Objective: Develop and validate a mortality risk calculator that could be utilized at the time of transfer, leveraging routinely collected variables that could be obtained by trained nonclinical transfer personnel.
Background: There are no objective tools to predict mortality at the time of interhospital transfer for Emergency General Surgery patients that are "unseen" by the accepting system.
Methods: Patients transferred to general or colorectal surgery services from January 2016 to August 2022 were retrospectively identified and randomly divided into training and validation cohorts (3:1 ratio). The primary outcome was admission-related mortality, defined as death during the index admission or within 30 days postdischarge. Multiple predictive models were developed and validated.
Results: Among 4664 transferred patients, 280 (6.0%) experienced mortality. Predictive models were generated utilizing 19 routinely collected variables; the penalized regression model was selected over other models due to excellent performance using only 12 variables. The model performance on the validating set resulted in an area under the receiver operating characteristic curve, sensitivity, specificity, and balanced accuracy of 0.851, 0.90, 0.67, and 0.79, respectively. After bias correction, the Brier score was 0.04, indicating a strong association between the assigned risk and the observed frequency of mortality.
Conclusions: A risk calculator using 12 variables has excellent predictive ability for mortality at the time of interhospital transfer among "unseen" Emergency General Surgery patients. Quantifying a patient's mortality risk at the time of transfer could improve patient triage, bed and resource allocation, and standardize care.
Keywords: hospital transfer; mortality risk; predictive model; risk calculator; surgical mortality; surgical transfer.
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