Dagmar F Hernandez-Suarez1, Sagar Ranka2, Yeunjung Kim3, Azeem Latib4, Jose Wiley4, Angel Lopez-Candales5, Duane S Pinto6, Maday C Gonzalez4, Harish Ramakrishna7, Cristina Sanina4, Brenda G Nieves-Rodriguez8, Jovaniel Rodriguez-Maldonado8, Roberto Feliu Maldonado8, Israel J Rodriguez-Ruiz8, Istoni da Luz Sant'Ana8, Karlo A Wiley9, Pedro Cox-Alomar10, Pedro A Villablanca11, Abiel Roche-Lima8. 1. Division of Cardiovascular Medicine, Department of Medicine, University of Puerto Rico School of Medicine, San Juan, PR, USA. Electronic address: dagmar.hernandez@upr.edu. 2. Division of Cardiovascular Medicine, Department of Medicine, University of Kansas School of Medicine, Kansas City, KS, USA. 3. Division of Cardiovascular Medicine, Department of Medicine, Yale University School of Medicine, New Haven, CT, USA. 4. Division of Cardiology, Department of Medicine, Montefiore Medical Center/Albert Einstein College of Medicine, New York, NY, USA. 5. Division of Cardiology, Department of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA. 6. Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA. 7. Division of Cardiovascular and Thoracic Anesthesiology, Mayo Clinic, Rochester, MN, USA. 8. Center for Collaborative Research in Health Disparities, University of Puerto Rico School of Medicine, San Juan, PR, USA. 9. College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, USA. 10. Division of Cardiology, Department of Medicine, Louisiana State University, New Orleans, LA, USA. 11. Division of Cardiovascular Medicine, Department of Medicine, Henry Ford Hospital, Detroit, MI, USA.
Abstract
BACKGROUND: Transcatheter mitral valve repair (TMVR) utilization has increased significantly in the United States over the last years. Yet, a risk-prediction tool for adverse events has not been developed. We aimed to generate a machine-learning-based algorithm to predict in-hospital mortality after TMVR. METHODS: Patients who underwent TMVR from 2012 through 2015 were identified using the National Inpatient Sample database. The study population was randomly divided into a training set (n = 636) and a testing set (n = 213). Prediction models for in-hospital mortality were obtained using five supervised machine-learning classifiers. RESULTS: A total of 849 TMVRs were analyzed in our study. The overall in-hospital mortality was 3.1%. A naïve Bayes (NB) model had the best discrimination for fifteen variables, with an area under the receiver-operating curve (AUC) of 0.83 (95% CI, 0.80-0.87), compared to 0.77 for logistic regression (95% CI, 0.58-0.95), 0.73 for an artificial neural network (95% CI, 0.55-0.91), and 0.67 for both a random forest and a support-vector machine (95% CI, 0.47-0.87). History of coronary artery disease, of chronic kidney disease, and smoking were the three most significant predictors of in-hospital mortality. CONCLUSIONS: We developed a robust machine-learning-derived model to predict in-hospital mortality in patients undergoing TMVR. This model is promising for decision-making and deserves further clinical validation.
BACKGROUND: Transcatheter mitral valve repair (TMVR) utilization has increased significantly in the United States over the last years. Yet, a risk-prediction tool for adverse events has not been developed. We aimed to generate a machine-learning-based algorithm to predict in-hospital mortality after TMVR. METHODS: Patients who underwent TMVR from 2012 through 2015 were identified using the National Inpatient Sample database. The study population was randomly divided into a training set (n = 636) and a testing set (n = 213). Prediction models for in-hospital mortality were obtained using five supervised machine-learning classifiers. RESULTS: A total of 849 TMVRs were analyzed in our study. The overall in-hospital mortality was 3.1%. A naïve Bayes (NB) model had the best discrimination for fifteen variables, with an area under the receiver-operating curve (AUC) of 0.83 (95% CI, 0.80-0.87), compared to 0.77 for logistic regression (95% CI, 0.58-0.95), 0.73 for an artificial neural network (95% CI, 0.55-0.91), and 0.67 for both a random forest and a support-vector machine (95% CI, 0.47-0.87). History of coronary artery disease, of chronic kidney disease, and smoking were the three most significant predictors of in-hospital mortality. CONCLUSIONS: We developed a robust machine-learning-derived model to predict in-hospital mortality in patients undergoing TMVR. This model is promising for decision-making and deserves further clinical validation.
Authors: Sachin S Goel; Navkaranbir Bajaj; Bhuvnesh Aggarwal; Supriya Gupta; Kanhaiya Lal Poddar; Mobolaji Ige; Hazem Bdair; Abed Anabtawi; Shiraz Rahim; Patrick L Whitlow; E Murat Tuzcu; Brian P Griffin; William J Stewart; Marc Gillinov; Eugene H Blackstone; Nicholas G Smedira; Guilherme H Oliveira; Benico Barzilai; Venu Menon; Samir R Kapadia Journal: J Am Coll Cardiol Date: 2013-09-11 Impact factor: 24.094
Authors: Ted Feldman; Saibal Kar; Sammy Elmariah; Steven C Smart; Alfredo Trento; Robert J Siegel; Patricia Apruzzese; Peter Fail; Michael J Rinaldi; Richard W Smalling; James B Hermiller; David Heimansohn; William A Gray; Paul A Grayburn; Michael J Mack; D Scott Lim; Gorav Ailawadi; Howard C Herrmann; Michael A Acker; Frank E Silvestry; Elyse Foster; Andrew Wang; Donald D Glower; Laura Mauri Journal: J Am Coll Cardiol Date: 2015-12-29 Impact factor: 24.094
Authors: Francesco Maisano; Olaf Franzen; Stephan Baldus; Ulrich Schäfer; Jörg Hausleiter; Christian Butter; Gian Paolo Ussia; Horst Sievert; Gert Richardt; Julian D Widder; Tiziano Moccetti; Wolfgang Schillinger Journal: J Am Coll Cardiol Date: 2013-06-07 Impact factor: 24.094
Authors: Ted Feldman; Elyse Foster; Donald D Glower; Donald G Glower; Saibal Kar; Michael J Rinaldi; Peter S Fail; Richard W Smalling; Robert Siegel; Geoffrey A Rose; Eric Engeron; Catalin Loghin; Alfredo Trento; Eric R Skipper; Tommy Fudge; George V Letsou; Joseph M Massaro; Laura Mauri Journal: N Engl J Med Date: 2011-04-04 Impact factor: 91.245
Authors: Gregg W Stone; JoAnn Lindenfeld; William T Abraham; Saibal Kar; D Scott Lim; Jacob M Mishell; Brian Whisenant; Paul A Grayburn; Michael Rinaldi; Samir R Kapadia; Vivek Rajagopal; Ian J Sarembock; Andreas Brieke; Steven O Marx; David J Cohen; Neil J Weissman; Michael J Mack Journal: N Engl J Med Date: 2018-09-23 Impact factor: 91.245