Systematic Review and Critical Appraisal of Prediction Models for Readmission in Coronary Artery Disease Patients: Assessing Current Efficacy and Future Directions

References

• Ralapanawa U, Sivakanesan R. Epidemiology and the Magnitude of Coronary Artery Disease and Acute Coronary Syndrome: a Narrative Review. J Epidemiol Glob Health. 2021;11(2):169–177. doi:10.2991/jegh.k.201217.001
   PubMed Web of Science ®Google Scholar
• Mozaffarian D, Benjamin EJ, Go AS, et al. Heart disease and stroke statistics--2015 update: a report from the American Heart Association. Circulation. 2015;131(4):e29–e322. doi:10.1161/CIR.0000000000000152
   PubMed Web of Science ®Google Scholar
• Huynh QL, Nghiem S, Byrnes J, Scuffham PA, Marwick T. Application of a risk-guided strategy to secondary prevention of coronary heart disease: analysis from a state-wide data linkage in Queensland, Australia. BMJ Open. 2022;12(5):e057856. doi:10.1136/bmjopen-2021-057856
   PubMed Web of Science ®Google Scholar
• Fox KAA, Dabbous OH, Goldberg RJ, et al. Prediction of risk of death and myocardial infarction in the six months after presentation with acute coronary syndrome: prospective multinational observational study (GRACE). BMJ. 2006;333(7578):1091. doi:10.1136/bmj.38985.646481.55
   PubMed Web of Science ®Google Scholar
• Li J, Dharmarajan K, Bai X, et al. Thirty-Day Hospital Readmission After Acute Myocardial Infarction in China. Circ Cardiovasc Qual Outcomes. 2019;12(5):656.
   Web of Science ®Google Scholar
• Data.gov. All-Cause Unplanned 30-Day Hospital Readmission Rate, California; 2023. Available from: https://catalog.data.gov/dataset/all-cause-unplanned-30-day-hospital-readmission-rate-california-lghc-indicator-36e12. Accessed February 7, 2024.
   Google Scholar
• AHRQ. All-Cause Hospital Readmissions; 2020. Available from: https://search.ahrq.gov/search?q=Characteristics+of+30-Day+All-Cause+Hospital+Readmissions%2C+2016-2020. Accessed February 7, 2024.
   Google Scholar
• CMS.gov. Hospital Readmissions Reduction Program; 2023. Available from: https://www.cms.gov/medicare/quality/value-based-programs/hospital-readmissions. Accessed February 7, 2024.
   Google Scholar
• Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009;360(14):1418–1428. doi:10.1056/NEJMsa0803563
   PubMed Web of Science ®Google Scholar
• McManus DD, Saczynski JS, Lessard D, et al. Reliability of Predicting Early Hospital Readmission After Discharge for an Acute Coronary Syndrome Using Claims-Based Data. Am J Cardiol. 2016;117(4):501–507. doi:10.1016/j.amjcard.2015.11.034
   PubMed Web of Science ®Google Scholar
• Litovchik I, Pereg D, Shlomo N, et al. Characteristics and outcomes associated with 30-day readmissions following acute coronary syndrome 2000-2013: the Acute Coronary Syndrome Israeli Survey. Eur Heart J Acute Cardiovasc Care. 2019;8(8):738–744. doi:10.1177/2048872618767997
   PubMed Web of Science ®Google Scholar
• Fischer C, Lingsma HF, Marang-van DMP, Kringos DS, Klazinga NS, Steyerberg EW. Is the readmission rate a valid quality indicator? A review of the evidence. PLoS One. 2014;9(11):e112282. doi:10.1371/journal.pone.0112282
   PubMed Web of Science ®Google Scholar
• Engoren M, Habib RH, Dooner JJ, Schwann TA. Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery. J Clin Monit Comput. 2013;27(4):455–464. doi:10.1007/s10877-013-9444-7
   PubMed Web of Science ®Google Scholar
• Alba AC, Agoritsas T, Walsh M, et al. Discrimination and Calibration of Clinical Prediction Models: users’ Guides to the Medical Literature. JAMA. 2017;318(14):1377–1384. doi:10.1001/jama.2017.12126
   PubMed Web of Science ®Google Scholar
• Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. doi:10.1136/bmj.n71
   PubMed Web of Science ®Google Scholar
• American Heart Association. Get With The Guidelines® - Coronary Artery Disease; 2024. Available from: https://www.heart.org/en/professional/quality-improvement/get-with-the-guidelines/get-with-the-guidelines-coronary-artery-disease. Accessed February 7, 2024.
   Google Scholar
• Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med. 2014;11(10):e1001744. doi:10.1371/journal.pmed.1001744
   PubMed Web of Science ®Google Scholar
• Moons K, Wolff RF, Riley RD, et al. PROBAST: a Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: explanation and Elaboration. Ann Intern Med. 2019;170(1):W1. doi:10.7326/M18-1377
   PubMed Web of Science ®Google Scholar
• Dodson JA, Hajduk AM, Murphy TE, et al. 180-day readmission risk model for older adults with acute myocardial infarction: the SILVER-AMI study. Open Heart. 2021;8(1):e001442. doi:10.1136/openhrt-2020-001442
   PubMed Web of Science ®Google Scholar
• Li D, Lin Y, Dong W, Hu Y, Li K. A nomogram for predicting the readmission within 6 months after treatment in patients with acute coronary syndrome. BMC Cardiovasc Disord. 2022;22(1). doi:10.1186/s12872-022-02873-6
   Web of Science ®Google Scholar
• Liu G, Zhang Y, Zhang W, et al. A Risk Prediction Model of Readmission for Chinese Patients after Coronary Artery Bypass Grafting. The Heart Surgery Forum. 2021;24(3):E479–E483. doi:10.1532/hsf.3773
   PubMed Web of Science ®Google Scholar
• Zhang Z, Qiu H, Li W, Chen Y. A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction. BMC Med Inform Decis Mak. 2020;20(1). doi:10.1186/s12911-020-01358-w
   Web of Science ®Google Scholar
• Kini V, Peterson PN, Spertus JA, et al. Clinical Model to Predict 90-Day Risk of Readmission After Acute Myocardial Infarction. Circ Cardiovasc Qual Outcomes. 2018;11(10).
   Web of Science ®Google Scholar
• Yang J, Zou S, Mao M, et al. Construction and Validation of the Risk Prediction Model for Readmission among Elderly Patients with Coronary Heart Disease. Military Nursing. 2021;38(8):20–23.
   Google Scholar
• Xv Y, Zhang R, Zhang C, Wang H, Hai R, Yang Y. Construction of a 5-year readmission risk prediction model for young and middle-aged patients with coronary heart disease. Chin Nurs Res. 2023;37(9):1556–1561.
   Google Scholar
• Dreyer RP, Raparelli V, Tsang SW, et al. Development and Validation of a Risk Prediction Model for 1‐Year Readmission Among Young Adults Hospitalized for Acute Myocardial Infarction. J Am Heart Assoc. 2021;10(18). doi:10.1161/JAHA.121.021047
   Web of Science ®Google Scholar
• Matheny ME, Ricket I, Goodrich CA, et al. Development of Electronic Health Record–Based Prediction Models for 30-Day Readmission Risk Among Patients Hospitalized for Acute Myocardial Infarction. JAMA Network Open. 2021;4(1):e2035782. doi:10.1001/jamanetworkopen.2020.35782
   PubMed Web of Science ®Google Scholar
• Gupta S, Ko DT, Azizi P, et al. Evaluation of Machine Learning Algorithms for Predicting Readmission After Acute Myocardial Infarction Using Routinely Collected Clinical Data. Can J Cardiol. 2020;36(6):878–885. doi:10.1016/j.cjca.2019.10.023
   PubMed Web of Science ®Google Scholar
• Nguyen OK, Makam AN, Clark C, Zhang S, Das SR, Halm EA. Predicting 30‐Day Hospital Readmissions in Acute Myocardial Infarction: the AMI “READMITS” (Renal Function, Elevated Brain Natriuretic Peptide, Age, Diabetes Mellitus, Nonmale Sex, Intervention with Timely Percutaneous Coronary Intervention, and Low Systolic Blood Pressure) Score. J Am Heart Assoc. 2018;7(8). doi:10.1161/JAHA.118.008882
   Web of Science ®Google Scholar
• Song X, Tong Y, Luo Y, et al. Predicting 7-day unplanned readmission in elderly patients with coronary heart disease using machine learning. Front Cardiovasc Med. 2023;10.
   Web of Science ®Google Scholar
• Benuzillo J, Caine W, Evans RS, Roberts C, Lappe D, Doty J. Predicting readmission risk shortly after admission for CABG surgery. J Card Surg. 2018;33(4):163–170. doi:10.1111/jocs.13565
   PubMed Web of Science ®Google Scholar
• Dharmarajan K, Hsieh AF, Lin Z, et al. Diagnoses and timing of 30-day readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia. JAMA. 2013;309(4):355–363. doi:10.1001/jama.2012.216476
   PubMed Web of Science ®Google Scholar
• Bernheim SM, Grady JN, Lin Z, et al. National patterns of risk-standardized mortality and readmission for acute myocardial infarction and heart failure. Update on publicly reported outcomes measures based on the 2010 release. Circ Cardiovasc Qual Outcomes. 2010;3(5):459–467. doi:10.1161/CIRCOUTCOMES.110.957613
   PubMed Web of Science ®Google Scholar
• Ananth CV, Rutherford C, Rosenfeld EB, et al. Epidemiologic trends and risk factors associated with the decline in mortality from coronary heart disease in the United States, 1990-2019. Am Heart J. 2023;263:46–55. doi:10.1016/j.ahj.2023.05.006
   PubMed Web of Science ®Google Scholar
• Commission TMPA. Update: medPAC’s evaluation of Medicare’s Hospital Readmission Reduction Program; 2019.
   Google Scholar
• Lam L, Ahn HJ, Okajima K, et al. Gender Differences in the Rate of 30-Day Readmissions after Percutaneous Coronary Intervention for Acute Coronary Syndrome. Womens Health Issues. 2019;29(1):17–22. doi:10.1016/j.whi.2018.09.002
   PubMed Web of Science ®Google Scholar
• de Marvao A, Alexander D, Bucciarelli-Ducci C, Price S. Heart disease in women: a narrative review. Anaesthesia. 2021;76(Suppl 4):118–130. doi:10.1111/anae.15376
   PubMedGoogle Scholar
• Shaw LJ, Bugiardini R, Merz CN. Women and ischemic heart disease: evolving knowledge. J Am Coll Cardiol. 2009;54(17):1561–1575. doi:10.1016/j.jacc.2009.04.098
   PubMed Web of Science ®Google Scholar
• Madhavan MV, Gersh BJ, Alexander KP, Granger CB, Stone GW. Coronary Artery Disease in Patients >/=80 Years of Age. J Am Coll Cardiol. 2018;71(18):2015–2040. doi:10.1016/j.jacc.2017.12.068
   PubMed Web of Science ®Google Scholar
• Krumholz HM. Post-hospital syndrome--an acquired, transient condition of generalized risk. N Engl J Med. 2013;368(2):100–102. doi:10.1056/NEJMp1212324
   PubMed Web of Science ®Google Scholar
• Naylor MD, Brooten DA, Campbell RL, Maislin G, McCauley KM, Schwartz JS. Transitional care of older adults hospitalized with heart failure: a randomized, controlled trial. J Am Geriatr Soc. 2004;52(5):675–684. doi:10.1111/j.1532-5415.2004.52202.x
   PubMed Web of Science ®Google Scholar
• Wang JG, Zhang W, Li Y, Liu L. Hypertension in China: epidemiology and treatment initiatives. Nat Rev Cardiol. 2023;20(8):531–545. doi:10.1038/s41569-022-00829-z
   PubMed Web of Science ®Google Scholar
• Xie X, He T, Kang J, Siscovick DS, Li Y, Pagan JA. Cost-effectiveness analysis of intensive hypertension control in China. Prev Med. 2018;111:110–114. doi:10.1016/j.ypmed.2018.02.033
   PubMed Web of Science ®Google Scholar
• Rapsomaniki E, Timmis A, George J, et al. Blood pressure and incidence of twelve cardiovascular diseases: lifetime risks, healthy life-years lost, and age-specific associations in 1.25 million people. Lancet. 2014;383(9932):1899–1911. doi:10.1016/S0140-6736(14)60685-1
   PubMed Web of Science ®Google Scholar
• Paez A. Gray literature: an important resource in systematic reviews. J Evid Based Med. 2017;10(3):233–240. doi:10.1111/jebm.12266
   PubMedGoogle Scholar