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Machine Learning for the Prediction of Complications in Patients After Mitral Valve Surgery.

ABSTRACT: Background: This study intended to use a machine learning model to identify critical preoperative and intraoperative variables and predict the risk of several severe complications (myocardial infarction, stroke, renal failure, and hospital mortality) after cardiac valvular surgery. Study Design and Methods: A total of 1,488 patients undergoing cardiac valvular surgery in eight large tertiary hospitals in China were examined. Fifty-four perioperative variables, such as essential demographic characteristics, concomitant disease, preoperative laboratory indicators, operation type, and intraoperative information, were collected. Machine learning models were developed and validated by 10-fold cross-validation. In each fold, Recursive Feature Elimination was used to select key variables. Ten machine learning models and logistic regression were developed. The area under the receiver operating characteristic (AUROC), accuracy (ACC), Youden index, sensitivity, specificity, F1-score, positive predictive value (PPV), and negative predictive value (NPV) were used to compare the prediction performance of different models. The SHapley Additive ex Planations package was applied to interpret the best machine learning model. Finally, a model was trained on the whole dataset with the merged key variables, and a web tool was created for clinicians to use. Results: In this study, 14 vital variables, namely, intraoperative total input, intraoperative blood loss, intraoperative colloid bolus, Classification of New York Heart Association (NYHA) heart function, preoperative hemoglobin (Hb), preoperative platelet (PLT), age, preoperative fibrinogen (FIB), intraoperative minimum red blood cell volume (Hct), body mass index (BMI), creatinine, preoperative Hct, intraoperative minimum Hb, and intraoperative autologous blood, were finally selected. The eXtreme Gradient Boosting algorithms (XGBOOST) algorithm model presented a significantly better predictive performance (AUROC: 0.90) than the other models (ACC: 81%, Youden index: 70%, sensitivity: 89%, specificity: 81%, F1-score:0.26, PPV: 15%, and NPV: 99%). Conclusion: A model for predicting several severe complications after cardiac valvular surgery was successfully developed using a machine learning algorithm based on 14 perioperative variables, which could guide clinical physicians to take appropriate preventive measures and diminish the complications for patients at high risk.

SUBMITTER: Jiang H

PROVIDER: S-EPMC8716451 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundMitral valve surgery (MVS) is an effective treatment for mitral valve diseases. There is a lack of reliable personalized risk prediction models for mortality in patients undergoing mitral valve surgery. Our aim was to develop a risk stratification system to predict all-cause mortality in patients after mitral valve surgery.MethodsDifferent machine learning models for the prediction of all-cause mortality were trained on a derivation cohort of 1,883 patients undergoing mitral valve surgery [split into a training cohort (70%) and internal validation cohort (30%)] to predict all-cause mortality. Forty-five clinical variables routinely evaluated at discharge were used to train the models. The best performance model (PRIME score) was tested in an externally validated cohort of 220 patients undergoing mitral valve surgery. The model performance was evaluated according to the area under the curve (AUC). Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were compared with existing risk strategies.ResultsAfter a median follow-up of 2 years, there were 133 (7.063%) deaths in the derivation cohort and 17 (7.727%) deaths in the validation cohort. The PRIME score showed an AUC of 0.902 (95% confidence interval [CI], 0.849-0.956) in the internal validation cohort and 0.873 (95% CI: 0.769-0.977) in the external validation cohort. In the external validation cohort, the performance of the PRIME score was significantly improved compared with that of the existing EuroSCORE II (NRI = 0.550, [95% CI 0.001-1.099], P = 0.049, IDI = 0.485, [95% CI 0.230-0.741], P < 0.001).ConclusionMachine learning-based model (the PRIME score) that integrate clinical, demographic, imaging, and laboratory features demonstrated superior performance for the prediction of mortality patients after mitral valve surgery compared with the traditional risk model EuroSCORE II.Clinical trial registration[http://www.clinicaltrials.gov], identifier [NCT05141292].

Project description:BackgroundInitial studies have suggested the familial clustering of mitral valve prolapse, but most of them were either community based among unselected individuals or applied non-specific diagnostic criteria. Therefore little is known about the familial distribution of mitral regurgitation in a referral-type population with a more severe mitral valve prolapse phenotype. The objective of this study was to evaluate the presence of familial mitral regurgitation in patients undergoing surgery for mitral valve prolapse, differentiating patients with Barlow's disease, Barlow forme fruste and fibro-elastic deficiency.MethodsA total of 385 patients (62 ± 12 years, 63% men) who underwent surgery for mitral valve prolapse were contacted to assess cardiac family history systematically. Only the documented presence of mitral regurgitation was considered to define 'familial mitral regurgitation'. In the probands, the aetiology of mitral valve prolapse was defined by surgical observations.ResultsA total of 107 (28%) probands were classified as having Barlow's disease, 85 (22%) as Barlow forme fruste and 193 (50%) patients as fibro-elastic deficiency. In total, 51 patients (13%) reported a clear family history for mitral regurgitation; these patients were significantly younger, more often diagnosed with Barlow's disease and also reported more sudden death in their family as compared with 'sporadic mitral regurgitation'. In particular, 'familial mitral regurgitation' was reported in 28 patients with Barlow's disease (26%), 15 patients (8%) with fibro-elastic deficiency and eight (9%) with Barlow forme fruste (P < 0.001).ConclusionsIn a large cohort of patients operated for mitral valve prolapse, the self-reported prevalence of familial mitral regurgitation was 26% in patients with Barlow's disease and still 8% in patients with fibro-elastic deficiency, highlighting the importance of familial anamnesis and echocardiographic screening in all mitral valve prolapse patients.

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Machine Learning for the Prediction of Complications in Patients After Mitral Valve Surgery.

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