Key result
A random forest machine learning model using electronic health record data accurately predicted heart failure in dogs with myxomatous mitral valve disease, achieving an area under the curve of 0.887.
Why the study?
Assessing the risk of heart failure in dogs with myxomatous mitral valve disease is often challenging, prompting the development of machine learning-based risk prediction models using electronic health records.
Observational (n=143)
No
Effect estimate: AUC 0.887
A random forest machine learning model utilizing routine electronic health record data can accurately predict the risk of heart failure in dogs with myxomatous mitral valve disease.
ML models from EHRs may aid HF risk stratification in canine MMVD; hypothesis-generating pending prospective validation.
Introduction: Myxomatous mitral valve disease (MMVD) is the most common cause of heart failure in dogs, and assessing the risk of heart failure in dogs with MMVD is often challenging. Machine learning applied to electronic health records (EHRs) is an effective tool for predicting prognosis in the medical field. This study aimed to develop machine learning-based heart failure risk prediction models for dogs with MMVD using a dataset of EHRs. Methods: A total of 143 dogs with MMVD between May 2018 and May 2022. Complete medical records were reviewed for all patients. Demographic data, radiographic measurements, echocardiographic values, and laboratory results were obtained from the clinical database. Four machine-learning algorithms (random forest, K-nearest neighbors, naïve Bayes, support vector machine) were used to develop risk prediction models. Model performance was represented by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC). The best-performing model was chosen for the feature-ranking process. Results: The random forest model showed superior performance to the other models (AUC = 0.88), while the performance of the K-nearest neighbors model showed the lowest performance (AUC = 0.69). The top three models showed excellent performance (AUC ≥ 0.8). According to the random forest algorithm's feature ranking, echocardiographic and radiographic variables had the highest predictive values for heart failure, followed by packed cell volume (PCV) and respiratory rates. Among the electrolyte variables, chloride had the highest predictive value for heart failure. Discussion: These machine-learning models will enable clinicians to support decision-making in estimating the prognosis of patients with MMVD.
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Kim et al. (2023) conducted an observational in Canine myxomatous mitral valve disease (MMVD) (n=143). Random forest machine learning model vs. K-nearest neighbors, naïve Bayes, and support vector machine models was evaluated on Area under the receiver operating characteristic curve (AUC) for predicting heart failure (AUC 0.887). A random forest machine learning model using electronic health record data accurately predicted heart failure in dogs with myxomatous mitral valve disease, achieving an area under the curve of 0.887.
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