Category: 3. Business

Introduction

Bloodstream infections (BSIs) are a major global health problem in the elderly as age-related immune defects, multiple comorbid conditions and diminished physiological reserves predispose these patients to high morbidity, prolonged hospitalization and increased mortality.^1,2 Although Escherichia coli (E.coli) is the predominant pathogen causing BSIs, non-E.coli organisms such as Pseudomonas aeruginosa and Klebsiella pneumoniae associated with BSIs are known to be more severe, and result in higher mortality and complex treatment requirements necessitating a longer hospital stay.³ The elderly are at particular risk for complicated clinical courses due to late diagnosis which may limit prompt initiation of appropriate treatment, thereby emphasizing the need for pathogen-specific risk stratification approach.

Neutrophil-to-platelet ratio (NPAR) also reflects both systemic inflammation and thrombotic risk.⁴ Elevated NPAR is associated with poor prognosis in sepsis and other bacterial infections, but the role of NPAR in predicting E.coli bloodstream infection (BSI) among elderly patients was not well studied.⁵ With development of microfluidics and novel phage-based detection systems, combining NPAR with pathogen specific diagnostic tools holds promise in the early management of E.coli BSI. Though evidence suggests that patients experience higher mortality in non-E.coli compared to E.coli infection causing BSIs, there is currently no study that has directly compared risk of mortality between E.coli and non- E.coli BSIs among elderly population. Furthermore, the utility of NPAR in predicting E.coli BSI among elderly patients remains unexplored.

This study aimed to compare mortality between E.coli versus non-E.coli BSIs among elderly inpatients, explore potential utility of NPAR as a diagnostic biomarker to predict E.coli BSI and its prognostic implications among them.

Methods

Study Design and Participants

This single-center, retrospective cohort study encompassed 527 elderly patients diagnosed with BSIs between December 2011 and February 2024 at the Second Medical Center of the Chinese PLA General Hospital through the hospital’s infection information system. The inclusion criteria were: (1) age greater than 65 years; and (2) availability of complete medical records. The exclusion criteria were as follows: (1) Incomplete medical records. The study protocol was reviewed and approved by the Chinese PLA Hospital Ethical Committee (Approval No.NO. S2024-359-02) and complied with the Declaration of Helsinki. Due to the retrospective design, informed consent was waived.

Data Collection

Data on NPAR levels and other covariates, including demographic and clinical factors, were collected at baseline. NPAR was measured as both a continuous variable and categorized into tertiles (T1, T2, and T3). The outcome of interest was the occurrence of E.coli BSI, which was confirmed by blood culture. A BACT/ALERT 3D automatic blood culture instrument (bioMérieux, France) was used for blood culture, a Vitek2 Compact automatic microbiological identification and antimicrobial susceptibility analysis system (bioMérieux, France) was used for strain identification and antimicrobial susceptibility testing. Baseline data were collected on a range of demographic and clinical characteristics, including age, gender, department, smoking status, comorbidities (eg, diabetes mellitus, hypertension, coronary disease), and clinical interventions (eg, number of operations, use of ventilator, central venous catheter, urinary catheter, chemotherapy, radiotherapy, blood transfusion, polypharmacy regimens). The hospitalization duration was also recorded as a continuous variable.

Statistical Analysis

Descriptive statistics were used to summarize the baseline characteristics of the participants. Continuous variables were expressed as means with standard deviations, and categorical variables were reported as frequencies and percentages. Categorical variables were compared using the chi-square or Fisher’s exact test; continuous variables were analyzed using the Student’s t-test, Mann–Whitney U-test or Kruskal–Wallis test as appropriate; logistic regression was used to assess the predictive value of NPAR for E. coli BSI; Cox proportional hazards models were applied for survival analysis; and the Kaplan–Meier method with Log rank test was used to compare mortality between groups. To evaluate the association between NPAR and E. coli BSI, we constructed three models in our analysis: Model 1: Unadjusted crude model. Model 2: Adjusted for age and sex. Model 3: Based on Model 2, we further adjusted for variables that showed statistical significance in the univariate analysis, as well as through reverse adjustment, including department, coronary disease, and combination of drug. The odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for both continuous NPAR and NPAR tertiles (T1 as the reference group). The dose-response relationship for continuous NPAR was assessed, and the trend across NPAR tertiles was evaluated using a likelihood ratio test. The non-linearity of the dose-response relationship was examined using a non-linear regression model. The p-value for trend was calculated to assess the monotonicity of the dose-response across tertiles of NPAR. Statistical significance was set at P < 0.05 for all analyses. All statistical analyses were performed using R (version 3.6.3) statistical software.

Results

Baseline Characteristics

A cohort of 510 elderly participants (mean age 89.9 ± 8.5 years) meeting inclusion and exclusion criteria was stratified into E.coli BSI (n=92, 18.2%) and non-E.coli BSIs groups (n=418, 81.8%) based on the causative agent of bloodstream infection. The baseline characteristics of the overall cohort and the two groups are summarized in Table 1. No intergroup differences in age (P=0.978), gender (P=0.229), smoking status (P=0.193), diabetes (P=0.614), or hypertension (P=1.0). Higher coronary disease prevalence in non-E.coli group (57.2% vs 32.3%, P<0.001) and comparable surgical frequency (P=0.441).

Clinical Interventions and Outcomes

As shown in Table 2, 49.1% (n = 510) patients did not require ventilator support, with a higher proportion of patients in the E. coli group (61.3%) compared to the non-E.coli group (46.4%) (P = 0.045). Similarly, the duration of central venous catheter use was significantly longer in the E.coli group, with 25.8% of patients requiring it for more than 90 days, compared to 14.1% in the non-E.coli group (P = 0.023). Blood transfusion was more frequently administered in the E.coli group (73.1% vs 58.1%, P = 0.01). The length of hospital stay did not differ significantly between the two groups (P = 0.563), with a median length of stay of 90.0 days (IQR: 65.4, 96.0) in the overall cohort.

Table 2 Clinical Interventions and Outcomes of Enrolled Bloodstream Infection Patients

Departments and Infection Source Distribution

There was a statistically significant difference between E.coli group and non-E.coli groups regarding the departments in which patients were hospitalized (P = 0.046) (Table 1). The distribution of departments of patients with E.coli bloodstream infections were as follows: Cardiology (16.30%), Endocrinology (3.26%), Gastroenterology (35.87%), Hematology (1.09%), ICU (4.35%), Nephrology (4.35%), Neurology (5.43%), Oncology (2.17%), and Respiratory Medicine (10.87%) (Figure 1). The distribution of infection sources among patients with E.coli bloodstream infections were as follows: Biliary infection source (28.26%), non-biliary intra-abdominal infection (3.26%), Pulmonary infection source (16.30%), Unidentified infection source (23.92%), and Urinary tract infection source (28.26%) (Figure 1). Also, we found that non-E. coli pathogens were the primary contributors. Among these, Staphylococcus species were the most prevalent, accounting for 125 isolates out of 510 total samples (Supplementary Table 1).

Figure 1 Distribution of infection sources and departments in patients with Escherichia coli bloodstream infections. (A) Distribution of infection sources in patients with Escherichia coli bloodstream infections. (B) Distribution of infection sources in internal medicine bloodstream Infection patients with Escherichia coli. (C) Distribution of infection sources in surgeon bloodstream infection patients with Escherichia coli. (D) Distribution of departments of patients with Escherichia coli bloodstream infections.

NPAR Associations with E.coli BSI

The association between NPAR and E.coli BSI was evaluated using three models (Table 3). All models demonstrated a statistically significant inverse relationship between continuous NPAR and E.coli BSI risk: Model 1, OR=0.88 (95% CI: 0.84, 0.93; P < 0.001), Model 2, OR =0.88 (95% CI: 0.84, 0.92; P < 0.001), and in Model 3, OR=0.89 (95% CI: 0.84, 0.94; P < 0.001). Using Tertile 1 (T1) as the reference group, in Tertile 2 (T2), the odds of infection were significantly reduced, with Model 1(OR=0.53; 95% CI: 0.32, 0.89; P = 0.017), Model 2(OR= 0.50; 95% CI: 0.29, 0.84; P = 0.01) and Model 3(OR =0.46; 95% CI: 0.26, 0.81; P = 0.008). In Tertile 3 (T3), the odds of infection were even more substantially reduced, with Model 1(OR =0.21; 95% CI: 0.11, 0.40; P < 0.001), Model 2 (OR=0.21; 95% CI: 0.11, 0.39; P < 0.001), and Model 3 (OR= 0.23; 95% CI: 0.11, 0.46; P < 0.001). Furthermore, a statistically significant trend towards decreasing odds of infection across increasing tertiles of NPAR was observed in all models (P for trend < 0.001). These findings suggest that higher NPAR levels correlate with a reduced likelihood of E.coli BSI, with the risk decreasing progressively across tertiles. This aligns with NPAR’s role as a composite inflammatory marker, where elevated values may reflect an attenuated susceptibility to systemic infection.

Table 3 Associations of NPAR with Escherichia coli Bloodstream Infections in Elder Patients

Dose-Response Relationship Between NPAR and E.coli BSI

A linear relationship between NPAR and E.coli BSI was statistically significant (P < 0.05), while a non-linear relationship was not evident (P = 0.424, Cutoff value =19.4) (Figure 2). Therefore, as NPAR increases, there is a linear decrease in E.coli BSI risk. Hence NPAR may serve as a continuous protective marker rather than having a threshold at a particular level.

Figure 2 The dose-response relationship between NPAR and Escherichia coli bloodstream infection. (NAPR: neutrophil-to-platelet ratio).

Survival Analysis

The Kaplan-Meier survival plots of patients with E.coli and non-E.coli BSIs are shown in Figure 3, which revealed higher survival probability of patients with E coli compared with non-E coli counterparts (HR=0.43; 95% CI 0.21, 0.88, P=0.021). Notably, the survival probability for patients with non-E.coli infections dropped more rapidly over time, whereas the E.coli group exhibited a more gradual decline in survival.

Figure 3 Kaplan-Meier curve analysis of patients in Escherichia coli and non-Escherichia coli bloodstream infection groups.

Antimicrobial Resistance Pattern

As shown in Table 4 and Figure 4, almost 97.1% of E.coli isolates were resistant to Ampicillin (AMP), 77.1% were resistant to Ciprofloxacin (CIP), 74.3% were resistant to Cefazolin (CEZ), 72.9% were resistant to Levofloxacin (LVX), and 72.7% were resistant to Ampicillin/Sulbactam (AMP/SUL). Overall, E.coli isolates exhibited no resistance to the following antibiotics: Amikacin (AMK), Ertapenem (ETP), Tigecycline (TGC), and Cefotetan (CET). The antibiotic with the highest resistance rate in E.coli isolates from bloodstream infection patients in the respiratory department were AMP (100%), Ceftriaxone (CTX, 100%), CEZ (100%). AMP (100%), Doxycycline (DXY, 100%), Gentamicin (GEN, 100%). In contrast, the general surgery department had a lower resistance rate for antibiotics like Ampicillin/Sulbactam (60.0%) and Gentamicin (66.7%). Notably, antibiotics like Amikacin, Ertapenem, and Tigecycline showed a resistance rate of 0% across all departments. Resistance to Cefotetan was absent across all departments tested.

Table 4 Antibiotic Resistance Rate of Escherichia coli in Bloodstream Infection Patients

Figure 4 Overall antibiotic resistance rate of Escherichia coli.

Discussion

Our study provides several important implications to be addressed clinically for BSIs in extremely elderly patients. First, we demonstrated that non-E. coli BSIs had a significantly higher risk of mortality compared to E. coli BSI in extremely elderly inpatients with the mean age of 89.9 ± 8.5 years, indicating the clinical importance of identifying the pathogen causing infection. We also provided that low NPAR was inversely associated with the presence of E. coli BSI which may be useful early identification and risk stratification in elderly, leading to a more tailored and early intervention to be initiated.

Among 30,923 cases of E.coli bloodstream infections, 2961 cases of 30-day mortality were observed, resulting in an overall 30-day mortality of 9.6% (2961/30,923).⁶ Hospital-acquired or third-generation-cephalosporin-resistant E.coli BSI showed significantly higher mortality rates compared to community-acquired or third-generation-cephalosporin-susceptible E.coli BSI.⁶ In our cohort of elderly patients, non-E.coli BSIs were associated with higher mortality compared to E.coli BSIs, even after adjustment for demographics and clinical factors, which is consistent with previous studies. Various studies have reported that the reasons why high mortality is associated with non-E.coli infections include difficulty in diagnosis, limited treatment options, and increased infection severity due to multidrug-resistant organisms or high virulence.^6,7 As elderly patients have weak immunity and often suffer from multiple underlying diseases, there would be a greater concern about their course and response to treatment.^8,9 Our findings indicate that it is vital to early detection and proper management for non-E.coli BSIs. Previous studies have reported that E.coli BSI in elderly patients predominantly originate from urinary tract infections,¹⁰ which is consistent with the findings of our study, where urinary and biliary tract infections were identified as the leading sources. These infections typically elicit a relatively mild systemic inflammatory response, characterized by only a modest elevation in neutrophil counts and minimal alterations in platelet levels. In contrast, non-E. coli BSIs – such as those caused by Staphylococcus aureus, Klebsiella pneumoniae, or Enterococcus faecalis – are more often associated with catheter-related infections, non-biliary intra-abdominal infections, and respiratory tract infections, which usually trigger a more severe systemic inflammatory response.¹¹ It is reported that thrombocytopenia was independently associated with mortality among patients with BSIs.¹² This aligns with prior studies showing NPAR and related indices (eg, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio) as strong, independent predictors of sepsis severity and mortality.¹³ A high NPAR captures a dual-risk profile: amplified inflammatory response and impaired hemostatic balance, both of which have been independently linked to increased mortality in bloodstream infections.¹⁴ In our multivariable Cox regression models, high NPAR remained significantly associated with mortality even after adjusting for age, comorbidities, and infection source, indicating its robust prognostic value. Clinically, low NPAR may serve as an early indicator of E. coli BSI and help clinicians stratify patients who are likely to have more benign infection courses, potentially guiding early empirical therapy decisions and resource allocation. Conversely, persistently elevated NPAR should prompt vigilance for non-E. coli pathogens or complicated infection sources.

The prevalence of E.coli producing extended-spectrum beta-lactamases (ESBL) among BSI patients was 40.98%. E.coli isolates were generally sensitive to carbapenems and β-lactam/β-lactamase inhibitor combinations. Hospital-acquired infections, biliary tract infections, gastric tube insertion procedures, and prior cephalosporin administration were identified as independent risk factors for the isolation of ESBL-producing strains. ESBL positivity, hospital-acquired infections, and cancer were independent risk factors for mortality.¹⁵ Meta-analysis results indicate that it is necessary to shift current treatment practices from antibiotic escalation strategies that delay appropriate therapy to early, relatively aggressive, and comprehensive antibiotic treatment, especially in patients with BSIs caused by Klebsiella pneumoniae or E.coli.¹⁶ Choi et al found that E.coli is the most common pathogenic microorganism in BSIs, accounting for 32.3%, and the adjusted hazard ratio (aHR) for 30-day mortality and subsequent medical costs for E.coli BSI was lower compared to other microorganisms causing BSI. E.coli-BSI resulted in lower mortality rates during the first 7 days and from days 8 to 30 compared to BSIs caused by other microorganisms.¹⁷ Our study found that the proportion of internal medicine patients was higher in the non-E.coli group, while E.coli infections were more common in the surgical department. The incidence of coronary artery disease was lower in the E.coli group, whereas it was higher in the non-E.coli group. There were significant differences in the duration of ventilator use and central venous catheter use between the E.coli and non-E.coli groups, with patients in the non-E.coli group having a longer durations of use.

NPAR is a simple, readily accessible measure derived from routine blood tests, and it has been proposed as a marker for various infectious and inflammatory conditions.^18–20 As shown in Figure 2, the OR for E.coli BSI decreases with increasing NPAR values. The analysis indicates a significant overall association between NPAR and the risk of E.coli bloodstream infection. Overall P-value: <0.001, indicating a strong statistical significance for the association between NPAR and the risk of E.coli bloodstream infection. In our analysis, the non-linear regression yielded a p-value of 0.424, indicating no significant evidence of a non-linear relationship between NPAR and infection risk. Therefore, we conclude that the relationship is best modeled as linear, suggesting a consistent, proportional association between NPAR and infection risk. OR (95% CI): The odds ratio decreases progressively with higher NPAR levels, approaching 1, indicating that higher NPAR values are associated with a reduced risk of E.coli bloodstream infection. Our findings suggest that low NPAR values are strongly associated with an increased risk of E.coli infection, with patients in the lowest NPAR tertile having substantially higher odds of having an E.coli infection compared to those in the highest tertile. This association remained consistent across various analytical models, further reinforcing the evidence for NPAR as a predictor of E.coli infections. Although the precise mechanisms linking NPAR to infection risk are not fully understood, it is believed that NPAR reflects the balance between inflammatory and immune responses.^21–23 During E. coli BSI, lipopolysaccharides (LPS) derived from the bacterial cell wall activate macrophages and other immune cells via Toll-like receptor 4 (TLR4) and related signaling pathways.²⁴ This activation triggers a cascade release of proinflammatory cytokines, including interleukin (IL)-6, IL-1, and TNF-α. Among these, IL-6 plays a central role in the IL-6–liver axis by markedly stimulating hepatic synthesis of thrombopoietin (TPO), the key regulator of megakaryocyte proliferation and differentiation.²⁵ Elevated TPO levels subsequently enhance platelet production, resulting in reactive thrombocytosis.²⁵ In addition, several inflammatory cytokines, such as IL-6, IL-11, and granulocyte-macrophage colony-stimulating factor (GM-CSF), may directly or indirectly act on hematopoietic stem and progenitor cells to promote megakaryocyte maturation and platelet release. In contrast, non-E. coli BSIs like S. aureus can induce platelet aggregation and clearance through α-toxin- and ClfA-mediated mechanisms, thereby promoting thrombocytopenia and contributing to an elevated NPAR.²⁶ Together, these inflammatory responses provide a plausible explanation for the increased platelet counts frequently observed in E. coli BSI and may partially underlie the association between a low NPAR and disease progression.

The antibiotic resistance profiles of E.coli isolates in this study reveal significant variability across different clinical departments. High resistance rates to Ampicillin, Ampicillin/Sulbactam, and Cefazolin are consistent with previous reports of widespread beta-lactam resistance.^27,28 However, the absence of resistance to Amikacin and Ertapenem is encouraging, as these antibiotics are vital for treating multidrug-resistant infections. The elevated resistance rates to Ciprofloxacin and Levofloxacin in the respiratory and cardiology departments may be linked to the frequent use of these antibiotics in those specialties. The lack of resistance to Tigecycline and Ertapenem across all departments suggests that these antibiotics could serve as effective treatment options for E.coli bloodstream infections. The relatively low resistance rates to Imipenem/Cilastatin and Meropenem in most departments further highlight the importance of carbapenems in managing severe E.coli infections. The significant resistance to Ticarcillin/Clavulanate, particularly in the general surgery department, underscores the need for more judicious use of this antibiotic to prevent the development of further resistance. Doctors should emphasize careful monitoring of antibiotic use, restricting the use of broad-spectrum antibiotics, and promoting individualized treatment guided by sensitivity testing to reduce the spread of resistance.^29,30

Conclusion

The NPAR, as demonstrated in our study, holds significant potential as a simple, cost-effective, and globally applicable biomarker for early identifying and targeted managing E. coli BSI in elderly patients. Low NPAR is associated with an increased likelihood of E. coli BSI and can help clinicians identify high-risk patients who may benefit from early therapeutic interventions. In future research, the role of NPAR as a predictive and prognostic biomarker for E.coli BSI could be further extended to populations across different age groups, with subsequent studies needed to explore longitudinal NPAR trends during treatment as a monitoring biomarker, and further elucidate the underlying biological mechanisms linking NPAR with infection susceptibility and clinical outcomes in E. coli BSI.

Data Confidentiality Statement

All patient data were handled in strict compliance with confidentiality regulations. The data were anonymized prior to analysis, and no identifiable personal information was disclosed or shared outside the research team.

Data Sharing Statement

The data are available from the corresponding author on reasonable request.

Consent to Participate

This research was approved and waived the consent by the Ethics Committee of Chinese PLA General Hospital (NO. S2020-25601). Informed consent was not required due to the retrospective nature of the study design. All authors confirm this study adheres to the Declaration of Helsinki.

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 retrospective study was supported by National Clinical Research Center for Geriatric Diseases Open Project: NCRCG-PLAGH-2022012 and NCRCG-PLAGH-2023004; Beijing Natural Science Foundation: L222014.

Disclosure

All authors in this study declare no competing conflicts.

References

1. Leibovici-Weissman Y, Tau N, Yahav D. Bloodstream infections in the elderly: what is the real goal? Aging Clin Exp Res. 2021;33(4):1101–1112. PMID: 31486996. doi:10.1007/s40520-019-01337-w

2. Lin H, Gao Y, Qiu Y, et al. The prognostic factors of bloodstream infection in immunosuppressed elderly patients: a retrospective, single-center, five-year cohort study. Clin Interv Aging. 2022;17:1647–1656. PMID: 36425478; PMCID: PMC9680683. doi:10.2147/CIA.S386922

3. Mondal U, Warren E, Bookstaver PB, et al. Incidence and predictors of complications in Gram-negative bloodstream infection. Infection. 2024;52(5):1725–1731. PMID: 38436912; PMCID: PMC11499525. doi:10.1007/s15010-024-02202-3

4. Gong Y, Li D, Cheng B, et al. Increased neutrophil percentage-to-albumin ratio is associated with all-cause mortality in patients with severe sepsis or septic shock. Epidemiol Infect. 2020;148:e87. PMID: 32238212; PMCID: PMC7189348. doi:10.1017/S0950268820000771

5. Mousa N, Salah M, Elbaz S, et al. Neutrophil percentage-to-albumin ratio is a new diagnostic marker for spontaneous bacterial peritonitis: a prospective multicenter study. Gut Pathog. 2024;16(1):18. PMID: 38561807; PMCID: PMC10985869. doi:10.1186/s13099-024-00610-2

6. MacKinnon MC, McEwen SA, Pearl DL, et al. Mortality in Escherichia coli bloodstream infections: a multinational population-based cohort study. BMC Infect Dis. 2021;21(1):606. PMID: 34172003; PMCID: PMC8229717. doi:10.1186/s12879-021-06326-x

7. Leistner R, Gürntke S, Sakellariou C, et al. Bloodstream infection due to extended-spectrum beta-lactamase (ESBL)-positive K. pneumoniae and E. coli: an analysis of the disease burden in a large cohort. Infection. 2014;42(6):991–997. PMID: 25100555. doi:10.1007/s15010-014-0670-9

8. Chen Q, Ma G, Cao H, et al. Risk factors and diagnostic markers for Escherichia coli bloodstream infection in older patients. Arch Gerontol Geriatr. 2021;93:104315. PMID: 33310397. doi:10.1016/j.archger.2020.104315

9. Giannella M, Pascale R, Toschi A, et al. Treatment duration for Escherichia coli bloodstream infection and outcomes: retrospective single-centre study. Clin Microbiol Infect. 2018;24(10):1077–1083. PMID: 29371138. doi:10.1016/j.cmi.2018.01.013

10. Choi HJ, Jeong SH, Shin KS, et al. Characteristics of Escherichia coli urine isolates and risk factors for secondary bloodstream infections in patients with urinary tract infections. Microbiol Spectr. 2022;10(4):e0166022. PMID: 35862950; PMCID: PMC9430824. doi:10.1128/spectrum.01660-22

11. Timsit JF, Ruppé E, Barbier F, et al. Bloodstream infections in critically ill patients: an expert statement. Intensive Care Med. 2020;46(2):266–284. PMID: 32047941; PMCID: PMC7223992. doi:10.1007/s00134-020-05950-6

12. Adelman MW, Casarin S, Kurian J, et al. Platelets and mortality in bloodstream infection: a multicenter cohort study. Clin Microbiol Infect. 2025;31(10):1733–1736. PMID: 40744277; PMCID: PMC12377423. doi:10.1016/j.cmi.2025.07.021

13. Gao L, Shi Q, Li H, et al. Prognostic value of the combined variability of mean platelet volume and neutrophil percentage for short-term clinical outcomes of sepsis patients. Postgrad Med. 2021;133(6):604–612. PMID: 32912023. doi:10.1080/00325481.2020.1823137

14. Cecconi M, Evans L, Levy M, et al. Sepsis and septic shock. Lancet. 2018;392(10141):75–87. PMID: 29937192. doi:10.1016/S0140-6736(18)30696-2

15. Zhao S, Wu Y, Dai Z, et al. Risk factors for antibiotic resistance and mortality in patients with bloodstream infection of Escherichia coli. Eur J Clin Microbiol Infect Dis. 2022;41(5):713–721. doi:10.1007/s10096-022-04423-6

16. Lodise TP, Zhao Q, Fahrbach K, et al. A systematic review of the association between delayed appropriate therapy and mortality among patients hospitalized with infections due to Klebsiella pneumoniae or Escherichia coli: how long is too long? BMC Infect Dis. 2018;18(1):625. doi:10.1186/s12879-018-3524-8

17. Choi MH, Kim D, Kim J, et al. Shift in risk factors for mortality by period of the bloodstream infection timeline. J Microbiol Immunol Infect. 2024;57(1):97–106. doi:10.1016/j.jmii.2023.11.008

18. Lan CC, Su WL, Yang MC, et al. Predictive role of neutrophil-percentage-to-albumin, neutrophil-to-lymphocyte and eosinophil-to-lymphocyte ratios for mortality in patients with COPD: evidence from NHANES 2011–2018. Respirology. 2023;28(12):1136–1146. doi:10.1111/resp.14589

19. Ji W, Li H, Qi Y, et al. Association between neutrophil-percentage-to-albumin ratio (NPAR) and metabolic syndrome risk: insights from a large US population-based study. Sci Rep. 2024;14(1):26646. PMID: 39496695; PMCID: PMC11535182. doi:10.1038/s41598-024-77802-y

20. Dong K, Zheng Y, Wang Y, et al. Predictive role of neutrophil percentage-to-albumin ratio, neutrophil-to-lymphocyte ratio, and systemic immune-inflammation index for mortality in patients with MASLD. Sci Rep. 2024;14(1):30403. PMID: 39638820; PMCID: PMC11621551. doi:10.1038/s41598-024-80801-8

21. Ding W, La R, Wang S, et al. Associations between neutrophil percentage to albumin ratio and rheumatoid arthritis versus osteoarthritis: a comprehensive analysis utilizing the NHANES database. Front Immunol. 2025;16:1436311. PMID: 39917306; PMCID: PMC11799277. doi:10.3389/fimmu.2025.1436311

22. Yang F, Dong R, Wang Y, et al. Prediction of pulmonary infection in patients with severe myelitis by NPAR combined with spinal cord lesion segments. Front Neurol. 2024;15:1364108. PMID: 38481940; PMCID: PMC10932995. doi:10.3389/fneur.2024.1364108

23. Zawiah M, Khan AH, Abu Farha R, et al. Predictors of stroke-associated pneumonia and the predictive value of neutrophil percentage-to-albumin ratio. Postgrad Med. 2023;135(7):681–689. PMID: 37756038. doi:10.1080/00325481.2023.2261354

24. Luo R, Yao Y, Chen Z, et al. An examination of the LPS-TLR4 immune response through the analysis of molecular structures and protein-protein interactions. Cell Commun Signal. 2025;23(1):142. PMID: 40102851; PMCID: PMC11921546. doi:10.1186/s12964-025-02149-4

25. Tsutsumi N, Masoumi Z, James SC, et al. Structure of the thrombopoietin-MPL receptor complex is a blueprint for biasing hematopoiesis. Cell. 2023;186(19):4189–4203.e22. PMID: 37633268; PMCID: PMC10528194. doi:10.1016/j.cell.2023.07.037

26. Xia Y, Sun C, Zhou K, et al. Platelet glycoprotein Ibα cytoplasmic tail exacerbates thrombosis during bacterial sepsis. Int J Mol Sci. 2024;25(21):11548. PMID: 39519103; PMCID: PMC11546206. doi:10.3390/ijms252111548

27. Nasrollahian S, Graham JP, Halaji M. A review of the mechanisms that confer antibiotic resistance in pathotypes of E. coli. Front Cell Infect Microbiol. 2024;14:1387497. PMID: 38638826; PMCID: PMC11024256. doi:10.3389/fcimb.2024.1387497

28. Sundaramoorthy NS, Shankaran P, Gopalan V, et al. New tools to mitigate drug resistance in Enterobacteriaceae – escherichia coli and Klebsiella pneumoniae. Crit Rev Microbiol. 2023;49(4):435–454. PMID: 35649163. doi:10.1080/1040841X.2022.2080525

29. Katip W, Rayanakorn A, Oberdorfer P, et al. Short versus long course of colistin treatment for carbapenem-resistant A. baumannii in critically ill patients: a propensity score matching study. J Infect Public Health. 2023;16(8):1249–1255. PMID: 37295057. doi:10.1016/j.jiph.2023.05.024

30. Katip W, Rayanakorn A, Sornsuvit C, et al. High-loading-dose colistin with nebulized administration for carbapenem-resistant acinetobacter baumannii pneumonia in critically ill patients: a retrospective cohort study. Antibiotics. 2024;13(3):287. PMID: 38534721; PMCID: PMC10967279. doi:10.3390/antibiotics13030287

New digital exhibition delivers a curated selection of artwork from seven of the world’s top galleries to living rooms worldwide

Samsung Art TV users gain access to fifth Art Basel digital art collection, exhibiting global reach of artists and galleries showcased

Samsung Electronics Co., Ltd., global display leader and provider of the Official Art TV of Art Basel, today announced the launch of the 2025 Art Basel Miami Beach Collection, a curated digital exhibition spotlighting 24 contemporary artists who will be showcased at Art Basel Miami Beach in December, available exclusively on Samsung Art Store^[1]

“With the 2025 Art Basel Miami Beach collection, we wanted to bring the distinct energy of the show directly into people’s homes,” said Daria Greene, Head of Content and Curation for Samsung Art Store. “Each artwork carries its own cultural perspective, expanding the ever-growing collection we offer on Samsung Art Store.”

The new 2025 Art Basel Miami Beach Collection features artwork by emerging and established artists from around the world, presented by seven of the world’s top galleries — Instituto de Visión, Kurimanzutto, Meredith Rosen Gallery, Nina Johnson, Vermelho, Sean Kelly and Charlie James Gallery. The hand-selected digital collection reflects the cultural richness and diverse voices that define contemporary art today.

Highlights include:

• Olinda Silvano, “Energía de la visión de Ayahuasca” (2022)
• The Pérez Bros., “Victoria Park” (2025)
• George Nelson Preston, “Apenas Cinco Semanas Da Kissama e as Colinas Do Brasil Nos Surpreenderam” (2019)
• Jennifer Rubell, “40 Hearts” (2018)
• Aycoobo, “Luna Ilena” (2024)

This is the fifth Art Basel digital art collection featured on Samsung Art Store. As part of Samsung’s longstanding partnership with the show, each collection aims to reflect the global reach of artists and galleries showcased at Art Basel, bringing that discovery directly into homes worldwide across the Samsung 2025 TV lineup.

“Art Basel’s partnership with Samsung continues to expand the ways in which our galleries and artists can reach new audiences,” said Vincenzo de Bellis, Chief Artistic Officer and Global Director of Art Basel Fair. “By bringing a curated selection from Art Basel Miami Beach into homes around the world, this initiative extends the fair’s artistic vision beyond the halls of the convention center and broadens the possibilities for discovery, engagement and visibility. We are delighted to see these works presented on Samsung Art Store, reflecting the depth of our exhibitors, the global resonance of their artists and the evolving formats through which contemporary art is experienced today.”

Samsung has led the global TV market for 19 consecutive years^[2], delivering the exceptional picture quality that fine art demands. Samsung Art Store features more than 4,000 works by over 800 artists, including the 2025 Art Basel Miami Beach Collection.

For more information, visit http://www.samsung.com/uk

[1]Art Store subscription and Samsung Account connection required to access full selection of artwork.

[2]Source: Omdia, Feb-2025 (Results are not an endorsement of Samsung)

• B7H3 and PTK7 is co-expressed in multiple solid tumor types, including lung, colorectal, and head and neck cancers, at approximately 30%, 46%, and 27%, respectively
• Deep and durable regressions observed with IDE034 monotherapy in multiple preclinical in-vivo models with B7H3 and PTK7 co-expression
• Enhanced durability with IDE034 and IDE161 PARG inhibitor combination in preclinical in vivo models; targeting to share additional preclinical data supporting mechanistic rationale at a medical conference in H1 2026

SOUTH SAN FRANCISCO, Calif. and SHANGHAI, Dec. 1, 2025 /PRNewswire/ — IDEAYA Biosciences, Inc. (NASDAQ: IDYA), a precision medicine oncology company committed to the discovery and development of targeted therapeutics, announced the clearance of an investigational new drug (IND) application with the U.S. Food and Drug Administration (FDA) for the initiation of a Phase 1 clinical trial to evaluate IDE034, a potential first-in-class bispecific B7H3/PTK7 TOP1 antibody-drug conjugate (ADC).  IDEAYA expects to begin enrolling the study in Q1 2026, initially evaluating patients with solid tumors known to express B7H3 and PTK7, including lung, colorectal, head and neck and ovarian/gynecological cancers.  Based on the Human Protein Atlas database, B7H3/PTK7 has been reported to be co-expressed in lung, colorectal, and head and neck cancers at approximately 30%, 46% and 27%, respectively.

“IND clearance for IDE034 is an important step in expanding our potential first-in-class TOP1 ADC clinical pipeline into bispecific, precision-guided approaches,” said Darrin M. Beaupre, M.D., Ph.D., Chief Medical Officer of IDEAYA Biosciences. “IDE034 has demonstrated robust antitumor activity and selective targeting of B7H3- and PTK7-expressing solid tumor models. The high prevalence of B7H3/PTK7 co-expression in solid tumors such as lung, colorectal, and head and neck cancers underscores its broad indication potential.”

“We are excited to advance our differentiated clinical strategy with now three potentially first-in-class clinical-stage programs focused on enhancing the efficacy of TOP1 ADCs through the PARG DDR combination mechanism.  We believe this approach addresses a key unmet need by improving the durability of response to TOP1 payload-based ADC therapies.  We are targeting to share additional preclinical data to support the PARG and TOP1 ADC combination rationale at a major medical conference in H1 2026,” said Yujiro S. Hata, President and Chief Executive Officer of IDEAYA Biosciences.

Preclinical studies have demonstrated strong anti-tumor activity in B7H3/PTK7-positive tumor models, including deep and durable tumor regressions with IDE034 monotherapy, supporting advancement into clinical development. This co-expression pattern supports the potential for broad monotherapy activity, while the TOP1 payload provides a strong mechanistic rationale for combining IDE034 with IDEAYA’s PARG inhibitor, IDE161.  TOP1 inhibition induces replication stress and DNA damage, which can increase reliance on the PARG pathway; therefore, a IDE034 and IDE161 combination approach may enhance anti-tumor activity in patients with solid tumors that co-express B7H3 and PTK7, consistent with the results that were observed preclinically with this combination.

About IDEAYA Biosciences

IDEAYA is a precision medicine oncology company committed to the discovery, development, and commercialization of transformative therapies for cancer.  Our approach integrates expertise in small-molecule drug discovery, structural biology and bioinformatics with robust internal capabilities in identifying and validating translational biomarkers to develop tailored, potentially first-in-class targeted therapies aligned to the genetic drivers of disease.  We have built a deep pipeline of product candidates focused on synthetic lethality and antibody-drug conjugates, or ADCs, for molecularly defined solid tumor indications.  Our mission is to bring forth the next wave of precision oncology therapies that are more selective, more effective, and deeply personalized with the goal of altering the course of disease and improving clinical outcomes for patients with cancer.

Forward-Looking Statements

This press release contains forward-looking statements, including, but not limited to, statements related to: (i) the timing of the initiation of and enrollment of subjects for  the Phase 1 clinical trial to evaluate IDE034; (ii) the potential frequency of B7H3/PTK7 co-expressed in solid tumors types, including lung, colorectal, and head and neck cancers; (iii) the potential therapeutic benefit of IDE034 as monotherapy and in combination with IDE161, a PARG inhibitor; and (iv) the timing of a data presentation related to the  IDE034 and IDE161, PARG inhibitor, combination at a medical conference.  Preclinical study results are not necessarily predictive of future clinical trial results and/or approval.  Such forward-looking statements involve substantial risks and uncertainties that could cause IDEAYA’s preclinical and clinical development programs, future results, performance or achievements to differ significantly from those expressed or implied by the forward-looking statements. Such risks and uncertainties include, among others, the uncertainties inherent in the drug development process, including IDEAYA’s programs’ early stage of development, the process of designing and conducting preclinical and clinical trials, the regulatory approval processes, the timing of regulatory filings, the challenges associated with manufacturing drug products, IDEAYA’s ability to successfully establish, protect and defend its intellectual property, and other matters that could affect the sufficiency of existing cash to fund operations. IDEAYA undertakes no obligation to update or revise any forward-looking statements. For a further description of the risks and uncertainties that could cause actual results to differ from those expressed in these forward-looking statements, as well as risks relating to the business of IDEAYA in general, see IDEAYA’s Annual Report on Form 10-K dated February 18, 2025 and any current and periodic reports filed with the U.S. Securities and Exchange Commission.

Investor and Media Contact
Joshua Bleharski, Ph.D.
Chief Financial Officer 
[email protected]

SOURCE IDEAYA Biosciences, Inc.

Regulated Open Banking has gone live in New Zealand as part of a phased rollout, “opening the door” to faster loan approvals, easier bill management, and personalised budgeting insights, according to Commerce and Consumer Affairs Minister Scott Simpson.

Open Banking in the country is supported by a set of regulations established under the Customer and Product Data Act 2025.

Released in October, the regulations require the four major banks – ANZ, ASB, BNZ and Westpac – to have certain Open Banking systems ready by 1 December 2025, while Kiwibank will need to be ready by June 2026 for payments, and by December 2026 for other Open Banking services.

“From budgeting tools to faster mortgage comparisons and low-cost payment options, the opportunities and innovations presented by Open Banking are endless,” Simpson said.

“Open Banking makes it easier to switch banks by giving customers a safe, regulated way to share their financial information.

“It will make mortgage applications faster by allowing third-party services to securely gather the right financial documents in one place, especially helpful for people with accounts across different banks.”

The Minister also pointed to the benefits for small businesses in New Zealand.

“Small businesses will also benefit from more choice in financial management and invoicing tools, helping them get paid faster and access innovative, lower-cost payment solutions,” he said.

The Ministry of Business, Innovation and Employment (MBIE), which is providing regulatory oversight of the regime, is accepting applications from organisations that want to become accredited data requestors. In turn, they will receive an ‘accreditation mark’ from MBIE to show they are trusted and verified.

Simpson said: “The regulations, released in October, align with global best practice and build on successful models in Australia and the UK, where Open Banking has sped up home loan approvals and enabled new consumer-friendly apps.

“Importantly, the regulations ensure that security of consumer data is paramount. Data can only be shared under the customer’s explicit consent, and third-party requestors (such as fintechs) must be accredited by the Ministry of Business, Innovation and Employment.”

Further reading: Westpac NZ becomes first bank to hit key Open Banking milestone

Introduction

The Global Cancer Statistics 2022 report indicated that there were nearly 20 million new cancer cases, including 1.9 million new colorectal cancer (CRC) cases, accounting for nearly one-tenth of cancer cases, making it the third most common malignant tumor in the world and the second leading cause of cancer-related deaths.¹ With advances in minimally invasive techniques, locally advanced colorectal cancer (LARC) patients may benefit from neoadjuvant therapy. However, in the presence of metastases, chances of survival are significantly reduced.² Most LARC patients were treated with total mesolectal excision (TME) and neoadjuvant chemoradiotherapy (NACRT) significantly improved the distant metastasis of the cancer.³ The 5-year survival rate and cumulative incidence of 5-year local recurrence of 76% and 6%, respectively, with NACRT prior to TME reduced local recurrence, metastasis, and improved survival of patients with LARC.⁴ This treatment achieved a high pathological complete remission rate (pCR) (44.3%), a high CRT compliance rate (98.8%), and significantly fewer postoperative complications than the TME group.⁵ Therefore, NACRT, followed by resection of en bloc rectum and mesorectum, has become the standard of care for LARC.³

Different factors have a significant role in the stage reduction of LARC after NACRT-TME. Several studies have shown that from a radiation oncology point of view, radiomics column maps of MRI predict good response to NACRT,⁶ and that short-term radiation therapy and long-term chemotherapy not only increase the local response to NACRT but also reduce the risk of systemic recurrence.⁷ In terms of hematological indices, carcinoembryonic antigen (CEA), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-neutrophil index (PNI)⁸ and CRP albumin-lymphocyte index (CALLY) were all recently proposed as novel inflammation-nutrition composite index.⁹ CALLY has been shown to have a critical role in gastric, hepatic and pot-bellied cancers.^9–11 Regarding sarcopenia, which is characterized by loss of skeletal muscle mass (SMA), it is associated with progression-free survival (PFS) and overall survival (OS) in short-term CRC patients undergoing surgery.^12,13

The above factors indicate that inflammatory nutritional indices are key determinants influencing cancer progression and prognosis and possess predictive capacity for postoperative PFS and OS in CRC patients. Unfortunately, no existing models are currently available to assess the risk of NACRT and postoperative outcomes in LARC, thereby limiting the provision of valuable post-treatment guidance. In this study, we developed nomograms combining clinical factors and inflammatory nutritional indices to comprehensively evaluate disease prognosis outcomes. It can predict the probability of recurrence or death in specific individuals, and its most important advantage is that it can assess the risk of clinical events independently based on patient and disease characteristics.¹⁴ Nomograms can incorporate continuous variables and independent risk factors of the disease into prognosis, outperforming clinicians in assessing locally advanced tumors and is widely applicable in practice.¹⁵

Materials and Methods

Patients and Participants

This study retrospectively investigated patients with LARC who were treated with NACRT between January 2020 and May 2024 at Jiangnan University Hospital. The inclusion criteria were as follows: (1) patients with complete peripheral blood cell count data and serum CEA; (2) patients aged 18–79 years; (3) patients diagnosed with LARC after pathological histology, illnesses intestinal obstruction, distant metastases (lung/ovarian/peritoneal), or concurrent bone marrow transplantation during NACRT; patients who were unable to undergo surgery and opted for conservative treatment after NACRT; critically ill patients suffering from cardiac failure, renal failure, or other serious illnesses; and patients with incomplete follow-up data were excluded from the analysis (Figure 1). All patients were staged preoperatively with contrast-enhanced computed tomography of the abdomen and pelvis using MRI of the pelvis and contrast-enhanced computed tomography CT. Pathologic histology of all patients was staged, according to the 8th AJCC TNM. Clinical staging before the start of NACRT (cTNM) was compared with the pathology of the resected specimens after treatment (ypTNM), down to ypTNM stage 0-I as the criterion for grouping.¹⁶ Owing to the specific nature of the disease, the sample size only met the minimum power requirement; however, multicenter studies on rare subgroups will be required in the future.

Treatment Strategies

All patients received a NACRT regimen consisting of a total radiation dose of 45 or 50.4 Gy and continuous infusion or oral (capecitabine) 5-fluorouracil chemotherapy-based chemotherapy. Radiotherapy was delivered in 25 fractions over 5 weeks.¹⁷ Surgery was performed for at least 6 weeks after the last radiotherapy session. All cases were treated by open or laparoscopic surgery. Surgical approaches included Dixon’s procedure, colostomy, Hartmann’s procedure, and Miles’ procedure. Based on pre-NACRT imaging, if lateral pelvic lymph node (LLN) metastasis was suspected, lateral pelvic lymph node dissection was performed concurrently. Adjuvant chemotherapy was considered for all patients regardless of pathological results. The regimen used for adjuvant chemotherapy was as follows: the Roswell Park regimen of intravenous 5-FU plus LV, oral UFT plus l-LV, or oral capecitabine plus oxaliplatin, capecitabine plus oxaliplatin.¹⁸

Date Collection

The following data were collected: (1) Baseline clinical variables: gender, age, Diabetes, Hypertension, body mass index (BMI), Charlson comorbidity index (CCI), nutritional risk screening 2002 (NRS2002) score and L3 skeletal muscle cross-sectional area (L3SM). (2) pre-NACRT, serologic parameters within 24 hours of admission: platelet count, lymphocyte count, neutrophil count, monocyte count, albumin count, C-reactive protein (CRP), CEA, carbohydrate antigen 19–9 (CA199) et al (3) Pathological information: TNM stage before and after NACRT, vascular invasion and nerve invasion et al (4) Follow-up data: overall survival (OS) and progression-free survival (PFS) (months). We calculated L3SMI, NLR, PLR, LMR, PNI and CALLY according to the following formula.

Slice-Omatic, version 5.0 (TomoVision, Magog, Canada) was used to analyse L3SM on CT images. Muscle tissue unit thresholder ranged from −29 HU to 150 HU. L3SMI (cm²/m²) = total area of all skeletal muscle at the L3 level (cm²)/height² (m²).^19,20 CALLY index = (albumin level in g/L) × (lymphocyte count in/μL)/(CRP level in mg/L) × 10.²¹ The formulas for the remaining inflammatory nutritional indices were presented in Supplementary Table S1.

Complication Assessment

Postoperative complications following LARC radical surgery are defined as medical or surgical complications occurring within 30 days postoperatively. The Claven-Dindo system is used for grading (Supplementary Table S2), with grade ≥ IIIa classified as severe postoperative complications.²² CCI includes all postoperative adverse events, which are weighted according to severity and comprehensively calculated (www.assessurgery.com) to derive the corresponding score (0–100 points, with 0 points indicating no complications and 100 points indicating death).^23,24

Endpoints and Follow-Up

All patients were followed by telephone and in the clinic. The endpoints event defined as any form of tumour recurrence, metastasis or death, with a follow-up deadline of May 2025. The primary endpoint of this study was OS, defined as the time interval from the date of randomization to death from any cause. The secondary endpoint was PFS, defined as the time from the start of treatment to the first imaging-confirmed disease progression or death from any cause.

Establishment and Validation of Prognostic Models

LASSO regression was used for preliminary analysis, and the selected variables were further screened using univariate Cox analysis to identify potential risk factors affecting survival. After identifying the potential risk factors, we performed backward multivariate analysis to select the optimal model. Based on the results of the multivariate Cox, statistically significant variables (P < 0.05) were included in the nomogram to predict 2-year and 3-year survival rates after NACRT and surgery. The predictive performance of the nomogram model was assessed using time-dependent receiver operating characteristics (ROC), bootstrap method and calibration curves. Time-dependent ROC were used to evaluate discriminatory ability. Bootstrap method was used to repeat 500 times for internal validation of the nomogram model. Calibration curves were used to compare the probabilities predicted by the nomogram with actual outcomes. Kaplan–Meier curves (KM) was used to validate the risk stratification ability of the nomogram model. Finally, the net benefit of the model was assessed using decision curve analysis (DCA).

Statistical Analysis

Statistical analysis was performed using SPSS 27.0 software. For continuous variables, normality was tested using the Kolmogorov–Smirnov test. Compliance was expressed as mean ± standard deviation (SD) for continuous variables and as median M [P25, P75] for non-continuous variables. For categorical variables, categorical information was presented as numbers and percentages (n,%). Comparisons between groups were performed using independent samples t-tests, non-parametric rank-sum tests, and chi-square (χ²) tests. ROC curves were plotted, and the Youden index was calculated to identify the optimal cutoff value for the inflammatory nutritional index in predicting survival. LASSO regression was used for preliminary screening of potential risk factors, followed by Cox proportional hazards models for univariate and multivariate analysis. Forest plots were created using the Dream Cloud statistical platform (https://mengte.pro/forest_plot), and the model was visualized using a nomogram. The calibration curve, bootstrap method and DCA were used to evaluate the model. All analyses were conducted using R (version 4.5.0; http://www.r-project.org/), with a two-sided P < 0.05 indicating statistical significance.

Results

Baseline Characteristics and ROC Curves That Affect the Survival

The baseline characteristics of LARC patients included in this retrospective study are presented in Table 1. The median OS and PFS for all patients were 27 months and 26 months, respectively. The 2-year and 3-year OS rates were 93.9% and 79.3%, respectively. The 2-year and 3-year PFS rates were 76.7% and 52.8%, respectively. Among the 131 patients (95 male, 36 female), the median age of LARC patients was 62 years [IQR: 55, 70]. According to the 8th AJCC TNM classification, postoperative histopathology showed that ypTNM stage 0-I was defined as a good response, and ypTNM stage II–IV was defined as a poor response. Moreover, according to postoperative pathology findings, the results showed that six inflammatory nutritional indices, the quantitative indicator CCI for complications occurring within 30 days postoperatively in 101 LARC patients (77.1%); histological differentiation, with moderate differentiation (71.7%); and no vascular invasion (77.9%) were significantly associated with tumor downstaging. All patient characteristics are listed in Table 1.

Table 1 Baseline Data and Clinicopathologic Features

According to ROC analyses, the optimal cutoffs for predicting survival using L3SMI, PLR, NLR, LMR, CALLY, and PNI were 43.44, 152.09, 2.83, 3.09, 1.47, and 45.85, respectively. The optimal inflammatory nutritional index for predicting survival was CALLY (AUC = 0.736, 95% CI: 0.609–0.863; P < 0.001). All inflammatory nutritional indices are listed in Figure 2 and Table 2.

Table 2 AUC Values from the ROC Analysis of Inflammatory Nutritional Indices for Predicting Survival

Figure 2 Receiver operating characteristic curves of inflammatory nutritional indices for predicting survival. The highest area under curve (AUC) value for survival is CALLY (0.736). Abbreviations: L3SMI, L3 Skeletal Muscle Index; PNI, prognostic nutrition index; PLR, platelet to lymphocyte ratio; NLR, neutrophil to lymphocyte ratio; LMR, lymphocyte to monocyte ratio; CALLY, C-reactive protein-albumin-lymphocyte index.

LASSO Regression Screening for Prognostic Factors of OS and PFS in LARC

By comparing the clinical characteristics of LARC patients undergoing NACRT and whether the tumor stage was downgraded post-surgery (Table 1), multiple potential prognostic factors were identified, including gender, age, diabetes, hypertension, BMI, NRS2002 score, CCI, and 19 other indicators. These factors were included in LASSO regression analysis, and the optimal assessment indicator for OS was achieved when the harmonic parameter log(λ) was 0.033. Ultimately, 17 variables were selected: gender, age, BMI, NRS2002, CCI, CEA, N stage, L3SMI, PLR, NLR, LMR, CALLY, PNI, histological differentiation, vascular and nerve invasion, and Her-2 (Figure 3A and B). These clinical characteristics were found to be associated with OS in LARC patients. Similarly, the optimal assessment indicator for PFS was achieved when the harmonic parameter log(λ) was 0.084, and ultimately 7 variables were selected that were associated with PFS: CEA, CA724, PLR, CALLY, histological differentiation, vascular invasion, and nerve invasion (Figure 3C and D).

Figure 3 A LASSO binary Cox regression model based on the minimum Lambda value was used to screen predictive indices for overall survival (OS) and progression-free survival (PFS) in LARC patients. (A) LASSO coefficient parameter for the 28 variables predicting OS. Vertical lines are drawn at the values selected by 10-fold cross-validation in (B). As the λ value decreases, the model’s compression increases, enhancing its ability to select important variables. (B) 10-fold cross-validation results for predicting OS. The values between the two dashed lines represent the positive and negative standard deviation ranges of log(λ). The left dashed line indicates the value of the harmonic parameter log(λ) when the model error is minimized. When log(λ) = 0.033, 17 variables were selected. (C) LASSO coefficient parameters for the 28 variables predicting PFS. Vertical lines are drawn at the values selected in the 10-fold cross-validation in (D). As the λ value decreases, the model’s accuracy also decreases. (D) 10-fold cross-validation results for predicting PFS. When log(λ) = 0.084, 7 variables were selected.

Prognostic Prediction of the CALLY Index in LARC

We performed Cox regression analysis using the variables selected through Lasso regression analysis. The results of univariate and multivariate Cox regression analysis were presented in Figure 4. Multivariate analysis of OS showed (Figure 4A) that elevated levels of high CALLY (HR = 0.344, 95% CI: 0.133–0.893; P = 0.028) reduced the risk of OS by 65.6%. For the PFS multivariate analysis results (Figure 4B), high CALLY (HR = 0.492, 95% CI: 0.266–0.912; P = 0.024) reduced the risk of PFS by 50.8%. Additionally, we found that CEA is not only a risk factor for OS (HR = 1.004, 95% CI: 1.001–1.007; P = 0.014) but also PFS (HR = 1.005, 95% CI: 1.002–1.008; P < 0.001).

Figure 4 Multivariate Cox regression forest plot analyzing the survival prognosis of LARC patients based on the LASSO regression model. (A) Forest plot obtained from univariate and multivariate Cox regression analysis with OS as the outcome. (B) Forest plot obtained from univariate and multivariate Cox regression analysis with PFS as the outcome. Abbreviations: LARC, locally advanced colorectal cancer; CCI, Charlson comorbidity index; CA724, carbohydrate Antigen 72–4; L3SMI, L3 Skeletal Muscle Index; PLR, platelet to lymphocyte ratio; NLR, neutrophil to lymphocyte ratio; LMR, lymphocyte to monocyte ratio; PNI, prognostic nutrition index; CALLY, C-reactive protein-albumin-lymphocyte index. Notes: *Indicate P < 0.05.

Development and Evaluation of Nomograms Based on CALLY

To develop a quantitative method for predicting the prognosis of LARC, we implement nomogram prediction models for OS and PFS based on the results of multivariate Cox analysis, including CALLY, CEA, and CCI (HR = 1.053, 95% CI: 1.000–1.108; P = 0.048) to predict the 2-year and 3-year OS of patients (Figure 5A), and CALLY, PLR (HR = 2.577, 95% CI: 1.321–5.027; P = 0.005), CEA, CA724 (per-unit increase HR = 1.006, 95% CI: 1.001–1.012; P = 0.023) and vascular invasion (HR = 2.263, 95% CI: 1.011–5.068; P = 0.047) to predict the 2-year and 3-year PFS of patients (Figure 6A). To utilize the nomogram, a vertical line is drawn from each variable’s value to the points axis, with total points calculated as the sum of all individual risk scores.

Figure 5 Comprehensive evaluation of the NACRT and postoperative OS prediction model for LARC patients: nomogram, calibration curve, and clinical decision curve (DCA). (A) Nomogram to predict 2-year and 3-year OS for LARC patients. (B) Time-ROC curves for 2-year and 3-year OS prediction based on the nomogram. (C) Calibration curves for 2-year and 3-year OS prediction based on the nomogram. The 45-degree diagonal line represents ideal prediction. (D and E) DCA of risk scores for OS prediction based on the nomogram. The net benefit, calculated by adding true positives and false positives, corresponds to the measurement value on the Y-axis; the X-axis represents the threshold probability. (D) 2-, (E) 3-year DCA for OS prediction based on the nomogram. Abbreviations: LARC, locally advanced colorectal cancer; NACRT, neoadjuvant chemoradiotherapy; CCI, Charlson comorbidity index; CALLY, C-reactive protein-albumin-lymphocyte index.

Figure 6 Comprehensive evaluation of the NACRT and postoperative PFS prediction model for LARC patients: nomogram, calibration curve, and clinical decision curve (DCA). (A) Nomogram to predict 2-year and 3-year PFS for LARC patients. (B) Time-ROC curves for 2-year and 3-year PFS prediction based on the nomogram. (C) Calibration curves for 2-year and 3-year PFS prediction based on the nomogram. The 45-degree diagonal line represents ideal prediction. (D and E) DCA of risk scores for PFS prediction based on the nomogram. The net benefit, calculated by adding true positives and false positives, corresponds to the measurement value on the Y-axis; the X-axis represents the threshold probability. (D) 2-, (E) 3-year DCA for PFS prediction based on the nomogram. Abbreviations: LARC, locally advanced colorectal cancer; NACRT, neoadjuvant chemoradiotherapy; CA724, carbohydrate Antigen 72–4; PLR, platelet to lymphocyte ratio; CALLY, C-reactive protein-albumin-lymphocyte index.

Model performance was evaluated using time-dependent ROC curves, calibration analyses, decision curve analysis (DCA) and bootstrap resampling method. The AUCs for 2- and 3-year OS predictions were 0.83 and 0.76, respectively (Figure 5B). However, the PFS model showed higher 3-year accuracy (AUC = 0.81), but lower 2-year accuracy (AUC = 0.71) compared to the OS model, respectively (Figure 6B). Calibration curves display the better consistency between predicted survival probabilities from the nomogram and the OS/PFS at 2- and 3-years (Figures 5C and 6C), indicating improved reliability in longer-term predictions. Furthermore, DCA shows that under the comparison of curve areas, the 3-year PFS model is much higher than that of the 2-year-3-year OS model (Figure 5D and E), which suggests that the value of the net benefit of the PFS model is much greater than that of the OS model, and even more strikingly, the 3-year PFS model exceeds the default strategy in terms of net benefit when the decision threshold exceeds 0.93 (Figure 6D and E). These results suggest that the PFS model improves the accuracy of long-term prognostic stratification and increases clinical value as the time horizon increases. At the same time, we performed internal validation of the models using the bootstrap resampling method 500 times and obtained the resampled ROC of the OS and PFS models: AUC = 0.866 (95% CI: 0.862–0.869) (Figure 7A); 0.861 (95% CI: 0.858–0.864) (Figure 7B). In summary, the nomogram model for PFS can better predict the value.

Figure 7 Bootstrap resampling 500 times for internal validation of the model. (A) Bootstrap resampling of the OS model 500 times, AUC = 0.866 (95% CI: 0.862–0.869); (B) Bootstrap resampling of the PFS model 500 times, AUC = 0.861 (95% CI: 0.858–0.864).

Survival Curves Based on the Nomograms

Patients were divided into low-risk and high-risk groups based on their survival status. Kaplan-Meier analysis showed that the two survival curves were significantly separated (Log rank test, P < 0.001). The OS and PFS rates indicated that the high-risk group had an 8.25 times higher risk of death (95% CI: 3.05–22.30) and a 6.98 times higher risk (95% CI: 3.75–12.99) compared to the low-risk group (Figure 8A and C). These results, along with the model, confirmed the clinical applicability of the nomogram. In addition, Kaplan-Meier curves for OS and PFS were plotted for CALLY (Figure 8B and D), showing that the risk of death in the low CALLY group was 3.99 times higher than that in the high CALLY group (95% CI: 1.437–10.80) and 2.89 times higher (95% CI: 1.335–6.259), respectively. In summary, compared to individual factors, the nomogram model can better predict OS and PFS.

Figure 8 Kaplan-Meier survival analysis based on nomogram risk groups and CALLY. (A) Nomogram risk stratification for predicting OS. The 5-year mortality risk in the high-risk group was 8.25 times higher than that in the low-risk group. (B) The effect of CALLY levels on OS. The mortality risk in the low CALLY group was 3.99 times higher than that in the high CALLY group. (C) Nomogram risk stratification for predicting PFS. The 5-year mortality risk in the high-risk group is 6.98 times higher than that in the low-risk group. (D) The impact of CALLY levels on PFS. The mortality risk in the low CALLY group is 2.89 times higher than that in the high CALLY group. Abbreviations: CALLY, C-reactive protein-albumin-lymphocyte index.

Discussion

The 5-year survival rate and 5-year cumulative local recurrence rate for LARC patients are 76% and 6%.⁴ The most effective treatment currently available involves NACRT followed by radical resection, with adjuvant chemotherapy administered subsequently. This approach improves preoperative tumor response rates and reduces postoperative local metastasis rates.²⁵ In short-term efficacy analyses, NACRT did improve the R0 resection rate and 3-year OS, but the distant metastasis rate remained high, and improvements in 5-year OS and disease-free survival (DFS) were not significant.²⁶ Therefore, as Okamura reported,²⁷ early identification of LARC with poor postoperative outcomes following NACRT, combined with adaptive treatment strategies, may improve the likelihood of tumor recurrence in the short term. While the Okamura model for esophageal cancer achieved an initial AUC of 0.679 with 1000 bootstrap validations of 0.670,²⁷ our nomogram demonstrates substantially improved discriminative ability in the LARC-specific context: initial 2-year AUC 0.830 further enhanced to 0.866 after 500 bootstraps (Figure 7A). This represents a relative improvement in predictive accuracy over current standards. We concluded that the model could enhance the treatment of NACRT patients by rapidly identifying high risk and then intervening with treatment regimens. In our study, we used OS and PFS as outcomes and analyzed 131 LARC patients using Lasso-Cox regression, screening out 3 and 5 variables respectively to establish two nomogram models predicting 2-year and 3-year outcomes. These indicators include tumor burden markers (CEA, CA724), inflammatory-nutritional markers (CALLY, PLR), and postoperative pathological and complication status (CCI, vascular invasion). The nomograms based on the inflammatory-nutritional index CALLY can rapidly stratify patients into risk groups prior to NACRT, thereby enabling individualized treatment.

Inflammation can promote carcinogenesis by inducing gene mutations, inhibiting apoptosis, stimulating angiogenesis and cell proliferation.²⁸ Mark Schmitt et al²⁹ demonstrated that even in the absence of exogenous carcinogens, inflammation itself can trigger tumor development by inducing DNA damage, and that part of the mechanism involves the release of innate immunity cells excessive reactive oxygen species leading to increased oxidative stress. CALLY, as an inflammatory nutritional index, has been reported to aid in the prognosis of various cancers.^9–11 It consists of CRP, serum albumin, and lymphocytes, playing a crucial role in inflammation, nutrition, and immunity in CRC.²¹ CRP and Alb are commonly used clinical markers for inflammation or nutrition. Previous studies had shown that elevated CRP levels indicated more severe inflammatory states and were closely associated with cancer prognosis.³⁰ Similarly, nutritional status should not be overlooked during cancer development. Inflammation can induce cancer, affecting metabolic processes and reducing the absorption and utilization of nutrition.³¹ Disease progression often leads to reduced food intake, and LARC may exhibit malnutrition or hypoproteinemia.³² This was highly consistent with our data: reduced CALLY values (reflecting elevated CRP and reduced albumin/lymphocytes) were independently associated with significant deterioration in 2- and 3-years OS and PFS. More importantly, our statistical analyses showed that CALLY demonstrated greater discriminatory power and comprehensive value in predicting the long-term survival prognosis of patients with LARC undergoing NACRT compared with CRP or Alb alone.

Surgery is a crucial step in the treatment of LARC-NACRT, and postoperative complications within 30 days can directly impact clinical outcomes and subsequent comprehensive treatment.³³ Slankamenac et al³⁴ first proposed the CCI scoring system in 2013, with a range of 0–100 points, offering a broader and more discriminative grading range for complications. David et al³⁵ reported that each one-point increase in CCI was associated with a 1.02-fold increase in risk, suggesting that CCI was an independent risk factor for postoperative outcomes in CRC. These findings were also validated in the present study, where CCI was closely associated with OS. Higher CCI scores (HR = 1.053, 95% CI: 1.000–1.108, P = 0.048) were associated with higher OS risk, indicating that patients with poorer baseline health status have a higher risk of postoperative mortality. Unlike CALLY, CCI is not a dynamic laboratory index but a clinical indicator reflecting the overall physiological reserve. Its value lies in its ability to indirectly reflect patients’ tolerance to treatment and postoperative recovery capacity, thereby predicting the prognosis of comprehensive treatment in the later stages.

Prognostic nomograms demonstrated superior versatility and efficiency in large-scale NACRT.³⁶ Our nomogram, integrating clinical serological markers, neoadjuvant strategies, postoperative pathology, genetic testing, and complication data effectively predicted OS and PFS in patients with LARC. Validation confirmed robust discriminatory performance that time-dependent ROC and bootstrap-validated AUC were all above 0.8. Additionally, calibration curves demonstrated close alignment with true outcomes, while DCA showed significant net benefit. It should be emphasized that the primary strength of models lies in their robust prediction of short-to-mid-term outcomes (AUC = 0.83 for 2-year OS), while extrapolation beyond 3 years remains limited by the current median follow-up of 27 months. Furthermore, based on the risk scores derived from the model, the high-risk group exhibited the worst prognosis. The risk stratification provided by this model directly guides precision treatment decisions. Low-risk patients can be exempted from chemotherapy, reducing treatment toxicity while lowering healthcare expenditures. Conversely, high-risk patients can initiate PD-L1 targeted therapy earlier, improving quality of life and optimizing survival benefits. At initial diagnosis, clinicians can rapidly assign risk scores using our designed scoring system to generate risk stratification reports, supporting multidisciplinary consultation decisions. Critically, our models’ advantages lie in clinical accessibility: it used routinely available clinical data, unlike costly multi-omics approaches (eg, radiomics, metagenomics, transcriptomics) that require specialized infrastructure. However, several limitations were presented in our study. First, this was a single-center design with a small sample size and potential selection bias. Second, median follow-up duration of 27 months, necessitating longer-term validation. Third, lack of dynamic perioperative biomarker monitoring. Finally, the models required further external validation in multi-center prospective cohorts to confirm clinical practicality.

Conclusion

In this study, we used nomograms to intuitively construct a prediction model of OS and PFS in patients with LARC by the inflammatory nutritional index CALLY. Higher CALLY levels were an independent protective factor for survival. Although the nomogram of OS showed strong discrimination, the PFS model not only presented a higher 3-year predictive accuracy but was more extensive in terms of clinical utility. All these results highlighted the prognostic value of CALLY in the treatment of LARC, and secondly, the nomogram of PFS could provide an individualized risk assessment before NACRT in patients and provide valuable guidance for clinical decision-making.

Data Sharing Statement

Data generated and analysed during the current study is not publicly available, due to patient confidentiality and hospital research site requirements. However, they can be obtained from the corresponding author, Chuanqing Bao, if reasonably requested.

Ethics Approval and Informed Consent

The Ethics Committee of Jiangnan University approved the study (JNU202306011RB15), and our study complied with the Declaration of Helsinki. Patients’ medical records were anonymized and de-identified before analysis. Since this study was a retrospective study using medical records from previous clinical visits and patients were recruited from across the country, the written informed consent form could not be signed in person. However, to ensure the legality of the study, we obtained consent from patients by telephone recording during telephone follow-up visits. If a patient was unable to speak or had died during the follow-up visit, consent was obtained from the family member.

Acknowledgments

We thank all the participants involved in this study.

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 research was supported by Wuxi Municipal Health Commission Major Project (Grant: Z202213). Author Chuanqing Bao has received research support from the Wuxi Municipal Health Commission.

Disclosure

The authors declare no potential conflicts of interest.

References

1. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA A Cancer J Clin. 2024;74(3):229–263. doi:10.3322/caac.21834

2. Klekowski J, Chabowski M, Krzystek-Korpacka M, et al. The utility of lipidomic analysis in colorectal cancer diagnosis and prognosis—a systematic review of recent literature. Int J Mol Sci. 2024;25(14). doi:10.3390/ijms25147722

3. Shadmanov N, Aliyev V, Bakir B, et al. The shifting and evolving neoadjuvant treatments and surgical platforms on oncological outcomes and sphincter preservation in distal rectal cancer: a 23-year retrospective experience. J Gastrointest Cancer. 2025;56(1):177. doi:10.1007/s12029-025-01303-y

4. Sauer R, Becker H, Hohenberger W, et al. Preoperative versus postoperative chemoradiotherapy for rectal cancer. N Engl J Med. 2004;351(17):1731–1740.

5. Serra-Aracil X, Pericay C, Badia-Closa J, et al. Short-term outcomes of chemoradiotherapy and local excision versus total mesorectal excision in T2-T3ab, N0, M0 rectal cancer: a multicentre randomised, controlled, Phase III trial (the TAU-TEM study). Annals Oncol. 2023;34(1):78–90. doi:10.1016/j.annonc.2022.09.160

6. Cheng Y, Luo Y, Hu Y, et al. Multiparametric MRI-based Radiomics approaches on predicting response to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer. Abdom Radiol. 2021;46(11):5072–5085. doi:10.1007/s00261-021-03219-0

7. Smith HG, Nilsson PJ, Shogan BD, et al. Neoadjuvant treatment of colorectal cancer: comprehensive review. BJS Open. 2024;8(3). doi:10.1093/bjsopen/zrae038

8. Armstrong J, Balasubramanian I, Brannigan A, et al. A multicentre cohort study assessing the utility of routine blood tests as adjuncts to identify complete responders in rectal cancer following neoadjuvant chemoradiotherapy. Int J Colorectal Dis. 2022;37(4):957–965. doi:10.1007/s00384-022-04103-z

9. Iida H, Tani M, Komeda K, et al. Superiority of CRP-albumin-lymphocyte index (CALLY index) as a non-invasive prognostic biomarker after hepatectomy for hepatocellular carcinoma. HPB. 2022;24(1):101–115. doi:10.1016/j.hpb.2021.06.414

10. Toda M, Musha H, Suzuki T, et al. Impact of C-reactive protein-albumin-lymphocyte index as a prognostic marker for the patients with undergoing gastric cancer surgery. Front Nutr. 2025;12. doi:10.3389/fnut.2025.1556062

11. Xi P, Huang G, Huang K, et al. The prognostic significance of the CALLY index in ampullary carcinoma: an inflammation-nutrition retrospective analysis. J Inflamm Res. 2025;18:621–635. doi:10.2147/jir.S495815

12. van Vugt JLA, Coebergh van den Braak RRJ, Lalmahomed ZS, et al. Impact of low skeletal muscle mass and density on short and long-term outcome after resection of stage I-III colorectal cancer. Eur J Surg Oncol. 2018;44(9):1354–1360. doi:10.1016/j.ejso.2018.05.029

13. Li Z, Yan G, Liu M, et al. Association of perioperative skeletal muscle index change with outcome in colorectal cancer patients. J Cachexia Sarcopenia Muscle. 2024;15(6):2519–2535. doi:10.1002/jcsm.13594

14. Huang Z, Chen S, Yin S, et al. Development and validation of a nomogram for predicting the risk of developing gastric cancer based on a questionnaire: a cross–sectional study. Front Oncol. 2024;14. doi:10.3389/fonc.2024.1351967

15. Gou M, Qian N, Zhang Y, et al. Construction of a nomogram to predict the survival of metastatic gastric cancer patients that received immunotherapy. Front Immunol. 2022;13. doi:10.3389/fimmu.2022.950868

16. Nagtegaal ID, Glynne-Jones R. How to measure tumour response in rectal cancer? An explanation of discrepancies and suggestions for improvement. Cancer Treatment Rev. 2020;84. doi:10.1016/j.ctrv.2020.101964.

17. Matsuda T, Sumi Y, Yamashita K, et al. Outcomes and prognostic factors of selective lateral pelvic lymph node dissection with preoperative chemoradiotherapy for locally advanced rectal cancer. Int J Colorectal Dis. 2018;33(4):367–374. doi:10.1007/s00384-018-2974-1

18. Horie K, Matsuda T, Yamashita K, et al. Sarcopenia assessed by skeletal muscle mass volume is a prognostic factor for oncological outcomes of rectal cancer patients undergoing neoadjuvant chemoradiotherapy followed by surgery. Eur J Surg Oncol. 2022;48(4):850–856. doi:10.1016/j.ejso.2021.10.018

19. Utsumi M, Inagaki M, Kitada K, et al. Combination of sarcopenia and systemic inflammation-based markers for predicting the prognosis of patients undergoing pancreaticoduodenectomy for pancreatic cancer. PLoS One. 2024;19(6):e0305844. doi:10.1371/journal.pone.0305844

20. Jin J, Xiong G, Peng F, et al. The ratio of skeletal muscle mass to body mass index combined with inflammatory immune markers to stratify survival of pancreatic cancer after pancreatoduodenectomy. Eur J Surg Oncol. 2024;50(7):108355. doi:10.1016/j.ejso.2024.108355

21. Yang M, Lin S-Q, Liu X-Y, et al. Association between C-reactive protein-albumin-lymphocyte (CALLY) index and overall survival in patients with colorectal cancer: from the investigation on nutrition status and clinical outcome of common cancers study. Front Immunol. 2023;14. doi:10.3389/fimmu.2023.1131496

22. Clavien PA, Barkun J, de Oliveira ML, et al. The Clavien-Dindo classification of surgical complications. Annals Surg. 2009;250(2):187–196. doi:10.1097/SLA.0b013e3181b13ca2

23. Yamashita S, Sheth RA, Niekamp AS, et al. Comprehensive complication index predicts cancer-specific survival after resection of colorectal metastases independent of RAS mutational status. Annals Surg. 2017;266(6):1045–1054. doi:10.1097/sla.0000000000002018

24. Tahiri M, Sikder T, Maimon G, et al. The impact of postoperative complications on the recovery of elderly surgical patients. Surg Endoscopy. 2015;30(5):1762–1770. doi:10.1007/s00464-015-4440-2

25. Aliyev V, Shadmanov N, Piozzi GN, et al. Comparing total mesorectal excision with partial mesorectal excision for proximal rectal cancer: evaluating postoperative and long-term oncological outcomes. Updates Surg. 2024;76(4):1279–1287. doi:10.1007/s13304-024-01926-z

26. Zhang Z-T, Xiao -W-W, Li L-R, et al. Neoadjuvant chemoradiotherapy versus neoadjuvant chemotherapy for initially unresectable locally advanced colon cancer: short-term outcomes of an open-label, single-centre, randomised, controlled, Phase 3 trial. eClinicalMedicine. 2024;76. doi:10.1016/j.eclinm.2024.102836

27. Okamura A, Watanabe M, Okui J, et al. Development and validation of a predictive model of therapeutic effect in patients with esophageal squamous cell carcinoma who received neoadjuvant treatment: a nationwide retrospective study in Japan. Annals Surg Oncol. 2022;30(4):2176–2185. doi:10.1245/s10434-022-12960-9

28. Kundu J, Surh Y. Inflammation: gearing the journey to cancer. Mutation Res /Rev Mutation Res. 2008;659(1–2):15–30. doi:10.1016/j.mrrev.2008.03.002

29. Schmitt M, Greten FR. The inflammatory pathogenesis of colorectal cancer. Nat Rev Immunol. 2021;21(10):653–667. doi:10.1038/s41577-021-00534-x

30. Zhu M, Ma Z, Zhang X, et al. C-reactive protein and cancer risk: a pan-cancer study of prospective cohort and Mendelian randomization analysis. BMC Med. 2022;20(1). doi:10.1186/s12916-022-02506-x

31. Martínez-Escribano C, Arteaga Moreno F, Pérez-López M, et al. Malnutrition and increased risk of adverse outcomes in elderly patients undergoing elective colorectal cancer surgery: a case-control study nested in a cohort. Nutrients. 2022;14(1). doi:10.3390/nu14010207

32. Kim WR, Han YD, Min BS. C-reactive protein level predicts survival outcomes in rectal cancer patients undergoing total mesorectal excision after preoperative chemoradiation therapy. Annals Surg Oncol. 2018;25(13):3898–3905. doi:10.1245/s10434-018-6828-4

33. Warps AK, Tollenaar RAEM, Tanis PJ, et al. Postoperative complications after colorectal cancer surgery and the association with long-term survival. Eur J Surg Oncol. 2022;48(4):873–882. doi:10.1016/j.ejso.2021.10.035

34. Slankamenac K, Nederlof N, Pessaux P, et al. The comprehensive complication index. Annals Surg. 2014;260(5):757–763. doi:10.1097/sla.0000000000000948

35. Ortiz-López D, Marchena-Gómez J, Nogués-Ramía E, et al. Utility of a new prognostic score based on the Comprehensive Complication Index (CCI^®) in patients operated on for colorectal cancer (S-CRC-PC score). Surg Oncol. 2022;42. doi:10.1016/j.suronc.2022.101780

36. Ribeiro U. Nomogram for predicting pathologic response following neoadjuvant chemotherapy or chemoradiotherapy in patients with esophageal cancer. Annals Surg Oncol. 2023;30(4):1945–1947. doi:10.1245/s10434-023-13133-y