Improved Pain and Perioperative Outcomes After Hip Arthroscopy With the Pericapsular Nerve Group Block.

PURPOSE: To compare early postoperative pain in patients undergoing hip arthroscopy with versus without the pericapsular nerve group (PENG) block. METHODS: A retrospective chart review of prospectively collected data was performed to identify patients who underwent hip arthroscopy at a single institution between May 2019 and October 2021. Patients were included if they received general anesthesia and were opioid naive. Patients who received the PENG block were compared with patients who did not. Opioid, benzodiazepine, and antiemetic medication administration was recorded both intraoperatively and for the duration of the patient's stay in the postanesthesia care unit (PACU). Opioids administered were converted to morphine milligram equivalents (MMEs). Pain was assessed with a visual analog scale. Time to discharge (in minutes) and complications were recorded. RESULTS: A total of 53 patients were identified for inclusion, of whom 28 received the PENG block and 25 did not. Opioid consumption was significantly lower in the PENG block group both intraoperatively (16.9 ± 14.1 MMEs vs 40.6 ± 18.3 MMEs, P < .001) and in the PACU (14.4 ± 11.4 MMEs vs 31.2 ± 20.1 MMEs, P < .001). The highest recorded PACU pain score was significantly greater in the no-PENG block group (7.0 ± 1.9 vs 5.3 ± 2.1, P = .004). Within the PENG block group, fewer patients required antiemetics (0 vs 4, P = .043). There was a greater time to discharge in the no-PENG block group (161 ± 50 minutes vs 129 ± 34 minutes, P = .008). No complications, including postoperative falls, were noted in either group. CONCLUSIONS: The PENG block improves perioperative outcomes by decreasing pain, opioid consumption, time to discharge, antiemetic requirements, and benzodiazepine requirements after hip arthroscopy. LEVEL OF EVIDENCE: Level III, retrospective comparative therapeutic trial.

Neural Network Prediction of ICU Length of Stay Following Cardiac Surgery Based on Pre-Incision Variables.

BACKGROUND: Advanced predictive analytical techniques are being increasingly applied to clinical risk assessment. This study compared a neural network model to several other models in predicting the length of stay (LOS) in the cardiac surgical intensive care unit (ICU) based on pre-incision patient characteristics. METHODS: Thirty six variables collected from 185 cardiac surgical patients were analyzed for contribution to ICU LOS. The Automatic Linear Modeling (ALM) module of IBM-SPSS software identified 8 factors with statistically significant associations with ICU LOS; these factors were also analyzed with the Artificial Neural Network (ANN) module of the same software. The weighted contributions of each factor ("trained" data) were then applied to data for a "new" patient to predict ICU LOS for that individual. RESULTS: Factors identified in the ALM model were: use of an intra-aortic balloon pump; O2 delivery index; age; use of positive cardiac inotropic agents; hematocrit; serum creatinine ≥ 1.3 mg/deciliter; gender; arterial pCO2. The r2 value for ALM prediction of ICU LOS in the initial (training) model was 0.356, p <0.0001. Cross validation in prediction of a "new" patient yielded r2 = 0.200, p <0.0001. The same 8 factors analyzed with ANN yielded a training prediction r2 of 0.535 (p <0.0001) and a cross validation prediction r2 of 0.410, p <0.0001. Two additional predictive algorithms were studied, but they had lower prediction accuracies. Our validated neural network model identified the upper quartile of ICU LOS with an odds ratio of 9.8(p <0.0001). CONCLUSIONS: ANN demonstrated a 2-fold greater accuracy than ALM in prediction of observed ICU LOS. This greater accuracy would be presumed to result from the capacity of ANN to capture nonlinear effects and higher order interactions. Predictive modeling may be of value in early anticipation of risks of post-operative morbidity and utilization of ICU facilities.