Dataset Information

Predicting Long-Term Mortality in TAVI Patients Using Machine Learning Techniques.

ABSTRACT: Whereas transcatheter aortic valve implantation (TAVI) has become the gold standard for aortic valve stenosis treatment in high-risk patients, it has recently been extended to include intermediate risk patients. However, the mortality rate at 5 years is still elevated. The aim of the present study was to develop a novel machine learning (ML) approach able to identify the best predictors of 5-year mortality after TAVI among several clinical and echocardiographic variables, which may improve the long-term prognosis. We retrospectively enrolled 471 patients undergoing TAVI. More than 80 pre-TAVI variables were collected and analyzed through different feature selection processes, which allowed for the identification of several variables with the highest predictive value of mortality. Different ML models were compared. Multilayer perceptron resulted in the best performance in predicting mortality at 5 years after TAVI, with an area under the curve, positive predictive value, and sensitivity of 0.79, 0.73, and 0.71, respectively. We presented an ML approach for the assessment of risk factors for long-term mortality after TAVI to improve clinical prognosis. Fourteen potential predictors were identified with the organic mitral regurgitation (myxomatous or calcific degeneration of the leaflets and/or annulus) which showed the highest impact on 5 years mortality.

SUBMITTER: Penso M

PROVIDER: S-EPMC8072967 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Predicting Long-Term Mortality in TAVI Patients Using Machine Learning Techniques.

Penso Marco M Pepi Mauro M Fusini Laura L Muratori Manuela M Cefalù Claudia C Mantegazza Valentina V Gripari Paola P Ali Sarah Ghulam SG Fabbiocchi Franco F Bartorelli Antonio L AL Caiani Enrico G EG Tamborini Gloria G

Journal of cardiovascular development and disease 20210416 4

<h4>Background</h4>Whereas transcatheter aortic valve implantation (TAVI) has become the gold standard for aortic valve stenosis treatment in high-risk patients, it has recently been extended to include intermediate risk patients. However, the mortality rate at 5 years is still elevated. The aim of the present study was to develop a novel machine learning (ML) approach able to identify the best predictors of 5-year mortality after TAVI among several clinical and echocardiographic variables, whic ...[more]

PMID: 33923465

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Project description:ObjectiveThis study aims to construct and validate several machine learning (ML) algorithms to predict long-term mortality and identify risk factors in unselected patients post-cardiac surgery.MethodsThe Medical Information Mart for Intensive Care (MIMIC-III) database was used to perform a retrospective administrative database study. Candidate predictors consisted of the demographics, comorbidity, vital signs, laboratory test results, scoring systems, and treatment information on the first day of ICU admission. Four-year mortality was set as the study outcome. We used the ML methods of logistic regression (LR), artificial neural network (NNET), naïve bayes (NB), gradient boosting machine (GBM), adapting boosting (Ada), random forest (RF), bagged trees (BT), and eXtreme Gradient Boosting (XGB). The prognostic capacity and clinical utility of these ML models were compared using the area under the receiver operating characteristic curves (AUC), calibration curves, and decision curve analysis (DCA).ResultsOf 7,368 patients in MIMIC-III included in the final cohort, a total of 1,337 (18.15%) patients died during a 4-year follow-up. Among 65 variables extracted from the database, a total of 25 predictors were selected using recursive feature elimination and included in the subsequent analysis. The Ada model performed best among eight models in both discriminatory ability with the highest AUC of 0.801 and goodness of fit (visualized by calibration curve). Moreover, the DCA shows that the net benefit of the RF, Ada, and BT models surpassed that of other ML models for almost all threshold probability values. Additionally, through the Ada technique, we determined that red blood cell distribution width (RDW), blood urea nitrogen (BUN), SAPS II, anion gap (AG), age, urine output, chloride, creatinine, congestive heart failure, and SOFA were the Top 10 predictors in the feature importance rankings.ConclusionsThe Ada model performs best in predicting 4-year mortality after cardiac surgery among the eight ML models, which might have significant application in the development of early warning systems for patients following operations.

Project description:Preterm birth prediction is essential for improving neonatal outcomes. While many machine learning techniques have been applied to predict preterm birth using health records, inflammatory markers, and vaginal microbiome data, the role of prenatal oral microbiome remains unclear. This study aimed to compare oral microbiome compositions between a preterm and a full-term birth group, identify oral microbiome associated with preterm birth, and develop a preterm birth prediction model using machine learning of oral microbiome compositions. Participants included singleton pregnant women admitted to Jeonbuk National University Hospital between 2019 and 2021. Subjects were divided into a preterm and a full-term birth group based on pregnancy outcomes. Oral microbiome samples were collected using mouthwash within 24 h before delivery and 16S ribosomal RNA sequencing was performed to analyze taxonomy. Differentially abundant taxa were identified using DESeq2. A random forest classifier was applied to predict preterm birth based on the oral microbiome. A total of 59 women participated in this study, with 30 in the preterm birth group and 29 in the full-term birth group. There was no significant difference in maternal clinical characteristics between the preterm and the full-birth group. Twenty-five differentially abundant taxa were identified, including 22 full-term birth-enriched taxa and 3 preterm birth-enriched taxa. The random forest classifier achieved high balanced accuracies (0.765 ± 0.071) using the 9 most important taxa. Our study identified 25 differentially abundant taxa that could differentiate preterm and full-term birth groups. A preterm birth prediction model was developed using machine learning of oral microbiome compositions in mouthwash samples. Findings of this study suggest the potential of using oral microbiome for predicting preterm birth. Further multi-center and larger studies are required to validate our results before clinical applications.

Project description:BackgroundAdverse pregnancy outcomes pose significant risk to maternal and neonatal health, contributing to morbidity, mortality, and long-term developmental challenges. This study aimed to predict these outcomes in Rwanda using supervised machine learning algorithms.MethodsThis cross-sectional study utilized data from the Rwanda Demographic and Health Survey (RDHS, 2019-2020) involving 14,634 women. K-fold cross-validation (k = 10) and synthetic minority oversampling technique (SMOTE) were used to manage dataset partitioning and class imbalance. Descriptive and multivariate analyses were conducted to identify the prevalence and risk factors for adverse pregnancy outcomes. Seven machine learning algorithms were assessed for their accuracy, precision, recall, F1 score, and area under the curve (AUC).ResultsOf the pregnancies analyzed, 93.4% resulted in live births, while 4.5% ended in miscarriage, and 2.1% in stillbirth. Advanced maternal age(>30 years),women aged 30-34 years (adjusted odds ratio [AOR] = 5.755; 95% confidence interval [CI] = 3.085-10.074; p < 0.001), 35-39 years (AOR = 8.458; 95% CI = 4.507-10.571; p < 0.001), 40-44 years (AOR = 11.86; 95% CI = 6.250-21.842; p < 0.001), and 45-49 years (AOR = 14.233; 95% CI = 7.359-25.922; p < 0.001), compared to those aged 15-19 years, and multiple unions (polyandry) (AOR = 1.320; 95% CI = 1.104-1.573, p = 0.002), and women not visited by healthcare provider during pregnancy (AOR = 1.421; 95%CI = 1.300-1.611, p<0.001) were factors associated with an increased risk of adverse pregnancy outcomes. In contrast, being married (AOR = 0.894; 95% CI = 0.787-0.966) and attending at least two antenatal care (ANC) visits (AOR = 0.801; 95% CI = 0.664-0.961) were linked to reduced risk. The K-nearest neighbors (KNN) model outperformed other ML Models in predicting adverse pregnancy outcomes, achieving 86% accuracy, 89% precision, 97% recall, 93% F1 score, and an area under the curve (AUC) of 0.842. The ML models constantly highlighted that woman with advanced maternal age, those in multiple unions, and inadequate ANC were more susceptible to adverse pregnancy outcomes.ConclusionsMachine learning algorithms, particularly KNN, are effective in predicting adverse pregnancy outcomes, facilitating early intervention and improved maternal and neonatal care.

Dataset Information

Predicting Long-Term Mortality in TAVI Patients Using Machine Learning Techniques.

Publications

Predicting Long-Term Mortality in TAVI Patients Using Machine Learning Techniques.

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