Can Machine Learning Aid the Selection of Percutaneous vs Surgical Revascularization?

Ninomiya, Kai; Kageyama, Shigetaka; Shiomi, Hiroki; Kotoku, Nozomi; Masuda, Shinichiro; Revaiah, Pruthvi C.; Garg, Scot; O'Leary, Neil; van Klaveren, David; Kimura, Takeshi; Onuma, Yoshinobu; Serruys, Patrick W.

doi:10.1016/j.jacc.2023.09.818

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…In addition, Ninomiya et al. ( 44 ) developed ML models to predict 5-year all-cause mortality in patients with CAD and assessed ML’s benefit in guiding decision-making between percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG). The results showed that the hybrid gradient boosting model was the most effective for predicting 5-year all-cause mortality (C-indexes of 0.78) and that ML is feasible and effective for identifying individuals who benefit from CABG or PCI.…”

Section: Discussionmentioning

confidence: 99%

“…For instance, a study based on electronic health records used ML to generate an in-silico marker for coronary artery disease (CAD) that can non-invasively quantify AS and risk of death on a continuous spectrum, and identify underdiagnosed individuals (43). In addition, Ninomiya et al (44) developed ML models to predict 5year all-cause mortality in patients with CAD and assessed ML's benefit in guiding decision-making between percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG). The results showed that the hybrid gradient boosting model was the most effective for predicting 5-year all-cause mortality (C-indexes of 0.78) and that ML is feasible and effective for identifying individuals who benefit from CABG or PCI.…”

Section: Discussionmentioning

confidence: 99%

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Predicting the risk of subclinical atherosclerosis based on interpretable machine models in a Chinese T2DM population

Tusongtuoheti,

Shu,

Huang

et al. 2024

Front. Endocrinol.

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BackgroundCardiovascular disease (CVD) has emerged as a global public health concern. Identifying and preventing subclinical atherosclerosis (SCAS), an early indicator of CVD, is critical for improving cardiovascular outcomes. This study aimed to construct interpretable machine learning models for predicting SCAS risk in type 2 diabetes mellitus (T2DM) patients.MethodsThis study included 3084 T2DM individuals who received health care at Zhenhai Lianhua Hospital, Ningbo, China, from January 2018 to December 2022. The least absolute shrinkage and selection operator combined with random forest-recursive feature elimination were used to screen for characteristic variables. Linear discriminant analysis, logistic regression, Naive Bayes, random forest, support vector machine, and extreme gradient boosting were employed in constructing risk prediction models for SCAS in T2DM patients. The area under the receiver operating characteristic curve (AUC) was employed to assess the predictive capacity of the model through 10-fold cross-validation. Additionally, the SHapley Additive exPlanations were utilized to interpret the best-performing model.ResultsThe percentage of SCAS was 38.46% (n=1186) in the study population. Fourteen variables, including age, white blood cell count, and basophil count, were identified as independent risk factors for SCAS. Nine predictors, including age, albumin, and total protein, were screened for the construction of risk prediction models. After validation, the random forest model exhibited the best clinical predictive value in the training set with an AUC of 0.729 (95% CI: 0.709-0.749), and it also demonstrated good predictive value in the internal validation set [AUC: 0.715 (95% CI: 0.688-0.742)]. The model interpretation revealed that age, albumin, total protein, total cholesterol, and serum creatinine were the top five variables contributing to the prediction model.ConclusionThe construction of SCAS risk models based on the Chinese T2DM population contributes to its early prevention and intervention, which would reduce the incidence of adverse cardiovascular prognostic events.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%