Dataset Information

Detection of congestive heart failure by Doppler echocardiography in cats with hypertrophic cardiomyopathy.

ABSTRACT: BACKGROUND:Left-sided congestive heart failure (CHF) is characterized by increased filling pressures and related Doppler echocardiographic (DE) filling patterns. HYPOTHESIS:Doppler echocardiographic variables of left ventricular filling derived from transmitral flow, pulmonary vein flow, and tissue Doppler can be used to detect CHF in cats with hypertrophic cardiomyopathy (HCM). ANIMALS:Forty-seven client-owned cats. METHODS:Prospective clinical cohort study. Cats underwent physical examination, thoracic radiography, analysis of N-terminal pro-brain natriuretic peptide (NT-proBNP), and transthoracic echocardiography and were divided into 3 age-matched groups: Group 1 (apparently healthy control), Group 2 (preclinical HCM), and Group 3 (HCM and CHF). Measured and calculated variables included respiratory rate, DE estimates, serum NT-proBNP concentration, and radiographic CHF score. Groups were compared using ANOVA, and receiver operating characteristic (ROC) curve and multivariate analyses were used to identify diagnostic cutoffs for the detection of CHF. RESULTS:Fifteen cats were in Group 1, 17 in Group 2, and 15 in Group 3. The ROC analysis indicated that the ratio of peak velocity of early diastolic transmitral flow to peak velocity of late diastolic transmitral flow (area under the curve [AUC], 1.0; diagnostic cutoff, 1.77; P =?.001), ratio of left atrial size to aortic annular dimension (AUC, 0.91; diagnostic cutoff, 1.96; P =?.003), left atrial diameter (AUC, 0.89; cutoff, 18.5?mm; P =?.004), diastolic functional class (AUC, 0.89; cutoff, class 2; P =?.005), respiratory (AUC, 0.79; cutoff, 36 breaths per minute [brpm]; P =?.02), and the ratio of the peak velocity of fused early and late transmitral flow velocities to the peak velocity of the fused early and late diastolic tissue Doppler waveforms (AUC, 0.74; cutoff, 15.1; P =?.05) performed best for detecting CHF. CONCLUSIONS AND CLINICAL IMPORTANCE:Various DE variables can be used to detect CHF in cats with HCM. Determination of the clinical benefit of such variables in initiating treatments and assessing treatment success needs further study.

SUBMITTER: Rohrbaugh MN

PROVIDER: S-EPMC7255654 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Detection of congestive heart failure by Doppler echocardiography in cats with hypertrophic cardiomyopathy.

Rohrbaugh Michelle N MN Schober Karsten E KE Rhinehart Jaylyn D JD Bonagura John D JD Habing Amy A Yildiz Vedat V

Journal of veterinary internal medicine 20200504 3

<h4>Background</h4>Left-sided congestive heart failure (CHF) is characterized by increased filling pressures and related Doppler echocardiographic (DE) filling patterns.<h4>Hypothesis</h4>Doppler echocardiographic variables of left ventricular filling derived from transmitral flow, pulmonary vein flow, and tissue Doppler can be used to detect CHF in cats with hypertrophic cardiomyopathy (HCM).<h4>Animals</h4>Forty-seven client-owned cats.<h4>Methods</h4>Prospective clinical cohort study. Cats un ...[more]

PMID: 32364632

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Project description: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.

Dataset Information

Detection of congestive heart failure by Doppler echocardiography in cats with hypertrophic cardiomyopathy.

Publications

Detection of congestive heart failure by Doppler echocardiography in cats with hypertrophic cardiomyopathy.

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