Copyright (c) 2026 Juan C. Batista M.
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Introduction: The COVID-19 pandemic had a critical impact on healthcare systems. Respiratory distress is associated with an increased risk of progression and mortality, highlighting the need for statistical tools that can anticipate this complication in hospitalized patients.
Materials and methods Clinical, sociodemographic, and laboratory data from 68 patients hospitalized in Panama (2020–2021) were analyzed. Exploratory tests were performed, and a logistic regression model stratified by sex was constructed, estimating odds ratios with 95% confidence intervals.
Results Sixty percent presented with respiratory distress. Age, cardiovascular history, and creatinine were associated with this outcome, although none were individually significant. The model identified predictors differentiated by sex, with areas under the curve (AUC) of 0.66 in men and 0.87 in women.
Conclusion Through logistic regression, we demonstrated that it is possible to predict the risk of respiratory distress in hospitalized patients with COVID-19, identifying clinically relevant variables and sex differences that improve early stratification. The model provides practical utility because it allows complications to be anticipated and supports clinical prioritization in resource-limited settings.
