Impact of incorporating echocardiographic screening into a clinical prediction model to optimise utilisation of echocardiography in primary care

Abstract

Introduction Access to public subspecialty healthcare is limited in underserved areas of Brazil, including echocardiography (echo). Long waiting lines and lack of a prioritisation system lead to diagnostic lag and may contribute to poor outcomes. We developed a prioritisation tool for use in primary care, aimed at improving resource utilisation, by predicting those at highest risk of having an abnormal echo, and thus in highest need of referral. Methods All patients in the existing primary care waiting list for echo were invited for participation and underwent a clinical questionnaire, simplified 7‐view echo screening by non‐physicians with handheld devices, and standard echo by experts. Two derivation models were developed, one including only clinical variables and a second including clinical variables and findings of major heart disease (HD) on echo screening (cut point for high/low‐risk). For validation, patients were risk‐classified according to the clinical score. High‐risk patients and a sample of low‐risk underwent standard echo. Intermediate‐risk patients first had screening echo, with a standard echo if HD was suspected. Discrimination and calibration of the two models were assessed to predict HD in standard echo. Results In derivation (N = 603), clinical variables associated with HD were female gender, body mass index, Chagas disease, prior cardiac surgery, coronary disease, valve disease, hypertension and heart failure, and this model was well calibrated with C‐statistic = 0.781. Performance was improved with the addition of echo screening, with C‐statistic = 0.871 after cross‐validation. For validation (N = 1526), 227 (14.9%) patients were classified as low risk, 1082 (70.9%) as intermediate risk and 217 (14.2%) as high risk by the clinical model. The final model with two categories had high sensitivity (99%) and negative predictive value (97%) for HD in standard echo. Model performance was good with C‐statistic = 0.720. Conclusion The addition of screening echo to clinical variables significantly improves the performance of a score to predict major HD.

Estatística Cardiovascular – Brasil 2021

“…O escore combinado – derivado de 603 pacientes na lista de espera por ecocardiografia e validado em uma amostra similar de 1.526 indivíduos – apresentou boa discriminação, com estatística C = 0,72, sensibilidade = 99% e valor preditivo negativo = 97%, emergindo como ferramenta promissora para o diagnóstico precoce de doença valvar/DCR, avaliação da carga de doença e a priorização de referenciamento. 193 …”

“…The combined score – derived from 603 patients in waiting lists for echocardiography, and validated in a similar sample of 1526 individuals – had good discrimination, with C-statistic = 0.72, sensitivity = 99% and negative predictive value = 97%, and emerges as a promising tool for early diagnosis of valve disease/RHD, estimation of disease burden and priority referrals. 193 …”

Left ventricular systolic dysfunction predicted by artificial intelligence using the electrocardiogram in Chagas disease patients–The SaMi-Trop cohort

“…Like other neglected tropical diseases, ChD is a chronic, stigmatizing condition, closely associated with poverty, and many patients live in remote areas of Latin America [ 30 ]. This limits the clinical evaluation to diagnose or rule out LVSD because the echo–and other cardiac imaging alternatives–is a scarce resource for this setting, and it is not readily available or not available at all [ 31 ]. Because of the lack of tertiary care facilities outside urban centers, an automatic diagnostic tool based on the ECG, which is a relatively simple exam that does not require human interpretation, would improve the capacity to recognize LVSD, which is in accordance to the World Heart Federation recommendations for the management of ChD [ 30 ].…”

“…imaging alternatives-is a scarce resource for this setting, and it is not readily available or not available at all [31]. Because of the lack of tertiary care facilities outside urban centers, an automatic diagnostic tool based on the ECG, which is a relatively simple exam that does not require human interpretation, would improve the capacity to recognize LVSD, which is in accordance to the World Heart Federation recommendations for the management of ChD [30].…”

Feasibility of a point‐of‐care ultrasound protocol for cardiorespiratory evaluation of horses in different clinical settings

“…Some of these protocols have become particularly relevant during the current coronavirus 2019 (COVID‐19) disease pandemic 3,4 . Multiple studies have evaluated the utility and accuracy of focused cardiac and lung ultrasound examinations as well as the training programs that have been designed to teach focused cardiac ultrasound examination in humans and small animals 5‐13 . Focused ultrasound examinations that combine evaluation of both the respiratory and cardiac systems exist for humans but none currently is described for small animals or horses 14 .…”