Predicting the development of adverse cardiac events in patients with hypertrophic cardiomyopathy using machine learning

Kochav, Stephanie M.; Raita, Yoshihiko; Fifer, Michael A.; Takayama, Hiroo; Ginns, Jonathan; Maurer, Mathew S.; Reilly, Muredach P; Hasegawa, Kohei; Shimada, Yuichi J.

doi:10.1016/j.ijcard.2020.11.003

Cited by 14 publications

(10 citation statements)

References 23 publications

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“…Machine learning (ML) algorithms provide a powerful tool for learning complex relationships between the risk predictors and outcomes from a representative sample of the patients. ML-based models have been used to predict cardiovascular events with improved accuracy and generalizability compared to traditional risk predictors ( 15 – 19 ). Several studies showed that further improvement can be achieved by combining a number of ML models in an ensemble utilizing their versatile characteristics ( 15 , 20 , 21 ).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Predicting Heart Failure Progression in Hypertrophic Cardiomyopathy

Fahmy

Rowin

Manning

et al. 2021

Front. Cardiovasc. Med.

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Background: Development of advanced heart failure (HF) symptoms is the most common adverse pathway in hypertrophic cardiomyopathy (HCM) patients. Currently, there is a limited ability to identify HCM patients at risk of HF.Objectives: In this study, we present a machine learning (ML)-based model to identify individual HCM patients who are at high risk of developing advanced HF symptoms.Methods: From a consecutive cohort of HCM patients evaluated at the Tufts HCM Institute from 2001 to 2018, we extracted a set of 64 potential risk factors measured at baseline. Only patients with New York Heart Association (NYHA) functional class I/II and LV ejection fraction (LVEF) by echocardiography >35% were included. The study cohort (n = 1,427 patients) was split into three disjoint subsets: development (50%), model selection (10%), and independent validation (40%). The least absolute shrinkage and selection operator was used to select the most influential clinical variables. An ensemble of ML classifiers, including logistic regression, was used to identify patients with high risk of developing a HF outcome. Study outcomes were defined as progression to NYHA class III/IV, drop in LVEF below 35%, septal reduction procedure, and/or heart transplantation.Results: During a mean follow-up of 4.7 ± 3.7 years, advanced HF occurred in 283 (20% out of 1,427) patients. The model features included patients' sex, NYHA class (I or II), HCM type (i.e., obstructive or not), LV wall thickness, LVEF, presence of HF symptoms (e.g., dyspnea, presyncope), comorbidities (atrial fibrillation, hypertension, mitral regurgitation, and systolic anterior motion), and type of cardiac medications. The developed risk stratification model showed strong differentiation power to identify patients at advanced HF risk in the testing dataset (c-statistics = 0.81; 95% confidence interval [CI]: 0.76, 0.86). The model allowed correct identification of high-risk patients with accuracy 74% (CI: 0.70, 0.78), sensitivity 80% (CI: 0.77, 0.83), and specificity 72% (CI: 0.68, 0.76). The model performance was comparable among different sex and age groups.Conclusions: A 5-year risk prediction of progressive HF in HCM patients can be accurately estimated using ML analysis of patients' clinical and imaging parameters. A set of 17 clinical and imaging variables were identified as the most important predictors of progressive HF in HCM.

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Section: Introductionmentioning

confidence: 99%

Machine Learning for Predicting Heart Failure Progression in Hypertrophic Cardiomyopathy

Fahmy

Rowin

Manning

et al. 2021

Front. Cardiovasc. Med.

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“…Using artificial intelligence to develop clinical prediction tools could reduce variability in decision making [38 && ]. Thus far, artificial intelligence has helped predict mortality and renal failure in patients undergoing cardiac surgery and in patient selection for transcatheter aortic valve replacement (TAVR), as well as other clinical outcomes (Table 2) [39][40][41][42][43][44][45].…”

Section: Clinical Outcome Predictionsmentioning

confidence: 99%

“…Graft failure post heart transplant [39] Adverse events post LVAD implantation [40] Adverse cardiac events in those with hypertrophic cardiomyopathy [41] Procedural outcomes…”

Section: Clinical Outcomesmentioning

confidence: 99%

The cardiac surgeon's guide to artificial intelligence

Nedadur¹,

Wang

Yanagawa

2021

Current Opinion in Cardiology

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Purpose of reviewArtificial intelligence is the ability for machines to perform intelligent tasks. Artificial intelligence is already penetrating many aspects of medicine including cardiac surgery. Here, we offer a platform introduction to artificial intelligence for cardiac surgeons to understand the implementations of this transformative tool. Recent findingsArtificial intelligence has contributed greatly to the automation of cardiac imaging, including echocardiography, cardiac computed tomography, cardiac MRI and most recently, in radiomics. There are also several artificial intelligence based clinical prediction tools that predict complex outcomes after cardiac surgery. Waveform analysis, specifically, automated electrocardiogram analysis, has seen significant strides with promise in wearables and remote monitoring. Experimentally, artificial intelligence has also entered the operating room in the form of augmented reality and automated robotic surgery.

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“…Machine learning (ML) techniques have become a novel method for modeling in the medical field and has been widely used for predicting clinical endpoints. 16,[20][21][22] ML is a discipline that uses computer algorithms to learn from datasets and analyses a multitude of variables with nonlinearity and complex relationships that may be correlated with outcome development, which can be better to improve the predicting ability of model, it is an alternative approach to conventional statistical analyses such as logistic or cox proportional hazard regression. 23 Therefore, we aim to determine the risk factors associated with bleeding risk in patients after heart valve replacement and develop a ML model to predict the high bleeding risk during hospitalization, thus may be better for adjusting therapeutic strategies in timely and reducing the occurrence of adverse reactions.…”

Section: Introductionmentioning

confidence: 99%

Using machine learning to predict the bleeding risk for patients with cardiac valve replacement treated with warfarin in hospitalized

Hu,

Zhang,

Wei

et al. 2024

Pharmacoepidemiology and Drug

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BackgroundDistinguishing warfarin‐related bleeding risk at the bedside remains challenging. Studies indicate that warfarin therapy should be suspended when international normalized ratio (INR) ≥ 4.5, or it may sharply increase the risk of bleeding. We aim to develop and validate a model to predict the high bleeding risk in valve replacement patients during hospitalization.MethodCardiac valve replacement patients from January 2016 to December 2021 across Nanjing First Hospital were collected. Five different machine‐learning (ML) models were used to establish the prediction model. High bleeding risk was an INR ≥4.5. The area under the receiver operating characteristic curve (AUC) was used for evaluating the prediction performance of different models. The SHapley Additive exPlanations (SHAP) was used for interpreting the model. We also compared ML with ATRIA score and ORBIT score.ResultsA total of 2376 patients were finally enrolled in this model, 131 (5.5%) of whom experienced the high bleeding risk after anticoagulation therapy of warfarin during hospitalization. The extreme gradient boosting (XGBoost) exhibited the best overall prediction performance (AUC: 0.882, confidence interval [CI] 0.817–0.946, Brier score, 0.158) compared to other prediction models. It also shows superior performance compared with ATRIA score and ORBIT score. The top 5 most influential features in XGBoost model were platelet, thyroid stimulation hormone, body surface area, serum creatinine and white blood cell.ConclusionA model for predicting high bleeding risk in valve replacement patients who treated with warfarin during hospitalization was successfully developed by using machine learning, which may well assist clinicians to identify patients at high risk of bleeding and allow timely adjust therapeutic strategies in evaluating individual patient.

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Predicting the development of adverse cardiac events in patients with hypertrophic cardiomyopathy using machine learning

Cited by 14 publications

References 23 publications

Machine Learning for Predicting Heart Failure Progression in Hypertrophic Cardiomyopathy

Machine Learning for Predicting Heart Failure Progression in Hypertrophic Cardiomyopathy

The cardiac surgeon's guide to artificial intelligence

Using machine learning to predict the bleeding risk for patients with cardiac valve replacement treated with warfarin in hospitalized

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