Machine learning-based risk prediction of intrahospital clinical outcomes in patients undergoing TAVI

Gomes, Bruna; Pilz, Maximilian; Reich, Christoph; Leuschner, Florian; Konstandin, Mathias; Katus, Hugo A.; Meder, Benjamin

doi:10.1007/s00392-020-01691-0

Cited by 26 publications

(28 citation statements)

References 44 publications

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“…While both LR and ML models were well-calibrated, ML was associated with a statistically significant improvement in discriminatory performance for both 90-day and 1-year mortality. These trends are similar to what has been observed in analyses of general adult cardiac surgical procedures where ML has been associated with improved predictive capability in risk modeling [5,9,10]. The implications of the current study are important as risk modeling plays a critical role in determining LVAD surgical candidacy, selection of type of advanced heart failure therapy, patient counseling and prognostication, and quality improvement.…”

Section: Discussionsupporting

confidence: 79%

Using machine learning to improve risk prediction in durable left ventricular assist devices

Kilic

Dochtermann²,

Padman³

et al. 2021

PLoS ONE

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Risk models have historically displayed only moderate predictive performance in estimating mortality risk in left ventricular assist device therapy. This study evaluated whether machine learning can improve risk prediction for left ventricular assist devices. Primary durable left ventricular assist devices reported in the Interagency Registry for Mechanically Assisted Circulatory Support between March 1, 2006 and December 31, 2016 were included. The study cohort was randomly divided 3:1 into training and testing sets. Logistic regression and machine learning models (extreme gradient boosting) were created in the training set for 90-day and 1-year mortality and their performance was evaluated after bootstrapping with 1000 replications in the testing set. Differences in model performance were also evaluated in cases of concordance versus discordance in predicted risk between logistic regression and extreme gradient boosting as defined by equal size patient tertiles. A total of 16,120 patients were included. Calibration metrics were comparable between logistic regression and extreme gradient boosting. C-index was improved with extreme gradient boosting (90-day: 0.707 [0.683–0.730] versus 0.740 [0.717–0.762] and 1-year: 0.691 [0.673–0.710] versus 0.714 [0.695–0.734]; each p<0.001). Net reclassification index analysis similarly demonstrated an improvement of 48.8% and 36.9% for 90-day and 1-year mortality, respectively, with extreme gradient boosting (each p<0.001). Concordance in predicted risk between logistic regression and extreme gradient boosting resulted in substantially improved c-index for both logistic regression and extreme gradient boosting (90-day logistic regression 0.536 versus 0.752, 1-year logistic regression 0.555 versus 0.726, 90-day extreme gradient boosting 0.623 versus 0.772, 1-year extreme gradient boosting 0.613 versus 0.742, each p<0.001). These results demonstrate that machine learning can improve risk model performance for durable left ventricular assist devices, both independently and as an adjunct to logistic regression.

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Section: Discussionsupporting

confidence: 79%

Using machine learning to improve risk prediction in durable left ventricular assist devices

Kilic

Dochtermann²,

Padman³

et al. 2021

PLoS ONE

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“…Clinical Epidemiology 2022:14 16 outcomes, 10,11 short-term prognosis 12,13 and long-term mortality 14 after TAVR. Evidences have shown that machine learning outperformed traditional linear regression models in these classification tasks.…”

Section: Dovepressmentioning

confidence: 99%

“…Machine learning has drawn significant attention in clinical prediction models in recent years, due to its ability to effectively model linear and non-linear relationships and interactions. 9 These techniques have been applied to predict in-hospital outcomes, 10,11 short-term prognoses 12,13 and long-term mortality 14 after TAVR. Despite the great performance of traditional machine learning methods (naive Bayes, random forest, support vector machine, etc) in predicting category outcomes (eg dead or alive), it is challenging for them to deal with time-to-event outcomes as in survival analysis.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Prediction of Late Major Bleeding After Transcatheter Aortic Valve Replacement

Jia¹,

Luosang²,

Li³

et al. 2022

CLEP

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Late major bleeding is one of the main complications after transcatheter aortic valve replacement (TAVR). We aimed to develop a risk prediction model based on deep learning to predict major or life-threatening bleeding complications (MLBCs) after TAVR. Patients and Methods: This was a retrospective study including TAVR patients from West China Hospital of Sichuan University Transcatheter Aortic Valve Replacement Registry (ChiCTR2000033419) between April 17, 2012 and May 27, 2020. A deep learning-based model named BLeNet was developed with 56 features covering baseline, procedural, and post-procedural characteristics. The model was validated with the bootstrap method and evaluated using Harrell's concordance index (c-index), receiver operating characteristics (ROC) curve, calibration curve, and Kaplan-Meier estimate. Captum interpretation library was applied to identify feature importance. The BLeNet model was compared with the traditional Cox proportional hazard (Cox-PH) model and the random survival forest model in the metrics mentioned above. Results: The BLeNet model outperformed the Cox-PH and random survival forest models significantly in discrimination [optimism-corrected c-index of BLeNet vs Cox-PH vs random survival forest: 0.81 (95% CI: 0.79-0.92) vs 0.72 (95% CI: 0.63-0.77) vs 0.70 (95% CI: 0.61-0.74)] and calibration (integrated calibration index of BLeNet vs Cox-PH vs random survival forest: 0.007 vs 0.015 vs 0.019). In Kaplan-Meier analysis, BLeNet model had great performance in stratifying high-and low-bleeding risk patients (p < 0.0001). Conclusion:Deep learning is a feasible way to build prediction models concerning TAVR prognosis. A dedicated bleeding risk prediction model was developed for TAVR patients to facilitate well-informed clinical decisions.

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“…Typically AUC-ROC values between 1-0.9 are considered 'excellent', 0.9 − 0.8 'good', 0.8 − 0.7 'fair' 0.7 − 0.6 'poor' and 0.6 − 0.5 'failed'. The highest AUC-ROC were recorded by Gomes et al, 2021, where ANN, Support Vector Machine (SVM) and RF models were applied to predict all-cause intrahospital mortality after Transcatheter Aortic Valve Implantation (TAVI). The most effective model was RF with an AUC-ROC of 0.97 [95%CI: 0.95-0.98], followed by ANN (AUC-ROC of 0.96 [95%CI: 0.94-0.97]) and SVM (AUC-ROC of 0.94 [95%CI: 0.91-0.96]).…”

Section: Meta-analysismentioning

confidence: 99%

Application of machine learning approaches in predicting clinical outcomes in older adults – a systematic review and meta-analysis.

Olender

Roy

Nishtala

2022

Preprint

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Background Machine learning-based prediction models have the potential to have a considerable positive impact on geriatric care. Design: Systematic review and meta-analyses. Participants: Older adults (≥ 65 years) in any setting. Intervention: Machine learning models for predicting clinical outcomes in older adults were evaluated. A meta-analysis was conducted where the predictive models were compared based on their performance in predicting mortality. Outcome measures: Studies were grouped by the clinical outcome, and the models were compared based on the area under the receiver operating characteristic curve metric. Results 29 studies that satisfied the systematic review criteria were appraised and six studies predicting a mortality outcome were included in the meta-analyses. We could only pool studies by mortality as there were inconsistent definitions and sparse data to pool studies for other clinical outcomes. The area under the receiver operating characteristic curve from six studies included in the meta-analysis yielded a summary estimate of 0.82 (95%CI: 0.76–0.87), signifying good discriminatory power in predicting mortality. Conclusion The meta-analysis indicates that machine learning models can predict mortality. As electronic healthcare databases grow larger and more comprehensive, the available computational power increases and machine learning models become more sophisticated; they should be integrated into a larger research setting to predict various clinical outcomes.

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Machine learning-based risk prediction of intrahospital clinical outcomes in patients undergoing TAVI

Cited by 26 publications

References 44 publications

Using machine learning to improve risk prediction in durable left ventricular assist devices

Using machine learning to improve risk prediction in durable left ventricular assist devices

Deep Learning in Prediction of Late Major Bleeding After Transcatheter Aortic Valve Replacement

Application of machine learning approaches in predicting clinical outcomes in older adults – a systematic review and meta-analysis.

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