Publications

OBJECTIVE:: We compare consensus recommendations for 5 surgical procedures to prospectively collected patient consumption data. To address local variation, we combined data from multiple hospitals across the country. SUMMARY OF BACKGROUND DATA: One approach to address the opioid epidemic has been to create prescribing consensus reports for common surgical procedures. However, it is unclear how these guidelines compare to patient-reported data from multiple hospital systems. METHODS: Prospective observational studies of surgery patients were completed between 3/2017 and 12/2018. Data were collected utilizing post-discharge surveys and chart reviews from 5 hospitals (representing 3 hospital systems) in 5 states across the USA. Prescribing recommendations for 5 common surgical procedures identified in 2 recent consensus reports were compared to the prospectively collected aggregated data. Surgeries included: laparoscopic cholecystectomy, open inguinal hernia repair, laparoscopic inguinal hernia repair, partial mastectomy without sentinel lymph node biopsy, and partial mastectomy with sentinel lymph node biopsy. RESULTS: Eight hundred forty-seven opioid-naïve patients who underwent 1 of the 5 studied procedures reported counts of unused opioid pills after discharge. Forty-one percent did not take any opioid medications, and across all surgeries, the median consumption was 3 5 mg oxycodone pills or less. Generally, consensus reports recommended opioid quantities that were greater than the 75th percentile of consumption, and for 2 procedures, recommendations exceeded the 90th percentile of consumption. CONCLUSIONS: Although consensus recommendations were an important first step to address opioid prescribing, our data suggests that following these recommendations would result in 47%-56% of pills prescribed remaining unused. Future multi-institutional efforts should be directed toward refining and personalizing prescribing recommendations.

BACKGROUND: Unanticipated hospital admission is regarded as a measure of adverse perioperative patient care. However, previously published studies for risk prediction after ambulatory procedures are sparse compared to those examining readmission after inpatient surgery. We aimed to evaluate the incidence and reasons for unplanned admission after ambulatory surgery and develop a prediction tool for preoperative risk assessment. METHODS: This retrospective cohort study included adult patients undergoing ambulatory, noncardiac procedures under anesthesia care at 2 tertiary care centers in Massachusetts, United States, between 2007 and 2017 as well as all hospitals and ambulatory surgery centers in New York State, United States, in 2014. The primary outcome was unplanned hospital admission within 30 days after discharge. We created a prediction tool (the PREdicting admission after Outpatient Procedures [PREOP] score) using stepwise backward regression analysis to predict unplanned hospital admission, based on criteria used by the Centers for Medicare & Medicaid Services, within 30 days after surgery in the Massachusetts hospital network registry. Model predictors included patient demographics, comorbidities, and procedural factors. We validated the score externally in the New York state registry. Reasons for unplanned admission were assessed. RESULTS: A total of 170,983 patients were included in the Massachusetts hospital network registry and 1,232,788 in the New York state registry. Among those, the observed rate of unplanned admission was 2.0% (3504) and 1.7% (20,622), respectively. The prediction model showed good discrimination in the training set with C-statistic of 0.77 (95% confidence interval [CI], 0.77-0.78) and satisfactory discrimination in the validation set with C-statistic of 0.71 (95% CI, 0.70-0.71). The risk of unplanned admission varied widely from 0.4% (95% CI, 0.3-0.4) among patients whose calculated PREOP scores were in the first percentile to 21.3% (95% CI, 20.0-22.5) among patients whose scores were in the 99th percentile. Predictions were well calibrated with an overall ratio of observed-to-expected events of 99.97% (95% CI, 96.3-103.6) in the training and 92.6% (95% CI, 88.8-96.4) in the external validation set. Unplanned admissions were most often related to malignancy, nonsurgical site infections, and surgical complications. CONCLUSIONS: We present an instrument for prediction of unplanned 30-day admission after ambulatory procedures under anesthesia care validated in a statewide cohort comprising academic and nonacademic hospitals as well as ambulatory surgery centers. The instrument may be useful in identifying patients at high risk for 30-day unplanned hospital admission and may be used for benchmarking hospitals, ambulatory surgery centers, and practitioners.

We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.

Open, or non-laparoscopic surgery, represents the vast majority of all operating room procedures, but few tools exist to objectively evaluate these techniques at scale. Current efforts involve human expert-based visual assessment. We leverage advances in computer vision to introduce an automated approach to video analysis of surgical execution. A state-of-the-art convolutional neural network architecture for object detection was used to detect operating hands in open surgery videos. Automated assessment was expanded by combining model predictions with a fast object tracker to enable surgeon-specific hand tracking. To train our model, we used publicly available videos of open surgery from YouTube and annotated these with spatial bounding boxes of operating hands. Our model's spatial detections of operating hands significantly outperforms the detections achieved using pre-existing hand-detection datasets, and allow for insights into intra-operative movement patterns and economy of motion.