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Introduction

Hypertension, specifically essential hypertension (also known as primary or idiopathic hypertension), is a major contributing risk factor for cardiovascular diseases (CVDs) and other diseases with adverse clinical outcomes, making it the largest single contributor to morbidity and mortality worldwide.¹ Pre-hypertension, defined as the intermediate stage between normotension and hypertension, is also significantly correlated with a heightened risk of various cardiovascular outcomes including total CVD, coronary heart disease, myocardial infarction, and stroke.² Elevated blood pressure and its consequent cardiovascular and cerebrovascular diseases have become the predominant burden of disease and leading cause of mortality globally and in most regions of the world,³ affecting approximately 1.39 billion individuals and resulting in over 10.8 million deaths annually.⁴ Owing to the efforts of governments and health organizations and the widespread use of anti-hypertensive medications, global mean blood pressure has remained constant or decreased slightly over the past five decades. However, the prevalence of hypertension has still increased, particularly in low- and middle-income countries.⁵ Consequently, moving the prevention gateway forward and implementing targeted preventive measures at the pre-hypertension stage may offer a breakthrough in reducing the incidence of hypertension.

Obesity is proven to be an established risk factor for hypertension and CVDs,^6–8 and the close association of obesity with blood pressure has long been recognized in diverse populations.^9–11 As conventional anthropometric indicators of obesity, body mass index (BMI) is widely used in research on predictive factors of pre-hypertension and hypertension.^12,13 Waist circumference (WC) and waist-height ratio (WHtR) also show good potential to predict pre-hypertension and hypertension.¹⁴ BMI reflects the overall body fat distribution and cannot accurately distinguish between fat and muscle proportions; WC and WHtR indicate abdominal obesity accurately and cannot distinguish between subcutaneous fat and visceral fat. In addition, other conventional anthropometric indicators such as basal metabolic rate (BMR), fat mass (FM), fat-free mass (FFM), fat mass index (FMI), and fat-free mass index (FFMI) are also considered effective predictors of hypertension.^15–17 However, the effects of these conventional anthropometric indicators on pre-hypertension are still limited. Furthermore, only a few studies reported the relationship between body fat percentage (BFP), visceral fat index (VFI) and pre-hypertension,^18,19 but the conclusions among them seemed to be controversial.

Recently, a number of new anthropometric indicators have garnered increasing attention due to their higher value in predicting disease risk. Waist circumference index (WCI), weight-adjusted-waist index (WWI), body surface area (BSA), conicity index (CI), body roundness index (BRI), and a body shape index (ABSI) are parameters based on specific combinations and calculations of physical examination indicators such as height, weight, or WC, while atherogenic index of plasma (AIP) and triglyceride-glucose index (TyG) are derived from the arithmetic operation of biochemical indicators such as triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), or fasting plasma glucose (FBG). Lipid accumulation product (LAP), visceral adiposity index (VAI), and cardiometabolic index (CMI) combine physical examination indicators such as height or WC with biochemical indicators such as TG and HDL-C. Some studies showed these unconventional anthropometric indicators are associated with hypertension, pre-hypertension, or blood pressure.^20–22 Differing from most studies that consider only one single indicator, this study is one of the first studies to compare a broad panel (24 indicators) simultaneously in a large, population-based Chinese survey. It would be of great clinical and practical importance to further explore the best anthropometric indicators for identifying pre-hypertension or hypertension.

Therefore, this study aimed to identify and compare the predictive value of 24 conventional and unconventional anthropometric indicators for pre-hypertension and hypertension among Chinese adults.

Methods

Study Design and Population

In this study, we used data from the Fujian Province part of the Chinese Residents Cardiovascular Disease and Risk Factors Surveillance Project, 2020. The project was carried out on a stratified multi-stage random sampling method, taking into account the economic development level of urban and rural areas, and selected 262 nationally and provincially representative districts and counties in 31 provinces, autonomous regions, and municipalities (excluding Hong Kong, Macao, and Taiwan) on the basis of their administrative divisions as monitoring sites. At each monitoring point, 1200 permanent residents aged 18 and above were selected according to sex and age group. A total of about 300,000 people were eventually surveyed on the status and distribution characteristics of important CVDs and risk factors such as obesity, hypertension, diabetes mellitus, dyslipidemia, coronary heart disease and stroke, etc.

As a result, three cities in urban areas and five counties in rural areas were selected. A total of 9790 participants living in Fujian Province for more than six months and aged 18 years or older were randomly selected to participate in this survey from August 2020 to April 2021. The exclusion criteria were set as follows: (1) participants with missing systolic blood pressure (SBP)/diastolic blood pressure (DBP) data (n = 59); (2) participants with missing data on total cholesterol (TC), TG, HDL-C, low-density lipoprotein cholesterol (LDL-C), FBG, or uric acid (UA) (n = 943); (3) participants with negative and clearly erroneous values of FFM (n = 1). After data filtration, a total of 8787 participants were ultimately selected for subsequent analyses. This study followed STROBE reporting guidelines (see Supplementary Table 1).

Blood Pressure Measurement and Definition of Pre-Hypertension or Hypertension

BP was measured using the same brand and model of electronic sphygmomanometer (Omron electronic sphygmomanometer HBP-1120U), with an accuracy of ±1 mm Hg. Participants were uniformly measured on the right upper arm, and three measurements were recorded, with no more than a 10 mm Hg difference in systolic or diastolic readings between any two of the three measurements (1 mm Hg = 0.133 k Pa). SBP or DBP was defined as the average of the three SBP or DBP readings.

The classification of normotension, pre-hypertension and hypertension was based on the criteria from JNC-8.²³ Hypertension was defined as an average SBP ≥140 mm Hg, and/or average DBP ≥90 mm Hg, and/or previously diagnosed with hypertension, and/or currently taking anti-hypertensive drugs; pre-hypertension was defined as an average SBP in the range of 120 to 139 mm Hg and/or an average DBP in the range of 80 to 89 mm Hg, without being on anti-hypertensive drugs; normotension was defined as an average SBP <120 mm Hg and an average DBP <80 mm Hg, without being on anti-hypertensive drugs.

Anthropometric Measurements

Anthropometric measurements were performed by trained staff according to standard procedures. Height was measured using a stadiometer of the same model with a length of 2.0 m and a minimum scale of 1 mm (Suhong BT-24). WC was measured at the superior border of the iliac crests using a waist circumference ruler of the same brand and model with a length of 1.5 m, a width of 1 cm, and a minimum scale of 1 mm. Weight, FM, FFM, BFP, VFI, and BMR were measured using the same brand and model of weight and body fat measuring device (InBody H20B body composition analyzer), with the scale function measuring accurately to 0.1 kg and a maximum weighing capacity of 150 kg. The above parameters must be measured on an empty stomach in the early morning, and participants should be dressed in light clothes. BMI, height-adjusted weight (HtaW), WHtR, FMI, FFMI, WCI, WWI, BSA, AIP, LAP, VAI, TyG, CI, BRI, ABSI, and CMI were calculated according to previous published formulae as followed (HtaW was calculated based on the coefficients of the sex-specific linear regression models of weight on height):

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Covariates

About 21 covariates were identified based on literature. Demographic characteristics, health-related behaviors, and family history were collected by a standardized questionnaire form through face-to-face interviews with trained staff and physical measurements. Demographic characteristics included age, sex, current residence, educational level, annual household income per capita, occupation, medical insurance, household size, marital status. Educational level was categorized into five levels: illiterate, primary school, junior high school, high school/technical secondary school, and junior college/undergraduate or above. Occupation was divided into three categories: unemployed, physical labor, and mental labor. Health-related behaviors included smoking, drinking, physical activity, regular sleep. Smoking was divided into three groups: never (never smoked in a lifetime), former (smoked in the past but not in the past 30 days), and current (smoked in the past 30 days). Drinking was also classified into three levels: never (never drank alcohol in a lifetime), former (used to drink alcohol but do not drink it anymore), and current (drank in the previous 30 days). Physical activity was defined as those who engaged in moderate-intensity or above physical activity for at least 5 days a week and at least 30 minutes per day during the past month. Regular sleep referred to those who slept between 7 and 9 hours per night on average in the past month. Family history involved hypertension and CVDs, and family history of CVDs was defined as one of the parents had coronary heart disease or stroke. Blood biochemical indicators included FBG, TC, TG, HDL-C, LDL-C, and UA, all tested using the Beckman AU680 instruments. The glucose oxidase method was used for FBG, the enzymatic method was used for TC and TG, the direct method was used for HDL-C and LDL-C, and the uricase method was used for serum UA.

Statistical Analysis

Continuous variables were presented as mean ± standard deviation or median (IQR) as appropriate and compared using the ANOVA, Welch ANOVA test or Kruskal–Wallis rank sum test, which depended on whether the quantitative data were consistent with the normal distribution. Categorical variables were expressed as percentages and analyzed by the Chi-square test or Fisher’s exact test as appropriate. Univariate logistic regression was used to judge the relationship between anthropometric indicators and pre-hypertension or hypertension, as well as a correlation matrix was calculated and a heat map was plotted to assess the multicollinearity of independent variables. Least absolute shrinkage and selection operator (LASSO) regression was employed to select independent variables, and multivariate logistic regression was further used to analyze the association between screened anthropometric indicators and pre-hypertension or hypertension, adjusting for confounders. Receiver-operating characteristic (ROC) curve was used to evaluate the discrimination ability of each screened anthropometric indicators for pre-hypertension or hypertension. The potential dose-response relationships between anthropometric indicators and the risk of pre-hypertension or hypertension was assessed by restricted cubic spline (RCS) models. A two-sided P-value <0.05 was considered significant. All statistical analyses were conducted using R 4.3.3 and DecisionLinnc. 1.0 softwares.

Results

Characteristics of the Study Population

Figure 1 showed a detailed flow chart of participants screening process. As shown in Table 1, among the 8787 participants, the prevalence of pre-hypertension and hypertension were 34.92% (3068/8787) and 35.84% (3149/8787), respectively. Compared with the normotension group, participants with pre-hypertension or hypertension tended to be older. The differences in sex, current residence, educational level, annual household income per capita, occupation, medical insurance, household size, marital status, smoking, drinking, regular sleep, family history of hypertension or CVDs within the blood pressure subgroups were statistically significant (all P < 0.05). Besides, the pre-hypertension and hypertension groups displayed notably higher FBG, TC, TG, LDL-C, UA and lower HDL-C in comparison to the normotension group (all P < 0.05).

Table 1 Characteristics of the Study Population

Figure 1 A detailed flow chart of participants screening process.

Distributions of Conventional and Unconventional Anthropometric Indicators

Table 2 showed the distributions of 24 conventional and unconventional anthropometric indicators in the normotension, pre-hypertension, and hypertension groups. There were statistically significant differences in height, weight, WC, FM, FFM, BFP, VFI, BMR, BMI, HtaW, WHtR, FMI, FFMI, WCI, WWI, BSA, AIP, LAP, VAI, TyG, CI, BRI, ABSI, and CMI among the blood pressure subgroups (all P < 0.05). Compared to the normotension group, the pre-hypertension group exhibited significantly higher height, weight, WC, FM, FFM, VFI, BMR, BMI, HtaW, WHtR, FMI, FFMI, WCI, WWI, BSA, AIP, LAP, VAI, TyG, CI, BRI, ABSI, and CMI; while the hypertension group showed significantly higher weight, WC, FM, FFM, BFP, VFI, BMR, BMI, HtaW, WHtR, FMI, FFMI, WCI, WWI, BSA, AIP, LAP, VAI, TyG, CI, BRI, ABSI, and CMI, but lower height (all P < 0.05).

Table 2 Distributions of Conventional and Unconventional Anthropometric Indicators in Different Blood Pressure Subgroups

Selection of Anthropometric Indicators by LASSO Regression

The results of univariate logistic regression analysis showed that 23 conventional and unconventional anthropometric indicators were associated with pre-hypertension (except for BFP), and all 24 conventional and unconventional anthropometric indicators were associated with hypertension (see Supplementary Table 2). However, the correlation matrix and heat map revealed multicollinearity among the independent variables (see Supplementary Table 3 and Supplementary Figure 1). Thus, we used LASSO regression to identify key anthropometric indicators associated with pre-hypertension or hypertension, respectively. As presented in Figure 2, the LASSO regression model identified 15 conventional and unconventional anthropometric indicators for pre-hypertension (including weight, FFM, VFI, BMR, BMI, HtaW, FMI, FFMI, WWI, AIP, LAP, VAI, TyG, BRI, and CMI), and 18 conventional and unconventional anthropometric indicators for hypertension (including height, WC, FM, BFP, VFI, BMR, BMI, HtaW, WHtR, FMI, FFMI, WWI, AIP, LAP, VAI, TyG, BRI, and CMI).

Figure 2 Selection of anthropometric indicators by LASSO Regression. Notes: (A) LASSO coefficient profiles of the 23 conventional and unconventional anthropometric indicators in pre-hypertension. A coefficient profile plot was produced against the log lambda sequence. In this study, anthropometric indicators were chosen according to the minimum criteria, where the optimal lambda resulted in 15 nonzero coefficients. (B) A 10-fold cross-validation was used in the LASSO regression for pre-hypertension. Binomial deviance was plotted versus log lambda. The dotted vertical lines were drawn at the optimal values using the minimum criteria (left dotted line) and the one standard error criteria (right dotted line). (C) LASSO coefficient profiles of the 24 conventional and unconventional anthropometric indicators in hypertension. A coefficient profile plot was produced against the log lambda sequence. In this study, anthropometric indicators were chosen according to the minimum criteria, where the optimal lambda resulted in 18 nonzero coefficients. (D) A 10-fold cross-validation was used in the LASSO regression for hypertension. Binomial deviance was plotted versus log lambda. The dotted vertical lines were drawn at the optimal values using the minimum criteria (left dotted line) and the one standard error criteria (right dotted line).

Association of Conventional and Unconventional Anthropometric Indicators with Pre-Hypertension and Hypertension

After excluded the variables with multi-collinearity, multivariate logistic regression analysis was used to analyze the association of conventional and unconventional anthropometric indicators with pre-hypertension and hypertension (Table 3). The final model adjusted for covariates such as demographic characteristics, health-related behaviors, family history, and blood biochemical indicators, and showed that weight (OR: 2.082, 95% CI: 1.280–3.411), BMI (OR: 4.047, 95% CI: 1.485–11.107), HtaW (OR: 0.998, 95% CI: 0.996–0.999), FMI (OR: 0.138, 95% CI: 0.034–0.556), FFMI (OR: 0.202, 95% CI: 0.061–0.661), AIP (OR: 0.331, 95% CI: 0.132–0.818), and TyG (OR: 2.467, 95% CI: 1.704–3.599) were significantly associated with pre-hypertension, while only FM (OR: 1.373, 95% CI: 1.004–1.895), AIP (OR: 0.115, 95% CI: 0.041–0.309), and TyG (OR: 5.450, 95% CI: 3.557–8.435) were significantly associated with hypertension.

Table 3 Multivariate Logistic Regression Analysis of Conventional and Unconventional Anthropometric Indicators with Pre-Hypertension and Hypertension

Discrimination Ability of Different Anthropometric Indicators by ROC Curves

Figure 3 showed the ROC curves for anthropometric indicators related to pre-hypertension and hypertension, and Table 4 demonstrated the area under curve (AUC), best threshold, sensitivity, and specificity of these anthropometric indicators. After adjusting for covariates, the predictive efficacy of the seven anthropometric indicators in pre-hypertension tended to be consistent, and the AUC of these anthropometric indicators were ranked from high to low as follows: Weight > BMI > FMI > FFMI > HtaW = TyG > AIP. Additionally, three anthropometric indicators had good predictive effects for hypertension, with their AUC ranked from high to low as follows: FM > TyG > AIP.

Table 4 AUCs and Best Thresholds for Anthropometric Indicators in Relation to Pre-Hypertension and Hypertension

Figure 3 ROC curves of anthropometric indicators for discriminating pre-hypertension (A) and hypertension (B). Notes: The ROC curves were adjusted for age, sex, current residence, educational level, annual household income per capita, occupation, medical insurance, household size, marital status, smoking, drinking, regular sleep, family history of hypertension, family history of cardiovascular diseases, FBG, TC, TG, HDL-C, LDL-C, UA (TG and HDL-C were not adjusted for AIP because they were included in the formula; TG and FBG were not adjusted for TyG because they were included in the formula).

Dose-Response Relationships of Anthropometric Indicators with Pre-Hypertension and Hypertension by RCS Models

According to Figure 4, RCS models suggested that weight, FMI, FFMI, AIP, and TyG had linear dose-response relationships with pre-hypertension risk (P for nonlinear > 0.05), while BMI and HtaW were nonlinearly associated with pre-hypertension risk (P for nonlinear < 0.05); FM, AIP and TyG had nonlinear dose-response relationships with hypertension risk (P for nonlinear < 0.05).

Figure 4 The restricted cubic splines of conventional and unconventional anthropometric indicators with the risk of pre-hypertension or hypertension. Notes: The restricted cubic spline analyses were adjusted for age, sex, current residence, educational level, annual household income per capita, occupation, medical insurance, household size, marital status, smoking, drinking, regular sleep, family history of hypertension, family history of cardiovascular diseases, FBG, TC, TG, HDL-C, LDL-C, UA (TG and HDL-C were not adjusted for AIP because they were included in the formula; TG and FBG were not adjusted for TyG because they were included in the formula). (A) dose-response relationship between weight and the risk of pre-hypertension; (B) dose-response relationship between BMI and the risk of pre-hypertension; (C) dose-response relationship between HtaW and the risk of pre-hypertension; (D) dose-response relationship between FMI and the risk of pre-hypertension; (E) dose-response relationship between FFMI and the risk of pre-hypertension; (F) dose-response relationship between AIP and the risk of pre-hypertension; (G) dose-response relationship between TyG and the risk of pre-hypertension; (H) dose-response relationship between FM and the risk of hypertension; (I) dose-response relationship between AIP and the risk of hypertension; (J) dose-response relationship between TyG and the risk of hypertension.

Subgroup Analyses and Sensitivity Analyses

Subgroup analyses revealed that among the conventional and unconventional anthropometric indicators of pre-hypertension, there were statistically significant interactions between age and weight, BMI, HtaW, FFMI, AIP; current residence and weight; educational level and weight, BMI, HtaW, FFMI, AIP, TyG; annual household income per capita, occupation, regular sleep and weight, BMI, HtaW, FFMI; medical insurance and weight, HtaW, FFMI; household size and weight, BMI, FFMI, AIP; marital status and weight, FFMI, AIP; smoking and HtaW, FMI, FFMI; drinking and BMI, FMI. Additionally, the interactions between age, occupation, family history of hypertension and AIP, TyG; sex, educational level, smoking and FM, AIP, TyG; medical insurance, marital status and AIP; household size and FM, AIP; drinking and FM were found to be statistically significant for hypertension (see Supplementary Figure 2). Furthermore, some sets of post hoc sensitivity analyses were performed to verify the robustness of the associations (see Supplementary Table 4). In most sensitivity analyses, the above associations between conventional and unconventional anthropometric indicators and pre-hypertension or hypertension remained unchanged.

Discussion

To the best of our knowledge, this is the first study to compare 24 indicators simultaneously in a large Chinese survey. We comprehensively assess the associations between a wide range of conventional and unconventional anthropometric indicators and pre-hypertension or hypertension in Chinese adults. In this study, we found that pre-hypertension was associated with weight, BMI, HtaW, FMI, FFMI, AIP, and TyG, with BMI showing the strongest association, followed by TyG. For hypertension, significant associations were found with FM, AIP, and TyG, with TyG exhibiting a notably stronger association compared to other anthropometric indicators. The ROC results demonstrated that conventional anthropometric indicators such as weight and BMI still had good predictive performance for pre-hypertension, while TyG and AIP, as unconventional anthropometric indicators, also have guiding significance to identify pre-hypertension. For hypertension, FM (a conventional anthropometric indicator), along with AIP and TyG (unconventional anthropometric indicators), showed excellent predictive values. The RCS models revealed that weight, FMI, FFMI, AIP, and TyG were linearly related to the risk of pre-hypertension, whereas BMI and HtaW were nonlinearly related to pre-hypertension risk; FM, AIP, and TyG were nonlinearly related to the risk of hypertension.

Weight, as one of the most fundamental conventional anthropometric indicators, serves as the most straightforward measure of obesity in individuals. Several cohort studies have demonstrated that weight change was associated with the incidence and mortality of metabolic diseases such as diabetes and hypertension,^39,40 as well as CVDs including coronary heart disease.⁴¹ In addition to being linked to the risk of disease in healthy populations, a prospective cohort study demonstrated that weight variability and weight change were both associated with higher risk of CVD mortality and all-cause mortality in individuals with hypertension.⁴² Another study also found that excessive weight can adversely affect kidney function through metabolic diseases.⁴³ This highlights the importance of daily weight management and monitoring individual weight changes, which are significant for both normotensive and hypertensive individuals. Although this study is a cross-sectional study, we still found that weight is an important predictor for pre-hypertension, suggesting that we should pay attention to our own weight and strive to maintain it within a normal range.

However, weight can only partially reflect overall obesity, and due to individual differences in height, using weight alone to predict hypertension risk is significantly biased. BMI and HtaW take into account the influence of height on weight, thereby partially addressing these shortcomings. Numerous cross-sectional studies have demonstrated that BMI was associated with pre-hypertension or hypertension.^44–46 A cohort study indicated that the risk of hypertension was not only related to the magnitude of BMI growth but also to the rate of growth, and that the risk of hypertension decreased significantly with the reduction in BMI.⁴⁷ Moreover, either a persistently high BMI or a rapid increase in BMI from childhood to adulthood may have adverse long-term effects on the development of hypertension and CVDs.⁴⁸ However, no studies have yet explored the relationship between HtaW and hypertension or pre-hypertension. Our study found that both BMI and HtaW have good predictive value for pre-hypertension, highlighting the importance of obesity indicators that consider height in the risk assessment of hypertension.

Although this study identified through ROC curve analysis that weight and BMI exhibit the highest predictive efficacy for pre-hypertension compared to other conventional anthropometric indicators, previous research indicated that BMI merely represented overall obesity without distinguishing between the proportions of muscle and fat in an individual’s weight, and this limitation gives rise to the obesity paradox, potentially introducing bias into the research outcomes.⁴⁹ FM refers to the total weight of all fat tissue within the body, typically measured using bioelectrical impedance analysis (BIA), dual-energy X-ray absorptiometry (DEXA), or skinfold thickness measurements. These methods provide a more accurate assessment of fat content, thereby evaluating individual obesity without the interference of muscle mass. FMI and FFMI, representing fat mass and fat-free mass adjusted for height, respectively, partially correct the bias caused by comparing FM across individuals of varying heights. Our study demonstrated that FMI and FFMI were predictive indicators for pre-hypertension risk, while FM was an indicator for hypertension risk, which aligns with the results of several other scholarly studies.^16,50,51 Besides, some researchers investigated the associations between FMI, FFMI and hypertension, but their findings have been inconsistent,^17,52,53 potentially due to differences in geographic regions, ethnicities, and study periods. However, our study does not support the predictive role of FMI and FFMI for hypertension risk. Future research should consider expanding the sample size to validate these findings in larger populations.

AIP is a biomarker composed of TG and HDL-C, which has been widely used in recent years to predict atherosclerosis and cardiovascular events, and has been demonstrated to have a higher predictive efficacy compared to individual lipid risk factors such as TG, TC, LDL-C, and HDL-C.³¹ Previous studies indicated that AIP was an important predictor of metabolic diseases such as hypertension, diabetes, and hyperuricemia,^54–56 and was associated with all-cause mortality and CVD-specific mortality in patients with hypertension.⁵⁷ The multivariate logistic regression results of this study showed that AIP was negatively correlated with the risk of pre-hypertension or hypertension, which is contrary to previous research.⁵⁸ However, the results of univariate logistic regression, RCS plots, and subgroup analyses indicated that AIP was positively correlated with the risk of pre-hypertension or hypertension. The possible reason is that despite controlling for multicollinearity among independent variables by screening variables through LASSO regression before conducting multivariate logistic regression, some multicollinearity or interaction among the selected variables still exists due to the intrinsic characteristics of anthropometric indicators, which led to a change in the association direction between AIP and pre-hypertension or hypertension. Further exploration is needed to investigate whether there are interactions between AIP and other anthropometric indicators. TyG is a recently popular “star” anthropometric indicator, combining TG and FBG, and has been identified as a reliable surrogate biomarker for insulin resistance.^34,59 Recent studies have also shown that it was closely related to the development and prognosis of CVDs.⁶⁰ Several scholars investigated the association between TyG and hypertension,^61,62 finding that TyG was an effective predictor of all-cause mortality in hypertensive patients.^63,64 However, no studies have yet focused on whether TyG is associated with pre-hypertension. Our study found that TyG was associated with both pre-hypertension and hypertension. Moreover, compared to AIP, TyG showed a stronger association and better predictive performance for pre-hypertension and hypertension, further confirming that TyG can serve as an effective predictor of the risk of pre-hypertension and hypertension.

Even if the diagnostic criteria for hypertension are well established, some scholars recently questioned the “one-size-fits-all” approach of only using blood pressure measurements to determine the presence of the condition. This is particularly significant for populous countries like China, where changes in the diagnostic threshold for hypertension could lead to substantial fluctuations in the number of diagnosed patients. Therefore, in the context of precision medicine, we should consider adopting more scientific and reasonable methods for individualized prediction of hypertension. Based on the above discussion, our study suggested that utilizing anthropometric indicators to predict the risk of pre-hypertension/hypertension could provide new insights for more accurate and scientific hypertension prediction in the future. We also attempt to propose a decision chart for individualized prediction of pre-hypertension/hypertension (Figure 5). In future primary care practice, screening strategies should be further refined, for example, by combining BMI with TyG testing to achieve more precise hypertension prediction.

Figure 5 Decision diagram for individualized prediction of pre-hypertension/hypertension.

This study innovatively explored the association of 24 conventional and unconventional anthropometric indicators with pre-hypertension or hypertension. By extensively adjusting for known confounding factors, we employed multiple methods to identify the optimal anthropometric indicators for predicting pre-hypertension or hypertension, which enriched the theoretical research on predictive factors for pre-hypertension and hypertension, providing scientific evidence and reasonable recommendations for reducing the incidence of hypertension. However, there are still some limitations in this study. Firstly, as a cross-sectional study, it cannot establish causal relationships. Secondly, despite our efforts to collect currently known anthropometric indicators through literature review, some new indicators might not have been measured. Thirdly, despite extensive covariate adjustment, lifestyle factors (eg, dietary intake, stress, salt consumption) were not captured and could influence blood pressure. Fourthly, due to the lack of hip circumference data, some related anthropometric indicators such as waist-to-hip ratio (WHR), abdominal volume index (AVI), and body adiposity index (BAI) could not be included in this study. We will further improve the survey design in future research. Lastly, it is undeniable that demographic characteristics may vary across different regions. Although our study subjects were selected through strict multi-stage stratified random sampling, it was limited to the population of Fujian Province. Therefore, caution should be exercised when generalizing the findings, and future studies should expand the sample size for further analysis.

In summary, the TyG index is emerging as a powerful marker for hypertension risk prediction and should be considered alongside conventional measures to strengthen primary care strategies.

Conclusions

In conclusion, this is one of the first comprehensive comparisons of 24 anthropometric indicators in a large Chinese population. We investigated the association of conventional and unconventional anthropometric indicators with the risk of pre-hypertension and hypertension in Chinese adults. Our results indicated that weight, BMI, HtaW, FMI, FFMI, AIP, and TyG were independently associated with pre-hypertension, among which the BMI and TyG had the strongest association with pre-hypertension, while hypertension was associated with FM, AIP, and TyG, with TyG showing a significantly stronger association with hypertension compared to other anthropometric indicators. Given the high prevalence of hypertension in China, simple conventional anthropometric measures still hold substantial potential for early population-level prevention. Emerging indicators such as TyG also deserve increased attention and could be integrated into existing clinical screening protocols to achieve more precise risk stratification, enabling clinicians to tailor lifestyle or therapeutic interventions accordingly. Future longitudinal studies are warranted to confirm causality and validate the predictive utility of novel indices such as TyG and AIP.

Abbreviations

CVD, cardiovascular disease; BMI, body mass index; WC, waist circumference; WHtR, waist-height ratio; BMR, basal metabolic rate; FM, fat mass; FFM, fat-free mass; FMI, fat mass index; FFMI, fat-free mass index; BFP, body fat percentage; VFI, visceral fat index; WCI, waist circumference index; WWI, weight-adjusted-waist index; BSA, body surface area; CI, conicity index; BRI, body roundness index; ABSI, a body shape index; AIP, atherogenic index of plasma; TyG, triglyceride-glucose index; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; FBG, fasting plasma glucose; LAP, lipid accumulation product; VAI, visceral adiposity index; CMI, cardiometabolic index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; UA, uric acid; HtaW, height-adjusted weight; LASSO, least absolute shrinkage and selection operator; ROC, receiver-operating characteristic; RCS, restricted cubic spline; CNY, Chinese yuan; UEBMI, urban employees basic medical insurance; URRBMI, urban and rural residents basic medical insurance; AUC, area under curve; BIA, bioelectrical impedance analysis; DEXA, dual-energy x-ray absorptiometry; WHR, waist-to-hip ratio; AVI, abdominal volume index; BAI, body adiposity index.

Data Sharing Statement

The datasets used and/or analyzed during the current study are available from the corresponding author (Xian-E Peng) on reasonable request.

Ethics Approval and Consent to Participate

Ethics approval was obtained from the Ethics Committee of Fuwai Hospital (No. 2020-1360), and written informed consent was obtained from each participant. According to Item 2 of Article 32 of the Measures for Ethical Review of Life Science and Medical Research Involving Human Subjects dated February 18, 2023, our study is exempt from ethical review as it involves anonymized data (https://www.gov.cn/zhengce/zhengceku/2023-02/28/content_5743658.htm).

Acknowledgments

The authors would like to express their sincere gratitude to all the participants who wholeheartedly provided invaluable information and their collaboration in this research.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This work was supported by the Project of the National Health Commission of the People’s Republic of China (grant numbers: NHC2020-609) and the Special Funding Project of Fujian Provincial Department of Finance (grant numbers: BPB-HY2021).

Disclosure

The authors report no conflicts of interest in this work.

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LONDON — LONDON (AP) — Amazon says a massive outage of its cloud computing service has been resolved as of Monday evening, after a problem disrupted internet use around the world, taking down a broad range of online services, including social media, gaming, food delivery, streaming and financial platforms.

The all-day disruption and the ensuing exasperation it caused served as the latest reminder that 21st century society is increasingly dependent on just a handful of companies for much of its internet technology, which seems to work reliably until it suddenly breaks down.

About three hours after the outage began early Monday morning, Amazon Web Services said it was starting to recover, but it wasn’t until 6 p.m. Eastern that “services returned to normal operations,” Amazon said on its AWS health website, where it tracks outages.

AWS provides behind-the-scenes cloud computing infrastructure to some of the world’s biggest organizations. Its customers include government departments, universities and businesses, including The Associated Press.

Cybersecurity expert Mike Chapple said “a slow and bumpy recovery process” is “entirely normal.”

As engineers roll out fixes across the cloud computing infrastructure, the process could trigger smaller disruptions, he said.

“It’s similar to what happens after a large-scale power outage: While a city’s power is coming back online, neighborhoods may see intermittent glitches as crews finish the repairs,” said Chapple, an information technology professor at the University of Notre Dame’s Mendoza College of Business.

Amazon pinned the outage on issues related to its domain name system that converts web addresses into IP addresses, which are numeric designations that identify locations on the internet. Those addresses allow websites and apps to load on internet-connected devices.

DownDetector, a website that tracks online outages, said in a Facebook post that it received over 11 million user reports of problems at more than 2,500 companies. Users reported trouble with the social media site Snapchat, the Roblox and Fortnite video games, the online broker Robinhood and the McDonald’s app, as well as Netflix, Disney+ and many other services.

The cryptocurrency exchange Coinbase and the Signal chat app both said on X that they were experiencing trouble related to the outage.

Amazon’s own services were also affected. Users of the company’s Ring doorbell cameras and Alexa-powered smart speakers reported that they were not working, while others said they were unable to access the Amazon website or download books to their Kindle.

Many college and K-12 students were unable to submit or access their homework or course materials Monday because the AWS outage knocked out Canvas, a widely used educational platform.

“I currently can’t grade any online assignments, and my students can’t access their online materials” because of the outage’s effect on learning-management systems, said Damien P. Williams, a professor of philosophy and data science at the University of North Carolina at Charlotte.

The exact number of schools impacted was not immediately known, but Canvas says on its website it is used by 50% of college and university students in North America, including all Ivy League schools in the U.S.

At the University of California, Riverside, students couldn’t submit assignments, take quizzes or access course materials, and online instruction was limited, the campus said.

Ohio State University informed its 70,000 students at all six campuses by email Monday morning that online course materials might be inaccessible due to the outage and that “students should connect with their instructors for any alternative plans.” As of 7:10 p.m. Eastern, access was restored, the university told students.

This is not the first time issues with Amazon cloud services have caused widespread disruptions.

Many popular internet services were affected by a brief outage in 2023. AWS’s longest outage in recent history occurred in late 2021, when a wide range of companies — from airlines and auto dealerships to payment apps and video streaming services — were affected for more than five hours. Outages also happened in 2020 and 2017.

The first signs of trouble emerged at around 3:11 a.m. Eastern time, when AWS reported on its “health dashboard” that it was “investigating increased error rates and latencies for multiple AWS services in the US-EAST-1 Region.” Later, the company reported that there were “significant error rates” and that engineers were “actively working” on the problem.

Around 6 a.m. Eastern time, the company reported seeing recovery across most of the affected services and said it was seeking a “full resolution.” As of midday, AWS was still working to resolve the trouble.

Sixty-four internal AWS services were affected, the company said.

Because much of the world now relies on three or four companies to provide the underlying infrastructure of the internet, “when there’s an issue like this, it can be really impactful” across many online services, said Patrick Burgess, a cybersecurity expert at U.K.-based BCS, The Chartered Institute for IT.

“The world now runs on the cloud,” Burgess said.

And because so much of the online world’s plumbing is underpinned by so few companies, when something goes wrong, “it’s very difficult for users to pinpoint what is happening because we don’t see Amazon, we just see Snapchat or Roblox,” Burgess said.

“The good news is that this kind of issue is usually relatively fast” to resolve, and there’s no indication that it was caused by a cyberattack, Burgess said.

“This looks like a good old-fashioned technology issue. Something’s gone wrong, and it will be fixed by Amazon,” he said.

There are “well-established processes” to deal with outages at AWS, as well as rivals Google and Microsoft, Burgess said, adding that such outages are usually over in “hours rather than days.”

___

Ortutay reported from San Francisco. Associated Press videojournalist Mustakim Hasnath in London and Jocelyn Gecker in San Francisco contributed to this report.

Introduction

Pancreatic cancer is one of the most aggressive malignancies, with only 20% of patients eligible for surgical resection at the time of diagnosis.^1–3 These patients often face prolonged hospitalization and significant postoperative challenges, among which pain control remains a major clinical concern. Poorly managed postoperative pain can stimulate catecholamine release, which may suppress natural killer cell activity—a component of innate immunity—and potentially influence anti-tumor responses.⁴ Additionally, it is associated with increased psychological distress and reduced quality of life. Despite its clinical significance, current pain management strategies after pancreatic surgery are often suboptimal, underscoring the need for more effective analgesic approaches and further investigation into their impact on postoperative outcomes.

Patient-controlled intravenous analgesia (PCIA) with opioids is widely used for postoperative pain control.^5–8 Sufentanil, a selective potent μ-receptor agonist, is widely used for its efficacy in postoperative pain management.⁹ Given the moderate to severe pain typically associated with pancreatic surgery, a potent analgesic strategy is essential. However, increasing the dosage of a single analgesic agent to achieve adequate pain relief may also elevate the risk of adverse effects, including respiratory depression, nausea, and vomiting. Dezocine, a partial μ-receptor agonist and κ-receptor antagonist, has emerged as a promising adjunct due to its analgesic and sedative effects, as well as its favorable safety profile compared to pure μ-receptor agonists.^10–12 By acting on κ-receptors in the spinal cord and brain, dezocine provides analgesic and sedative effects without the typical µ-receptor dependence, potentially reducing adverse reactions such as smooth muscle relaxation.¹⁰ Previous studies have demonstrated that dezocine offers significant postoperative antihyperalgesic and analgesic effects, with benefits lasting up to 48 hours in patients undergoing open gastrectomy.¹³

Several studies have demonstrated that dezocine, when combined with morphine, enhances postoperative analgesia and reduces opioid-related side effects, such as nausea and pruritus, making it a valuable option in anesthesia practice.^14–16 At our institution, the combination of sufentanil and dezocine has been used in PCIA for pancreatic cancer patients for several years. However, the efficacy and safety of this combination have not been thoroughly investigated. To address this gap, we conducted a propensity score-matched (PSM) study at a high-volume pancreatic center to evaluate the role of dezocine as an adjunct to sufentanil in PCIA for postoperative pain management following pancreatic surgery, which, to our knowledge, is the first study to investigate the analgesic effects of this combination in PCIA for pancreatic surgery patients.

Materials and Methods

Ethics Approval

This retrospective study was approved by the Ethics Committee of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (Ethics Approval Number: (2023) No. 48), with a waiver of patient written informed consent due to the use of de-identified, archival medical records (no active patient intervention). All patient identifiers were removed, and data were stored securely on encrypted servers accessible only to the research team, adhering to the Declaration of Helsinki (as revised in 2013).

Patients

A total of 1485 patients who underwent elective open or minimally invasive pancreatic tumor surgery and received patient-controlled intravenous analgesia (PCIA) for postoperative pain management at the Pancreas Center of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, between January 2022 and January 2023 were retrospectively enrolled. The center is one of the largest pancreatic surgery centers in Asia. Among them, 794 were male and 691 were female, with an age range of 18 to 85 years (mean age: 60.55 ± 12.55 years) and American Society of Anesthesiologists (ASA) physical status classification ranging from I to IV. Based on the PCIA regimen, patients were allocated into two groups: the sufentanil group (n = 251) and the sufentanil-dezocine combination group (n = 1234). Surgical approach (Laparotomy/Laparoscopic/Robotic) was documented based on the description of the surgical procedure in the operative notes. All operative notes were reviewed and signed off by the attending surgeon or a senior resident physician to ensure consistency in classification. To minimize confounding and selection bias, PSM was performed using a logistic regression model based on age, sex, BMI, surgical approach (laparotomy, laparoscopic, robotic), surgery type (pancreatoduodenectomy, total pancreatectomy, middle-preserving pancreatectomy, distal pancreatectomy, as different techniques may affect pain severity due to varying tissue trauma), and dexmedetomidine dose. A caliper of 0.02 and nearest-neighbor matching were applied in a 1:3 ratio using R software (v.4.3.1, The R Foundation for Statistical Computing, Vienna, Austria. http://www.r-project.org). Exclusion criteria included: (1) known allergies to study drugs; (2) inability to use patient-controlled intravenous analgesia (PCIA); (3) history of chronic pain or long-term use of analgesic medications; (4) requirement for reoperation due to postoperative bleeding or severe abdominal infection; (5) severe cardiopulmonary or hepatorenal insufficiency and (6) cognitive dysfunction.

Anesthesia Procedure

All patients fasted for 8 hours (solids) and 6 hours (clear liquids) preoperatively and were transferred to the operating room without premedication. Standard monitoring included electrocardiography (ECG), non-invasive blood pressure (BP), respiratory rate (RR), oxygen saturation (SpO₂), end-tidal carbon dioxide pressure (PetCO₂), and bispectral index (BIS). A uniform anesthetic regimen was administered to all patients, with surgeries performed by the same surgical team.

General anesthesia was induced with propofol (2–2.5mg/kg), sufentanil (0.3–0.5 µg/kg), rocuronium (0.6–0.8mg/kg) or cisatracurium (0.2–0.3mg/kg), dexamethasone 5 mg, and dexmedetomidine 0.6 µg/kg. Preoxygenation with 100% oxygen was administered for at least 3 minutes via a face mask. Anesthesia was maintained with sevoflurane (3vol%, 0.8–1.3MAC), remifentanil (0.2–0.4µg/kg/min), supplemental rocuronium (1/3–1/5 of the induction dose), and intermittent sufentanil (0.4 µg/kg). Ventilation was set at a tidal volume of 8 mL/kg, with respiratory frequency adjusted to maintain PetCO₂ at 35–45 mmHg. Anesthesia depth was titrated to maintain a BIS between 40 and 60, ensuring mean arterial pressure (MAP) and heart rate (HR) remained within 20% of baseline values. Patient temperature was maintained above 36°C using infusion heaters and warming blankets. A sufentanil loading dose (0.1 µg/kg) was administered 30 minutes before the end of surgery. Intraoperative fluid balance was defined as the net change in a patient’s total body fluid volume during surgery, calculated as the difference between the total intraoperative fluids inputs and outputs. Postoperatively, patients were transferred to the post-anesthesia care unit (PACU), where residual neuromuscular blockade was reversed with neostigmine (40 µg/kg) and atropine (20 µg/kg).

Postoperative PCIA Regimen

After meeting extubation criteria, patients were extubated and connected to an Artificial Intelligence Patient-Controlled Analgesia (AI-PCA) system (Model ZZB-IB, Nantong AIPU Medical Inc., China). Patients were divided into two groups based on the PCIA solution: the sufentanil group received sufentanil (1.0 µg/mL), and the combination group received sufentanil (1.0 µg/mL) plus dezocine (2.5 mg/mL). Group allocation was guided by clinical judgment of the anesthesiologist considering factors reflected in our dataset such as patient demographics, surgical complexity, intraoperative management details.

The Acute Pain Service team prepared the PCIA solution in 100 mL normal saline bags, containing either sufentanil alone or the combination and monitored patients. If the Numerical Rating Scale (NRS) at rest was ≥4, a 2 mL bolus of PCIA solution was administered at 15-minute intervals until NRS <4. Patients were then encouraged to self-administer PCIA as needed.

The PCIA pump was set to a background infusion rate of 2 mL/h, with a 2 mL bolus dose and a 15-minute lockout interval. PCIA was maintained for 48 hours postoperatively, during which vital signs including respiratory rate, oxygen saturation, and sedation scores were closely monitored.

Outcome Measures

Demographic and intraoperative data, including surgery type, site, anesthetic drug dosages, blood loss, transfusion, and fluid balance, were recorded. Postoperative data included PCIA pump usage duration, total input, cumulative and effective press counts, rescue analgesia, and adverse events (eg, vomiting, pruritus, respiratory depression, hypotension, dizziness, delirium). We assessed Functional Activity Score (FAS) and Level of Sedation (LOS) at 1 and 2 days post-surgery. FAS (1–3 grades) quantifies pain impact on daily functions: 1=no limitation (normal coughing/limb movement despite pain); 2=mild limitation (slight difficulty/slower actions), and 3=severe limitation (struggles with basic activities). LOS (0–3 grades) evaluates consciousness via responsiveness: 0=alert (follows instructions), 1=somnolent (wakes to calls but drifts off), 2=stuporous (brief pain wakefulness), 3=comatose (no response to calls or pain). Both were graded during routine checks to guide pain management and monitor recovery. Pain intensity was evaluated using the NRS at rest (NRSR) and during coughing (NRSC) at 24, 48, and 72 hours post-surgery. The NRS ranges from 0 (no pain) to 10 (worst imaginable pain). Moderate-to-severe pain was defined as NRS ≥4. Mild pain (NRS 1–3) was also recorded in postoperative data. Adverse events were recorded based on routine clinical documentation in the hospital’s electronic medical records (EMR) and nursing care logs.

Primary endpoints were the incidence of moderate-to-severe pain at rest and during coughing within 48 hours post-surgery. Secondary endpoints included the incidence of moderate-severe pain at rest and during coughing at 24 hours and 72 hours post-surgery, LOS, FAS, and adverse events.

Statistical Analysis

Continuous variables were first assessed for normality, those with normal distribution were expressed as mean ± standard deviation (SD) and compared using independent t-tests. Skewed distributed continuous variables were presented as median (Q1, Q3) and analyzed with the Mann–Whitney U-test. Categorical variables were expressed as frequencies and percentage, and compared using Pearson’s chi-square or Fisher’s exact test. Missing data for demographic characteristics, intraoperative and postoperative data were imputed using the expectation-maximization algorithm. Univariate and multivariate logistic regression models, alongside with post-PSM analysis and inverse probability weighting (IPW) analysis were conducted to calculate odds ratios (OR) and 95% confidence intervals (CI). Analyses were performed using SAS (v.9.2, SAS Institute Inc., USA). All tests were two-sided, and statistical significance was set at the 5% level. No adjustments have been made for multiple testing.

Results

Patient Characteristics

Before PSM, the sufentanil group comprised 251 patients, while the combination group included 1234 patients. The sufentanil group was older (mean age 63.73 ± 13.69 years vs 59.90 ± 13.44 years, P< 0.05), had a higher proportion of pancreatoduodenectomy (PD) procedures (55.8% vs 44.8%, P< 0.05), and a greater rate of laparotomy (80.5% vs 73.9%, P < 0.05). Additionally, the sufentanil group had a lower BMI (22.25 ± 3.30 vs 22.73 ± 3.31, P < 0.05) and received a lower dexmedetomidine dosage (16.85 ± 15.75 µg vs 22.50 ± 16.32 µg, P < 0.05) compared to the combination group. No significant difference was observed in sex distribution. After PSM, the study included 247 patients in the sufentanil group and 704 in the combination group, with all baseline variables balanced between the two groups (Table 1).

Table 1 Demographic Characteristics and Perioperative Outcomes of Patients Between the Sufentanial Group and the Combination Group

Perioperative Outcomes

After PSM, no significant differences were observed in blood loss, blood transfusion volume, or total PCIA input between the two groups, despite differences before matching. The dosages of sufentanil and rocuronium bromide, as well as effective and cumulative PCIA press counts, showed no significant differences before or after PSM. However, the sufentanil group exhibited greater fluid balance difference and longer pump usage duration, which were statistically significant both before and after PSM (Table 1).

Primary Endpoint

The incidence of moderate-to-severe pain at rest and during coughing within 48 hours post-surgery is summarized in Table 2. After PSM, 19 patients (7.7%) in the sufentanil group experienced moderate-to-severe pain at rest, compared to 20 patients (2.8%) in the combination group (P < 0.05). Similarly, the incidence of pain during coughing was significantly higher in the sufentanil group (74 patients, 30.0%) than in the combination group (166 patients, 23.6%) during the same period (P < 0.05). These differences were also observed before PSM.

Table 2 Moderate-Severe Pain at Rest and During Coughing After Surgery Between the Sufentanial Group and the Combination Group

At 48 hours post-surgery, NRSR was significantly higher in the sufentanil group (1.97 ± 1.26) compared to the combination group (1.77 ± 0.91) (P= 0.018). Similarly, NRSC at 48 hours was higher in the sufentanil group (3.13 ± 1.57) than in the combination group (2.89 ± 1.17) (P= 0.022). All four analytical approaches including univariate and multivariate logistic regression analyses, post- PSM analysis and IPW analysis consistently identified sufentanil monotherapy as an independent predictor of moderate-to-severe pain, with odds ratios (ORs) and 95% confidence intervals (CIs) presented in Table 3.

Table 3 Logistic Regression Results for Moderate-Severe Pain at Rest and During Coughing After Surgery Between the Sufentanial Group and the Combination Group

Secondary Endpoints

Significant differences in the incidence of pain at rest and during coughing were observed at 24 and 72 hours post-surgery before PSM (P < 0.05). After PSM, these differences remained significant, except for pain during coughing at 72 hours (Table 2). No significant inter-group differences were noted in vomiting, hypotension, dizziness, delirium, or rescue analgesia on the first and second postoperative days, either before or after PSM. However, the functional activity scale (FAS) scores on the first and second postoperative days revealed significant differences between the two groups. Additionally, the proportion of fully alert patients on the second postoperative day was significantly higher in the combination group compared to the sufentanil group, both before and after PSM (Table 4).

Table 4 Adverse Events Between the Sufentanial Group and the Combination Group

Discussion

Pancreatic surgery is a critical intervention for pancreatic cancer, yet patients often experience prolonged postoperative pain, which can hinder physical and mental recovery. Effective pain management is therefore essential for improving patient outcomes and has garnered significant clinical attention. Opioid-based analgesia, particularly sufentanil, is widely used in patient-controlled intravenous analgesia (PCIA). However, the adverse effects of opioids, such as addiction, respiratory depression, pruritus, and sedation, have driven the search for alternative strategies to reduce opioid dosages and minimize side effects.¹⁷ Multimodal analgesia has emerged as a promising approach.¹⁸

In this propensity score-matched study, we evaluated the efficacy of combining sufentanil with dezocine in PCIA for postoperative pain management in patients undergoing pancreatic surgery. After matching, baseline characteristics and perioperative outcomes were comparable between the groups. Our findings demonstrated that the sufentanil-dezocine combination significantly reduced the incidence of moderate-to-severe pain at rest and during coughing within the first 48 hours postoperatively, without increasing the risk of clinically relevant side effects such as vomiting, hypotension, dizziness, delirium, or the need for rescue analgesia. Patients in the combination group exhibited significantly lower NRSR and NRSC at 48 hours post-surgery compared to the sufentanil group. Multivariate logistic regression analysis identified sufentanil monotherapy as an independent predictor of postoperative pain, suggesting that the addition of dezocine enhances analgesic efficacy, consistent with previous findings.¹⁶ These findings align with dezocine’s proposed mechanism: by targeting κ-receptors (which modulate pain perception) and partially activating μ-receptors (avoiding overstimulation), the combination may enhance analgesia while mitigating pure μ-agonist-related side effects. Notably, the reduction in pain during coughing–a high-pain activity critical for pancreatic surgery recovery–suggests the combination may be particularly beneficial for patients requiring early mobilization.

A primary concern with combining dezocine and sufentanil in PCIA is the potential for excessive sedation. However, our study found no evidence of increased sedation in the combination group during the 48-hour postoperative period. While sedation levels on the first postoperative day did not differ significantly, the proportion of fully alert patients was significantly higher in the combination group on the second postoperative day. This finding suggests that dezocine may enhance patient alertness while maintaining effective analgesia–Its ability to improve alertness and reduce sedation-related complications supports its value as a “balanced” adjunct in postoperative pain management^19–21 likely due to κ-receptor activation inducing lighter sedation compared to μ-agonists.

Postoperative adverse events, such as vomiting, hypotension, and dizziness, can negatively impact patient satisfaction and prolong hospital stays.¹⁰ Our study found that the addition of dezocine to sufentanil did not exacerbate these side effects. Notably, the combination group had a significantly lower incidence of respiratory depression compared to the sufentanil group, with no significant differences in vomiting, hypotension, dizziness, or delirium. These results align with previous research^22–25 and further support the safety profile of the sufentanil-dezocine combination.

Despite these promising findings, several limitations should be acknowledged. First, the retrospective design of the study introduces potential for selection bias, although this was mitigated by propensity score matching and the uniformity of our surgical team, Sensitivity analyses using alternative matching strategies (eg inverse probability weighting, multivariate logistic regression) yielded consistent results, suggesting no major residual confounding affected our conclusions. Second, generalizability of our findings may be limited. Due to our single-center design, even though our cohort meets high-volume criteria. As emphasized in a recent review on gastric cancer surgery outcomes, institutional factors can create variability in textbook outcomes (TOs) even among high-volume centers, highlighting the need for cross-institutional validation.²⁶ Future multi-center collaborations will compare textbook outcomes across 10+ high-volume centers using a pragmatic, standardized protocol to address this gap. Third, retrospective data precluded optimization of sufentanil/dezocine dosing. Prospective dose-response studies are needed to refine postoperative pain management in high-risk surgical populations.

In conclusion, our study demonstrates that the sufentanil-dezocine combination in PCIA significantly reduces moderate-to-severe pain at rest and during coughing within the first 48 hours after pancreatic surgery, without increasing the incidence of clinically relevant adverse effects, which has not been previously reported in the context of pancreatic surgery, suggesting it as a promising and safe approach for postoperative pain management in pancreatic cancer patients. Future research should focus on optimizing dosing strategies and confirming these results in prospective, multicenter trials.

Data Sharing Statement

The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding authors.

Funding

This work was supported by the National Natural Science Foundation of China (No: T2293734).

Disclosure

The authors declare that they have no conflicts of interest in this work.

References

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Introduction

Hepatocellular carcinoma (HCC) is one of the most prevalent and lethal malignancies worldwide, with hepatectomy remaining a primary curative treatment option for early-stage disease.^1,2 However, postoperative recurrence poses a significant challenge, with 5-year recurrence rate as high as 60%.³ Recurrence can be categorized as early recurrence (<2 years), caused by micro metastases after resection, or late recurrence (>2 years), caused by new tumors arising in a microenvironment prone to carcinogenesis.⁴ Research has shown that HCC patients who experience early recurrence post-resection have a worse prognosis compared to those with late recurrence.⁵ Therefore, accurate prediction of early recurrence is critical for personalized postoperative surveillance and adjuvant therapy strategies.

Certain clinicopathological features, including the Barcelona Clinic Liver Cancer (BCLC) staging system, alpha-fetoprotein (AFP), microvascular invasion (MVI), and satellite nodules (SNs), have been linked to postoperative prognosis.^1,6 However, conventional predictive assessments based on these biomarkers lack sufficient predictive accuracy, and information on MVI and SNs is not available preoperatively. Several recent studies have used artificial intelligence models based on CT/MRI data to effectively predict the risk of postsurgical HCC recurrence risk.^7–10 Compared with CT and MRI, ultrasound possesses the advantages of real-time, radiation-free, and cost-effective, and can also be used for intraoperative evaluation and guide the operation pathway. Additionally, contrast-enhanced ultrasound (CEUS) has emerged as a valuable imaging modality for HCC, offering real-time dynamic assessment of tumor vascularity and perfusion characteristics.¹¹

Previous studies have reported that certain CEUS characteristics such as rapid wash-in, early wash-out phase, rapid portal phase signal regression, and high LI-RADS grading are risk factors associated with early recurrence after radical HCC resection.^12,13 However, a critical gap remains: the interpretation of these characteristics is highly subjective and difficult to quantify, which limits their consistent application in prognostication. To address this gap in objectivity, deep learning (DL) emerges as a powerful tool. DL algorithms can automatically extract massive, quantitative features from medical images to model disease diagnosis and prognosis.¹⁴ The application of DL to liver CEUS is already established for tasks such as focal liver lesion classification and predicting HCC biological behavior,^15,16 demonstrating its potential in this domain. However, these existing applications primarily focus on classification limited to single-phase analysis or static images, potentially overlooking the critical time-dependent kinetic information. The specific knowledge gap our study aims to fill is the use of DL to predict long-term clinical outcomes directly from pre-operative dynamic CEUS cines. Therefore, we hypothesize that a DL-based framework, by leveraging the rich temporal data in multiphase CEUS, can overcome the limitations of subjective interpretation. This study aims to develop and validate such a framework, integrating CEUS data with clinicopathological variables to provide a noninvasive and efficient preoperative tool for predicting early recurrence in patients with early-stage HCC after surgical resection.

Methods

Study Population and Design

This study was performed in accordance with the principles of the Declaration of Helsinki. This retrospective study was approved by the Ethics Committee of the Second Affiliated Hospital of Nanchang University (Approval No: IIT-O-2025-251). The requirement for informed consent was waived by the Ethics Committee due to the retrospective and minimal-risk nature of the study. All patient data were kept strictly confidential and de-identified prior to analysis. From January 2010 to January 2023, 155 patients with HCC who underwent CEUS within two weeks before hepatectomy were enrolled. The diagnosis of HCC was based on the diagnostic criteria established by the European Association for the Study of the Liver.² Inclusion criteria were: BCLC stage 0 or A; liver function with Child-Pugh class A or B; performance status Eastern Cooperative Oncology Group score 0 or 1; R0 resection;¹⁷ regular follow-up within two years after hepatectomy. The exclusion criteria were poor CEUS imaging quality, recurrent HCC or primary HCC combined with other primary tumors, and a history of antitumor treatment before surgery, such as radiofrequency ablation, microwave ablation, transcatheter arterial chemoembolization, or chemotherapy. Pretreatment baseline clinical characteristics, including demographic data, laboratory test results, and clinical diagnoses, were collected from the Institutional Picture Archiving and Communication System (PACS^®; Carestream Health, Toronto, Canada). Finally, 115 patients’ preoperative CEUS cines and clinical variables were retrospectively analyzed. We developed four CEUS-based AI models to preoperatively predict early recurrence: CEUS-AP, CEUS-PP, CEUS-LP, and CEUS-MP. We further integrated clinical variables into the CEUS AI model to construct an individualized nomogram for preoperative prediction of early recurrence. The patient enrollment workflow and study design were shown in Figure 1.

Figure 1 Study workflow flowchart. Abbreviations: CEUS, contrast-enhanced ultrasound; HCC, hepatocellular carcinoma; ROI annotation illustration; AP, arterial phase; PP, portal venous phase; LP, late phase; MP, multiple phases.

CEUS Data Acquisition

CEUS was performed by one of two radiologists (ZH and YC) with over 10 years of experience in liver CEUS. Two types of ultrasound instruments were used in this study, Toshiba Aplio (n = 98) and SuperSonic Imagine (n = 17). The system parameters, including gain, dynamic range, mechanical index, output power, and focal zone, were fine-tuned to ensure effective tissue signal reduction without compromising penetration capability. A volume of 2.4 mL of the second-generation contrast agent (SonoVue^®; Bracco Imaging, Milan, Italy) was injected within one second via the elbow, followed by a 5-mL saline flush. The transducer was maintained in a stable position to continuously observe perfusion of the lesion. Three-phase contrast enhancement was dynamically monitored, consisting of arterial phase (AP, 0–30 s), portal venous phase (PP, 31–120 s), and late phase (LP, 121–360 s). The CEUS digital video recordings were stored as DICOM format.

Hepatectomy Procedure and Follow-Up Protocol

Anatomical partial hepatectomy was performed with a resection margin of ≥ 10 mm. All enrolled tumors were safely and completely removed, with no residual tumors following resection (R0 classification). The patients underwent regular follow-up assessments at 1, 3, 6, 9, and 12 months postoperatively, followed by evaluations every 3–6 months thereafter. During these visits, contrast-enhanced imaging (CEUS, CECT, or CEMRI) and serum biomarker testing (AFP and liver function) were performed. In this study, early recurrence was defined as a new lesion with typical imaging features of HCC that appeared in imaging examinations within 2 years after hepatectomy or as confirmed by pathological results from percutaneous liver biopsy or a second surgery.

Developing Prediction Model Based on CEUS

This study employed CEUS cines from three phases (arterial phase, portal venous phase, and late phase) to construct the corresponding single-phase models (CEUS-AP, CEUS-PP, and CEUS-LP). We then developed a comprehensive CEUS-MP (multi-phase) model that integrated all three phases. In each patient, three separate regions of interest (ROIs) were outlined by a radiologist with 5-year experience in CEUS to enable quantitative assessment of the CEUS videos across the arterial, portal venous, and late phases. The annotation tool was ITK-SNAP, which is an open-source software.¹⁸ Specifically, the doctor first annotated the contour of the tumor in the CEUS frame when the tumor displayed clearly. A rectangle to cover the tumor contour and an approximately 0.5 cm wide liver parenchyma surrounding the tumor were created (Figure 2). All frames with the same ROI location determined in the annotated frame were cropped and manually adjusted if necessary. To assess inter-observer variability in ROI delineation, two doctors (one with more than 10 years of experience in CEUS and the other with more than 5 years of experience) independently contoured the ROIs for a randomly selected cohort of 30 patients, blinded to patient outcomes and clinical characteristics. Radiomic features extracted from both sets of ROIs were compared using the intraclass correlation coefficient (ICC) to determine feature reproducibility.

Figure 2 ROI annotation illustration. (a–c) An example of the red/green/blue rectangle ROI segmented in one AP/PP/LP CEUS frame when the tumor displayed clearly. Abbreviations: ROI, region of interest; AP, arterial phase; PP, portal venous phase; LP, late phase; CEUS, contrast-enhanced ultrasound.

Enrolled patients were randomly divided into training (n = 75) and validation (n = 40) cohorts. To minimize model-induced bias, convolutional neural networks (CNN) were employed for the four models based on CEUS cines in different phases consisting of a two-dimensional (2D) convolution layer, 2D max-pooling layer, ReLU activation function, vector of locally aggregated descriptors (VLAD), and a fully connecting layer (FCL) (Figure 3). The inputs of the CEUS-AP, CEUS-PP, CEUS-LP, and CEUS-MP models were different CEUS cines in the arterial, portal venous, late, and the above-mentioned three phases, respectively. In the four CEUS AI models, convolution layers automatically learn the imaging features from each frame of CEUS, and VLAD is responsible for the quantitative analysis of enhancement patterns over time for the CEUS cines. The FCL guide model predicted the probability of early recurrence for each patient. A ten-fold validation method was adopted in the training phase to optimize the four CEUS AI models. To improve the prediction performance and reduce the risk of overfitting, CEUS cines were split over time into multiple independent samples at two frames per second; in this way, the training population was augmented for better learning. Data augmentation techniques, including random rotations, flips, and intensity variations, were employed to augment the diversity of the training dataset. Moreover, depth-wise convolution was selected to reduce the number of parameters in the training phase. Both L1 and L2 regularization were employed to combat overfitting. L1 regularization promoted sparsity and can perform feature selection by driving less important weights to zero, while L2 regularization penalized large weights to ensure the model was less sensitive to small fluctuations in the input data. This combined approach was chosen to create a more robust and generalizable model given the high-dimensional feature space relative to our sample size. We implemented early stopping based on the validation loss to halt training once performance on the validation set ceased to improve. In this study, the convolutional layers employed a depthwise separable convolution structure to replace the conventional 3D convolution operations, separating the processes of feature convolution filtering and the integration of cross-channel features. A global pooling layer was incorporated before the fully connected layers, aggregating features across temporal and spatial dimensions to reduce the number of neural nodes that the fully connected layers need to process. Through a series of specialized network design strategies, the parameter count of R-DLCEUS was reduced to approximately 20,000. During the training phase, the model used cross-entropy as the objective function and optimized the parameters using the stochastic gradient descent method. Each batch consisted of 16 samples, and the model was trained for 70 epochs. The initial learning rate was set to 0.001 and was reduced by a factor of 0.5 at the 15th, 30th, and 55th epochs. A momentum coefficient of 0.9 was applied during training. For the CEUS-MP model, the single-channel cine loops from the AP, PP, and VP were concatenated along the channel dimension to form a multi-channel input, which was then processed by the subsequent 2D convolutional network. The area under the receiver operating characteristic curve (AUC) was adopted to quantitatively measure the prediction performance of the CEUS AI models.

Figure 3 The overall network structure of CEUS AI models. CNN were employed for the four models based on CEUS cines in different phases. Input step: The CEUS cines of AP/ PP/LP/MP were inputted into the CNN model, respectively. Feature extraction step: The features extracted from three phases were then integrated into one feature collection which demonstrated the characteristics of the entire CEUS cine. 2D Conv x included 2D convolution layer, 2D max-pooling layer, and ReLU activation function. Output step: probability was calculated to estimate risk of early recurrence after hepatectomy. Abbreviations: CEUS, contrast-enhanced ultrasound; AI, artificial intelligence; CNN, convolutional neural network; AP, arterial phase; PP, portal phase; LP, late phase; MP, multiple phases; 2D Conv, two-dimensional convolution; VLAD, vector of locally aggregated descriptors; FCL, full connected layer.

Building Combined Individualized Nomogram

To incorporate information from clinical variables with CEUS cines, an individualized model was developed to preoperatively predict early recurrence after hepatectomy. Specifically, four models developed from CEUS video data (CEUS-AP, CEUS-PP, CEUS-LP, and CEUS-MP) were evaluated to identify the optimal predictive model. Subsequently, the predictive probability of the selected model was used as a Radiomics signature and integrated with clinical variables to build a combined nomogram prediction model based on the multi-variable logistic regression analysis. In order to demonstrate the incremental value of DL-CEUS-based nomogram, univariate and multivariate logistic regression analyses were performed to build the Clinical-only model. The analyzed clinical variables included 16 basic clinical characteristics (Table 1). Variable selection followed a two-stage process. First, all parameters showing marginal significance (P < 0.10) in univariate analyses were included; subsequently, these were incorporated into a stepwise multivariable regression model. Only variables demonstrating statistical significance (P < 0.05) in the final multivariable analysis were retained as independent predictors and were used for combined nomogram development. The prediction performance of the nomogram was assessed using AUC, calibration curves, and decision curves.

Table 1 Baseline Characteristics of Patients

Visualization of the CEUS-MP Model

To enhance the interpretability of the DL model’s predictions regarding early recurrence, we employed Selvaraju R.’s method to transform the DL feature maps into pseudo-colored visualization maps.¹⁹ In these maps, warm-colored (red) pixels represent regions with stronger predictive relevance, indicating high-weight features that significantly contribute to the model’s output. Conversely, the cool-colored (blue) pixels denote areas of weaker correlation corresponding to the low-weight factors in the prediction. This visualization approach effectively highlights the critical image regions that influence the model’s decision-making process.

Statistical Analysis

The statistical software and packages used were Python (version 3.11), PyTorch (version 2.0.1), R (version 3.4.4), and computeC. The chi-square test was used to compare categorical variables. Student’s t-test or the Mann–Whitney test, as appropriate, was used to compare continuous variables. All statistical tests were two-sided, and differences were considered significant at P < 0.05.

Results

Clinical Characteristics

Among 115 patients, there were 93 males (93/115, 80.9%) and 22 females (22/115, 19.1%). The mean tumor size was 3.1 ± 0.9 cm (range, 1.0–5.0 cm). Postoperative follow-up results revealed that among the115 patients, 30 (30/115, 26.1%) experienced early recurrence, whereas 85 (85/115, 73.9%) showed no evidence of early recurrence. There were no significant differences in the baseline characteristics between the training (n = 75) and validation (n = 40) cohorts. Detailed baseline characteristics of the two cohorts are shown in Table 1.

Predictive Performance of CEUS AI Models

The features demonstrated high reproducibility, with 91% achieving excellent ICC values, indicating strong inter-observer agreement in ROI segmentation. In the training cohort, AUCs of CEUS-MP, CEUS-AP, CEUS-PP, and CEUS-LP reached 0.922 (95% CI: 0.816–0.971), 0.829 (95% CI: 0.712–0.954), 0.816 (95% CI: 0.697–0.941) and 0.808 (95% CI: 0.691–0.938), respectively. The DL-based AI model using multiple CEUS cines (CEUS-MP) achieved the best prediction performance compared with the other three models based on single-phase CEUS cine (CEUS-AP, CEUS-PP, and CEUS-LP) (Figure 4a). Similar results were observed in the validation cohort. In the validation cohort, the AUCs of CEUS-MP, CEUS-AP, CEUS-PP, and CEUS-LP were 0.840 (95% CI: 0.701–0.989), 0.741 (95% CI: 0.670–0.921), 0.719 (95% CI: 0.637–0.909), and 0.703 (95% CI: 0.661–0.889), respectively (Figure 4b). Table 2 provides an overview of the quantitative assessment of the four models. CEUS-MP offered significantly higher AUCs than CEUS-AP, CEUS-PP, and CEUS-LP in both training (P = 0.026, P = 0.014, P = 0.001) and validation (P = 0.029, P = 0.016, P = 0.001) cohorts. However, no significant differences in AUCs were observed between CEUS-AP, CEUS-PP, and CEUS-LP in either the training or validation cohorts (all P > 0.05).

Table 2 Predictive Performance of CEUS-MP, CEUS-AP, CEUS-PP, and CEUS-LP in Training and Validation Cohorts

Figure 4 ROC curves of four CEUS AI models. (a) ROC curves of CEUS AI models in training cohort. (b) ROC curves of CEUS AI models in validation cohort. CEUS-MP achieved the best prediction performance compared with CEUS-AP, CEUS-PP, and CEUS-LP in both cohorts.

The Individualized Predictive Value of Combined Nomogram

Multivariable regression analysis revealed that serum albumin (< 35 g/L) (HR = 1.146, P = 0.034), AFP (> 1000 ng/mL) (HR = 1.908, P = 0.028), and the CEUS-MP signature (HR = 5.141, P < 0.001) were independent preoperative predictors of early recurrence (Table 3). An individualized nomogram was built based on these three significant values (Figure 5a). In the training and validation cohorts, the AUCs of the nomogram for early recurrence was 0.945 (95% CI: 0.861–0.965) and 0.871 (95% CI: 0.751–0.970) for early recurrence, respectively. (Figure 5b). The nomogram achieved a sensitivity of 88.9% (95% CI: 75.9–96.1%) and specificity of 98.1% (95% CI: 81.6–99.1%) in the training cohort. In the validation cohort, the sensitivity and specificity of the nomogram were 83.0% (95% CI: 70.4–90.8%) and 82.5% (95% CI: 70.8–91.5%), respectively. The nomogram showed good calibration for early recurrence prediction (Figure 5c). The Hosmer-Lemeshow test showed that there were no significant differences in early recurrence prediction between the nomogram and the ideal reference curve (P = 0.415). The decision curve analysis of the nomogram is presented in Figure 5d. AFP and serum albumin were selected to construct a Clinical-only model. The Clinical-only model had an AUC of 0.573 (95% CI: 0.501–0.675) in the training cohort with a 0.554 (95% CI: 0.512–0.685) accuracy and an AUC of 0.528 (95% CI: 0.500–0.618) in the validation cohort with a 0.545 (95% CI: 0.509–0.678) accuracy. The nomogram achieved a significantly higher predictive performance compared to the Clinical-only model in both training and validation cohorts (Both P < 0.001). The decision curves showed that if the threshold probability of a patient was > 30%, integrating CEUS-MP signatures and clinical variables (AFP and serum albumin) to predict the patient’s early recurrence added more net benefit than using only clinical variables. In addition, the net benefit of the CEUS-MP signature was similar to that of the nomograms.

Table 3 Prognostic Factors to Predict Early Recurrence of HCC Patients After Hepatectomy

Figure 5 Prediction performance of individualized nomogram, ROC curves, calibration curves and Decision curve analysis. (a) Nomogram for predicting the probability of a patient occur early recurrence after hepatectomy. A patient who obtained high total scores of the three factors tends to have high probability of early recurrence after hepatectomy. ALB, 0 means < 35 g/L, 1 means ≥ 35 g/L; AFP, 0 means < 200ng/ mL, 1 means ≥ 200ng/mL. (b) ROC curves of nomogram in training and validation cohort. (c) Calibration curves of the nomogram for predicting early recurrence in training and validation cohorts. (d) Decision curve analysis for nomogram.

Understanding the R-DLCEUS Model

From the visualization maps of the CEUS-LP model, it can be observed that the model focuses more on the early period AP, suggesting its potential ability to capture the “fast-in” feature, which may be closely associated with the early recurrence of HCC. Additionally, we observed that for some early cases, the attention of the model exhibited a “patchy” pattern (Figure 6). We hypothesize that these heterogeneous attention regions may correspond to areas of necrotic or fibrotic tissue, features often associated with aggressive tumor biology. However, this requires further pathological correlation.

Figure 6 Visualization of the CEUS-LP model. (a–c) Representative time-point images (9s, 12s, 15s; early period AP) from dynamic CEUS in a post-hepatectomy patient with early recurrence. Each set of images displayed, from left to right, the original monochrome ultrasound image, the pseudo-color heatmap, and the heatmap overlaid on the original ultrasound image.

Discussion

This study developed a DL-based model using CEUS to preoperatively predict early recurrence in patients with early-stage HCC patients following hepatectomy. Our results demonstrated that the multiphase CEUS model (CEUS-MP) achieved superior predictive performance compared with single-phase models, with AUC of 0.922 and 0.840 in the training and validation cohorts, respectively. Furthermore, the integration of CEUS-MP signatures with clinical variables (serum albumin and AFP) into a nomogram further enhanced predictive accuracy, yielding AUCs of 0.945 and 0.871 in the training and validation cohorts, respectively. These findings highlighted the potential of CEUS combined with DL as an auxiliary method for preoperative risk stratification in patients with HCC.

While conventional ultrasound remains the first-line imaging tool for liver screening, its inability to assess the tumor vasculature limits its diagnostic and prognostic utility. Since CEUS cines overcome these limitations by providing dynamic, contrast-enhanced visualization of tumor microvasculature, leading to more accurate HCC diagnosis, recurrence prediction, and treatment monitoring.²⁰ Given these advantages, we utilized CEUS cines, rather than conventional ultrasound images, to develop our prediction models in this study, with the help of advanced quantification techniques for more precise analysis. The superior performance of the CEUS-MP model highlighted the importance of leveraging multiphase CEUS data, which capture dynamic tumor perfusion characteristics across the arterial, portal venous, and late phases. This aligns with previous studies emphasizing the prognostic value of CEUS features, such as rapid wash-in and early wash-out, in HCC recurrence prediction.¹² However, traditional CEUS interpretation is limited by subjectivity and inter-observer variability. Our DL approach addressed this limitation by automatically extracting quantitative features from CEUS cines, thereby standardizing the analysis and improving the reproducibility. Huang et al conducted a quantitative analysis of CEUS images of patients with hepatocellular carcinoma (HCC) following radical resection to predict early recurrence.²¹ However, their model achieved an AUC of only 0.57 in the testing cohort, which was significantly lower than that of our proposed model. A potential limitation of their approach was the reliance on a single-frame analysis, the peak contrast intensity of the lesion on CEUS, which may have decreased predictive accuracy. By contrast, our study leveraged continuous multiphase CEUS cine imaging, enabling a more comprehensive assessment and significantly improving the precision of early recurrence prediction.

The integration of CEUS-MP signatures with clinical variables into a nomogram represented a significant advancement in personalized HCC management. Multivariate analysis identified serum albumin and AFP as the independent clinical prognostic predictors. Notably, low ALB levels served as a reliable indicator of advanced hepatic dysfunction, reflecting both the progression of liver carcinogenesis and deteriorating hepatic synthetic capacity.²² Concurrently, elevated AFP served as a biological marker indicating high invasiveness in HCC.²³ In such patients, some microlesions may escape detection by imaging examinations during the initial HCC diagnosis, contributing to early recurrence after surgical resection. This study demonstrated that higher AFP levels were correlated with an increased risk of early tumor recurrence. Therefore, the AFP level held a significant value in predicting the prognosis of therapy. The high sensitivity and specificity of the nomogram in both cohorts suggested its clinical utility in identifying high-risk patients who may benefit from other treatment selections or intensified surveillance. Notably, the decision curve analysis indicated that the nomogram provided a greater net benefit than the clinical variables alone when the threshold probability exceeded 30%, supporting its practical application in clinical decision-making. The visualization maps further enhanced interpretability, revealing that the model focused on regions with heterogeneous enhancement patterns, which may correlate with aggressive tumor biology. The heterogeneous or peripheral enhancement patterns highlighted by the model were often associated with active tumor regions, while the lack of enhancement in certain areas may correlate with central necrosis or fibrosis commonly found in larger lesions.

Limitations

Our study has several limitations. First, it is a retrospective study conducted at a single center, which introduces potential selection bias. Our patient cohort may not fully represent the more heterogeneous patient populations seen across different healthcare institutions. Second, the uniform high-standard scanning protocols and specialized operators may have inflated the performance estimates of our model. Moreover, the relatively small cohort, particularly the limited number of early recurrence events, is a recognized limitation that warrants caution in interpreting the findings and underscores the need for future validation in larger populations. Furthermore, the use of two different ultrasound systems for CEUS acquisition, despite our efforts at intensity normalization, represents a potential technical confounder. Therefore, the generalizability of our findings needs to be further validated in real-world, multi-center settings, and should also explore incorporating additional imaging modalities (eg, MRI or CT) and molecular biomarkers to refine predictive accuracy.

Conclusion

In conclusion, our study demonstrates that a DL-based CEUS framework, particularly when combined with clinical variables, shows strong potential for the preoperative prediction of early recurrence in patients with HCC undergoing hepatectomy. While these initial results are promising, further validation in large-scale, multi-center, prospective cohorts is essential to confirm its generalizability and establish clinical utility. If validated, this approach could facilitate personalized postoperative management and potentially improve long-term patient outcomes. Future work should also focus on integrating this model with other imaging modalities, such as CT or MRI, to further enhance predictive accuracy.

Ethics Approval and Consent to Participate

This study was approved by the Institutional Review Board of The Second Affiliated Hospital of Nanchang University.

Acknowledgments

The authors thank the data collectors for their efforts and interest in participating in data collection. We would like to thank the patients who willingly provided all the necessary information without any reservation.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This study was supported by grants from the National Natural Science Foundation of China (Grant No. 82360348), the Jiangxi Provincial Natural Science Foundation (Grant No. 20232BAB216096), and the Jiangxi Ganpo Outstanding Talent Support Program–Academic and Technical Discipline Leader Development Project (Grant No. 20243BCE51175).

Disclosure

All authors have no conflicts of interest to declare in this work.

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