Analysis of factors influencing re-hospitalization and death in coronary heart disease patients with heart failure based on the joint fragility model: a prospective cohort study_Chinese Journal of Evidence-Based Medicine

Authors：

ZHU Yu ¹ , YAN Jingjing ^2,3 , YANG Yaqi ¹ , WANG Fei ¹ , YAN Juanjuan ⁴ ,  ZHANG Yanbo ^2,3,4

1. School of Traditional Chinese Medicine and Food Engineering, Shanxi University of Traditional Chinese Medicine, Taiyuan 030619, P. R. China;
2. Department of Biostatistics, School of Public Health, Shanxi Medical University, Taiyuan 030000, P. R. China;
3. Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan 030000, P. R. China;
4. School of Health Services and Management, Shanxi University of Chinese Medicine, Taiyuan 030619, P. R. China;

Corresponding author：

ZHANG Yanbo, Email: sxmuzyb@126.com

Keywords：

Heart failure caused by coronary heart; Joint frailty model; Re-hospitalization; Mortality; Event dependency

DOI：

10.7507/1672-2531.202404104

Video：

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Objective The re-hospitalization and death events of patients heart failure caused by coronary heart disease are characterized by non-independence, heterogeneity, and censored data. A joint frailty model is established to jointly model the events, explore the risk factors affecting the prognosis of patients, and reduce the re-hospitalization rate and mortality of patients. Methods The sample includes 4 682 patients with heart failure caused by coronary heart disease in two tertiary hospitals from January 2014 and June 2019. The electronic medical record information of patients during hospitalization and their follow-up information were collected. The Cox model, conditional frailty model and joint frailty model were used to analyze patient re-hospitalization and death. Results The joint frailty model identifies patients with a higher risk of both relapse and death (θ=0.209, P<0.001). Risk factors for re-hospitalization were advanced age, Grade 3 hypertension, mental work, no medical insurance, high cystatin C, and low ejection fraction, plus, within certain limits, low free thyroxine-3 and thyroxine-4. Antiplatelet drugs and statins significantly reduced the risk of re-hospitalization. Risk factors for death were advanced age, New York Heart Association classification Ⅲ to Ⅳ, no medical insurance, mental work, high cystatin C level, high troponin-I level, low free thyroxine-3, and low ejection fraction. Percutaneous coronary intervention, and taking antiplatelet drugs and statins significantly reduced the risk of death. Conclusion The joint frailty model can simultaneously model recurring and terminal events, and accurately predict them. Our results suggest that thyroid hormone levels and cystatin C levels of patients should be considered more carefully. People with mental jobs should change bad working habits to reduce adverse outcomes.

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2.	Redfield MM, Borlaug BA. Heart failure with preserved ejection fraction: a review. JAMA, 2023, 329(10): 827-838.
3.	Savarese G, Becher PM, Lund LH, et al. Global burden of heart failure: a comprehensive and updated review of epidemiology. Cardiovasc Res, 2023, 118(17): 3272-3287.
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7.	Charles-Nelson A, Katsahian S, Schramm C. How to analyze and interpret recurrent events data in the presence of a terminal event: an application on readmission after colorectal cancer surgery. Stat Med, 2019, 38(18): 3476-3502.
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12.	闫晶晶, 田晶, 王可, 等. 冠心病合并慢性心力衰竭患者再住院条件脆弱模型分析. 中国卫生统计, 2020, 37(1): 66-69.
13.	于智凯, 郭强, 罗天娥, 等. 联合脆弱模型在含有终止事件的复发事件数据分析中的应用. 中国卫生统计, 2018, 35(6): 825-830.
14.	Ghosh D, Lin DY. Nonparametric analysis of recurrent events and death. Biometrics, 2000, 56(2): 554-562.
15.	Wang MC, Chiang CT. Non-parametric methods for recurrent event data with informative and non-informative censorings. Stat Med, 2002, 21(3): 445-456.
16.	Wang MC, Qin J, Chiang CT. Analyzing recurrent event data with informative censoring. J Am Stat Assoc, 2001, 96(455): 10.1198/016214501753209031.
17.	Ye Y, Kalbfleisch JD, Schaubel DE. Semiparametric analysis of correlated recurrent and terminal events. Biometrics, 2007, 63(1): 78-87.
18.	Heidenreich PA, Bozkurt B, Aguilar D, et al. 2022 AHA/ACC/HFSA guideline for the management of heart failure: a report of the American college of cardiology/American heart association joint committee on clinical practice guidelines. Circulation, 2022, 145(18): e895-e1032.
19.	颜红兵. 美国冠心病诊断与治疗指南(第2版). 北京: 中国环境科学出版社, 2006.
20.	Box-Steffensmeier JM, De Boef S. Repeated events survival models: the conditional frailty model. Stat Med, 2006, 25(20): 3518-3533.
21.	Huang X, Wolfe RA. A frailty model for informative censoring. Biometrics, 2002, 58(3): 510-520.
22.	Andersen P, Gill R. Cox's regression model for counting processes: a large sample study. Ann Stat, 1982, 10(4): 1100-1120.
23.	Prentice RL, Williams BJ, Peterson AV. On the regression analysis of multivariate failure time data. Biometrika, 1981, (2): 373-379.
24.	Austin PC. A tutorial on multilevel survival analysis: methods, models and applications. Int Stat Rev, 2017, 85(2): 185-203.
25.	Braga JR, Tu JV, Austin PC, et al. Recurrent events analysis for examination of hospitalizations in heart failure: insights from the enhanced feedback for effective cardiac treatment (EFFECT) trial. Eur Heart J Qual Care Clin Outcomes, 2018, 4(1): 18-26.
26.	Ullah S, Gabbett TJ, Finch CF. Statistical modelling for recurrent events: an application to sports injuries. Br J Sports Med, 2014, 48(17): 1287-1293.
27.	Di Mauro M, Petroni R, Clemente D, et al. Clinical profile of patients with heart failure can predict rehospitalization and quality of life. J Cardiovasc Med (Hagerstown), 2018, 19(3): 98-104.
28.	Bunevicius A, Kazlauskas H, Raskauskiene N, et al. Ischemic stroke functional outcomes are independently associated with C-reactive protein concentrations and cognitive outcomes with triiodothyronine concentrations: a pilot study. Endocrine, 2014, 45(2): 213-220.
29.	全军民, 姚琪, 陶袁, 等. 亚临床甲状腺功能紊乱对急性心力衰竭患者远期预后的影响. 中国心血管杂志, 2016, 21(6): 456-461.
30.	薛超, 卞玲, 解玉水, 等. 急性冠状动脉综合征患者游离三碘甲状腺原氨酸水平降低与预后的关系研究. 中国心血管杂志, 2016, 21(2): 114-119.
31.	Moruzzi P, Doria E, Agostoni PG, et al. Usefulness of L-thyroxine to improve cardiac and exercise performance in idiopathic dilated cardiomyopathy. Am J Cardiol, 1994, 73(5): 374-378.
32.	Han WK, Wagener G, Zhu Y, et al. Urinary biomarkers in the early detection of acute kidney injury after cardiac surgery. Clin J Am Soc Nephrol, 2009, 4(5): 873-882.
33.	Hawkins NM, Jhund PS, McMurray JJ, et al. Heart failure and socioeconomic status: accumulating evidence of inequality. Eur J Heart Fail, 2012, 14(2): 138-146.
34.	Tsuchihashi M, Tsutsui H, Kodama K, et al. Medical and socioenvironmental predictors of hospital readmission in patients with congestive heart failure. Am Heart J, 2001, 142(4): E7.
35.	Pacho C, Domingo M, Núñez R, et al. Predictive biomarkers for death and rehospitalization in comorbid frail elderly heart failure patients. BMC Geriatr, 2018, 18(1): 109.
36.	Rørth R, Fosbøl EL, Mogensen UM, et al. Employment status at time of first hospitalization for heart failure is associated with a higher risk of death and rehospitalization for heart failure. Eur J Heart Fail, 2018, 20(2): 240-247.
37.	Shi J, Wu Y, Zhu S, et al. The association between serum creatinine/cystatin C ratio and cardiovascular morbidity and mortality: insights from NHANES. Rev Cardiovasc Med, 2023, 24(9): 275.
38.	Grubb AO. Cystatin C-properties and use as diagnostic marker. Adv Clin Chem, 2000, 35: 63-99.

1. Emmons-Bell S, Johnson C, Roth G. Prevalence, incidence and survival of heart failure: a systematic review. Heart, 2022, 108(17): 1351-1360.
2. Redfield MM, Borlaug BA. Heart failure with preserved ejection fraction: a review. JAMA, 2023, 329(10): 827-838.
3. Savarese G, Becher PM, Lund LH, et al. Global burden of heart failure: a comprehensive and updated review of epidemiology. Cardiovasc Res, 2023, 118(17): 3272-3287.
4. Rizzuto N, Charles G, Knobf MT. Decreasing 30-day readmission rates in patients with heart failure. Crit Care Nurse, 2022, 42(4): 13-19.
5. Huang J, Yin H, Zhang M, et al. Understanding the economic burden of heart failure in China: impact on disease management and resource utilization. J Med Econ, 2017, 20(5): 549-553.
6. Li Y, Sun XL, Qiu H, et al. Long-term outcomes and independent predictors of mortality in patients presenting to emergency departments with acute heart failure in Beijing: a multicenter cohort study with a 5-year follow-up. Chin Med J (Engl), 2021, 134(15): 1803-1811.
7. Charles-Nelson A, Katsahian S, Schramm C. How to analyze and interpret recurrent events data in the presence of a terminal event: an application on readmission after colorectal cancer surgery. Stat Med, 2019, 38(18): 3476-3502.
8. Clayton D. Some approaches to the analysis of recurrent event data. Stat Methods Med Res, 1994, 3(3): 244-262.
9. Fonarow GC. Clinical risk prediction tools in patients hospitalized with heart failure. Rev Cardiovasc Med, 2012, 13(1): e14-e23.
10. Kelly PJ, Lim LL. Survival analysis for recurrent event data: an application to childhood infectious diseases. Stat Med, 2000, 19(1): 13-33.
11. Tian J, Yan J, Zhang Q, et al. Analysis of re-hospitalizations for patients with heart failure caused by coronary heart disease: data of first event and recurrent event. Ther Clin Risk Manag, 2019, 15: 1333-1341.
12. 闫晶晶, 田晶, 王可, 等. 冠心病合并慢性心力衰竭患者再住院条件脆弱模型分析. 中国卫生统计, 2020, 37(1): 66-69.
13. 于智凯, 郭强, 罗天娥, 等. 联合脆弱模型在含有终止事件的复发事件数据分析中的应用. 中国卫生统计, 2018, 35(6): 825-830.
14. Ghosh D, Lin DY. Nonparametric analysis of recurrent events and death. Biometrics, 2000, 56(2): 554-562.
15. Wang MC, Chiang CT. Non-parametric methods for recurrent event data with informative and non-informative censorings. Stat Med, 2002, 21(3): 445-456.
16. Wang MC, Qin J, Chiang CT. Analyzing recurrent event data with informative censoring. J Am Stat Assoc, 2001, 96(455): 10.1198/016214501753209031.
17. Ye Y, Kalbfleisch JD, Schaubel DE. Semiparametric analysis of correlated recurrent and terminal events. Biometrics, 2007, 63(1): 78-87.
18. Heidenreich PA, Bozkurt B, Aguilar D, et al. 2022 AHA/ACC/HFSA guideline for the management of heart failure: a report of the American college of cardiology/American heart association joint committee on clinical practice guidelines. Circulation, 2022, 145(18): e895-e1032.
19. 颜红兵. 美国冠心病诊断与治疗指南(第2版). 北京: 中国环境科学出版社, 2006.
20. Box-Steffensmeier JM, De Boef S. Repeated events survival models: the conditional frailty model. Stat Med, 2006, 25(20): 3518-3533.
21. Huang X, Wolfe RA. A frailty model for informative censoring. Biometrics, 2002, 58(3): 510-520.
22. Andersen P, Gill R. Cox's regression model for counting processes: a large sample study. Ann Stat, 1982, 10(4): 1100-1120.
23. Prentice RL, Williams BJ, Peterson AV. On the regression analysis of multivariate failure time data. Biometrika, 1981, (2): 373-379.
24. Austin PC. A tutorial on multilevel survival analysis: methods, models and applications. Int Stat Rev, 2017, 85(2): 185-203.
25. Braga JR, Tu JV, Austin PC, et al. Recurrent events analysis for examination of hospitalizations in heart failure: insights from the enhanced feedback for effective cardiac treatment (EFFECT) trial. Eur Heart J Qual Care Clin Outcomes, 2018, 4(1): 18-26.
26. Ullah S, Gabbett TJ, Finch CF. Statistical modelling for recurrent events: an application to sports injuries. Br J Sports Med, 2014, 48(17): 1287-1293.
27. Di Mauro M, Petroni R, Clemente D, et al. Clinical profile of patients with heart failure can predict rehospitalization and quality of life. J Cardiovasc Med (Hagerstown), 2018, 19(3): 98-104.
28. Bunevicius A, Kazlauskas H, Raskauskiene N, et al. Ischemic stroke functional outcomes are independently associated with C-reactive protein concentrations and cognitive outcomes with triiodothyronine concentrations: a pilot study. Endocrine, 2014, 45(2): 213-220.
29. 全军民, 姚琪, 陶袁, 等. 亚临床甲状腺功能紊乱对急性心力衰竭患者远期预后的影响. 中国心血管杂志, 2016, 21(6): 456-461.
30. 薛超, 卞玲, 解玉水, 等. 急性冠状动脉综合征患者游离三碘甲状腺原氨酸水平降低与预后的关系研究. 中国心血管杂志, 2016, 21(2): 114-119.
31. Moruzzi P, Doria E, Agostoni PG, et al. Usefulness of L-thyroxine to improve cardiac and exercise performance in idiopathic dilated cardiomyopathy. Am J Cardiol, 1994, 73(5): 374-378.
32. Han WK, Wagener G, Zhu Y, et al. Urinary biomarkers in the early detection of acute kidney injury after cardiac surgery. Clin J Am Soc Nephrol, 2009, 4(5): 873-882.
33. Hawkins NM, Jhund PS, McMurray JJ, et al. Heart failure and socioeconomic status: accumulating evidence of inequality. Eur J Heart Fail, 2012, 14(2): 138-146.
34. Tsuchihashi M, Tsutsui H, Kodama K, et al. Medical and socioenvironmental predictors of hospital readmission in patients with congestive heart failure. Am Heart J, 2001, 142(4): E7.
35. Pacho C, Domingo M, Núñez R, et al. Predictive biomarkers for death and rehospitalization in comorbid frail elderly heart failure patients. BMC Geriatr, 2018, 18(1): 109.
36. Rørth R, Fosbøl EL, Mogensen UM, et al. Employment status at time of first hospitalization for heart failure is associated with a higher risk of death and rehospitalization for heart failure. Eur J Heart Fail, 2018, 20(2): 240-247.
37. Shi J, Wu Y, Zhu S, et al. The association between serum creatinine/cystatin C ratio and cardiovascular morbidity and mortality: insights from NHANES. Rev Cardiovasc Med, 2023, 24(9): 275.
38. Grubb AO. Cystatin C-properties and use as diagnostic marker. Adv Clin Chem, 2000, 35: 63-99.

Chinese Journal of Evidence-Based Medicine

Latest ArticlesAnalysis of factors influencing re-hospitalization and death in coronary heart disease patients with heart failure based on the joint fragility model: a prospective cohort study

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