Faculty Bibliography

BACKGROUND:The National Surgical Quality Improvement Program (NSQIP) American College of Surgeons (ACS) risk calculator is a validated method of predicting postoperative complications that was recently updated to a machine-learning structure. The objective of this study was to measure the accuracy of this calculator in our institution on paraoesophageal hernia (PEH) repair. METHOD/METHODS:Procedures performed between 2019 and 2023 were retrospectively collected regarding demographics, operative variables, and outcomes with a 30-day follow-up. Thirteen outcomes measured by NSQIP-ACS calculator were measured. Observed and predicted rates were compared by receiver operating curves (ROC) and length of stay was compared by Wilcoxon signed rank test. RESULTS:A total of 203 paraoesophageal hernia repairs on patients with a median age of 68 (IQR 61-75) and 70.9% (n = 144) predominantly female. The size of the paraoesophageal hernia (PEH) was large or giant in 59.1% (n = 120) and mesh was placed in 70.4% (n = 143). The predicted risk was consistently higher than observed events on all but discharge destinations. Eight outcomes had no event to measure; however, the calculator accurately predicted a risk of ≤ 1% on all of these. The area under the curve (AUC) was fair (0.6-0.79) on discharge to nursing or rehabilitation facilities and failed in the rest of the measurable outcomes. CONCLUSION/CONCLUSIONS:The ACS-NSQIP risk calculator correctly predicted a low occurrence of postoperative outcomes in patients undergoing robotic paraoesophageal hernia repair.

BACKGROUND AND OBJECTIVES/UNASSIGNED:Operating rooms (ORs) are critical for hospital revenue and cost management, with utilization efficiency directly affecting financial outcomes. Traditional surgical scheduling often results in suboptimal OR use. We aim to build a machine learning (ML) model to predict incision times for robotic-assisted hysterectomies, enhancing scheduling accuracy and hospital finances. METHODS/UNASSIGNED:A retrospective study was conducted using data from robotic-assisted hysterectomy cases performed between January 2017 and April 2021 across 3 hospitals within a large academic health system. Cases were filtered for surgeries performed by high-volume surgeons and those with an incision time of under 3 hours (n = 2,702). Features influencing incision time were extracted from electronic medical records and used to train 5 ML models (linear ridge regression, random forest, XGBoost, CatBoost, and explainable boosting machine [EBM]). Model performance was evaluated using a dynamic monthly update process and novel metrics such as wait-time blocks and excess-time blocks. RESULTS/UNASSIGNED: < .001, 95% CI [-329 to -89]), translating to approximately 52-hours over the 51-month study period. The model predicted more surgeries within a 15% range of the true incision time compared to traditional methods. Influential features included surgeon experience, number of additional procedures, body mass index (BMI), and uterine size. CONCLUSION/UNASSIGNED:The ML model enhanced the prediction of incision times for robotic-assisted hysterectomies, providing a potential solution to reduce OR underutilization and increase surgical throughput and hospital revenue.

The utility of point-of-care ultrasound is well supported by the medical literature. Consequently, pediatric emergency medicine providers have embraced this technology in everyday practice. Recently, the American Academy of Pediatrics published a policy statement endorsing the use of point-of-care ultrasound by pediatric emergency medicine providers.  To date, there is no standard guideline for the practice of point-of-care ultrasound for this specialty. This document serves as an initial step in the detailed "how to" and description of individual point-of-care ultrasound examinations.  Pediatric emergency medicine providers should refer to this paper as reference for published research, objectives for learners, and standardized reporting guidelines.