Por favor, use este identificador para citar o enlazar este ítem: http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/7875
Development and Validation of the Patient History COVID-19 (PH-Covid19) Scoring System: A Multivariable Prediction Model of Death in Mexican Patients with COVID-19
GERARDO REYES VELAZQUEZ
FRANCISCO JAVIER TEPEPA LÓPEZ
Javier Mancilla-Galindo
Juan Mauricio Vera Zertuche
Addí Rhode Navarro-Cruz
Patricia Aguilar-Alonso
JOSE DE JESUS VIDAL MAYO
Ashuin Kammar-García
Orietta Segura- Badilla
Acceso Abierto
Atribución-NoComercial-SinDerivadas
https://doi.org/10.1101/2020.09.05.20189142
We sought to develop and validate a multivariable prediction model of death in Mexican patients with COVID-19, by using demographic and patient history predictors. We conducted a national retrospective cohort in two different sets of patients from the Mexican COVID-19 Epidemiologic Surveillance Study. To develop the model, we included 264,026 patients tested for SARS-CoV-2 between February 28 and May 30, 2020. To validate the model, 592,160 patients studied between June 1 and July 23, 2020 were included. Patients with a positive RT-PCR for SARS-CoV-2 and complete unduplicated data were eligible. Demographic and patient history variables were analyzed through Multivariable Cox regression models to evaluate predictors to be included in the prognostic scoring system called PH-Covid19. 83,779 patients were included to develop the model; 100,000, to validate the model. Eight predictors (age, sex, diabetes, COPD, immunosuppression, hypertension, obesity, and CKD) were included in the PH-Covid19 scoring system (range of values: −2 to 25 points). The predictive model has a discrimination of death of 0.8 (95%CI:0.796-0.804). The PH-Covid19 scoring system was developed and validated in Mexican patients to aid clinicians to stratify patients with COVID-19 at risk of fatal outcomes, allowing for better and efficient use of resources.
Medrxiv
08-09-2020
Preimpreso
medrxiv.org/
Inglés
VIRUS RESPIRATORIOS
Aparece en las colecciones: Artículos científicos

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