Introduction
Acute myocardial infarction (AMI) occurs when a thrombus forms in the coronary artery, blocking the vessel on the basis of coronary atherosclerosis, leading to a decrease in the blood supply to myocardial cells. Myocardial hypoxia and coronary artery spasms can also cause myocardial ischemia and necrosis in the cells.1 AMI may result in persistent arrhythmias, heart failure, and other complications in patients, with a high mortality rate, posing a serious threat to life and health.2 Cardiac rehabilitation can effectively alleviate the symptoms of AMI patients and control the progression of the disease. Early comprehensive rehabilitation activities are an important method of cardiac rehabilitation for AMI,3,4 mainly including educational counseling, exercise therapy, and other interventions. Through active cooperation from the patients, cardiovascular function recovery is promoted, ensuring that the patient’s physiological and psychological state remains in good condition. Moreover, compliance with cardiac rehabilitation directly impacts the rehabilitation outcome.5 Therefore, identifying the factors affecting compliance with cardiac rehabilitation in AMI patients is crucial for improving patient prognosis. Nomograms are simple to operate and highly readable, allowing the individualized prediction of the risk of an event by integrating the risk factors screened from regression analysis.6,7 Based on this, research on the compliance of elderly AMI patients with cardiac rehabilitation in nomograms is rarely reported. Therefore, this study aims to explore the factors affecting the compliance of elderly AMI patients with cardiac rehabilitation and to construct a predictive model using nomograms.
Materials and Methods
General Data
A retrospective selection of 239 elderly AMI patients admitted to our hospital from April 2022 to April 2024 was made. The patients were randomly divided into the modeling group (167 cases) and the validation group (72 cases) according to a 7:3 ratio (random number table method). The patients in the modeling group were divided into good compliance and poor compliance groups according to their cardiac rehabilitation compliance. The case collection process is shown in Figure 1. Inclusion criteria: (1) Meeting the diagnostic criteria for AMI;8 (2) Age ≥ 60 years; (3) Complete data. Exclusion criteria: (1) Liver and kidney dysfunction; (2) Systemic infectious diseases; (3) Unstable vital signs; (4) Malignant tumors; (5) Blood system diseases; (6) Hearing and visual impairment. This study was approved by the hospital ethics committee. See Figure 1.
|
Figure 1 Flow chart of case collection.
|
Cardiac Rehabilitation Compliance Assessment
The cardiac rehabilitation compliance of the patients was evaluated using the Cardiac Rehabilitation Scale.9 This scale includes 3 dimensions (autonomy, process anxiety, and outcome anxiety) and 18 items. Each item is rated on a 5-point Likert scale. Autonomy scores ≤ 15 indicate poor autonomy, process anxiety ≥ 19 indicates high process anxiety, and outcome anxiety ≥ 10 indicates high outcome anxiety. The scale has good reliability and validity, with a coefficient of 0.825.
Clinical Data
Clinical routine examination and electronic medical record data were collected, including age, gender, body mass index (BMI), first-time diagnosis, marital status, education level, place of residence, history of hypertension, history of diabetes, history of kidney disease, anxiety and depression, smoking history, alcohol abuse, disease perception, social support, healthcare supervision, method of medical expense payment, rehabilitation location, total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), glycosylated hemoglobin (HbA1c), fasting blood glucose (FBG), albumin, and uric acid levels.
Statistical Analysis
Data were analyzed using SPSS 25.0. Categorical data were tested using the χ²-test and expressed as cases (%). Continuous data that followed a normal distribution were tested using the t-test and expressed as 
. Multivariate logistic regression analysis was used to identify factors influencing cardiac rehabilitation compliance in elderly AMI patients. The nomogram model for cardiac rehabilitation compliance in elderly AMI patients was constructed using R software. The ROC curve was drawn to evaluate the discrimination of the nomogram model for cardiac rehabilitation compliance in elderly AMI patients. The calibration curve was drawn to evaluate model consistency. The clinical decision curve (DCA) was used to assess the clinical application value of the model. P < 0.05 was considered statistically significant.
Results
Comparison of Clinical Data Between the Modeling Group and Validation Group
There were no differences in the clinical data between the modeling group and the validation group (P > 0.05). See Table 1.
 |
Table 1 Comparison of Clinical Data Between Modelling and Validation Group
|
Status of Cardiac Rehabilitation Compliance in Elderly AMI Patients
In the modeling group of 167 patients, 67 patients had poor compliance, with an incidence rate of 40.12%. The scores for process anxiety, outcome anxiety, poor autonomy, high process anxiety, and high outcome anxiety in the poor compliance group were higher than those in the good compliance group (P < 0.05), and the autonomy score was also higher in the poor compliance group than in the good compliance group (P < 0.05). See Table 2.
 |
Table 2 Current Status of Adherence to Cardiac Rehabilitation Compliance in Elderly AMI Patients
|
Comparison of Clinical Data Between Poor Compliance and Good Compliance Groups
There were differences in age, education level, disease perception, anxiety and depression, social support, and healthcare supervision between the two groups (P < 0.05). No differences were found in other clinical data between the two groups (P > 0.05). See Table 3.
 |
Table 3 Comparison of Clinical Data Between the Poor Adherence Group and the Good Adherence Group
|
Analysis of Factors Affecting Cardiac Rehabilitation Compliance in Elderly AMI Patients
With poor cardiac rehabilitation compliance in elderly AMI patients as the dependent variable (yes = 1, no = 0), the factors with significant differences mentioned above were taken as independent variables. The variable assignment method is shown in Table 4. Multivariate logistic regression analysis results showed that age, education level, disease perception, anxiety and depression, social support, and healthcare supervision were risk factors for poor cardiac rehabilitation compliance in elderly AMI patients (P < 0.05). See Table 5.
 |
Table 4 Independent Variable Assignment Methods
|
 |
Table 5 Analysis of Factors Influencing Adherence to Cardiac Rehabilitation in Elderly Patients with AMI
|
Establishment of the Nomogram Model for Cardiac Rehabilitation Compliance in Elderly AMI Patients
In this model, the factors affecting the scores were, in order: social support, age, disease perception, healthcare supervision, anxiety and depression, and education level. See Figure 2.
 |
Figure 2 Development of a nomogram modelling of adherence to cardiac rehabilitation in elderly patients with AMI.
|
Nomogram Model of the Modeling Group
The AUC of the modeling group was 0.955 (95% CI: 0.927~0.984), and the slope of the calibration curve was close to 1. The H-L test yielded χ² = 7.863, P = 0.789, indicating good consistency. See Figure 3.
 |
Figure 3 Nomogram Model of the Modeling Group, (A) ROC curve for modelling group; (B) Modelling group calibration curves.
|
Nomogram Model of the Validation Group
The AUC of the validation group was 0.937 (95% CI: 0.884~0.991), and the slope of the calibration curve was close to 1. The H-L test yielded χ² = 7.453, P = 0.775, indicating good consistency. See Figure 4.
 |
Figure 4 Nomogram Model of the Validation Group, (A) ROC curve for the validation group; (B) Calibration curve for the validation group.
|
DCA Curve of the Nomogram Model
The DCA curve shows that the clinical utility of using this nomogram model to evaluate the cardiac rehabilitation compliance of elderly AMI patients was high when the high-risk threshold probability was between 0.08 and 0.93. See Figure 5.
 |
Figure 5 DCA curve for the nomogram.
|
Discussion
AMI can cause the rupture of atherosclerotic plaques, thereby damaging the vascular endothelium. Moreover, thrombosis in multiple coronary arteries can lead to vascular occlusion, reducing coronary blood flow and resulting in persistent ischemia and hypoxia, eventually causing myocardial necrosis.10 The clinical symptoms manifest as chest pain and chest tightness, and in severe cases, they can lead to arrhythmias and heart failure in patients.11 Cardiac rehabilitation is a major clinical intervention for treating AMI. It mainly reduces the psychological and physiological effects of the disease on patients through correcting cardiac risk factors and behavioral interventions, thus improving patients’ quality of life. Its effectiveness is related to the patients’ compliance.12 The results of this study found that in the modeling group of 167 patients, 67 patients had poor compliance, with an incidence rate of 40.12%. There were differences in autonomy scores, process anxiety, and outcome anxiety between the two groups. Therefore, identifying factors affecting cardiac rehabilitation compliance in AMI patients and timely prevention can effectively improve patient prognosis.
This study screened out six factors (age, education level, disease perception, anxiety and depression, social support, and healthcare supervision) through multivariate analysis and analyzed the reasons: (1) Elderly AMI patients often experience a decline in physical function and poor health, often accompanied by chronic metabolic complications and limited mobility. On the one hand, cardiac rehabilitation may not be pursued due to rehabilitation contraindications. On the other hand, the physical changes brought about by cardiac rehabilitation in elderly patients may not be apparent, leading to lower compliance.13,14 (2) Studies have shown that education level is related to treatment compliance in AMI patients. Patients with higher education levels can communicate more effectively,4 while those with lower education levels often lack sufficient awareness of cardiac rehabilitation. Some patients with lower economic levels also do not prioritize their health, all of which affect compliance,15 which is similar to the results of this study. For patients with lower education levels, healthcare professionals need to repeatedly emphasize its importance and guide family members to supervise cardiac rehabilitation training.(3) Disease perception is also a risk factor affecting cardiac rehabilitation compliance. Patients with poor perception are less aware of the importance of cardiac rehabilitation, more likely to fear the disease, and have low expectations for treatment, resulting in the misconception that rehabilitation therapy has little significance, which reduces compliance.16 (4) Anxiety and depression are also reasons for poor compliance with cardiac rehabilitation treatment. Patients who are long-term in a negative emotional state of anxiety and depression tend to neglect cardiac rehabilitation training. Furthermore, AMI is prone to complications, and patients worry about the occurrence of complications, which leads to emotional lows and reduced compliance.17,18 (5) Social support is also a risk factor affecting cardiac rehabilitation. Most patients acquire relevant knowledge about the disease through family and friends. Their support and encouragement help patients build confidence in overcoming the disease and improve their participation in cardiac rehabilitation.19 (6) Healthcare providers must select appropriate cardiac rehabilitation training based on the patient’s actual situation, ensuring the effectiveness of the training while avoiding exacerbating the patient’s financial burden. Professional support should be provided to supervise the patient’s training, encourage emotional support from family and friends, and actively guide patients to participate in cardiac rehabilitation training to improve compliance.20 Some studies have found that anthropometric indicators (such as thoracic morphology) can influence adherence to cardiac rehabilitation.21 However, this study mainly focused on patients’ general information and social factors, which is a limitation. Further research will be conducted to explore this aspect in the future.
Through the construction of a nomogram model, this study found that the AUC of the modeling group and validation group was 0.955 and 0.937, respectively, indicating high discrimination. The slope of the calibration curve was close to 1, showing good consistency between the model’s risk assessment and actual risk. Additionally, the DCA curve showed that when the high-risk threshold probability was between 0.08 and 0.93, the clinical application value of this nomogram model was high. It can help clinicians assess the risk of cardiac rehabilitation compliance in patients based on influencing factors and prevent non-compliance early to improve patient outcomes.
In conclusion, age, education level, disease perception, anxiety and depression, social support, and healthcare supervision are factors affecting cardiac rehabilitation compliance in elderly AMI patients. The nomogram model built on these factors showed good discrimination and consistency and can predict patients’ cardiac rehabilitation compliance. Further validation with larger sample sizes is needed in future studies. This study has several limitations. As a retrospective study, there may be selection bias in the sample size. Additionally, it is a single-center study with a relatively small sample size. Future research will involve prospective, multicenter studies with a larger sample size for further validation.
Data Sharing Statement
The original contributions presented in the study are included in the article.
Ethics Statement
The study was in accordance with Yueyang People’s Hospital ethics review board (2022-04-047) and with the 1964 Helsinki Declaration. Written informed consent to participate in this study was provided by the participants.
Disclosure
All authors declare no conflicts of interest.
References
1. Chen Q, Su L, Liu C, et al. PRKAR1A and SDCBP serve as potential predictors of heart failure following acute myocardial infarction. Front Immunol. 2022;13:878876. doi:10.3389/fimmu.2022.878876
2. Zhang L, Zhang X, Zhong X, et al. Soluble Flt-1 in AMI patients serum inhibits angiogenesis of endothelial progenitor cells by suppressing Akt and Erk’s activity. Biology. 2022;11(8).
3. Taylor RS, Dalal HM, Mcdonagh STJ. The role of cardiac rehabilitation in improving cardiovascular outcomes. Nat Rev Cardiol. 2022;19(3):180–194. doi:10.1038/s41569-021-00611-7
4. Goldstein DW, Hajduk AM, Song X, et al. Factors associated with cardiac rehabilitation participation in older adults after myocardial infarction: THE SILVER-AMI STUDY. J Cardiopulm Rehabil Prev. 2022;42(2):109–114. doi:10.1097/HCR.0000000000000627
5. Vilela EM, Ladeiras-Lopes R, João A, et al. Cardiac rehabilitation in elderly myocardial infarction survivors: focus on circulatory power. Rev Cardiovasc Med. 2021;22(3):903–910. doi:10.31083/j.rcm2203097
6. Wang X, Fu X. Predicting AKI in patients with AMI: development and assessment of a new predictive nomogram. Medicine. 2023;102(24):e33991. doi:10.1097/MD.0000000000033991
7. Chen S, Pan X, Mo J, et al. Establishment and validation of a prediction nomogram for heart failure risk in patients with acute myocardial infarction during hospitalization. BMC Cardiovasc Disord. 2023;23(1):619. doi:10.1186/s12872-023-03665-2
8. Pendell Meyers H, Bracey A, Lee D, et al. Accuracy of OMI ECG findings versus STEMI criteria for diagnosis of acute coronary occlusion myocardial infarction. Int J Cardiol Heart Vasc. 2021;33:100767. doi:10.1016/j.ijcha.2021.100767
9. Liu X, Fowokan A, Grace SL, et al. Translation, cross-cultural adaptation, and psychometric validation of the Chinese/mandarin cardiac rehabilitation barriers scale (CRBS-C/M). Rehabil Res Pract. 2021;2021:5511426. doi:10.1155/2021/5511426
10. Matter MA, Paneni F, Libby P, et al. Inflammation in acute myocardial infarction: the good, the bad and the ugly. Eur Heart J. 2024;45(2):89–103. doi:10.1093/eurheartj/ehad486
11. Zhang Y, Guo Z, Wu T, et al. SULT2B1b inhibits reverse cholesterol transport and promotes cholesterol accumulation and inflammation in lymphocytes from AMI patients with low LDL-C levels. Clin Sci. 2020;134(2):273–287. doi:10.1042/CS20190459
12. Kim C, Choi I, Cho S, et al. Correction: do cardiac rehabilitation affect clinical prognoses such as recurrence, readmission, revascularization, and mortality after AMI?: systematic review and meta-analysis. Ann Rehabil Med. 2021;45(2):165. doi:10.5535/arm.20080.e
13. Snoek JA, Prescott EI, van der Velde AE, et al. Effectiveness of home-based mobile guided cardiac rehabilitation as alternative strategy for nonparticipation in clinic-based cardiac rehabilitation among elderly patients in Europe: a randomized clinical trial. JAMA Cardiol. 2021;6(4):463–468. doi:10.1001/jamacardio.2020.5218
14. Zhu E, Liu Y, Zhong M, et al. Targeting NK-1R attenuates renal fibrosis via modulating inflammatory responses and cell fate in chronic kidney disease. Front Immunol. 2023;14:1142240. doi:10.3389/fimmu.2023.1142240
15. Hald K, Larsen FB, Nielsen KM, et al. Medication adherence, biological and lifestyle risk factors in patients with myocardial infarction: a ten-year follow-up on socially differentiated cardiac rehabilitation. Scand J Prim Health Care. 2019;37(2):182–190. doi:10.1080/02813432.2019.1608046
16. Tenbult N, Asten IV, Traa S, et al. Determinants of information needs in patients with coronary artery disease receiving cardiac rehabilitation: a prospective observational study. BMJ Open. 2023;13(2):e068351. doi:10.1136/bmjopen-2022-068351
17. Helmark C, Harrison A, Pedersen SS, et al. Systematic screening for anxiety and depression in cardiac rehabilitation – are we there yet?. Int J Cardiol. 2022;352:65–71. doi:10.1016/j.ijcard.2022.02.004
18. Bermudez T, Bierbauer W, Scholz U, et al. Depression and anxiety in cardiac rehabilitation: differential associations with changes in exercise capacity and quality of life. Anxiety Stress Coping. 2022;35(2):204–218. doi:10.1080/10615806.2021.1952191
19. Arian M, Valinejadi A, Soleimani M. Reviews evaluating information technology-based cardiac rehabilitation programs and support: a systematic review. Iran J Public Health. 2022;51(7):1525–1537. doi:10.18502/ijph.v51i7.10086
20. Bhat AG, Farah M, Szalai H, et al. Evaluation of the American association of cardiovascular and pulmonary rehabilitation exercise risk stratification classification tool without exercise testing. J Cardiopulm Rehabil Prev. 2021;41(4):257–263. doi:10.1097/HCR.0000000000000584
21. Sonaglioni A, Nicolosi GL, Lombardo M. The relationship between mitral valve prolapse and thoracic skeletal abnormalities in clinical practice: a systematic review. J Cardiovasc Med. 2024;25(5):353–363. doi:10.2459/JCM.0000000000001614