Epidemiology of non-oncological high-impact chronic pain in Ecuadorian adults in 2022

Supplementary document - Survey</p

Area under the curve of the receiver operating characteristic (ROC) curve of the COVID-19 in-hospital mortality score in both, the derivation and validation cohorts.

Area under the curve of the receiver operating characteristic (ROC) curve of the COVID-19 in-hospital mortality score in both, the derivation and validation cohorts.</p

Prediction model development and validation checklist (adapted from (1))—for illustrative purposes only.

Prediction model development and validation checklist (adapted from (1))—for illustrative purposes only.</p

The COVID-19 in-hospital mortality score.

ObjectiveTo develop and validate a scoring system to predict mortality among hospitalized patients with COVID-19.MethodsRetrospective 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.ResultsThere 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.ConclusionMale 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.</div

Crude and adjusted associations between baseline variables at admission and in-hospital death, according to our parsimonious model on Table 3 of the main text (Cox proportional hazards model) stratified by sex (validation cohort).

Crude and adjusted associations between baseline variables at admission and in-hospital death, according to our parsimonious model on Table 3 of the main text (Cox proportional hazards model) stratified by sex (validation cohort).</p

Cox regression modelling with multiple imputations was applied.

Cox regression modelling with multiple imputations was applied.</p

Imputed dataset.

ObjectiveTo develop and validate a scoring system to predict mortality among hospitalized patients with COVID-19.MethodsRetrospective 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.ResultsThere 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.ConclusionMale 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.</div

Comparative detailed report of sensitivity and specificity between Guayaquil and Quito.

Comparative detailed report of sensitivity and specificity between Guayaquil and Quito.</p

Comparison of characteristics between imputed and non-imputed data sets (derivation cohort).

Comparison of characteristics between imputed and non-imputed data sets (derivation cohort).</p

Comparison of the discriminatory power of the score between hospitals and between sex categories, calculating the area under the ROC curve in the validation cohort.

Comparison of the discriminatory power of the score between hospitals and between sex categories, calculating the area under the ROC curve in the validation cohort.</p