The association of ICU admission time with mortality in patients with severe pneumonia requiring invasive mechanical ventilation: a retrospective, single-center, multicampus cohort study

Nighttime admission to the intensive care unit (ICU) has been associated with worse outcomes in some critically ill populations. However, evidence specific to patients with severe pneumonia is limited. We evaluated the association between ICU admission time and 30-day mortality among patients with severe pneumonia requiring invasive mechanical ventilation (IMV). We conducted a retrospective cohort study of adults with ICD-coded severe pneumonia who received IMV in four campuses at a tertiary medical center between 2020 and 2024. Patients were categorized according to their ICU admission time and our institutional staffing schedule: daytime admission (08:00–15:59) and nighttime admission (16:00–07:59 the following day). The primary outcome was 30-day mortality after ICU admission. Survival analysis and multivariable Cox regression were used to test the association between admission time and 30-day mortality. A total of 9,611 patients were included in the analysis. Among them, 6,058 patients were admitted at night, and 3,553 during the daytime. After 1:1 propensity score matching (PSM), 3,476 patients remained in each group. There was no statistically significant difference in 30-day mortality following ICU admission between the two groups (hazard ratio [HR], 1.00; 95% confidence interval [CI], 0.92–1.09; P = 0.914). After adjustment for age, sex, relative weight, lactate, patient source, type of admission, and septic shock, multivariable Cox regression showed that daytime ICU admission was not an independent protective factor for 30-day mortality (adjusted HR, 1.01; 95% CI, 0.93–1.10; P = 0.794). In this cohort of patients with severe pneumonia requiring IMV, ICU admission timing was not significantly associated with 30-day mortality. The findings should be interpreted in light of residual confounding and the institution-specific staffing model.