Data Modeling Using Vital Sign Dynamics for In-hospital Mortality Classification in Patients with Acute Coronary Syndrome

Overview

Journal Healthc Inform Res

Date 2023 May 16

PMID 37190736

Authors

Sarawuth Limprasert

Ajchara Phu-Ang

Affiliations

Soon will be listed here.

Abstract

Objectives: This study compared feature selection by machine learning or expert recommendation in the performance of classification models for in-hospital mortality among patients with acute coronary syndrome (ACS) who underwent percutaneous coronary intervention (PCI).

Methods: recursive feature elimination with cross-validation (RFECV) and selection by interventional cardiologists. We used five simple models: support vector machine (SVM), random forest, decision tree, logistic regression, and artificial neural network. The performance metrics were accuracy, recall, and the false-negative rate, measured with 10-fold cross-validation in the training set and validated in the test set.

Results: Patients' mean age was 66.22 ± 12.88 years, and 33.63% had ST-elevation ACS. Fifteen of 34 features were selected as important with the RFECV method, while the experts chose 11 features. All models with feature selection by RFECV had higher accuracy than the models with expert-chosen features. In the training set, the random forest model had the highest accuracy (0.96 ± 0.01) and recall (0.97 ± 0.02). After validation in the test set, the SVM model displayed the highest accuracy (0.81) and a recall of 0.61.

Conclusions: Models with feature selection by RFECV had higher accuracy than those with feature selection by experts in identifying patients with ACS at high risk for in-hospital mortality.

References

Pitsavos C, Kourlaba G, Kurlaba G, Panagiotakos D, Kogias Y, Mantas Y . Association of creatinine clearance and in-hospital mortality in patients with acute coronary syndromes: the GREECS study. Circ J. 2006; 71(1):9-14. DOI: 10.1253/circj.71.9. View

Goldhill D, McNarry A, Mandersloot G, McGinley A . A physiologically-based early warning score for ward patients: the association between score and outcome. Anaesthesia. 2005; 60(6):547-53. DOI: 10.1111/j.1365-2044.2005.04186.x. View

Bittl J . Percutaneous coronary interventions in the diabetic patient: where do we stand?. Circ Cardiovasc Interv. 2015; 8(4):e001944. DOI: 10.1161/CIRCINTERVENTIONS.114.001944. View

Dai Y, Yang J, Gao Z, Xu H, Sun Y, Wu Y . Atrial fibrillation in patients hospitalized with acute myocardial infarction: analysis of the china acute myocardial infarction (CAMI) registry. BMC Cardiovasc Disord. 2017; 17(1):2. PMC: 5210261. DOI: 10.1186/s12872-016-0442-9. View

Srimahachota S, Boonyaratavej S, Kanjanavanit R, Sritara P, Krittayaphong R, Kunjara-Na-Ayudhya R . Thai Registry in Acute Coronary Syndrome (TRACS)--an extension of Thai Acute Coronary Syndrome registry (TACS) group: lower in-hospital but still high mortality at one-year. J Med Assoc Thai. 2012; 95(4):508-18. View

Bloch E, Rotem T, Cohen J, Singer P, Aperstein Y . Machine Learning Models for Analysis of Vital Signs Dynamics: A Case for Sepsis Onset Prediction. J Healthc Eng. 2019; 2019:5930379. PMC: 6925691. DOI: 10.1155/2019/5930379. View

Radhachandran A, Garikipati A, Zelin N, Pellegrini E, Ghandian S, Calvert J . Prediction of short-term mortality in acute heart failure patients using minimal electronic health record data. BioData Min. 2021; 14(1):23. PMC: 8010502. DOI: 10.1186/s13040-021-00255-w. View

Liu J, Wu J, Liu S, Li M, Hu K, Li K . Predicting mortality of patients with acute kidney injury in the ICU using XGBoost model. PLoS One. 2021; 16(2):e0246306. PMC: 7861386. DOI: 10.1371/journal.pone.0246306. View

Cei M, Bartolomei C, Mumoli N . In-hospital mortality and morbidity of elderly medical patients can be predicted at admission by the Modified Early Warning Score: a prospective study. Int J Clin Pract. 2009; 63(4):591-5. DOI: 10.1111/j.1742-1241.2008.01986.x. View

10.

Alam N, Hobbelink E, Van Tienhoven A, van de Ven P, Jansma E, Nanayakkara P . The impact of the use of the Early Warning Score (EWS) on patient outcomes: a systematic review. Resuscitation. 2014; 85(5):587-94. DOI: 10.1016/j.resuscitation.2014.01.013. View

11.

Terkelsen C, Lassen J, Norgaard B, Gerdes J, Jensen T, Gotzsche L . Mortality rates in patients with ST-elevation vs. non-ST-elevation acute myocardial infarction: observations from an unselected cohort. Eur Heart J. 2004; 26(1):18-26. DOI: 10.1093/eurheartj/ehi002. View

12.

Seib C, Roose J, Hubbard A, Suh I . Ensemble machine learning for the prediction of patient-level outcomes following thyroidectomy. Am J Surg. 2020; 222(2):347-353. PMC: 8172667. DOI: 10.1016/j.amjsurg.2020.11.055. View

13.

Rojas J, Carey K, Edelson D, Venable L, Howell M, Churpek M . Predicting Intensive Care Unit Readmission with Machine Learning Using Electronic Health Record Data. Ann Am Thorac Soc. 2018; 15(7):846-853. PMC: 6207111. DOI: 10.1513/AnnalsATS.201710-787OC. View

14.

Chehab O, Qannus A, Eldirani M, Hassan H, Tamim H, Dakik H . Predictors of In-Hospital Mortality in Patients Admitted with Acute Myocardial Infarction in a Developing Country. Cardiol Res. 2018; 9(5):293-299. PMC: 6188047. DOI: 10.14740/cr772w. View

15.

Granger C, Goldberg R, Dabbous O, Pieper K, Eagle K, Cannon C . Predictors of hospital mortality in the global registry of acute coronary events. Arch Intern Med. 2003; 163(19):2345-53. DOI: 10.1001/archinte.163.19.2345. View

16.

Kim S, Kim S, Cho J, Kim Y, Sol I, Sung Y . A deep learning model for real-time mortality prediction in critically ill children. Crit Care. 2019; 23(1):279. PMC: 6694497. DOI: 10.1186/s13054-019-2561-z. View

17.

Groarke J, Gallagher J, Stack J, Aftab A, Dwyer C, McGovern R . Use of an admission early warning score to predict patient morbidity and mortality and treatment success. Emerg Med J. 2008; 25(12):803-6. DOI: 10.1136/emj.2007.051425. View

18.

Santopinto J, Fox K, Goldberg R, Budaj A, Pinero G, Avezum A . Creatinine clearance and adverse hospital outcomes in patients with acute coronary syndromes: findings from the global registry of acute coronary events (GRACE). Heart. 2003; 89(9):1003-8. PMC: 1767853. DOI: 10.1136/heart.89.9.1003. View

19.

Kwon J, Lee Y, Lee Y, Lee S, Park J . An Algorithm Based on Deep Learning for Predicting In-Hospital Cardiac Arrest. J Am Heart Assoc. 2018; 7(13). PMC: 6064911. DOI: 10.1161/JAHA.118.008678. View