Mortality Predictors and Retrospective Concordance with ICU Admission Prioritization Guidelines in Patients with Cancer: A Single-Center Cohort Study

Background/Objectives: Advances in cancer treatment and an aging population have increased ICU admissions among patients with cancer, while mortality remains high and ICU resources are limited. We aimed to identify mortality predictors available at admission and factors arising during the ICU stay and to evaluate retrospective concordance with ICU admission-prioritization guidelines. Methods: This retrospective single-center cohort included 287 patients with an active malignancy admitted to an oncology ICU in Lublin, Poland, from 1 January 2024 through 31 December 2025. Two independent reviewers, blinded to outcomes, retrospectively assigned ICU admission priorities according to Polish Society of Anaesthesiology and Intensive Therapy guidelines. Univariable and multivariable logistic regression analyses were used to identify factors associated with ICU mortality. Results: Overall ICU mortality was 41.1% (118/287). In the adjusted admission-time model, APACHE II score (odds ratio (OR), 1.15 per point), ECOG 4 (OR, 6.37), and postoperative admission (OR, 0.29) were associated with mortality (area under the receiver operating characteristic curve (AUC), 0.84). In the exploratory ICU-course model, mechanical ventilation >48 h or death within 48 h while mechanically ventilated (OR, 6.79) and vasopressor therapy >48 h or death within 48 h while receiving vasopressors (OR, 4.33) were associated with mortality. The proportion of priority-4 admissions decreased from 19.2% (24/125) in 2024 to 4.9% (8/162) in 2025 (p < 0.001). Priority-4 classification remained associated with ICU mortality even after adjustment for APACHE II score. Conclusions: Cancer, even at an advanced stage, should not be regarded as an absolute contraindication to intensive care. Clinical and oncological information available at admission may support evidence-based ICU triage. Structured prioritization may help identify patients with a very limited likelihood of benefiting from intensive care and support responsible allocation of critical care resources.