Identification of risk factors of 30-day readmission and 180-day in-hospital mortality, and its corresponding relative importance in patients with Ischemic heart disease: a machine learning approach

References

• Kurian AK, Cardarelli KM. Racial and ethnic differences in cardiovascular disease risk factors: a systematic review. Ethn Dis. 2007;17:143–152.
   PubMed Web of Science ®Google Scholar
• Bonow RO, Grant AO, Jacobs AK. The cardiovascular state of the union: confronting healthcare disparities. Circulation. 2005;111(10):1205–1207.
   PubMed Web of Science ®Google Scholar
• Mensah GA, Mokdad AH, Ford ES, et al. State of disparities in cardiovascular health in the United States. Circulation. 2005;111:1233–1241.
   PubMed Web of Science ®Google Scholar
• McClellan M, Brown N, Califf RM, et al. Call to action: urgent challenges in cardiovascular disease: a presidential advisory from the American Heart Association. Circulation. 2019;139:e44–e54.
   PubMed Web of Science ®Google Scholar
• Centers for Medicare & Medicaid Services. Hospital readmissions reduction program (HRRP); 2019 [cited 2020 Feb]. Available from: https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Readmissions-Reduction-Program
   Google Scholar
• Santschi V, Chiolero A, Burnand B, et al. Impact of pharmacist care in the management of cardiovascular disease risk factors: a systematic review and meta-analysis of randomized trials. Arch Intern Med. 2011;171:1441–1453.
   PubMedGoogle Scholar
• Piepoli MF, Hoes AW, Agewall S, et al. European guidelines on cardiovascular disease prevention in clinical practice: the sixth joint task force of the European Society of Cardiology and other societies on cardiovascular disease prevention in clinical practice (constituted by representatives of 10 societies and by invited experts) developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Eur Heart J. 2016;37:2315–2381. Epub 2016 May 23.
   PubMed Web of Science ®Google Scholar
• Wang N, Gallagher R, Sze D, et al. Predictors of frequent readmissions in patients with heart failure. Heart Lung Circ. 2019;28:277–283.
   PubMed Web of Science ®Google Scholar
• Hernandez MB, Schwartz RS, Asher CR, et al. Predictors of 30-day readmission in patients hospitalized with decompensated heart failure. Clin Cardiol. 2013;36(9):542–547.
   PubMed Web of Science ®Google Scholar
• McNamara RL, Kennedy KF, Cohen DJ, et al. Predicting in-hospital mortality in patients with acute myocardial infarction. J Am Coll Cardiol. 2016;68:626–635.
   PubMedGoogle Scholar
• Ranganathan P, Pramesh C, Aggarwal R. Common pitfalls in statistical analysis: logistic regression. Perspect Clin Res. 2017;8:148–151.
   PubMedGoogle Scholar
• Bishop CM. Model-based machine learning. Philos Trans A Math Phys Eng Sci. 2013;371:20120222.
   PubMed Web of Science ®Google Scholar
• Deo RC. Machine learning in medicine. Circulation. 2015;132:1920–1930.
   PubMed Web of Science ®Google Scholar
• Sidey-Gibbons JAM, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction. BMC Med Res Methodol. 2019;19:64.
   PubMed Web of Science ®Google Scholar
• Kakadiaris IA, Vrigkas M, Yen AA, et al. Machine learning outperforms ACC/AHA CVD risk calculator in MESA. J Am Heart Assoc. 2018;7:e009476.
   PubMed Web of Science ®Google Scholar
• Ambale-Venkatesh B, Yang X, Wu CO, et al. Cardiovascular event prediction by machine learning: the multi-ethnic study of atherosclerosis. Circ Res. 2017;121:1092–1101.
   PubMed Web of Science ®Google Scholar
• Morgan DJ, Bame B, Zimand P, et al. Assessment of machine learning vs standard prediction rules for predicting hospital readmissions. JAMA Network Open. 2019;2:e190348–e190348.
   PubMed Web of Science ®Google Scholar
• Mortazavi BJ, Downing NS, Bucholz EM, et al. Analysis of machine learning techniques for heart failure readmissions. Circ Cardiovasc Qual Outcomes. 2016;9:629–640.
   PubMed Web of Science ®Google Scholar
• Steele AJ, Denaxas SC, Shah AD, et al. Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease. PLoS One. 2018;13:e0202344.
   PubMed Web of Science ®Google Scholar
• Al’Aref SJ, Anchouche K, Singh G, et al. Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. Eur Heart J. 2019;40:1975–1986.
   PubMed Web of Science ®Google Scholar
• Goldstein BA, Navar AM, Carter RE. Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges. Eur Heart J. 2017;38:1805–1814.
   PubMed Web of Science ®Google Scholar
• Preen DB, Holman CD, Spilsbury K, et al. Length of comorbidity lookback period affected regression model performance of administrative health data. J Clin Epidemiol. 2006;59:940–946. Epub 2006 Jun 19.
   PubMed Web of Science ®Google Scholar
• Tripathi A, Abbott JD, Fonarow GC, et al. Thirty-day readmission rate and costs after percutaneous coronary intervention in the United States. Circ Cardiovasc Interv. 2017;10(12). DOI:https://doi.org/10.1161/CIRCINTERVENTIONS.117.005925
   Google Scholar
• Wadhera RK, Joynt Maddox KE, Wasfy JH, et al. Association of the hospital readmissions reduction program with mortality among medicare beneficiaries hospitalized for heart failure, acute myocardial infarction, and pneumonia. JAMA. 2018;320:2542–2552.
   PubMed Web of Science ®Google Scholar
• Andersen RM. Revisiting the behavioral model and access to medical care: does it matter? J Health Soc Behav. 1995;36:1–10.
   PubMed Web of Science ®Google Scholar
• Van den Broeck J, Cunningham SA, Eeckels R, et al. Data cleaning: detecting, diagnosing, and editing data abnormalities. PLoS Med. 2005;2:e267.
   PubMed Web of Science ®Google Scholar
• Grafarend EW. Linear and nonlinear models: fixed effects, random effects, and mixed models. Berlin, NY: Walter de Gruyter; 2006.
   Google Scholar
• Chawla NV, Bowyer KW, Hall LO, et al. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321–357.
   Web of Science ®Google Scholar
• Sun Y, Wong AK, Kamel MS. Classification of imbalanced data: a review. Intern J Pattern Recognit Artif Intell. 2009;23:687–719.
   Web of Science ®Google Scholar
• Alghamdi M, Al-Mallah M, Keteyian S, et al. Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: the Henry Ford exercise testing (FIT) project. PLoS One. 2017;12:e0179805.
   PubMed Web of Science ®Google Scholar
• Cava W, Bauer C, Moore JH, et al. Interpretation of machine learning predictions for patient outcomes in electronic health records. AMIA Annu Symp Proc. 2020;2019:572–581.
   PubMedGoogle Scholar
• Goff DC Jr, Lloyd-Jones DM, Bennett G, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association task force on practice guidelines. Circulation. 2014;129(25Suppl 2):S49–73.
   PubMed Web of Science ®Google Scholar
• Khan H, Greene SJ, Fonarow GC, et al. Length of hospital stay and 30-day readmission following heart failure hospitalization: insights from the EVEREST trial. Eur J Heart Fail. 2015;17:1022–1031.
   PubMed Web of Science ®Google Scholar
• Kwok CS, Rao SV, Gilchrist IC, et al. Relation of length of stay to unplanned readmissions for patients who undergo elective percutaneous coronary intervention. Am J Cardiol. 2019;123:33–43.
   PubMed Web of Science ®Google Scholar
• Rosen OZ, Fridman R, Rosen BT, et al. Medication adherence as a predictor of 30-day hospital readmissions. Patient Prefer Adherence. 2017 Apr 20;11:801–810.
   PubMed Web of Science ®Google Scholar
• Zhang Y, Kaplan CM, Baik SH, et al. Medication adherence and readmission after myocardial infarction in the Medicare population. Am J Manag Care. 2014;20:e498–505. PMID: 25651604.
   PubMed Web of Science ®Google Scholar