Predictive value of the CURB-65, qSOFA, and APACHE II for in-hospital mortality in patients with acute exacerbation chronic obstructive pulmonary disease.

This study aimed to evaluate the efficacy of Quick Sequential Organ Failure Assessment, Confusion, Urea, Respiratory Rate, Blood Pressure, and Age Above or Below 65 Years (CURB-65), and Acute Physiology and Chronic Health Evaluation (APACHE) II in predicting the in-hospital mortality of patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). This retrospective study was conducted on 1583 hospitalized patients diagnosed with AECOPD from 2017 to 2019. Appropriate clinical data were retrieved from medical records from the time of admission up until the patients were discharged. The patients' most severe physiological condition and laboratory data within the first 24 hours of admission were used to determine CURB-65, Quick Sequential Organ Failure Assessment, and APACHE II scores. The accuracy of these 3 instruments in predicting the in-hospital mortality of patients with AECOPD was compared. It was observed that patients who had died had significantly higher APACHE II and CURB-65 scores (P < .05). Binary logistic regression analysis confirmed their significant association with mortality. The APACHE II score had a sensitivity of 91.6% and a specificity of 89.2%, while CURB-65 had a sensitivity of 94% and a specificity of 67.2%. The receiver operating characteristic curves for APACHE II and CURB-65 showed high predictive accuracy (area under the curve, 0.965 and 0.882, P < .001), respectively. Mortality rates substantially increased with higher scores (P < .001), reaching 38.2% for APACHE II scores ≥16 and 15.8% for CURB-65 scores ≥2. Our findings reveal a clear link between higher mortality rates and higher APACHE II and CURB-65 scores. The receiver operating characteristic curves' strong predictive ability highlights the dependability of these scoring systems in assessing the risk of in-hospital mortality, making them useful tools for predicting the outcomes in critical care.