Background-Accurately estimating operative case-time duration is critical for optimizing operating room utilization. Current estimates are inaccurate and prior models include data not available at the time of scheduling. Our objective was to develop statistical models in a large retrospective dataset to improve estimation of case-time duration relative to current standards. Study Design-We developed models to predict case-time duration using linear regression and supervised machine learning (ML). For each of these models, we generated: 1) service-specific models and 2) surgeon-specific models in which surgeons were modeled individually. Our dataset included 46,986 scheduled surgeries performed at our center from January 2014 to December 2017, with 80% used for training and 20% for model testing/validation. Predictions derived from each model were compared to our institutional standard. Models were evaluated based on accuracy, overage (case duration > predicted + 10%), underage (case duration < predicted-10%), and the predictive capability of being within a 10% tolerance threshold. Results-The ML algorithm resulted in the highest predictive capability. The surgeon-specific model was superior to the service-specific model, with higher accuracies, lower percentage of overage and underage, and higher percentage of cases within the 10% threshold. The ability to predict cases within 10% improved from 32% using our institutional standard to 39% with the ML surgeon-specific model. The majority of the information utilized in the models was based on procedure and personnel data rather than patient health status.

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BackgroundPain from rib fractures is associated with significant pulmonary morbidity. Epidural and paravertebral blocks (EPVBs) have been recommended as part of a multimodal approach to rib fracture pain, but their utility is often challenging in the trauma intensive care unit (ICU). The serratus anterior plane block (SAPB) has potential as an alternative approach for chest wall analgesia.MethodsThis retrospective study compared critically injured adults sustaining multiple rib fractures who had SAPB (n=14) to EPVB (n=25). Patients were matched by age, body mass index, American Society of Anesthesiology Physical Status, whether the patient required intubation, number of rib fractures and injury severity score. Outcome measures included hospital length of stay, ICU length of stay, preblock and post block rapid shallow breathing index (RSBI) in intubated patients, pain scores and morphine equivalent doses administered 24-hour preblock and post-block in non-intubated patients, and mortality.ResultsThere were no demographic differences between the two groups after matching. Nearly all of the patients who received either SAPB or EPVB demonstrated a reduction in RSBI or pain scores. The preblock RSBI was higher in the serratus anterior plane block group, but there was no difference between any of the other outcome measures.DiscussionThis retrospective study of our institutional data suggests no difference in efficacy between the serratus anterior plane block and neuraxial block for traumatic rib fracture pain in critically ill patients, but the sample size was too small to show statistical equivalence. Serratus anterior plane block is technically easier to perform with fewer theoretical contraindications compared with traditional neuraxial block. Further study with prospective comparative trials is warranted.Level of evidenceRetrospective matched cohort; Level IV.

BACKGROUND: Predictive analytics systems may improve perioperative care by enhancing preparation for, recognition of, and response to high-risk clinical events. Bradycardia is a fairly common and unpredictable clinical event with many causes; it may be benign or become associated with hypotension requiring aggressive treatment. Our aim was to build models to predict the occurrence of clinically significant intraoperative bradycardia at 3 time points during an operative course by utilizing available preoperative electronic medical record and intraoperative anesthesia information management system data. METHODS: The analyzed data include 62,182 scheduled noncardiac procedures performed at the University of Washington Medical Center between 2012 and 2017. The clinical event was defined as severe bradycardia (heart rate <50 beats per minute) followed by hypotension (mean arterial pressure <55 mm Hg) within a 10-minute window. We developed models to predict the presence of at least 1 event following 3 time points: induction of anesthesia (TP1), start of the procedure (TP2), and 30 minutes after the start of the procedure (TP3). Predictor variables were based on data available before each time point and included preoperative patient and procedure data (TP1), followed by intraoperative minute-to-minute patient monitor, ventilator, intravenous fluid, infusion, and bolus medication data (TP2 and TP3). Machine-learning and logistic regression models were developed, and their predictive abilities were evaluated using the area under the ROC curve (AUC). The contribution of the input variables to the models were evaluated. RESULTS: The number of events was 3498 (5.6%) after TP1, 2404 (3.9%) after TP2, and 1066 (1.7%) after TP3. Heart rate was the strongest predictor for events after TP1. Occurrence of a previous event, mean heart rate, and mean pulse rates before TP2 were the strongest predictor for events after TP2. Occurrence of a previous event, mean heart rate, mean pulse rates before TP2 (and their interaction), and 15-minute slopes in heart rate and blood pressure before TP2 were the strongest predictors for events after TP3. The best performing machine-learning models including all cases produced an AUC of 0.81 (TP1), 0.87 (TP2), and 0.89 (TP3) with positive predictive values of 0.30, 0.29, and 0.15 at 95% specificity, respectively. CONCLUSIONS: We developed models to predict unstable bradycardia leveraging preoperative and real-time intraoperative data. Our study demonstrates how predictive models may be utilized to predict clinical events across multiple time intervals, with a future goal of developing real-time, intraoperative, decision support.