Lavoie‐Gagne, Ophelie

Background: In professional sports, injuries resulting in loss of playing time have serious implications for both the athlete and the organization. Efforts to quantify injury probability utilizing machine learning have been met with renewed interest, and the development of effective models has the potential to supplement the decision-making process of team physicians. Purpose/Hypothesis: The purpose of this study was to (1) characterize the epidemiology of time-loss lower extremity muscle strains (LEMSs) in the National Basketball Association (NBA) from 1999 to 2019 and (2) determine the validity of a machine-learning model in predicting injury risk. It was hypothesized that time-loss LEMSs would be infrequent in this cohort and that a machine-learning model would outperform conventional methods in the prediction of injury risk. Study Design: Case-control study; Level of evidence, 3. Methods: Performance data and rates of the 4 major muscle strain injury types (hamstring, quadriceps, calf, and groin) were compiled from the 1999 to 2019 NBA seasons. Injuries included all publicly reported injuries that resulted in lost playing time. Models to predict the occurrence of a LEMS were generated using random forest, extreme gradient boosting (XGBoost), neural network, support vector machines, elastic net penalized logistic regression, and generalized logistic regression. Performance was compared utilizing discrimination, calibration, decision curve analysis, and the Brier score. Results: A total of 736 LEMSs resulting in lost playing time occurred among 2103 athletes. Important variables for predicting LEMS included previous number of lower extremity injuries; age; recent history of injuries to the ankle, hamstring, or groin; and recent history of concussion as well as 3-point attempt rate and free throw attempt rate. The XGBoost machine achieved the best performance based on discrimination assessed via internal validation (area under the receiver operating characteristic curve, 0.840), calibration, and decision curve analysis. Conclusion: Machine learning algorithms such as XGBoost outperformed logistic regression in the prediction of a LEMS that will result in lost time. Several variables increased the risk of LEMS, including a history of various lower extremity injuries, recent concussion, and total number of previous injuries.

Background: Clinically significant outcomes (CSOs) connect patient-reported outcome measures data to patient-perceived benefit. Although investigators have established threshold values for various CSOs, the timeline to achieve these outcomes after isolated biceps tenodesis (BT) has yet to be defined. Purpose: To define the time-dependent nature of minimal clinically important difference (MCID), substantial clinical benefit (SCB), and Patient Acceptable Symptom State (PASS) achievement after isolated BT. Study Design: Case series; Level of evidence, 4. Methods: The American Shoulder and Elbow Surgeons score (ASES), the Single Assessment Numeric Evaluation, and the Constant-Murley score (CMS) were administered preoperatively and at 6 and 12 months postoperatively to patients undergoing isolated BT between 2014 and 2018 at our institution. Cumulative probabilities for achieving MCID, SCB, and PASS were calculated using Kaplan-Meier survival analysis. Weibull parametric regression evaluated the hazard ratios (HRs) of achieving earlier MCID, SCB, and PASS. Results: Overall cohort (N = 190) achievement rates ranged between 77.8% and 83.2% for MCID, between 42.2% and 80.2% for SCB, and between 59.7% and 62.9% for PASS. Median achievement time was 5.3 to 6.1 months for MCID, 5.9 to 6.4 months for SCB, and 6.07 to 6.1 months for PASS. Multivariate Weibull parametric regression identified older age, male sex, higher body mass index, preoperative thyroid disease, smoking history, and higher preoperative CMS as predictors of delayed CSO achievement (HR, 1.01-6.41), whereas normal tendon on arthroscopy, defined as absence of tenosynovitis or tendon tear on arthroscopy, predicted earlier CSO achievement (HR, 0.19-0.46). Location of tenodesis and worker compensation status did not significantly predict the time to achieve CSOs on multivariate analysis. Conclusion: After isolated BT, patients can expect to attain CSO by 13 months postoperatively, with most patients achieving this between 5 and 8 months. Patients tend to take longer to achieve PASS than MCID and SCB.

Purpose To construct an algorithm to optimize clinical outcomes in subacromial impingement based on current, high‐level evidence. Methods A systematic review of all clinical trials on subacromial impingement published from 1999 to 2020 was performed. Demographic, clinical, range of motion (ROM), and patient‐reported outcome measure (PROM) data were collected. Interventions were compared via arm‐based Bayesian network meta‐analysis in a random‐effects model and treatments ranked via surface under the cumulative ranking curves with respect to 3 domains: pain, PROMs, and ROM. Results A total of 35 studies comprising 3,643 shoulders (42% female, age 50 ± 5 years) were included. Arthroscopic decompression with acromioplasty ranked much greater than arthroscopic decompression alone for pain relief and PROM improvement, but the difference in absolute PROMs was not statistically significant. Corticosteroid injection (CSI) alone demonstrated inferior outcomes across all 3 domains (pain, PROMs, and ROM) with low cumulative rankings. Physical therapy (PT) with CSI demonstrated moderate‐to‐excellent clinical improvement across all 3 domains whereas PT alone demonstrated excellent ROM and low‐moderate outcomes in pain and PROM domains. PT with nonsteroidal anti‐inflammatory drugs or alternative therapies ranked highly for PROM outcomes and moderate for pain and ROM domains. Finally, platelet‐rich plasma injections demonstrated moderate outcomes for pain, forward flexion, and abduction with very low‐ranking outcomes for PROMs and external rotation. Conclusions Arthroscopic decompression with acromioplasty and PT demonstrated superior outcomes whereas CSI demonstrated poor outcomes in all 3 domains (pain, PROMs, and ROM). For patients with significant symptoms, the authors recommend PT with CSI as a first‐line treatment, followed by acromioplasty and PT if conservative treatment fails. For patients with symptoms limited to 1 to 2 domains, the authors recommend a shared decision‐making approach focusing on treatment rankings within domains pertinent to individual patient symptomatology. Level of Evidence I, systematic review and network meta‐analysis of Level I studies.

Purpose To review the literature on postoperative corticosteroid injections (CSIs) following primary rotator cuff repair (RCR) to evaluate efficacy and adverse effects. Methods A systematic review of the MEDLINE, EMBASE, and Cochrane databases were performed to identify all studies published within the last 15 years, which reported on outcomes of postoperative CSIs following RCR. Studies including patients who received only preoperative CSIs and revision RCRs were excluded. Included studies were evaluated for study methodology, patient demographics, outcome measures, physical examination parameters, results of imaging studies, and adverse effects or clinical complications. Results Seven studies comprising 5,528 patients satisfied inclusion criteria. Among included patients, 54.8% were female and mean age range from 52.3 ± 13.0 to 62.7 ± 6.6 years. Only 1 included investigation was a Level I study. Overall, 4 of 5 studies reported significant improvements in pain and outcome scores (Constant score, American Shoulder and Elbow Surgeons score) compared with controls. Across all studies, the majority of these effects were statistically significant at 3 months postoperatively but not beyond this time point. Five of the 6 included investigations reported no increased rate of retears after postoperative CSIs. One study did find an increase in retear in patients receiving postoperative CSIs but was unable to determine whether these retears were present before the patient received the CSI. Another investigation reported an increased rate of infection only if the CSI was administered in the first postoperative month. Conclusions Postoperative CSIs may improve pain and function for up to 3 months following primary RCR but not at later follow‐up time points. CSIs should be administered only after the first postoperative month to minimize the potential risk for adverse events. Level of Evidence Systematic review of level I‐IV studies.

Purpose To develop a machine learning algorithm to predict total charges after ambulatory hip arthroscopy and create a risk‐adjusted payment model based on patient comorbidities. Methods A retrospective review of the New York State Ambulatory Surgery and Services database was performed to identify patients who underwent elective hip arthroscopy between 2015 and 2016. Features included in initial models consisted of patient characteristics, medical comorbidities, and procedure‐specific variables. Models were generated to predict total charges using 5 algorithms. Model performance was assessed by the root‐mean‐square error, root‐mean‐square logarithmic error, and coefficient of determination. Global variable importance and partial dependence curves were constructed to show the impact of each input feature on total charges. For performance benchmarking, the best candidate model was compared with a multivariate linear regression using the same input features. Results A total of 5,121 patients were included. The median cost after hip arthroscopy was $19,720 (interquartile range, $12,399‐$26,439). The gradient‐boosted ensemble model showed the best performance (root‐mean‐square error, $3,800 [95% confidence interval, $3,700‐$3,900]; logarithmic root‐mean‐square error, 0.249 [95% confidence interval, 0.24‐0.26]; R 2 = 0.73). Major cost drivers included total hours in facility less than 12 or more than 15, longer procedure time, performance of a labral repair, age younger than 30 years, Elixhauser Comorbidity Index (ECI) of 1 or greater, African American race, residence in extreme urban and rural areas, and higher household and neighborhood income. Conclusions The gradient‐boosted ensemble model effectively predicted total charges after hip arthroscopy. Few modifiable variables were identified other than anesthesia type; nonmodifiable drivers of total charges included duration of care less than 12 hours or more than 15 hours, operating room time more than 100 minutes, age younger than 30 years, performance of a labral repair, and ECI greater than 0. Stratification of patients based on the ECI highlighted the increased financial risk borne by physicians via flat reimbursement schedules given variable degrees of comorbidities. Level of Evidence Level III, retrospective cohort study.

Purpose To develop and internally validate a machine‐learning algorithm to reliably predict cost after anterior cruciate ligament reconstruction (ACLR). Methods A retrospective review of the New York State Ambulatory Surgery and Services database was performed to identify patients who underwent elective ACLR from 2015 to 2016. Features included in initial models consisted of patient characteristics (age, sex, insurance status, income, medical comorbidities as classified by the Clinical Classifications Software diagnosis code) as well as intraoperative variables (type of anesthesia and procedure‐specific factors). Models were generated to predict total charges using 4 algorithms: random forest, extreme gradient boost, elastic net penalized regression, and support vector machines with radial kernels. Training was performed with 10‐fold cross‐validation followed by internal validation via 0.632 bootstrapping. Model discriminative performance was assessed by area under the receiver operating characteristic curve, calibration, and the Brier score. Decision curve analysis was performed to demonstrate the net benefit of using the final model in practice. Results In total, 7,311 patients undergoing ambulatory ACLR were included. The random forest model demonstrated the best performance assessed via internal validation (area under the curve = 0.85), calibration, and the Brier score (0.208). Cost incurred was influenced by anesthesia type, operating room time, and number of chronic comorbidities. Decision curve analysis revealed a net benefit for use of the random forest model and the model was integrated into a web‐based open‐access application. Conclusions The random forest model predicted cost after ambulatory ACLR using a large, statewide database with good performance. The top variables found to predict increased charges were general anesthesia, operating room time, meniscal repair, self‐pay insurance, patient neighborhood characteristics, and number of chronic conditions. Level of Evidence III, retrospective cohort study.

Purpose The purposes of this study were to identify patient characteristics and risk factors for overnight admission following outpatient hip arthroscopy and to develop a machine learning algorithm that can effectively identify patients requiring admission following elective hip arthroscopy. Methods A retrospective review of a prospectively collected national surgical outcomes database was performed to identify patients who underwent elective outpatient hip arthroscopy from 2006 to 2018. Patients admitted overnight postoperatively were identified as those with length of stay of 1 or more days. Models were generated using random forest (RF), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), elastic net penalized logistic regression, and an additional model was produced as a weighted ensemble of the four final algorithms. Results Overall, 1,276 patients were included. The median age was 43 years, and 64.2% (819) were female. Of the included patients, 109 (8.5%) required an overnight stay following elective outpatient hip arthroscopy. The most important factors for inpatient admission were increasing operative time, general anesthesia, age extremes, male gender, greater body mass index (BMI), American Society of Anesthesiologists classification >1, and the following preoperative lab values outside of normal ranges: sodium, platelet count, hematocrit, and leukocyte count. The ensemble model achieved the best performance based on discrimination assessed via internal validation (area under the curve = .71), calibration, and decision curve analysis. The model was integrated into a Web‐based open‐access application able to provide both personalized predictions and explanations. Conclusion A machine learning algorithm developed based on preoperative features identified increasing operative time, age extremes, greater BMI, sodium, hematocrit, platelets, and leukocyte count as the most important variables associated with inpatient admission with fair validity.

AbstractPurposeRecovery following elective knee arthroscopy can be compromised by prolonged postoperative opioid utilization, yet an effective and validated risk calculator for this outcome remains elusive. The purpose of this study is to develop and validate a machine‐learning algorithm that can reliably and effectively predict prolonged opioid consumption in patients following elective knee arthroscopy.MethodsA retrospective review of an institutional outcome database was performed at a tertiary academic medical centre to identify adult patients who underwent knee arthroscopy between 2016 and 2018. Extended postoperative opioid consumption was defined as opioid consumption at least 150 days following surgery. Five machine‐learning algorithms were assessed for the ability to predict this outcome. Performances of the algorithms were assessed through discrimination, calibration, and decision curve analysis.ResultsOverall, of the 381 patients included, 60 (20.3%) demonstrated sustained postoperative opioid consumption. The factors determined for prediction of prolonged postoperative opioid prescriptions were reduced preoperative scores on the following patient‐reported outcomes: the IKDC, KOOS ADL, VR12 MCS, KOOS pain, and KOOS Sport and Activities. The ensemble model achieved the best performance based on discrimination (AUC = 0.74), calibration, and decision curve analysis. This model was integrated into a web‐based open‐access application able to provide both predictions and explanations.ConclusionFollowing appropriate external validation, the algorithm developed presently could augment timely identification of patients who are at risk of extended opioid use. Reduced scores on preoperative patient‐reported outcomes, symptom duration and perioperative oral morphine equivalents were identified as novel predictors of prolonged postoperative opioid use. The predictive model can be easily deployed in the clinical setting to identify at risk patients thus allowing providers to optimize modifiable risk factors and appropriately counsel patients preoperatively.Level of evidenceIII.

AbstractPurposeOvernight admission following anterior cruciate ligament reconstruction has implications on clinical outcomes as well as cost benefit, yet there are few validated risk calculators for reliable identification of appropriate candidates. The purpose of this study is to develop and validate a machine learning algorithm that can effectively identify patients requiring admission following elective anterior cruciate ligament (ACL) reconstruction. MethodsA retrospective review of a national surgical outcomes database was performed to identify patients who underwent elective ACL reconstruction from 2006 to 2018. Patients admitted overnight postoperatively were identified as those with length of stay of 1 or more days. Models were generated using random forest (RF), extreme gradient boosting (XGBoost), linear discriminant classifier (LDA), and adaptive boosting algorithms (AdaBoost), and an additional model was produced as a weighted ensemble of the four final algorithms.ResultsOverall, of the 4,709 patients included, 531 patients (11.3%) required at least one overnight stay following ACL reconstruction. The factors determined most important for identification of candidates for inpatient admission were operative time, anesthesia type, age, gender, and BMI. Smoking history, history of COPD, and history of coagulopathy were identified as less important variables. The following factors supported overnight admission: operative time > 200 min, age < 35.8 or > 53.5 years, male gender, BMI < 25 or > 31.2 kg/m2, positive smoking history, history of COPD and the presence of preoperative coagulopathy. The ensemble model achieved the best performance based on discrimination assessed via internal validation (AUC = 0.76), calibration, and decision curve analysis. The model was integrated into a web‐based open‐access application able to provide both predictions and explanations.ConclusionModifiable risk factors identified by the model such as increased BMI, operative time, anesthesia type, and comorbidities can help clinicians optimize preoperative status to prevent costs associated with unnecessary admissions. If externally validated in independent populations, this algorithm could use these inputs to guide preoperative screening and risk stratification to identify patients requiring overnight admission for observation following ACL reconstruction.Level of evidenceIV.