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Predicting Unplanned Readmissions to the Intensive Care Unit in the Trauma Population
Payton C O'Quinn1, Kaylan N Gee2, Sarah A King2
1Quillen College of Medicine, East Tennessee State University, Johnson City, TN, USA.
Insights
Unplanned readmission to the intensive care unit (UR-ICU) in trauma patients is linked to higher mortality. Key predictors include shock, intracranial surgery, anemia, sedation, infection, leukocytosis, and COPD. A nomogram aids in assessing discharge readiness.
Area of Science:
- Trauma surgery
- Critical care medicine
- Health services research
Background:
- Unplanned readmission to the intensive care unit (UR-ICU) in trauma patients is a significant concern, associated with increased length of stay, morbidity, and mortality.
- Identifying predictors of UR-ICU is crucial for improving patient outcomes and optimizing resource allocation in trauma care.
Purpose of the Study:
- To identify independent predictors of UR-ICU in trauma patients.
- To develop a nomogram for estimating the probability of UR-ICU and assist in discharge decision-making.
Main Methods:
- Retrospective case-control study at a Level I trauma center (January 2019 - December 2021).
- Matched 175 patients with UR-ICU to 175 patients without readmission (NR-ICU).
- Utilized univariate and multivariable binary linear regression and nomogram construction.
Main Results:
- UR-ICU patients had a significantly higher mortality rate (22.29%) compared to NR-ICU patients (6.29%).
- Seven independent predictors for UR-ICU were identified: shock, intracranial surgery, low hematocrit, sedation before discharge, active infection treatment, leukocytosis, and COPD.
- A nomogram was developed to predict UR-ICU probability.
Conclusions:
- UR-ICU in trauma is associated with poor outcomes and increased mortality.
- Shock, intracranial surgery, anemia, sedation, infection, leukocytosis, and COPD are significant risk factors.
- A predictive nomogram can aid in assessing patient readiness for ICU discharge.
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.
