Predicting Unplanned Readmissions to the Intensive Care Unit in the Trauma Population

Abstract

Background: Unplanned readmission to intensive care units (UR-ICU) in trauma is associated with increased hospital length of stay and significant morbidity and mortality. We identify independent predictors of UR-ICU and construct a nomogram to estimate readmission probability. Materials and Methods: We performed an IRB-approved retrospective case-control study at a Level I trauma center between January 2019 and December 2021. Patients with UR-ICU (n = 175) were matched with patients who were not readmitted (NR-ICU) (n = 175). Univariate and multivariable binary linear regressionanalyses were performed (SPSS Version 28, IBM Corp), and a nomogram was created (Stata 18.0, StataCorp LLC). Results: Demographics, comorbidities, and injury- and hospital course-related factors were examined as potential prognostic indicators of UR-ICU. The mortality rate of UR-ICU was 22.29% vs 6.29% for NR-ICU (P < .001). Binary linear regression identified seven independent predictors that contributed to UR-ICU: shock (P < .001) or intracranial surgery (P = .015) during ICU admission, low hematocrit (P = .001) or sedation administration in the 24 hours before ICU discharge (P < .001), active infection treatment (P = .192) or leukocytosis on ICU discharge (P = .01), and chronic obstructive pulmonary disease (COPD) (P = .002). A nomogram was generated to estimate the probability of UR-ICU and guide decisions on ICU discharge appropriateness. Discussion: In trauma, UR-ICU is often accompanied by poor outcomes and death. Shock, intracranial surgery, anemia, sedative administration, ongoing infection treatment, leukocytosis, and COPD are significant risk factors for UR-ICU. A predictive nomogram may help better assess readiness for ICU discharge.