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Impact of Incorporating Echocardiographic Screening into a Clinical Prediction Model to Optimize Utilization of Echocardiography in Primary Care
  • +13
  • Adriana Diamantino,
  • Bruno Nascimento,
  • Maria Carmo Nunes,
  • Craig Sable,
  • Kaciane Oliveira,
  • Lara Rabelo,
  • Juliane Franco,
  • Luciana Diamantino,
  • Márcia Barbosa,
  • Alison Reese,
  • Laura Olivieri,
  • Emily Lima,
  • Larissa Natany Martins,
  • Enrico Antônio Colosimo,
  • Andrea Beaton,
  • Antonio Luiz Ribeiro
Adriana Diamantino
Hospital das Clinicas da Universidade Federal de Minas Gerais
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Bruno Nascimento
Hospital das Clinicas da Universidade Federal de Minas Gerais
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Maria Carmo Nunes
Hospital das Clinicas da Universidade Federal de Minas Gerais
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Craig Sable
Children's National Health System
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Kaciane Oliveira
Hospital das Clinicas da Universidade Federal de Minas Gerais
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Lara Rabelo
Hospital das Clinicas da Universidade Federal de Minas Gerais
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Juliane Franco
Hospital das Clinicas da Universidade Federal de Minas Gerais
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Luciana Diamantino
Hospital das Clinicas da Universidade Federal de Minas Gerais
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Márcia Barbosa
Hospital das Clinicas da Universidade Federal de Minas Gerais
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Alison Reese
Children's National Health System
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Laura Olivieri
Children's National Health System
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Emily Lima
Hospital das Clinicas da Universidade Federal de Minas Gerais
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Larissa Natany Martins
Hospital das Clinicas da Universidade Federal de Minas Gerais
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Enrico Antônio Colosimo
Hospital das Clinicas da Universidade Federal de Minas Gerais
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Andrea Beaton
Cincinnati Children's Hospital Medical Center
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Antonio Luiz Ribeiro
Hospital das Clinicas da Universidade Federal de Minas Gerais
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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.

Peer review status:ACCEPTED

13 Jul 2020Submitted to International Journal of Clinical Practice
14 Jul 2020Submission Checks Completed
14 Jul 2020Assigned to Editor
14 Jul 2020Reviewer(s) Assigned
28 Jul 2020Review(s) Completed, Editorial Evaluation Pending
04 Aug 20201st Revision Received
05 Aug 2020Reviewer(s) Assigned
05 Aug 2020Submission Checks Completed
05 Aug 2020Assigned to Editor
14 Aug 2020Review(s) Completed, Editorial Evaluation Pending
16 Aug 2020Editorial Decision: Accept