Machine Learning Models to Predict Major Adverse Cardiovascular Events After Orthotopic Liver Transplantation: A Cohort Study

Jain, Vardhmaan; Radakovich, Nathan; Sharma, Vikram; Khan, Muhammad Zarrar; Harris, Kevin B; Bachour, Salam; Kleb, Cerise; Cywiński, Jacek B.; Argalious, Maged; Quintini, Cristiano; Menon, Krishna; Nair, Ravi; Tong, Michael Z.; Kapadia, Samir; Fares, Maan

doi:10.1053/j.jvca.2021.02.006

Cited by 18 publications

(19 citation statements)

References 18 publications

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“…The average AUROC for the best model across all 36 articles was 0.83; of the 8 articles with sample sizes of less than 2000 (ie, less than 1000 samples per class), 7 22,[26][27][28]31,35,37 (87.5%) had below-average (ie, less than 0.83) AUROC or accuracy. Twenty-four 9,18,20,[24][25][26][27][28][29][32][33][34][35]37,[39][40][41][43][44][45][46][47][48][49] articles (66.7%) presented confidence intervals for performance metrics. Among the 8 articles with sample sizes of less than 2000, 5 [26][27][28]35,37 presented confidence intervals.…”

Section: Resultsmentioning

confidence: 97%

“…Twenty-three articles9,19,21,23–25,27,29,30,32–34,36,37,39,41,42,44–46,48–50 (63.9%) reported precision metrics (area under the precision-recall curve, positive predictive value, or F1 score). Twenty-five articles9,20,21,23–28,31,33,34,36,38,40,42–50 (69.4%) included explainability mechanisms to convey the relative importance of input features in determining outputs. Thirteen articles9,16,17,20,25,27,29,30,35,38,45,46,50 (36.1%) presented a framework that could be used for clinical implementation; none of the articles assessed the efficacy of clinical implementation.…”

Section: Resultsmentioning

confidence: 99%

“…Table 1 summarizes study populations, model architecture, performance, validation type, and explainability, and whether precision and implementation frameworks were reported. The overall sample size range was 163-2,882,526, with 8 22,23,[26][27][28]31,35,37 of 36 articles (22.2%) featuring sample sizes of less than 2000. The average AUROC for the best model across all 36 articles was 0.83; of the 8 articles with sample sizes of less than 2000 (ie, less than 1000 samples per class), 7 22,[26][27][28]31,35,37 (87.5%) had below-average (ie, less than 0.83) AUROC or accuracy.…”

Section: Resultsmentioning

confidence: 99%

“…The overall sample size range was 163-2,882,526, with 8 22,23,[26][27][28]31,35,37 of 36 articles (22.2%) featuring sample sizes of less than 2000. The average AUROC for the best model across all 36 articles was 0.83; of the 8 articles with sample sizes of less than 2000 (ie, less than 1000 samples per class), 7 22,[26][27][28]31,35,37 (87.5%) had below-average (ie, less than 0.83) AUROC or accuracy. Twenty-four 9,18,20,[24][25][26][27][28][29][32][33][34][35]37,[39][40][41][43][44][45][46][47][48][49] articles (66.7%) presented confidence intervals for performance metrics.…”

Section: Resultsmentioning

confidence: 99%

“…Twenty-four 9,18,20,[24][25][26][27][28][29][32][33][34][35]37,[39][40][41][43][44][45][46][47][48][49] articles (66.7%) presented confidence intervals for performance metrics. Among the 8 articles with sample sizes of less than 2000, 5 [26][27][28]35,37 presented confidence intervals. Among these 5 articles, 4 [26][27][28]35 had wide confidence intervals ranging from 0.15 to 0.35, and 1, 37 which used waveform input features, had a narrow confidence interval of 0.01 (albeit with moderate performance in predicting postoperative deterioration, AUROC 0.71).…”

Section: Resultsmentioning

confidence: 99%

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Artificial Intelligence–enabled Decision Support in Surgery

et al. 2023

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Objective: To summarize state-of-the-art artificial intelligence-enabled decision support in surgery and to quantify deficiencies in scientific rigor and reporting. Background: To positively affect surgical care, decision-support models must exceed current reporting guideline requirements by performing external and real-time validation, enrolling adequate sample sizes, reporting model precision, assessing performance across vulnerable populations, and achieving clinical implementation; the degree to which published models meet these criteria is unknown. Methods: Embase, PubMed, and MEDLINE databases were searched from their inception to September 21, 2022 for articles describing artificial intelligence-enabled decision support in surgery that uses preoperative or intraoperative data elements to predict complications within 90 days of surgery. Scientific rigor and reporting criteria were assessed and reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. Results: Sample size ranged from 163-2,882,526, with 8/36 articles (22.2%) featuring sample sizes of less than 2000; 7 of these 8 articles (87.5%) had below-average (< 0.83) area under the receiver operating characteristic or accuracy. Overall, 29 articles (80.6%) performed internal validation only, 5 (13.8%) performed external validation, and 2 (5.6%) performed real-time validation. Twenty-three articles (63.9%) reported precision. No articles reported performance across sociodemographic categories. Thirteen articles (36.1%) presented a framework that could be used for clinical implementation; none assessed clinical implementation efficacy. Conclusions: Artificial intelligence-enabled decision support in surgery is limited by reliance on internal validation, small sample sizes that risk overfitting and sacrifice predictive performance, and failure to report confidence intervals, precision, equity analyses, and clinical implementation. Researchers should strive to improve scientific quality.

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Section: Resultsmentioning

confidence: 97%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Artificial Intelligence–enabled Decision Support in Surgery

et al. 2023

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Machine learning prediction of major adverse cardiac events after elective bariatric surgery

Romero-Velez,

Dang,

Barajas-Gamboa

et al. 2023

Surg Endosc

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A Systematic Review of Artificial Intelligence Models for Time-to-Event Outcome Applied in Cardiovascular Disease Risk Prediction

Teshale,

Htun,

Vered

et al. 2024

J Med Syst

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Artificial intelligence (AI) based predictive models for early detection of cardiovascular disease (CVD) risk are increasingly being utilised. However, AI based risk prediction models that account for right-censored data have been overlooked. This systematic review (PROSPERO protocol CRD42023492655) includes 33 studies that utilised machine learning (ML) and deep learning (DL) models for survival outcome in CVD prediction. We provided details on the employed ML and DL models, eXplainable AI (XAI) techniques, and type of included variables, with a focus on social determinants of health (SDoH) and gender-stratification. Approximately half of the studies were published in 2023 with the majority from the United States. Random Survival Forest (RSF), Survival Gradient Boosting models, and Penalised Cox models were the most frequently employed ML models. DeepSurv was the most frequently employed DL model. DL models were better at predicting CVD outcomes than ML models. Permutation-based feature importance and Shapley values were the most utilised XAI methods for explaining AI models. Moreover, only one in five studies performed gender-stratification analysis and very few incorporate the wide range of SDoH factors in their prediction model. In conclusion, the evidence indicates that RSF and DeepSurv models are currently the optimal models for predicting CVD outcomes. This study also highlights the better predictive ability of DL survival models, compared to ML models. Future research should ensure the appropriate interpretation of AI models, accounting for SDoH, and gender stratification, as gender plays a significant role in CVD occurrence.

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Machine Learning Models to Predict Major Adverse Cardiovascular Events After Orthotopic Liver Transplantation: A Cohort Study

Cited by 18 publications

References 18 publications

Artificial Intelligence–enabled Decision Support in Surgery

Artificial Intelligence–enabled Decision Support in Surgery

Machine learning prediction of major adverse cardiac events after elective bariatric surgery

A Systematic Review of Artificial Intelligence Models for Time-to-Event Outcome Applied in Cardiovascular Disease Risk Prediction

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