Development and validation of a scoring system to predict mortality in patients hospitalized with COVID-19: A retrospective cohort study in two large hospitals in Ecuador

• Published in: PloS one 2023-07, Vol.18 (7), p.e0288106-e0288106
• Main Authors: Dueñas-Espín, Iván, Echeverría-Mora, María, Montenegro-Fárez, Camila, Baldeón, Manuel, Chantong Villacres, Luis, Espejo Cárdenas, Hugo, Fornasini, Marco, Ochoa Andrade, Miguel, Solís, Carlos
• Format: Article
• Language: English
• Subjects: Altitude; Analysis; Biology and Life Sciences; C-reactive protein; Calibration; Cohort analysis; Confidence intervals; COVID-19; COVID-19 vaccines; Disease transmission; Drug dosages; Ecuador; Evaluation; Goodness of fit; Hazard assessment; Health hazards; Health risks; Hospital patients; Hospitalization; Hospitals; Hyperglycemia; Hypoglycemia; Hypoxemia; Immunization; Males; Medical prognosis; Medical research; Medicine and Health Sciences; Medicine, Experimental; Modelling; Mortality; Mortality risk; Pandemics; Patient outcomes; Patients; pH effects; Population; Prognosis; Public health; Regression models; Risk factors; Risk groups; Severe acute respiratory syndrome coronavirus 2; Sex; Sex ratio; Statistical analysis; Survival analysis; Taiwan; Variables
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0288106

To develop and validate a scoring system to predict mortality among hospitalized patients with COVID-19. Retrospective cohort study. We analyzed 5,062 analyzed hospitalized patients with COVID-19 treated at two hospitals; one each in Quito and Guayaquil, from February to July 2020. We assessed predictors of mortality using survival analyses and Cox models. We randomly divided the database into two sets: (i) the derivation cohort (n = 2497) to identify predictors of mortality, and (ii) the validation cohort (n = 2565) to test the discriminative ability of a scoring system. After multivariate analyses, we used the final model's β-coefficients to build the score. Statistical analyses involved the development of a Cox proportional hazards regression model, assessment of goodness of fit, discrimination, and calibration. There was a higher mortality risk for these factors: male sex [(hazard ratio (HR) = 1.32, 95% confidence interval (95% CI): 1.03-1.69], per each increase in a quartile of ages (HR = 1.44, 95% CI: 1.24-1.67) considering the younger group (17-44 years old) as the reference, presence of hypoxemia (HR = 1.40, 95% CI: 1.01-1.95), hypoglycemia and hospital hyperglycemia (HR = 1.99, 95% CI: 1.01-3.91, and HR = 1.27, 95% CI: 0.99-1.62, respectively) when compared with normoglycemia, an AST-ALT ratio >1 (HR = 1.55, 95% CI: 1.25-1.92), C-reactive protein level (CRP) of >10 mg/dL (HR = 1.49, 95% CI: 1.07-2.08), arterial pH 10 × 103 per μL (HR = 1.76, 95% CI: 1.35-2.29). We found a strong discriminative ability in the proposed score in the validation cohort [AUC of 0.876 (95% CI: 0.822-0.930)], moreover, a cutoff score ≥39 points demonstrates superior performance with a sensitivity of 93.10%, a specificity of 70.28%, and a correct classification rate of 72.66%. The LR+ (3.1328) and LR- (0.0981) values further support its efficacy in identifying high-risk patients. Male sex, increasing age, hypoxemia, hypoglycemia or hospital hyperglycemia, AST-ALT ratio >1, elevated CRP, altered arterial pH, and leucocytosis were factors significantly associated with higher mortality in hospitalized patients with COVID-19. A statistically significant Cox regression model with strong discriminatory power and good calibration was developed to predict mortality in hospitalized patients with COVID-19, highlighting its potential clinical utility.