# Machine learning voor LV diastolische functie op ECG

*geplaatst 2020-08-25 · Hartfalen · Journal of the American College of Cardiology · doi 10.1016/j.jacc.2020.06.061 · https://hartvaat.nl/2020/08/25/machine-learning-voor-lv-diastolische-functie-op-ecg/*

Studie die machine learning toepaste op ECG-kenmerken voor beoordeling van LV diastolische functie.

## English: Machine Learning Assessment of Left Ventricular Diastolic Function Based on Electrocardiographic Features.

This study used machine learning applied to standard ECG features to assess left ventricular diastolic function, developing an AI-based screening tool for identifying diastolic dysfunction without echocardiography.

## Abstract (original, from the publication)

BACKGROUND: Left ventricular (LV) diastolic dysfunction is recognized as playing a major role in the pathophysiology of heart failure; however, clinical tools for identifying diastolic dysfunction before echocardiography remain imprecise. OBJECTIVES: This study sought to develop machine-learning models that quantitatively estimate myocardial relaxation using clinical and electrocardiography (ECG) variables as a first step in the detection of LV diastolic dysfunction. METHODS: A multicenter prospective study was conducted at 4 institutions in North America enrolling a total of 1,202 subjects. Patients from 3 institutions (n = 814) formed an internal cohort and were randomly divided into training and internal test sets (80:20). Machine-learning models were developed using signal-processed ECG, traditional ECG, and clinical features and were tested using the test set. Data from the fourth institution was reserved as an external test set (n = 388) to evaluate the model generalizability. RESULTS: Despite diversity in subjects, the machine-learning model predicted the quantitative values of the LV relaxation velocities (e') measured by echocardiography in both internal and external test sets (mean absolute error: 1.46 and 1.93 cm/s; adjusted R2 = 0.57 and 0.46, respectively). Analysis of the area under the receiver operating characteristic curve (AUC) revealed that the estimated e' discriminated the guideline-recommended thresholds for abnormal myocardial relaxation and diastolic and systolic dysfunction (LV ejection fraction) the internal (area under the curve [AUC]: 0.83, 0.76, and 0.75) and external test sets (0.84, 0.80, and 0.81), respectively. Moreover, the estimated e' allowed prediction of LV diastolic dysfunction based on multiple age- and sex-adjusted reference limits (AUC: 0.88 and 0.94 in the internal and external sets, respectively). CONCLUSIONS: A quantitative prediction of myocardial relaxation can be performed using easily obtained clinical and ECG features. This cost-effective strategy may be a valuable first clinical step for assessing the presence of LV dysfunction and may potentially aid in the early diagnosis and management of heart failure patients.

Auteurs: Nobuyuki Kagiyama, Marco Piccirilli, Naveena Yanamala, Sirish Shrestha, Peter D Farjo, Grace Casaclang-Verzosa, Wadea M Tarhuni, Negin Nezarat, Matthew J Budoff, Jagat Narula, Partho P Sengupta

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Bron: Journal of the American College of Cardiology, https://doi.org/10.1016/j.jacc.2020.06.061. Bijgewerkt 2026-07-03T13:27:53Z. Citeer vrij, met bronvermelding en een link naar hartvaat.nl (de url van het record). Samenvattingen zijn redactioneel werk van HartVaat; de oorspronkelijke publicaties blijven van hun uitgevers (doi). Geen medisch advies.