Oncologic and Physiologic Predictors of Mortality after ICU Admission in Patients with Lung Cancer

Rationale: Critically ill lung cancer patients are a growing, high-risk population. However, prognostic tools to guide intensive care unit (ICU) decision-making are limited. Objective: To identify oncologic and critical illness factors associated with 90-day mortality following ICU admission in patients with lung cancer. Methods: We conducted a retrospective cohort study of lung cancer patients admitted to the ICU across Mayo Clinic Health System between 2018 and 2024. The primary outcome was 90-day mortality. Two complementary Cox proportional hazards models were developed: a focused model including cancer-specific variables (stage, cause of critical illness, ECOG performance status prior to admission, code status at admission, and time since last systemic therapy) and a full model that added broader clinical factors (age and Sequential Organ Failure Assessment SOFA score). We also evaluated discharge disposition and transitions in code status. Results: Among 528 patients, 90-day mortality was 58.7%. In the focused model (C-index 0.67), independent predictors of mortality included late-stage disease, cancer-specific reason for ICU admission, DNR/DNI status, and ECOG >1. In the full model (C-index 0.95), only age and SOFA score remained significant. Among early-stage patients, recent targeted or combination therapy was associated with higher mortality. Patients who changed code status during admission had high mortality (83%) and a low rate of discharge home (11%). Conclusions: Lung cancer patients admitted to ICU experience high short-term mortality, with outcomes shaped by both oncologic status and the severity of acute illness. A dual-model approach suggests a risk hierarchy: cancer-specific factors inform baseline mortality risk with an ICU admission, but age and physiologic derangement are the dominant drivers of survival once critical illness develops. These findings support dynamic, multidisciplinary prognostication and underscore the need to integrate oncology and critical care expertise in ICU decision-making. Word count: 310