Use of physical examination, electrocardiography, radiography, and biomarkers to predict echocardiographic stage B2 myxomatous mitral valve disease in preclinical Cavalier King Charles Spaniels

IF 1.5 2区 农林科学 Q2 VETERINARY SCIENCES Journal of Veterinary Cardiology Pub Date : 2023-10-07 DOI:10.1016/j.jvc.2023.10.001
S. Wesselowski DVM, MS , S.G. Gordon DVM, DVSc , R. Fries DVM , A.B. Saunders DVM , K.T. Sykes DVM , J. Vitt DVM , B. Boutet DVM , J. Häggström DVM , S. Kadotani DVM, MS , J. Stack DVM , B.G. Barnett DVM
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Abstract

Introduction

Cavalier King Charles Spaniels (CKCS) are predisposed to developing myxomatous mitral valve disease (MMVD). Dogs with stage B2 MMVD benefit from medication.

Objectives

To develop (1) breed-specific cut-offs for individual screening tests and (2) predictive models utilizing physical examination (PE), ECG, radiograph, and blood-based biomarker variables in combination for identification of echocardiographic stage B2 MMVD in preclinical CKCS.

Animals

Adult, preclinical CKCS not receiving cardiac medications (N = 226).

Materials and methods

Prospective, cross-sectional study. Enrolled CKCS underwent PE, ECG, radiography, Doppler blood pressure measurement, echocardiography, and biomarker testing. Dogs were grouped by MMVD stage using echocardiography only. The discriminatory ability of individual tests to identify stage B2 was assessed, and prediction models were developed using variables derived from four ‘tests’ (PE, ECG, radiography, and biomarkers).

Results

N-terminal pro-B-type natriuretic peptide (NT-proBNP) and radiographic vertebral heart size (VHS) had the best discriminatory ability of individual diagnostic tests to differentiate stage A/B1 CKCS from stage B2, with an area under the curve (AUC) of 0.855 and 0.843, respectively. An NT-proBNP ≥1138 pmol/L or a VHS ≥11.5 had high specificity for predicting stage B2 (90.1% and 90.6%, respectively). Prediction models incorporating variables from multiple tests had better discriminatory ability than single tests. The four-test prediction model had an AUC of 0.971. Three and two-test models had AUCs ranging between 0.925–0.959 and 0.895–0.949, respectively.

Conclusions

Both NT-proBNP and VHS have good utility for predicting echocardiographic stage B2 MMVD in CKCS as individual tests. Prediction models incorporating multiple test variables have superior discriminatory ability.

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应用体格检查、心电图、射线照相和生物标志物预测临床前骑士查尔斯王犬超声心动图B2期黏液瘤性二尖瓣疾病。
简介:骑士查尔斯国王猎犬(CKCS)易患黏液性二尖瓣病(MMVD)。患有B2期MMVD的狗受益于药物治疗。目的:开发(1)用于个体筛查测试的特定品种截止值,以及(2)利用体检(PE)、心电图、射线照片和基于血液的生物标志物变量组合的预测模型,用于识别临床前CKCS中超声心动图B2期MMVD。动物:未接受心脏药物治疗的成年临床前CKCS(N=226)。材料和方法:前瞻性、横断面研究。纳入的CKCS接受了PE、心电图、放射照相、多普勒血压测量、超声心动图和生物标志物测试。仅使用超声心动图对狗进行MMVD分期分组。评估了个体测试识别B2期的辨别能力,并使用四种“测试”(PE、ECG、放射照相和生物标志物)得出的变量开发了预测模型。结果:N-末端B型钠尿肽原(NT-proBNP)和放射学椎体心脏大小(VHS)在个体诊断测试中对A/B1期CKCS和B2期的区分能力最好,曲线下面积(AUC)分别为0.855和0.843。NT-proBNP≥1138pmol/L或VHS≥11.5对预测B2期具有较高的特异性(分别为90.1%和90.6%)。包含多个测试变量的预测模型比单个测试具有更好的判别能力。四个测试预测模型的AUC为0.971。三个和两个测试模型的AUC分别在0.925-0.959和0.895-0.949之间。结论:NT-proBNP和VHS在CKCS超声心动图B2期MMVD预测中具有良好的实用性。包含多个测试变量的预测模型具有优越的判别能力。
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来源期刊
Journal of Veterinary Cardiology
Journal of Veterinary Cardiology VETERINARY SCIENCES-
CiteScore
2.50
自引率
25.00%
发文量
66
审稿时长
154 days
期刊介绍: The mission of the Journal of Veterinary Cardiology is to publish peer-reviewed reports of the highest quality that promote greater understanding of cardiovascular disease, and enhance the health and well being of animals and humans. The Journal of Veterinary Cardiology publishes original contributions involving research and clinical practice that include prospective and retrospective studies, clinical trials, epidemiology, observational studies, and advances in applied and basic research. The Journal invites submission of original manuscripts. Specific content areas of interest include heart failure, arrhythmias, congenital heart disease, cardiovascular medicine, surgery, hypertension, health outcomes research, diagnostic imaging, interventional techniques, genetics, molecular cardiology, and cardiovascular pathology, pharmacology, and toxicology.
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