Predicting Mortality Prior to Interhospital Hospital for "Unseen" General Surgery Patients: Development, Validation and Feasibility Trial of a Mortality Risk Calculator.

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 non-clinical transfer personnel. SUMMARY BACKGROUND DATA: There are no objective tools to predict mortality at the time of inter-hospital transfer for Emergency General Surgery (EGS) patients that are "unseen" by the accepting system. METHODS: Patients transferred to general or colorectal surgery services from January 2016 through 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 post-discharge. Multiple predictive models were developed and validated. RESULTS: Among 4,664 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, Brier score was 0.04, indicating a strong association between the assigned risk and the observed frequency of mortality. CONCLUSION: A risk calculator using twelve variables has excellent predictive ability for mortality at the time of interhospital transfer among "unseen" EGS patients. Quantifying a patient's mortality risk at the time of transfer could improve patient triage, bed and resource allocation, and standardize care.

Development of a reproducible porcine model of infected burn wounds.

Severe burns are traumatic and physically debilitating injuries with a high rate of mortality. Bacterial infections often complicate burn injuries, which presents unique challenges for wound management and improved patient outcomes. Currently, pigs are used as the gold standard of pre-clinical models to study infected skin wounds due to the similarity between porcine and human skin in terms of structure and immunological response. However, utilizing this large animal model for wound infection studies can be technically challenging and create issues with data reproducibility. We present a detailed protocol for a porcine model of infected burn wounds based on our experience in creating and evaluating full thickness burn wounds infected with Staphylococcus aureus on six pigs. Wound healing kinetics and bacterial clearance were measured over a period of 27 d in this model. Enumerated are steps to achieve standardized wound creation, bacterial inoculation, and dressing techniques. Systematic evaluation of wound healing and bacterial colonization of the wound bed is also described. Finally, advice on animal housing considerations, efficient bacterial plating procedures, and overcoming common technical challenges is provided. This protocol aims to provide investigators with a step-by-step guide to execute a technically challenging porcine wound infection model in a reproducible manner. Accordingly, this would allow for the design and evaluation of more effective burn infection therapies leading to better strategies for patient care.