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.
Subject(s)
Echocardiography , Models, Statistical , Brazil , Female , Humans , Male , Primary Health Care , PrognosisABSTRACT
Background Risk stratification of Chagas disease patients in the limited-resource setting would be helpful in crafting management strategies. We developed a score to predict 2-year mortality in patients with Chagas cardiomyopathy from remote endemic areas. Methods and Results This study enrolled 1551 patients with Chagas cardiomyopathy from Minas Gerais State, Brazil, from the SaMi-Trop cohort (The São Paulo-Minas Gerais Tropical Medicine Research Center). Clinical evaluation, ECG, and NT-proBNP (N-terminal pro-B-type natriuretic peptide) were performed. A Cox proportional hazards model was used to develop a prediction model based on the key predictors. The end point was all-cause mortality. The patients were classified into 3 risk categories at baseline (low, <2%; intermediate, ≥2% to 10%; high, ≥10%). External validation was performed by applying the score to an independent population with Chagas disease. After 2 years of follow-up, 110 patients died, with an overall mortality rate of 3.505 deaths per 100 person-years. Based on the nomogram, the independent predictors of mortality were assigned points: age (10 points per decade), New York Heart Association functional class higher than I (15 points), heart rate ≥80 beats/min (20 points), QRS duration ≥150 ms (15 points), and abnormal NT-proBNP adjusted by age (55 points). The observed mortality rates in the low-, intermediate-, and high-risk groups were 0%, 3.6%, and 32.7%, respectively, in the derivation cohort and 3.2%, 8.7%, and 19.1%, respectively, in the validation cohort. The discrimination of the score was good in the development cohort (C statistic: 0.82), and validation cohort (C statistic: 0.71). Conclusions In a large population of patients with Chagas cardiomyopathy, a combination of risk factors accurately predicted early mortality. This helpful simple score could be used in remote areas with limited technological resources.
