INTRODUCTION: With the globalization of Chagas disease, unexperienced health care providers may have difficulties in identifying which patients should be examined for this condition. This study aimed to develop and validate a diagnostic clinical prediction model for chronic Chagas disease. METHODS: This diagnostic cohort study included consecutive volunteers suspected to have chronic Chagas disease. The clinical information was blindly compared to serological tests results, and a logistic regression model was fit and validated. RESULTS: The development cohort included 602 patients, and the validation cohort included 138 patients. The Chagas disease prevalence was 19.9%. Sex, age, referral from blood bank, history of living in a rural area, recognizing the kissing bug, systemic hypertension, number of siblings with Chagas disease, number of relatives with a history of stroke, ECG with low voltage, anterosuperior divisional block, pathologic Q wave, right bundle branch block, and any kind of extrasystole were included in the final model. Calibration and discrimination in the development and validation cohorts (ROC AUC 0.904 and 0.912, respectively) were good. Sensitivity and specificity analyses showed that specificity reaches at least 95% above the predicted 43% risk, while sensitivity is at least 95% below the predicted 7% risk. Net benefit decision curves favor the model across all thresholds. CONCLUSIONS: A nomogram and an online calculator (available at http://shiny.ipec.fiocruz.br:3838/pedrobrasil/chronic_chagas_disease_prediction/) were developed to aid in individual risk estimation.
INTRODUCTION: With the globalization of Chagas disease, unexperienced health care providers may have difficulties in identifying which patients should be examined for this condition. This study aimed to develop and validate a diagnostic clinical prediction model for chronic Chagas disease. METHODS: This diagnostic cohort study included consecutive volunteers suspected to have chronic Chagas disease. The clinical information was blindly compared to serological tests results, and a logistic regression model was fit and validated. RESULTS: The development cohort included 602 patients, and the validation cohort included 138 patients. The Chagas disease prevalence was 19.9%. Sex, age, referral from blood bank, history of living in a rural area, recognizing the kissing bug, systemic hypertension, number of siblings with Chagas disease, number of relatives with a history of stroke, ECG with low voltage, anterosuperior divisional block, pathologic Q wave, right bundle branch block, and any kind of extrasystole were included in the final model. Calibration and discrimination in the development and validation cohorts (ROC AUC 0.904 and 0.912, respectively) were good. Sensitivity and specificity analyses showed that specificity reaches at least 95% above the predicted 43% risk, while sensitivity is at least 95% below the predicted 7% risk. Net benefit decision curves favor the model across all thresholds. CONCLUSIONS: A nomogram and an online calculator (available at http://shiny.ipec.fiocruz.br:3838/pedrobrasil/chronic_chagas_disease_prediction/) were developed to aid in individual risk estimation.
Authors: Fernanda de Souza Nogueira Sardinha Mendes; Pedro Emmanuel Alvarenga Americano do Brasil Journal: Arq Bras Cardiol Date: 2017-05 Impact factor: 2.000
Authors: Marco Antonio Prates Nielebock; Otacílio C Moreira; Samanta Cristina das Chagas Xavier; Luciana de Freitas Campos Miranda; Ana Carolina Bastos de Lima; Thayanne Oliveira de Jesus Sales Pereira; Alejandro Marcel Hasslocher-Moreno; Constança Britto; Luiz Henrique Conde Sangenis; Roberto Magalhães Saraiva Journal: PLoS One Date: 2020-12-02 Impact factor: 3.240
Authors: Richard D Riley; Kym Ie Snell; Joie Ensor; Danielle L Burke; Frank E Harrell; Karel Gm Moons; Gary S Collins Journal: Stat Med Date: 2018-10-24 Impact factor: 2.373