Forecasting a Crisis: Machine-Learning Models Predict Occurrence of Intraoperative Bradycardia Associated With Hypotension.

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.

Improving Operating Room Efficiency: Machine Learning Approach to Predict Case-Time Duration.

BACKGROUND: Accurate estimation of operative case-time duration is critical for optimizing operating room use. Current estimates are inaccurate and earlier models include data not available at the time of scheduling. Our objective was to develop statistical models in a large retrospective data set 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. For each of these models, we generated an all-inclusive model, service-specific models, and surgeon-specific models. In the latter 2 approaches, individual models were created for each surgical service and surgeon, respectively. Our data set included 46,986 scheduled operations performed at a large academic medical 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 with our institutional standard of using average historic procedure times and surgeon estimates. 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 machine learning algorithm resulted in the highest predictive capability. The surgeon-specific model was superior to the service-specific model, with higher accuracy, 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 machine learning surgeon-specific model. CONCLUSIONS: Our study is a notable advancement toward statistical modeling of case-time duration across all surgical departments in a large tertiary medical center. Machine learning approaches can improve case duration estimations, enabling improved operating room scheduling, efficiency, and reduced costs.

Does Attending Surgeon Presence at the Preinduction Briefing Improve Operating Room Efficiency?

OBJECTIVE: To examine if attending surgeon presence at the preinduction briefing is associated with a shorter time to incision. STUDY DESIGN: Retrospective cohort study and survey. SETTING: Tertiary academic medical center. SUBJECTS AND METHODS: A retrospective cohort study was conducted of 22,857 operations by 141 attending surgeons across 12 specialties between August 3, 2016, and June 21, 2018. The independent variable was attending surgeon presence at the preinduction briefing. Linear regression models compared time from room entry to incision overall, by service line, and by surgeon. We hypothesized a shorter time to incision when the attending surgeon was present and a larger effect for cases with complex surgical equipment or positioning. A survey was administered to evaluate attending surgeons' perceptions of the briefing, with a response rate of 68% (64 of 94 attending surgeons). RESULTS: Cases for which the attending surgeon was present at the preinduction briefing had a statistically significant yet operationally minor reduction in mean time to incision when compared with cases when the attending surgeon was absent. After covariate adjustment, the mean time to incision was associated with an efficiency gain of 1.8 ± 0.5 minutes (mean ± SD; P < .001). There were no statistically significant differences in the subgroups of complex surgical equipment and complex positioning or in secondary analysis comparing service lines. The surgeon was the strongest confounding variable. Survey results demonstrated mild support: 55% of attending surgeons highly prioritized attending the preinduction briefing. CONCLUSION: Attending surgeon presence at the preinduction briefing has only a minor effect on efficiency as measured by time to incision.

Comparison of Micro-Computed Tomography and Clinical Computed Tomography Protocols for Visualization of Nasal Cartilage Before Surgical Planning for Rhinoplasty.

IMPORTANCE: There is no imaging standard to model nasal cartilage for the planning of rhinoplasty procedures. Preoperative visualization of cartilage may improve objective evaluation of nasal deformities, surgical planning, and surgical reconstruction. OBJECTIVES: To evaluate the feasibility of visualizing nasal cartilage using high resolution micro-computed tomography (CT) compared with the criterion standard of pathologic findings in a cadaveric specimen and to evaluate its accuracy compared with various clinical CT protocols. DESIGN, SETTING, AND PARTICIPANTS: Anatomic study at the University of Washington using single human cadaveric nasal specimens performed from July 10, 2017, to March 30, 2018. INTERVENTIONS: A micro-CT acquisition with 60-micron resolution was obtained of a nasal specimen. The specimen was then scanned with 5 different clinical CT protocols to span both clinical care and machine limits. The specimen was then sectioned in 5-mm axial slices for pathologic analysis. MAIN OUTCOMES AND MEASURES: Micro-CT images were registered to pathologic specimen cross-sections using a graphite fiducial system. Cartilage substructures were manually segmented and analyzed. A library of matched images across the micro-CT and various clinical CT protocols was then developed. Region of interest analysis was performed for each of the cartilage structures and their boundaries on clinical CT protocols and micro-CT, with the outcome of mean (SD) density using Hounsfield units. RESULTS: A single human cadaveric nasal specimen was used to obtain the following results. Lower lateral cartilage, upper lateral cartilage, and septal cartilage were accurately delineated on the micro-CT images compared with pathologic findings. The mean absolute deviation from pathologic findings was 0.30 mm for septal cartilage thickness, 0.98 mm for maximal upper lateral cartilage length, and 1.40 mm for maximal lower lateral cartilage length. On clinical CT protocols, only septal cartilage was well discriminated from boundary. Higher radiation dose resulted in more accurate density measurements of cartilage, but it did not ultimately improve ability to discriminate cartilage. CONCLUSIONS AND RELEVANCE: The results of this anatomic study may represent a notable step toward advancing knowledge of the capabilities and pitfalls of nasal cartilage visualization on CT. Nasal cartilage visualization was feasible on the micro-CT compared with pathologic findings. Future research may further examine the barriers to accurately visualizing upper lateral cartilage and lower lateral cartilage, a prerequisite for clinical application. LEVEL OF EVIDENCE: NA.

Maximizing Operational Efficiency Using an In-House Ambulatory Surgery Model at an Academic Medical Center.

EXECUTIVE SUMMARY: Given the rising costs of healthcare delivery and reimbursement constraints, large academic medical centers (AMCs) must improve efficiency while delivering high-quality care. With standardized cases and high volumes, ambulatory surgery is a high-value target for efficiency improvement. Mining a data set of more than 7,500 cases consisting of the three highest-volume ambulatory procedures in orthopedics, otolaryngology-head and neck surgery, and urology, we analyzed process times and wait times involved in patient flow. We examined differences among delayed versus early versus on-time cases, as well as differences in scheduled start times, day of the week, and each individual operating room. Our analysis found statistically and clinically significant differences in registration and setup wait times when comparing delayed versus early versus on-time cases. We then developed recommendations to increase value-added time. Using activity-based cost accounting, we created a model to quantify economic impact. Hospitals can adopt these methods to identify operational bottlenecks and employ our financial model to forecast changes in revenue. Application of this model can position AMCs for success in an increasingly competitive landscape.

The varying presentations of type I laryngeal cleft: illustrative cases in a set of triplets.

We present the first known instance of type 1 laryngeal cleft (T1LC) in a set of triplets. Individuals with T1LC typically present on a spectrum, from asymptomatic to recurrent aspiration pneumonias and failure to thrive. Each of the triplets in this family had a unique clinical presentation and treatment plan. Management of T1LC should be influenced by the severity of symptoms rather than the presence or absence of the anomaly itself. These three cases also support the notion that severity of symptoms may be independent of shared biological parents, home environment, and age.

Social media utilization in the cochlear implant community.

BACKGROUND: More than 200,000 individuals worldwide have received a cochlear implant (CI). Social media Websites may provide a paramedical community for those who possess or are interested in a CI. The utilization patterns of social media by the CI community, however, have not been thoroughly investigated. PURPOSE: The purpose of this study was to investigate participation of the CI community in social media Websites. RESEARCH DESIGN: We conducted a systematic survey of online CI-related social media sources. Using standard search engines, the search terms cochlear implant, auditory implant, forum, and blog identified relevant social media platforms and Websites. Social media participation was quantified by indices of membership and posts. STUDY SAMPLE: Social media sources included Facebook, Twitter, YouTube, blogs, and online forums. Each source was assigned one of six functional categories based on its description. INTERVENTION: No intervention was performed. DATA COLLECTION AND ANALYSIS: We conducted all online searches in February 2014. Total counts of each CI-related social media source were summed, and descriptive statistics were calculated. RESULTS: More than 350 sources were identified, including 60 Facebook groups, 36 Facebook pages, 48 Twitter accounts, 121 YouTube videos, 13 forums, and 95 blogs. The most active online communities were Twitter accounts, which totaled 35,577 members, and Facebook groups, which totaled 17,971 members. CI users participated in Facebook groups primarily for general information/support (68%). Online forums were the next most active online communities by membership. The largest forum contained approximately 9,500 topics with roughly 127,000 posts. CI users primarily shared personal stories through blogs (92%), Twitter (71%), and YouTube (62%). CONCLUSIONS: The CI community engages in the use of a wide range of online social media sources. The CI community uses social media for support, advocacy, rehabilitation information, research endeavors, and sharing of personal experiences. Future studies are needed to investigate how social media Websites may be harnessed to improve patient-provider relationships and potentially used to augment patient education.