Mortality prediction of mitral valve replacement surgery by machine learning

Langarizadeh, Mostafa; HosseiniNezhad, Marziyeh; Hosseini, Saeid

doi:10.4103/rcm.rcm_50_21

Cited by 3 publications

(12 citation statements)

References 18 publications

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“…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%

“…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: 99%

“…Twentythree articles 9,19,21,[23][24][25]27,29,30,[32][33][34]36,37,39,41,42,[44][45][46][48][49][50] (63.9%) reported precision metrics (area under the precision-recall curve, positive predictive value, or F1 score). Twenty-five articles 9,20,21,[23][24][25][26][27][28]31,33,34,36,38,40,[42][43][44][45][46][47][48][49][50] (69.4%) included explainability mechanisms to convey the relative importance of input features in determining outputs. Thirteen articles 9,16,17,…”

Section: Resultsmentioning

confidence: 99%

“…Twenty-five articles 9,20,21,[23][24][25][26][27][28]31,33,34,36,38,40,[42][43][44][45][46][47][48][49][50] (69.4%) included explainability mechanisms to convey the relative importance of input features in determining outputs. Thirteen articles 9,16,17,20,25,27,29,30,35,38,45,46,50 (36.1%) presented a framework that could be used for clinical implementation; none of the articles assessed the efficacy of clinical implementation. in 5 articles performed external or real-time validation; 1 in 5 likely might have had overfitting and lower predictive performance due to small sample sizes; only two-thirds presented confidence intervals for performance metrics; less than two-thirds reported measures of precision; and none of the articles reported analyses across sex or racial groups or assessed the efficacy of clinical implementation.…”

Section: Resultsmentioning

confidence: 99%

“…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%

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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: 99%

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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Artificial intelligence in cardiac surgery: A systematic review

Sulague,

Beloy,

Medina

et al. 2024

World j. surg.

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BackgroundArtificial intelligence (AI) has emerged as a tool to potentially increase the efficiency and efficacy of cardiovascular care and improve clinical outcomes. This study aims to provide an overview of applications of AI in cardiac surgery.MethodsA systematic literature search on AI applications in cardiac surgery from inception to February 2024 was conducted. Articles were then filtered based on the inclusion and exclusion criteria and the risk of bias was assessed. Key findings were then summarized.ResultsA total of 81 studies were found that reported on AI applications in cardiac surgery. There is a rapid rise in studies since 2020. The most popular machine learning technique was random forest (n = 48), followed by support vector machine (n = 33), logistic regression (n = 32), and eXtreme Gradient Boosting (n = 31). Most of the studies were on adult patients, conducted in China, and involved procedures such as valvular surgery (24.7%), heart transplant (9.4%), coronary revascularization (11.8%), congenital heart disease surgery (3.5%), and aortic dissection repair (2.4%). Regarding evaluation outcomes, 35 studies examined the performance, 26 studies examined clinician outcomes, and 20 studies examined patient outcomes.ConclusionAI was mainly used to predict complications following cardiac surgeries and improve clinicians' decision‐making by providing better preoperative risk assessment, stratification, and prognostication. While the application of AI in cardiac surgery has greatly progressed in the last decade, further studies need to be conducted to verify accuracy and ensure safety before use in clinical practice.

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Artificial Intelligence in Cardiac Surgery: A Systematic Review

Sulague,

Beloy,

Medina

et al. 2023

Preprint

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BACKGROUND Artificial intelligence has emerged as a tool to potentially increase efficiency and efficacy of healthcare and improve clinical outcomes. The growing body of knowledge of artificial intelligence applications in cardiac surgery necessitates evaluation of past studies to gain insights to the future direction of artificial intelligence applications in cardiac surgery. This study aims to provide a systematic review of the applications of artificial intelligence in cardiac surgery. METHODS A systematic literature search on artificial intelligence applications in cardiac surgery from 2000 to 2022 was conducted in the following databases: PubMed, Embase, Europe PMC, Epistemonikos, CINAHL, Cochrane Central, Google Scholar, Web of Science, Scopus, Cambridge Core, clinicaltrials.gov, and science. Studies on the implementation of artificial intelligence applications in cardiac surgery and the provision of decision support by the application through simulating clinical decision-making processes of healthcare providers were included. Studies not in English, published only as abstracts, review papers, meta-analyses, clinical trials that were still in progress, and published study protocols were excluded. This study was registered on Prospero (CRD42022377530). RESULTS A total of 42 studies were found that reported on artificial intelligence applications in cardiac surgery, all of which are cohort studies. Nine (21.43%) of the studies measured different parameters regarding cardiac surgeries in general. Meanwhile, 6 (14.29%) studies focused on Heart Transplantation (HT), 4 (9.52%) on Transcatheter Aortic Valve Replacement (TAVR), 3 (7.14%) anchored on Aortic Stenosis, and another 3 (7.14%) on Perioperative Complications. Three topics had 2 (4.76%) studies dedicated to them, namely Coronary Artery Bypass Graft (CABG), Postoperative Atrial Fibrillation (POAF), and Acute Kidney Injury (AKI). The remaining eleven studies have their own unique disease topics, procedures or surgeries in focus (n=11, 1 (2.38%), namely Postoperative Major Bleeding, Early Coronary Revascularization, Heart Valve Surgery, Isolated Mitral Valve Replacement (IMVR), Surgical Aortic Valve Replacement (SAVR), Open-Chest Surgery, Infective endocarditis, Post-Operative Deterioration, Red Blood Cell Transfusion, AKI - related Hippocampal Damage, and Open-Heart Surgery. Regarding evaluation outcomes, 26 studies examined the performance, 32 studies examined clinician outcomes, and 2 studies examined patient outcomes. Of the 42 studies, only 13 were conducted in Lower- and Middle-Income Countries. CONCLUSION Artificial intelligence was used to predict mortality, postoperative length of stay, and complications following cardiac surgeries. It can also improve clinicians’ medical decisions by providing better preoperative risk assessment, stratification, and prognostication. While the application of artificial intelligence in cardiac surgery has greatly progressed in the last two decades, more highly powered studies need to be done to assess challenges and to ensure accuracy and safety for use in clinical practice.

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Mortality prediction of mitral valve replacement surgery by machine learning

Cited by 3 publications

References 18 publications

Artificial Intelligence–enabled Decision Support in Surgery

Artificial Intelligence–enabled Decision Support in Surgery

Artificial intelligence in cardiac surgery: A systematic review

Artificial Intelligence in Cardiac Surgery: A Systematic Review

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