Machine-Learning-Based In-Hospital Mortality Prediction for Transcatheter Mitral Valve Repair in the United States.
Machine-Learning-Based In-Hospital Mortality Prediction for Transcatheter Mitral Valve Repair in the United States.
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DOI：
10.1016/j.carrev.2020.06.017
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发表时间：
2021-01
期刊：
影响因子：
--
通讯作者：
Roche-Lima A
中科院分区：
文献类型：
--
作者：
Hernandez-Suarez DF;Ranka S;Kim Y;Latib A;Wiley J;Lopez-Candales A;Pinto DS;Gonzalez MC;Ramakrishna H;Sanina C;Nieves-Rodriguez BG;Rodriguez-Maldonado J;Feliu Maldonado R;Rodriguez-Ruiz IJ;da Luz Sant'Ana I;Wiley KA;Cox-Alomar P;Villablanca PA;Roche-Lima A
Transcatheter mitral valve repair 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. 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. 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. 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.
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影响因子：
3.5
作者：
Shouval, Roni;Hadanny, Amir;Beigel, Roy
通讯作者：
Beigel, Roy
影响因子：
5.4
作者：
Al'Aref, Subhi J.;Singh, Gurpreet;Minutello, Robert M.
通讯作者：
Minutello, Robert M.
影响因子：
39.3
作者：
Puls M;Lubos E;Boekstegers P;von Bardeleben RS;Ouarrak T;Butter C;Zuern CS;Bekeredjian R;Sievert H;Nickenig G;Eggebrecht H;Senges J;Schillinger W
通讯作者：
Schillinger W
DOI：
10.1161/circinterventions.117.006179
发表时间：
2018-04-01
影响因子：
5.6
作者：
Gupta, Tanush;Khera, Sahil;Bhatt, Deepak L.
通讯作者：
Bhatt, Deepak L.
影响因子：
3
作者：
Elixhauser, A;Steiner, C;Coffey, RN
通讯作者：
Coffey, RN