tetano
Editor, Senior Moderator
J Gen Intern Med
. 2022 Jan 7.
doi: 10.1007/s11606-021-07235-0. Online ahead of print.
An Analysis COVID-19 in Mexico: a Prediction of Severity
Marco Ulises Martínez-Martínez[SUP] 1 2 [/SUP], Deshiré Alpízar-Rodríguez[SUP] 3 [/SUP], Rogelio Flores-Ramírez[SUP] 4 [/SUP], Diana Patricia Portales-Pérez[SUP] 5 [/SUP], Ruth Elena Soria-Guerra[SUP] 6 [/SUP], Francisco Pérez-Vázquez[SUP] 5 [/SUP], Fidel Martinez-Gutierrez[SUP] 7 5 [/SUP]
Affiliations
Abstract
Background: Coronavirus disease 2019 (COVID-19) causes a mild illness in most cases; forecasting COVID-19-associated mortality and the demand for hospital beds and ventilators are crucial for rationing countries' resources.
Objective: To evaluate factors associated with the severity of COVID-19 in Mexico and to develop and validate a score to predict severity in patients with COVID-19 infection in Mexico.
Design: Retrospective cohort.
Participants: We included 1,435,316 patients with COVID-19 included before the first vaccine application in Mexico; 725,289 (50.5%) were men; patient's mean age (standard deviation (SD)) was 43.9 (16.9) years; 21.7% of patients were considered severe COVID-19 because they were hospitalized, died or both.
Main measures: We assessed demographic variables, smoking status, pregnancy, and comorbidities. Backward selection of variables was used to derive and validate a model to predict the severity of COVID-19.
Key results: We developed a logistic regression model with 14 main variables, splines, and interactions that may predict the probability of COVID-19 severity (area under the curve for the validation cohort = 82.4%).
Conclusions: We developed a new model able to predict the severity of COVID-19 in Mexican patients. This model could be helpful in epidemiology and medical decisions.
Keywords: COVID-19; Hospitalization; Mortality; Severity.
. 2022 Jan 7.
doi: 10.1007/s11606-021-07235-0. Online ahead of print.
An Analysis COVID-19 in Mexico: a Prediction of Severity
Marco Ulises Martínez-Martínez[SUP] 1 2 [/SUP], Deshiré Alpízar-Rodríguez[SUP] 3 [/SUP], Rogelio Flores-Ramírez[SUP] 4 [/SUP], Diana Patricia Portales-Pérez[SUP] 5 [/SUP], Ruth Elena Soria-Guerra[SUP] 6 [/SUP], Francisco Pérez-Vázquez[SUP] 5 [/SUP], Fidel Martinez-Gutierrez[SUP] 7 5 [/SUP]
Affiliations
- PMID: 34993853
- DOI: 10.1007/s11606-021-07235-0
Abstract
Background: Coronavirus disease 2019 (COVID-19) causes a mild illness in most cases; forecasting COVID-19-associated mortality and the demand for hospital beds and ventilators are crucial for rationing countries' resources.
Objective: To evaluate factors associated with the severity of COVID-19 in Mexico and to develop and validate a score to predict severity in patients with COVID-19 infection in Mexico.
Design: Retrospective cohort.
Participants: We included 1,435,316 patients with COVID-19 included before the first vaccine application in Mexico; 725,289 (50.5%) were men; patient's mean age (standard deviation (SD)) was 43.9 (16.9) years; 21.7% of patients were considered severe COVID-19 because they were hospitalized, died or both.
Main measures: We assessed demographic variables, smoking status, pregnancy, and comorbidities. Backward selection of variables was used to derive and validate a model to predict the severity of COVID-19.
Key results: We developed a logistic regression model with 14 main variables, splines, and interactions that may predict the probability of COVID-19 severity (area under the curve for the validation cohort = 82.4%).
Conclusions: We developed a new model able to predict the severity of COVID-19 in Mexican patients. This model could be helpful in epidemiology and medical decisions.
Keywords: COVID-19; Hospitalization; Mortality; Severity.