Excessive resident duty hours (RDH) are a recognized issue with implications for physician well-being and patient safety. A major component of the RDH concern is on-call duty. While considerable work has been done to reduce resident call workload, there is a paucity of research in optimizing resident call scheduling. Call coverage is scheduled manually rather than demand-based, which generally leads to over-scheduling to prevent a service gap. Machine learning (ML) has been widely applied in other industries to prevent such issues of a supply-demand mismatch. However, the healthcare field has been slow to adopt these innovations. As such, the aim of this study was to use ML models to 1) predict demand on orthopaedic surgery residents at a level I trauma centre and 2) identify variables key to demand prediction.

Daily surgical handover emails over an eight year (2012-2019) period at a level I trauma centre were collected. The following data was used to calculate demand: spine call coverage, date, and number of operating rooms (ORs), traumas, admissions and consults completed. Various ML models (linear, tree-based and neural networks) were trained to predict the workload, with their results compared to the current scheduling approach. Quality of models was determined by using the area under the receiver operator curve (AUC) and accuracy of the predictions. The top ten most important variables were extracted from the most successful model.

During training, the model with the highest AUC and accuracy was the multivariate adaptive regression splines (MARS) model, with an AUC of 0.78±0.03 and accuracy of 71.7%±3.1%. During testing, the model with the highest AUC and accuracy was the neural network model, with an AUC of 0.81 and accuracy of 73.7%. All models were better than the current approach, which had an AUC of 0.50 and accuracy of 50.1%. Key variables used by the neural network model were (descending order): spine call duty, year, weekday/weekend, month, and day of the week.

This was the first study attempting to use ML to predict the service demand on orthopaedic surgery residents at a major level I trauma centre. Multiple ML models were shown to be more appropriate and accurate at predicting the demand on surgical residents as compared to the current scheduling approach. Future work should look to incorporate predictive models with optimization strategies to match scheduling with demand in order to improve resident well being and patient care.

Open debridement and Outerbridge and Kashiwagi debridement arthroplasty (OK procedure) are common surgical treatments for elbow arthritis. However, the literature contains little information on the long-term survivorship of these procedures. The purpose of this study was to determine the survivorship after elbow debridement techniques until conversion to total elbow arthroplasty and revision surgery.

We performed a retrospective chart review of patients who underwent open elbow surgical debridement (open debridement, OK procedure) between 2000 and 2015. Patients were diagnosed with primary elbow osteoarthritis, post-traumatic arthritis, or inflammatory arthritis. A total of 320 patients had primary surgery including open debridement (n=142) and OK procedure (n=178), and of these 33 patients required a secondary revision surgery (open debridement, n=14 and OK procedure, n=19). The average follow-up time was 11.5 years (5.5 - 21.5 years). Survivorship was analyzed with Kaplan-Meier curves and Log Rank test. A Cox proportional hazards model was used assess the likelihood of conversion to total elbow arthroplasty or revision surgery while adjusting for covariates (age, gender, diagnosis). Significance was set p<0.05.

Kaplan-Meier survival curves showed open debridement was 100.00% at 1 year, 99.25% at 5 years, and 98.49% at 10 years and for OK procedure 100.00% at 1 year, 98.80% at 5 years, 97.97% at 10 years (p=0.87) for conversion to total elbow arthroplasty. There was no difference in survivorship between procedures after adjusting for significant covariates with the cox proportional hazard model. The rate of revision for open debridement and OK procedure was similar at 11.31% rand 11.48% after 10 years respectively. There were higher rates of revision surgery in patients with open debridement (hazard ratio, 4.84 CI 1.29 – 18.17, p = 0.019) compared to OK procedure after adjusting for covariates. We also performed a stratified analysis with radiographic severity as an effect modifier and showed grade 3 arthritis did better with the OK procedure compared to open debridement for survivorship until revision surgery (p=0.05). However, this difference was not found for grade 1 or grade 2 arthritis. This may suggest that performing the OK procedure for more severe grade 3 arthritis could decrease reoperation rates. Further investigations are needed to better understand the indications for each surgical technique.

This study is the largest cohort of open debridement and OK procedure with long term follow-up. We showed that open elbow debridement and the OK procedure have excellent survivorship until conversion to total elbow arthroplasty and are viable options in the treatment of primary elbow osteoarthritis and post traumatic cases. The OK procedure also has lower rates of revision surgery than open debridement, especially with more severe radiographic arthritis.

Open debridement and Outerbridge and Kashiwagi debridement arthroplasty (OK procedure) are common surgical treatments for elbow arthritis. However, the literature contains little information on the long-term survivorship of these procedures. The purpose of this study was to determine the survivorship after elbow debridement techniques until conversion to total elbow arthroplasty and revision surgery.

We performed a retrospective chart review of patients who underwent open elbow surgical debridement (open debridement, OK procedure) between 2000 and 2015. Patients were diagnosed with primary elbow osteoarthritis, post-traumatic arthritis, or inflammatory arthritis. A total of 320 patients had primary surgery including open debridement (n=142) and OK procedure (n=178), and of these 33 patients required a secondary revision surgery (open debridement, n=14 and OK procedure, n=19). The average follow-up time was 11.5 years (5.5 - 21.5 years). Survivorship was analyzed with Kaplan-Meier curves and Log Rank test. A Cox proportional hazards model was used assess the likelihood of conversion to total elbow arthroplasty or revision surgery while adjusting for covariates (age, gender, diagnosis). Significance was set p<0.05.

Kaplan-Meier survival curves showed open debridement was 100.00% at 1 year, 99.25% at 5 years, and 98.49% at 10 years and for OK procedure 100.00% at 1 year, 98.80% at 5 years, 97.97% at 10 years (p=0.87) for conversion to total elbow arthroplasty. There was no difference in survivorship between procedures after adjusting for significant covariates with the cox proportional hazard model. The rate of revision for open debridement and OK procedure was similar at 11.31% rand 11.48% after 10 years respectively. There were higher rates of revision surgery in patients with open debridement (hazard ratio, 4.84 CI 1.29 - 18.17, p = 0.019) compared to OK procedure after adjusting for covariates. We also performed a stratified analysis with radiographic severity as an effect modifier and showed grade 3 arthritis did better with the OK procedure compared to open debridement for survivorship until revision surgery (p=0.05). However, this difference was not found for grade 1 or grade 2 arthritis. This may suggest that performing the OK procedure for more severe grade 3 arthritis could decrease reoperation rates. Further investigations are needed to better understand the indications for each surgical technique.

This study is the largest cohort of open debridement and OK procedure with long term follow-up. We showed that open elbow debridement and the OK procedure have excellent survivorship until conversion to total elbow arthroplasty and are viable options in the treatment of primary elbow osteoarthritis and post traumatic cases. The OK procedure also has lower rates of revision surgery than open debridement, especially with more severe radiographic arthritis.

The primary objectives of this study were to: 1) identify risk factors for subsequent surgery following initial treatment of proximal humerus fractures, stratified by initial treatment type; 2) generate risk prediction tools to predict subsequent shoulder surgery following initial treatment; and 3) internally validate the discriminative ability of each tool.

We identified patients ≥ 50 years with a diagnosis of proximal humerus fracture from 2004 to 2015 using linkable health datasets in Ontario, Canada. We used procedural and fee codes within 30 days of the index fracture to classify patients into treatment groups: 1) surgical fixation; 2) shoulder replacement; and 3) conservative. We used intervention and diagnosis codes to identify all instances of complication-related subsequent shoulder surgery following initial treatment within two years post fracture. We developed logistic regression models for randomly selected two thirds of each treatment group to evaluate the association of patient, fracture, surgical, and hospital variables on the odds of subsequent shoulder surgery following initial treatment. We used regression coefficients to compute points associated with each of the variables within each category, and calculated the risk associated with each point total using the regression equation. We used the final third of each cohort to evaluate the discriminative ability of the developed risk tools (via the continuous point total and a dichotomous point cut-off value for “higher” vs. “lower” risk determined by Receiver Operating Curves) using c-statistics.

We identified 20,897 patients with proximal humerus fractures that fit our inclusion criteria for analysis, 2,414 treated with fixation, 1,065 treated with replacement, and 17,418 treated conservatively. The proportions of patients who underwent subsequent shoulder surgery within two years were 13.8%, 5.1%, and 1.3%, for fixation, replacement, and conservative groups, respectively. Predictors of reoperation following fixation included the use of a bone graft, and fixation with a nail or wire vs. a plate. The only significant predictor of reoperation following replacement was poor bone quality. The only predictor of subsequent shoulder surgery following conservative treatment was more comorbidities while patients aged 70+, and those discharged home following initial presentation (vs. admitted or transferred to another facility) had lower odds of subsequent shoulder surgery. The risk tools developed were able to discriminate between patients who did or did not undergo subsequent shoulder surgery in the derivation cohorts with c-statistics of 0.75–0.88 (continuous point total), and 0.82–0.88 (dichotomous cut-off), and 0.53–0.78 (continuous point total) and 0.51–0.79 (dichotomous cut-off) in the validation cohorts.

Our results present potential factors associated with subsequent shoulder surgery following initial treatment of proximal humerus fractures, stratified by treatment type. Our developed risk tools showed good to strong discriminative ability in both the derivation and validation cohorts for patients treated with fixation, and conservatively. This indicates that the tools may be useful for clinicians and researchers. Future research is required to develop risk tools that incorporate clinical variables such as functional demands.

Distracted driving is now the number one cause of death among teenagers in the United States of America according to the National Highway Traffic Safety Administration. However, the risks and consequences of driving while distracted spans all ages, gender, and ethnicity. The Distractions on the Road: Injury eValuation in Surgery And FracturE Clinics (DRIVSAFE) Study aimed to examine the prevalence of distracted driving among patients attending hospital-based orthopaedic surgery fracture clinics. We further aimed to explore factors associated with distracted driving.

In a large, multi-center prospective observational study, we recruited 1378 adult patients with injuries treated across four clinics (Hamilton, Ontario, Toronto, Ontario, Calgary, Alberta, Halifax, Nova Scotia) across Canada. Eligible patients included those who held a valid driver's license and were able to communicate and understand written english. Patients were administered questions about distracted driving. Data were analyzed with descriptive statistics.

Patients average age was 45.8 years old (range 16 – 87), 54.3% male, and 44.6% female (1.1% not disclosed). Of 1361 patients, 1358 self-reported distracted driving (99.8%). Common sources of distractions included talking to passengers (98.7%), outer-vehicle distractions (95.5%), eating/drinking (90.4%), music listening/adjusting the radio (97.6%/93.8%), singing (83.2%), accepting phone calls (65.6%) and daydreaming (61.2%). Seventy-nine patients (6.3%), reported having been stopped by police for using a handheld device in the past. Among 113 drivers who disclosed the cause of their injury as a motor vehicle crash (MVC), 20 of them (17%) acknowledged being distracted at the time of the crash. Of the participants surveyed, 729 reported that during their lifetime they had been the driver in a MVC, with 226 (31.1%) acknowledging they were distracted at the time of the crash.

Approximately, 1 in 6 participants in this study had a MVC where they reported to be distracted. Despite the overwhelming knowledge that distracted driving is dangerous and the recognition by participants that it can be dangerous, a staggering amount of drivers engage in distracted driving on a fairly routine basis. This study demonstrates an ongoing need for research and driver education to reduce distracted driving and its devastating consequences.