Predictive Model of Surgical Time for Revision Total Hip Arthroplasty

Variability and relative contribution of surgeon- and anesthesia-specific time components to total procedural time in cardiac surgery

BACKGROUND

Decreasing variability in time-intensive tasks during cardiac surgery may reduce total procedural time, lower costs, reduce clinician burnout, and improve patient access. The relative contribution and variability of surgeon control time (SCT) and anesthesia control time (ACT) to total procedural time is unknown.

METHODS

A total of 669 patients undergoing coronary artery bypass graft (CABG) surgery were enrolled. Using linear regression, we estimated adjusted SCTs and ACTs, controlling for patient and procedural covariates. The primary endpoint compared overall SCTs and ACTs. The secondary endpoint compared the variability in adjusted SCTs and ACTs. Sensitivity analyses quantified the relative importance of the specific surgeon and anesthesiologist in the adjusted linear models.

RESULTS

The median SCT was 4.1 hours (interquartile range [IQR], 3.4-4.9 hours) compared to a median ACT of 1.0 hours (IQR, 0.8-1.2 hours; P < .001). Using linear regression, the variability in adjusted SCT among surgeons (range, 1.8 hours) was 3.5-fold greater than the variability in adjusted ACT among anesthesiologists (range, 0.5 hour; P < .001). The specific surgeon and anesthesiologist accounted for 50% of the explanatory power of the predictive model (P < .001).

CONCLUSIONS

SCT variability is significantly greater than ACT variability and is strongly associated with the surgeon performing the procedure. Although these results suggest that SCT variability is an attractive operational target, further studies are needed to determine practitioner specific and modifiable attributes to reduce variability and improve efficiency.

Determining pre-procedure fasting alert time using procedural and scheduling data

Before a medical procedure requiring anesthesia, patients are required to not eat or drink non-clear fluids for 6 h and not drink clear fluids for 2 h. Fasting durations in standard practice far exceed these minimum thresholds due to uncertainties in procedure start time. The aim of this retrospective, observational study was to compare fasting durations arising from standard practice with different approaches for calculating the timepoint at which patients are instructed to stop eating and drinking. Scheduling data for procedures performed in the cardiac catheterization laboratory of an academic hospital in Canada (January 2020 to April 2022) were used. Four approaches utilizing machine learning (ML) and simulation were used to predict procedure start times and calculate when patients should be instructed to start fasting. Median fasting duration for standard practice was 10.08 h (IQR 3.5) for both food and clear fluids intake. The best performing alternative approach, using tree-based ML models to predict procedure start time, reduced median fasting from food/non-clear fluids to 7.7 h (IQR 2) and clear liquids fasting to 3.7 h (IQR 2.4). 97.3% met the minimum fasting duration requirements (95% CI 96.9% to 97.6%). Further studies are required to determine the effectiveness of operationalizing this approach as an automated fasting alert system.

Which patient-specific parameters correlate with operation time for total hip arthroplasty? - A retrospective analysis of the direct anterior approach

PURPOSE

The current study aims to identify patient-specific factors that correlate with operation time for total hip arthroplasty (THA) performed via the direct anterior approach (DAA).

METHODS

In this retrospective study, patient-specific factors were tabulated from the charts and measured from preoperative templating radiographs. These factors were correlated with operation time by bivariate analysis. Significant factors were used for stepwise multiple regression analysis.

RESULTS

Nine hundred-sixty procedures were included. BMI (R = 0.283), the distance from the superior iliac spine to the greater trochanter (DAA Plane) (R = - 0.154), patients age (R = 0.152) and the abdominal fat flap (R = 0.134) showed the strongest correlations (p < 0.005) with operation time. The multiple regression model including BMI, Kellgren and Lawrence Score, Age, DAA Plane and the Canal to Calcar ratio had the best predictive accuracy (corrected R2 = 0.122).

CONCLUSIONS

Patient-specific factors that make the entry into the femur difficult correlate significantly with operation time of THA via the DAA.

Predicting surgical operative time in primary total knee arthroplasty utilizing machine learning models

BACKGROUND

Prolonged surgical operative time is associated with postoperative adverse outcomes following total knee arthroplasty (TKA). Increasing operating room efficiency necessitates the accurate prediction of surgical operative time for each patient. One potential way to increase the accuracy of predictions is to use advanced predictive analytics, such as machine learning. The aim of this study is to use machine learning to develop an accurate predictive model for surgical operative time for patients undergoing primary total knee arthroplasty.

METHODS

A retrospective chart review of electronic medical records was conducted to identify patients who underwent primary total knee arthroplasty at a tertiary referral center. Three machine learning algorithms were developed to predict surgical operative time and were assessed by discrimination, calibration and decision curve analysis. Specifically, we used: (1) Artificial Neural Networks (ANNs), (2) Random Forest (RF), and (3) K-Nearest Neighbor (KNN).

RESULTS

We analyzed the surgical operative time for 10,021 consecutive patients who underwent primary total knee arthroplasty. The neural network model achieved the best performance across discrimination (AUC = 0.82), calibration and decision curve analysis for predicting surgical operative time. Based on this algorithm, younger age (< 45 years), tranexamic acid non-usage, and a high BMI (> 40 kg/m²) were the strongest predictors associated with surgical operative time.

CONCLUSIONS

This study shows excellent performance of machine learning models for predicting surgical operative time in primary total knee arthroplasty. The accurate estimation of surgical duration is important in enhancing OR efficiency and identifying patients at risk for prolonged surgical operative time.

LEVEL OF EVIDENCE

Level III, case control retrospective analysis.

Favourable clinical outcomes following cemented arthroplasty after metal-on-metal total hip replacement: a retrospective study with a mean follow-up of 10 years

BACKGROUND

Given the unexpected high rate of failure following metal-on-metal total hip replacement (MoM-THR), it is expected that more MoM-THR patients will experience revision. The long-term outcomes regarding the primary MoM-THR revised to cemented THR (CTHR) remain controversial. The purpose of this retrospective review was to evaluate the long-term outcomes of patients who underwent conversion from MoM-THR to CTHR.

METHODS

A total of 220 patients (220 hips) who underwent a conversion of primary MoM-THR to CTHR from March 2006 to October 2016 were retrospectively reviewed. The primary outcomes were the functional outcomes assessed using the Harris hip scores (HHS) and major radiographic outcomes. Follow-ups occurred at 3 months, 6 months, 1 year, 2 years, and then every two years after revision.

RESULTS

Mean follow-up was 10.1 years (5-13 years). Distinct improvements were detected in the mean HHS between the preoperative and last follow-up analysis (62.35[±8.49] vs. 84.70[±14.68], respectively, p < 0.001). The key orthopaedic complication rate was 18.2% (27/148). Seven (4.7%) cases experienced a CTHR failure at a mean of 3.4 (±1.2) years after revision MoM-THR, mostly attributed to recurrent dislocation.

CONCLUSION

CTHR might yield an acceptable functional score and a low rate of the key orthopaedic complications.

The SIFK score: a validated predictive model for arthroplasty progression after subchondral insufficiency fractures of the knee

PURPOSE

The purpose of this study was to create a predictive model utilizing baseline demographic and radiographic characteristics for the likelihood that a patient with subchondral insufficiency fracture of the knee will progress to knee arthroplasty with emphasis on clinical interpretability and usability.

METHODS

A retrospective review of baseline and final radiographs in addition to MRIs were reviewed for evaluation of insufficiency fractures and associated injuries. Patient and radiographic factors were used in building predictive models for progression to arthroplasty with Train: Validation: Test subsets. Multiple models were compared with emphasis on clinical utility.

RESULTS

Total of 249 patients with a mean age of 64.6 (SD 10.5) years were included. Knee arthroplasty rate was 27% at mean of 4 years of follow-up. Lasso Regression was non-inferior to other models and was chosen for ease of interpretability. In order of importance, predictors for progression to arthroplasty included lateral meniscus extrusion, Kellgren-Lawrence Grade 4, SIFK on MFC, lateral meniscus root tear, and medial meniscus extrusion. The final SIFK Score stratified patients into low-, medium-, and high-risk categories with arthroplasty rates of 8.8%, 40.4%, and 78.9% (p < 0.001) and an area under the curve of 82.5%.

CONCLUSION

In this validated model, lateral meniscus extrusion, K-L Grade 4, SIFK on MFC, lateral meniscus root tear, and medial meniscus extrusion were the most important factors in predicting progression to arthroplasty (in that order). This model assists in patient treatment and counseling in providing prognostic information based on patient-specific risk factors by classifying them into a low-, medium-, and high-risk categories. This model can be used both by medical professionals treating musculoskeletal injuries in guiding patient decision making.

LEVEL OF EVIDENCE

Level III.

A Machine Learning Approach to Predicting Case Duration for Robot-Assisted Surgery

Robot-assisted surgery (RAS) requires a large capital investment by healthcare organizations. The cost of a robotic unit is fixed, so institutions must maximize use of each unit by utilizing all available operating room block time. One way to increase utilization is to accurately predict case durations. In this study, we sought to use machine learning to develop an accurate predictive model for RAS case duration. We analyzed a random sample of robotic cases at our institution from January 2014 to June 2017. We compared the machine learning models to the baseline model, which is the scheduled case duration (determined by previous case duration averages and surgeon adjustments). Specifically, we used: 1) multivariable linear regression, 2) ridge regression, 3) lasso regression, 4) random forest, 5) boosted regression tree, and 6) neural network. We found that all machine learning models decreased the average root-mean-squared error (RMSE) as compared to the baseline model. The average RMSE was lowest with the boosted regression tree (80.2 min, 95% CI 74.0-86.4), which was significantly lower than the baseline model (100.4 min, 95% CI 90.5-110.3). Using boosted regression tree, we can increase the number of accurately booked cases from 148 to 219 (34.9% to 51.7%, p < 0.001). This study shows that using various machine learning approaches can improve the accuracy of RAS case length predictions, which will increase utilization of this limited resource. Further work is needed to operationalize these findings.