Multi‐parametric system for risk stratification in mitral regurgitation: A multi‐task Gaussian prediction approach

Gary Tse; Jiandong Zhou; Sharen Lee; Yingzhi Liu; Keith Sai Kit Leung; Rachel Wing Chuen Lai; Anthony Burtman; C. J. Wilson; Tong Liu; Ka Hou Christien Li; Ishan Lakhani; Qingpeng Zhang

doi:10.1111/eci.13321

June 14, 2020European Journal of Clinical Investigation

Multi‐parametric system for risk stratification in mitral regurgitation: A multi‐task Gaussian prediction approach

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Why the study?

It was hypothesized that a multi-parametric approach incorporating comorbidities, ECG P-wave indices, echocardiography, NLR, and PNI can improve risk stratification in MR.

Does a multi-task Gaussian process learning model improve risk stratification for incident AF, TIA/stroke, and mortality in patients with mitral regurgitation compared to traditional statistical methods?

Structured PICO

Does a multi-task Gaussian process learning model improve risk stratification for incident AF, TIA/stroke, and mortality in patients with mitral regurgitation compared to traditional statistical methods?

P

Population

706 patients diagnosed with mitral regurgitation from a single centre between 1 March 2005 and 30 October 2018. 171 had normal inter-atrial conduction, 257 had inter-atrial block (IAB), and 266 had AF at baseline.

I

Intervention

Multi-task Gaussian process learning model incorporating medical comorbidity information, electrocardiographic P-wave indices, echocardiographic assessment, neutrophil-to-lymphocyte ratio (NLR) and prognostic nutritional index (PNI)

C

Comparator

Logistic regression and decision tree methods

O

Outcome

Incident atrial fibrillation (AF), transient ischemic attack (TIA)/stroke, and all-cause mortalityhard clinical

A multi-task machine learning approach incorporating multi-modality clinical data significantly improves risk stratification for adverse outcomes in patients with mitral regurgitation.

Implication

Multi-parametric MR risk stratification may aid prognostication; leaves open prospective validation before clinical use.

Abstract

BACKGROUND: We hypothesized that a multi-parametric approach incorporating medical comorbidity information, electrocardiographic P-wave indices, echocardiographic assessment, neutrophil-to-lymphocyte ratio (NLR) and prognostic nutritional index (PNI) calculated from laboratory data can improve risk stratification in mitral regurgitation (MR). METHODS: Patients diagnosed with mitral regurgitation between 1 March 2005 and 30 October 2018 from a single centre were retrospectively analysed. Outcomes analysed were incident atrial fibrillation (AF), transient ischemic attack (TIA)/stroke and mortality. RESULTS: This study cohort included 706 patients, of whom 171 had normal inter-atrial conduction, 257 had inter-atrial block (IAB) and 266 had AF at baseline. Logistic regression analysis showed that age, hypertension and mean P-wave duration (PWD) were significant predictors of new-onset AF. Low left ventricular ejection fraction (LVEF), abnormal P-wave terminal force in V1 (PTFV1) predicted TIA/stroke. Age, smoking, hypertension, diabetes mellitus, hypercholesterolaemia, ischemic heart disease, secondary mitral regurgitation, urea, creatinine, NLR, PNI, left atrial diameter (LAD), left ventricular end-diastolic dimension, LVEF, pulmonary arterial systolic pressure, IAB, baseline AF and heart failure predicted all-cause mortality. A multi-task Gaussian process learning model demonstrated significant improvement in risk stratification compared to logistic regression and a decision tree method. CONCLUSIONS: A multi-parametric approach incorporating multi-modality clinical data improves risk stratification in mitral regurgitation. Multi-task machine learning can significantly improve overall risk stratification performance.