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IntroductionPatient outcome prediction models are underused in clinical practice because of lack of integration with real-time patient data. The electronic health record (EHR) has the ability to use machine learning (ML) to develop predictive models. While an EHR ML model has been developed to predict clinical deterioration, it has yet to be validated for use in trauma. We hypothesized that the Epic Deterioration Index (EDI) would predict mortality and unplanned intensive care unit (ICU) admission in trauma patients.MethodsA retrospective analysis of a trauma registry was used to identify patients admitted to a level 1 trauma center for >24 hours from October 2019 to July 2020. We evaluated the performance of the EDI, which is constructed from 125 objective patient measures within the EHR, in predicting mortality and unplanned ICU admissions. We performed a 5 to 1 match on age because it is a major component of EDI, then examined the area under the receiver operating characteristic curve (AUROC), and benchmarked it against Injury Severity Score (ISS) and new injury severity score (NISS).ResultsThe study cohort consisted of 1,325 patients admitted with a mean age of 52.5 years and 91% following blunt injury. The in-hospital mortality rate was 2%, and unplanned ICU admission rate was 2.6%. In predicting mortality, the maximum EDI within 24 hours of admission had an AUROC of 0.98 compared with 0.89 of ISS and 0.91 of NISS. For unplanned ICU admission, the EDI slope within 24 hours of ICU admission had a modest performance with an AUROC of 0.66.ConclusionEpic Deterioration Index appears to perform strongly in predicting in-patient mortality similarly to ISS and NISS. In addition, it can be used to predict unplanned ICU admissions. This study helps validate the use of this real-time EHR ML-based tool, suggesting that EDI should be incorporated into the daily care of trauma patients.Level of evidencePrognostic, level III.

BACKGROUND: The extent to which obesity and genetics determine post-operative complications is incompletely understood. METHODS: We performed a retrospective study using two population cohorts with electronic health record (EHR) data. The first included 736,726 adults with body mass index (BMI) recorded between 1990-2017 at Vanderbilt University Medical Center. The second cohort consisted of 65,174 individuals from 12 institutions contributing EHR and genome-wide genotyping data to the Electronic Medical Records & Genomics (eMERGE) Network. Pairwise logistic regression analyses were used to measure the association of BMI categories with postoperative complications derived from International Classification of Disease-9 codes, including postoperative infection, incisional hernia, and intestinal obstruction. A genetic risk score (GRS) was constructed from 97 obesity-risk single nucleotide polymorphisms for a Mendelian randomization study to determine the association of genetic risk for obesity on postoperative complications. Logistic regression analyses were adjusted for sex, age, site, and race/principal components. RESULTS: Individuals with overweight or obese BMI (≥25 kg/m(2)) had increased risk for incisional hernia (Odds ratio [OR] 1.7-5.5, p

Brain-derived neurotrophic factor (BDNF) plays a key role in energy balance. In population studies, single nucleotide polymorphisms (SNPs) of the BDNF locus have been linked with obesity, but the mechanism by which these variants cause weight gain is unknown. Here, we examined human hypothalamic BDNF expression in association with 44 BDNF SNPs. We discovered that the minor C allele of rs12291063 is associated with lower human ventromedial hypothalamic BDNF expression (p