Derivation and Evaluation of Age-Specific Multivariate Reference Regions to Aid in Identification of Abnormal Filling Patterns: The HUNT and VaMIS Studies.

This study aimed to derive age-specific multivariate reference regions (MVRs) able to classify subjects into those having normal or abnormal filling patterns and to evaluate the prognostic impact of this classification.The integration of several parameters is necessary to diagnose disorders of left ventricular (LV) filling because no single measurement accurately describes the complexity of diastolic function. However, no generally accepted validated multiparametric algorithm currently exists.A cohort of 1,240 apparently healthy subjects from HUNT (the Nord-Trøndelag Health Study) with measured early (E) and late (A) mitral inflow velocity and early mitral annular diastolic tissue velocity (e') were used to derive univariate 95% reference bands and age-specific MVRs. Subsequently, the prognostic impact of this MVR-based classification was evaluated by Cox regression in a community-based cohort (n = 726) and in a cohort of subjects with recent acute myocardial infarction (n = 551). Both evaluation cohorts were derived from VaMIS (the Västmanland Myocardial Infarction Study).Univariate reference bands and MVRs are presented graphically and as regression equations. After adjustment for sex, age, hypertension, body mass index, diabetes, prior ischemic heart disease, LV mass, LV ejection fraction, and left atrial size, the hazard ratio associated with abnormal filling patterns in the community-based cohort was 3.5 (95% confidence interval: 1.7 to 7.0; p < 0.001) and that in the acute myocardial infarction cohort was 1.8 (95% confidence interval: 1.1 to 2.8; p = 0.011).This study derived age-specific MVRs for identification of abnormal LV filling patterns and showed, in a community-based cohort and in a cohort of patients with recent acute myocardial infarction, that these MVRs conveyed important prognostic information. An MVR-based classification of LV filling patterns could lead to more consistent diagnostic algorithms for identification of different filling disorders.