Comparison of CURB-65 and qSOFA scores in predicting outcomes in community-acquired pneumonia

Community-acquired pneumonia (CAP) is a significant cause of morbidity and mortality worldwide, with high hospitalization and death rates. Early and accurate assessment of disease severity in emergency settings is crucial for guiding treatment and reducing associated costs and mortality. The confusion, urea, respiratory rate, blood pressure, and age ≥ 65 (CURB-65) and quick Sequential Organ Failure Assessment (qSOFA) scoring systems are commonly used tools for prognostication, each with unique strengths. This study aims to compare the predictive capabilities of CURB-65 and qSOFA for intensive care unit (ICU) admission, mortality, and hospital stay duration among CAP patients.

This cross-sectional, prospective study was conducted over a year at a tertiary hospital. Ninety-three adult CAP patients meeting the inclusion criteria were enrolled. Data on clinical presentation, CURB-65, and qSOFA scores were recorded upon emergency admission. Patients were followed until discharge or death. Statistical analyses, including chi-square tests and area under the receiver operating characteristic (AUROC) curves, were used to compare outcomes predicted by the two scoring systems.

Both scoring systems demonstrated utility in predicting adverse outcomes, with qSOFA outperforming CURB-65 for ICU admission (AUROC: 0.817 vs. 0.810). However, CURB-65 was superior in predicting mortality (AUROC: 0.582 vs. 0.535) and hospital stay duration (AUROC: 0.848 vs. 0.829). Positive CURB-65 and qSOFA scores correlated significantly with higher ICU admissions, mortality, and longer hospital stays. Clinical features and comorbidities, such as chronic obstructive pulmonary disease and diabetes, were more prevalent in patients with higher scores.

Both CURB-65 and qSOFA effectively predict ICU admission, mortality, and hospital stay duration in CAP patients. While qSOFA is more sensitive for ICU admission, CURB-65 provides better mortality and length-of-stay predictions, underscoring their complementary roles in clinical decision-making.