Citations

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.

Purpose To compare the performance of the Patient‐Reported Outcomes Measurement Information System (PROMIS) physical function (PF), pain interference (PIF), and depression computer adaptive tests (CAT) relative to legacy instruments in patients undergoing meniscal surgery. Methods Patients scheduled to undergo meniscal surgery completed legacy knee function PROMs (International Knee Documentation Committee [IKDC], Knee Injury and Osteoarthritis Outcome Score [KOOS] subscores), Marx Activity Rating Scale (MARS), Veterans‐Rand 12 (VR12), Short Form 12 (SF12), and the Brief Resilience Scale (BRS) alongside PROMIS PF, PIF, and Depression preoperatively. Spearman rank correlations were calculated, and score distributions were examined for floor and ceiling effects. Results 152 patients (46.6 ± 14.9 years, 67.1% male) completed PROMs for appropriate inclusion. PROMIS PF yielded high‐moderate to high correlations with the IKDC and KOOS subscales ( r = 0.61 to 0.73), demonstrating similar performance to the IKDC. PROMIS PIF demonstrated moderately high‐moderate to high correlations with the IKDC, KOOS subscales, VR‐12 Physical Component Score (PCS), and SF12 PCS ( r = 0.62 to 0.71), performing comparably to KOOS Pain ( r = 0.55 to 0.92). PROMIS Depression demonstrated moderate to high‐moderate correlations with the mental health legacies ( r = 0.46 to 0.66). Significant ceiling effects were observed for MARS (n = 29, 18.8%), and significant floor effects were exhibited by PROMIS Depression (n = 38, 25%) and MARS (n = 27, 17.6%). Conclusion The PROMIS PF, PIF CAT, and Depression instruments exhibit comparable performance profiles relative to legacy knee PROMs. PROMIS PF and PIF demonstrated no floor and ceiling effects, whereas PROMIS Depression exhibited a significant relative floor effect. PROMIS PF and PIF may be appropriately used to establish functional baselines preoperatively. Level of Evidence IV, diagnostic case series.

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.

Background There is increased emphasis on properly allocating healthcare resources to optimize value within orthopedic surgery. Establishing time to maximal medical improvement (MMI) can inform clinical decision-making and practice guidelines. Purpose We sought (1) to evaluate the time to MMI as predicted by commonly used patient-reported outcome measures (PROMs) for evaluation of shoulder stabilization and (2) to evaluate typical time to return to sports and employment following surgery. Methods A systematic review of the Medline database was conducted to identify outcome studies reporting sequential follow-up at multiple time points, up to a minimum of 2 years after shoulder stabilization surgery. The included studies examined the outcomes of arthroscopic or open surgical techniques on anterior instability. Clinically significant improvements were evaluated utilizing the minimal clinically important difference specific to each PROM. Secondary outcomes included range of motion, return to sport/work, and recurrent instability. Results Ten studies comprising 590 surgically managed cases of anterior shoulder instability were included (78% arthroscopic, 22% open). Clinically significant improvements in PROMs were achieved up to 1 year post-operatively for Rowe, Western Ontario Instability Index (WOSI), American Shoulder and Elbow Surgeons (ASES), and Simple Shoulder Test (SST) scores. For the three most utilized tools (Rowe, WOSI, ASES), the majority of improvement occurred in the first 6 post-operative months. Clinically significant improvements in Constant Score and Oxford Shoulder Instability Score (OSIS) were achieved up to 6 months and 2 years after surgery, respectively. No clinically significant improvements were achieved on the Disabilities of the Arm, Shoulder, and Hand (DASH) tool. Conclusion Maximal medical improvement as determined by commonly utilized PROMs occurs by 1 year after operative management of anterior shoulder instability. The DASH tool does not appear to demonstrate a reliable time frame for clinically significant outcome improvement.

Purpose (1) To determine the area of posterior cruciate ligament (PCL) insertion sites on the lateral wall of the medial femoral condyle (LWMFC) that demonstrates the least amount of length change through full range of motion (ROM) and (2) to identify a range of flexion that would be favorable for graft tensioning for single‐bundle (SB) and double‐bundle (DB) PCL reconstruction. Methods Six fresh‐frozen cadaveric knees were obtained. Three‐dimensional computed tomography point‐cloud models were obtained from 0° to 135°. A point grid was placed on the LWMFC and the tibial PCL facet. Intra‐articular length was calculated for each point on the femur to the tibia at all flexion angles and grouped to represent areas for bone tunnels of SB and DB PCLR. Normalized length changes were evaluated. Results Femoral tunnel location and angle of graft fixation were significant contributors to mean, minimum, and maximum normalized length of the PCL (all p < .001). Tibial tunnel location was not significant in any case (all p < .22). A femoral tunnel in the location of the posteromedial bundle of the PCL resulted in the least length change at all tibial positions (maximum change 13%). Fixation of the anterolateral bundle in extension or at 30° flexion resulted in significant overconstraint of the PCL graft. The femoral tunnel location for a SB PCLR resulted in significant laxity at lower ranges of flexion. Conclusion PCL length was significantly dependent on femoral tunnel position and angle of fixation, whereas tibial tunnel position did not significantly contribute to observed differences. All PCL grafts demonstrated anisometry, with the anterolateral bundle being more anisometric than the posteromedial bundle. For DB PCLR, the posteromedial bundle demonstrated the highest degree of isometry throughout ROM, although no area of the LWMFC was truly isometric. The anterolateral bundle should be fixed at 90° to avoid overconstraint, and SB PCLR demonstrated significant laxity at lower ranges of flexion. Clinical Relevance Surgeons can apply the results of this investigation to surgical planning in PCLR to optimize isometry, which may ultimately reduce graft strain and the risk of graft failure. Additionally, DB PCLR demonstrated superiority compared with SB PCLR regarding graft isometry, as significant laxity was encountered at lower ranges of flexion in SB PCLRs. Fixation of the ALB at 90° flexion should be performed to avoid overconstraint in knee extension.

Purpose The purpose of this study is to define the impact of preoperative opioid use on postoperative opioid use, patient‐reported outcomes, and revision rates in a cohort of patients receiving arthroscopic shoulder surgery. Methods Patients who underwent shoulder arthroscopy were identified from an institutional database. Inclusion criteria were completion of preoperative and postoperative patient‐reported outcome measures (PROMs) at 1‐year follow‐up and completion of a questionnaire on use of opioids and number of pills per day. Outcomes assessed included postoperative PROM scores, postoperative opioid use, persistent pain, and achievement of the patient acceptable symptomatic state. A matched cohort analysis was performed to evaluate the impact of opioid use on achievement of postoperative outcomes, whereas a multivariate regression was performed to determine additional risk factors. Receiver operating characteristic curves were used to establish threshold values in oral morphine equivalents (OMEs) that predicted each outcome. Results A total of 184 (16.3%) patients were included in the opioid use (OU) group and 1,058 in the no opioid use (NOU) group. The OU and NOU groups showed statistically significant differences in both preoperative and postoperative scores across all PROMs ( P < .001). Multivariate logistic regression identified preoperative opioid use as a significant predictor of reduced achievement of the patient acceptable symptomatic state (odds ratio [OR], 0.69, 95% confidence interval [CI], 0.29‐0.83, P = .008), increased likelihood of endorsing persistent pain (OR, 1.73, 95% CI, 1.17‐2.56, P = .006), and increased opioid use at 1 year (OR, 21.3, 95% CI, 12.2‐37.2, P < .001). Consuming a high dosage during the perioperative period increased risk of revision surgery (OR, 8.59, 95% CI, 2.12‐34.78, P < .003). Results were confirmed by matched cohort analysis. Receiver operating characteristic analysis found that total OME >1430 mg/d in the perioperative period (area under the curve, 0.76) and perioperative daily OME >32.5 predicted postoperative opioid consumption (area under the curve, 0.79). Conclusions Patients with a history of preoperative opioid use can achieve significant improvements in patient‐reported outcomes after arthroscopic shoulder surgery. However, preoperative opioid use negatively impacts patients’ level of satisfaction and is a significant predictor of pain and continued opioid usage. Level of evidence Level III, retrospective cohort study