Impact of Incorporating Echocardiographic Screening into a Clinical
Prediction Model to Optimize Utilization of Echocardiography in Primary
Care
Abstract
Introduction: Access to public healthcare is limited in Brazilian
underserved areas, and long waiting lists remain for echocardiography
(echo). We aimed to develop a tool to optimize indications and shorten
waiting lists for standard echo in primary care. Methods: Patients in
waiting list for standard echo were enrolled. For derivation, patients
underwent a clinical questionnaire, simplified 7-view echo screening by
non-physicians with handheld devices (GE-VSCAN), and standard echo
(Vivid-Q) by experts. Two models were adjusted, one including clinical
variables and other adding screen-detected major heart disease (HD). 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
complete study if HD was suspected. Discrimination and calibration of
the 2 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=1,526), 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 2
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.