Forlenza, Enrico M.

Background: Distal femoral osteotomy (DFO) may be performed via either a lateral opening-wedge (LOW) or medial closing-wedge (MCW) osteotomy with meaningful clinical improvements; however, the effects of each DFO technique on leg length have not been well characterized. Purpose: To (1) validate a radiographic measurement technique to predict leg length changes after DFO and (2) compare the change in leg length after DFO for the LOW and MCW techniques. Study Design: Cohort study; Level of evidence, 3. Methods: Preoperative and postoperative full-length standing radiographs obtained from patients who underwent DFO at 1 of 2 academic medical centers were evaluated. The region on the medial (for LOW) or lateral (for MCW) distal femur cortex that would be the “hinge point” during DFO was identified on the radiographs. The distance from the center of the femoral head to the hinge point ( A ), the distance from the hinge point to the center of the talus ( B ), and the resultant angle (α) were measured. The equation presented within the text was used to plot the predicted leg length changes corresponding to the change in alpha angle produced by DFO. Comparisons were made between (1) the predicted and measured leg length changes after DFO and (2) the measured leg length change after LOW versus MCW osteotomies. Results: For both LOW (n = 10) and MCW (n = 10) osteotomies, the predicted leg length change was equivalent to the true change measured on postoperative radiographs (LOW: P = .16; MCW: P = .85). There was a mean lengthening of 0.85 mm (range, 0.5-1.3 mm) for every 1° of mechanical axis correction with LOW DFO, compared to a mean shortening of 0.45 mm (range, 0.1-1.4 mm) per 1° of MCW correction. Conclusion: Preoperative radiographic imaging can be used to predict leg length change after DFO with high reliability. Surgeons can expect approximately 0.85 mm of lengthening per 1° of DFO correction when performing LOW osteotomies, compared to 0.45 mm of shortening per 1° of correction for MCW osteotomies. However, the most accurate prediction results from patient-specific calculations.

Purpose To assess the relation between tendon migration, as measured by radiostereometric analysis, and patient‐reported outcome measures (PROMs) after biceps tenodesis (BT); to determine the likelihood of achieving clinically significant outcomes (CSOs) after BT; and to identify factors that impact CSO achievement. Methods Patients undergoing arthroscopic suprapectoral or open subpectoral BT at a single, high‐volume academic medical center were prospectively enrolled. A tantalum bead sutured to the tenodesis construct was used as a radiopaque marker. Biceps tendon migration was measured on calibrated radiographs at 12 weeks postoperatively. PROMs (Constant‐Murley, Single Assessment Numeric Evaluation [SANE], and Patient‐Reported Outcomes Measurement Information System–Upper Extremity [PROMIS‐UE] scores) were collected preoperatively and at minimum 2‐year follow‐up. Results Of 115 patients enrolled, 94 (82%) were included (median age, 52 years; median body mass index, 31.4). At a mean follow‐up of 2.9 years, the median Constant‐Murley, SANE, and PROMIS‐UE scores were 33 (interquartile range [IQR], 26‐35), 90 (IQR, 80‐99), and 47 (IQR, 42‐58), respectively. Median tantalum bead migration was 6.5 mm (IQR, 1.8‐13.8 mm). There were significant correlations between migration and Constant‐Murley score ( r 2 = 0.222; β = –0.554 [95% confidence interval (CI), –1.027 to –0.081]; P = .022), SANE score ( r 2 = 0.238; β = –0.198 [95% CI, –0.337 to –0.058]; P = .006), and PROMIS‐UE score ( r 2 = 0.233; β = –0.406 [95% CI, –0.707 to –0.104]; P = .009). On univariable analysis, higher body mass index was associated with achievement of substantial clinical benefit (unadjusted odds ratio [OR], 1.078 [95% CI, 1.007 to 1.161]; P = .038). Greater bead migration was negatively associated with achievement of the minimal clinically important difference (unadjusted OR, 0.969 [95% CI, 0.943 to 0.993]; P = .014) and patient acceptable symptomatic state (unadjusted OR, 0.965 [95% CI, 0.937 to 0.989]; P = .008) on all 3 instruments. Conclusions A 1‐cm increase in post‐tenodesis biceps tendon migration was associated with a decrease in the Constant‐Murley, SANE, and PROMIS‐UE scores of 6, 2, and 4 points, respectively, at a mean of 2.9 years after surgery. Most patients achieved CSOs for these PROMs by latest follow‐up, and greater biceps tendon construct migration was negatively associated with the likelihood of CSO achievement. Level of Evidence Level IV, retrospective case series.

Objectives: A DFO can be performed via two techniques: a lateral opening wedge (LOW) osteotomy and a medial closing wedge (MCW) osteotomy. Small case series have looked at how leg length is affected by a lateral opening wedge DFO, however, there is a lack of research comparing these leg length changes to medial closing DFO. Additionally, no studies have presented a model for predicting the leg length changes that will occur following DFO of either technique. Given that limb length differences can lead to accelerated osteoarthritis, back pain, hip pain, and other issues, being able to predict which technique can help prevent this pathology without a secondary procedure would be an important finding. Finally, the medial closing wedge osteotomy can allow for immediate weight-bearing while the lateral opening wedge does not and typically has associated cost with bone grafting. Therefore, we designed this study to validate a tool designed to predict leg length changes after distal femoral osteotomy (DFO) and compare changes following medial closing wedge (MCW) and lateral opening wedge (LOW) techniques. Methods: A collaborative retrospective review was performed of patients from Rush and Mayo Clinic databases who received a DFO and had full-length standing radiographs both pre-and postoperatively. For each preoperative radiograph, the region on the medial (for LOW) or lateral (for MCW) distal femur cortex that would be the “hinge point” during DFO was identified. The distances from the center of the femoral head to the hinge point (“A”), from the hinge point to the center of the tibial plafond (“B”), and the resultant angle at the hinge point (“α”) were measured (Figure 1). Figure 2 demonstrates the equation used to plot a graph of the predicted leg length changes corresponding to the change in α angle produced by DFO. Final leg length was calculated on postoperative radiographs, and the difference between predicted and true leg length changes was compared using paired Wilcoxon signed rank exact tests. Results: 10 MCW and 10 LOW patients were included. For both LOW (n=10) and MCW (n=10) osteotomies, the predicted leg length change was equivalent to the true change measured on postoperative radiographs (LOW P=0.16; MCW P=0.85). LOW DFO’s had 5.10 ± 2.77 mm (range: 1.45-10.87 mm) of leg lengthening, compared to 2.61 ± 1.25 mm (range: 0.50-4.56 mm) of leg shortening (p<0.001) for MCW (Figure 3). On average, there was 0.85 mm of lengthening (range 0.5-1.3 mm) for every 1° of mechanical axis correction with LOW DFO, compared to 0.45 mm of shortening (range: 0.1-1.4 mm) per 1° of MCW correction. Conclusions: This study presents a tool to accurately and reliably predict the leg length changes seen after both medial closing and lateral opening wedge DFO’s. Knowing what leg length changes to expect with each DFO technique is a useful tool that surgeons can utilize during surgical planning. Preoperative radiographic imaging can be used to predict leg length change following DFO with high reliability and accuracy. Surgeons can expect approximately 0.85mm of lengthening per 1° of DFO correction when performing LOW, compared to 0.45mm of shortening per 1° correction for MCW osteotomies.

Background: Studies suggest that similar clinical results are achieved via arthroscopic and open biceps tenodesis (BT) techniques. Purpose: To quantify the postoperative migration of the BT construct between arthroscopic suprapectoral BT (ASPBT) and open subpectoral BT (OSPBT) techniques via interference screw (IS) or single-suture suture anchor (SSSA) fixation using radiostereometric analysis. Study Design: Cohort study; Level of evidence, 2. Methods: Distal migration of the biceps tendon after OSPBT with a polyetheretherketone IS, OSPBT with 1 SSSA, ASPBT with polyetheretherketone IS, and ASPBT with 2 SSSAs was measured prospectively. Patients with symptomatic biceps tendinopathy and preoperative patient-reported outcome measures (PROMs) including Constant-Murley subjective, Single Assessment Numeric Evaluation, or Patient-Reported Outcomes Measurement Information System–Upper Extremity scores were included. A tantalum bead was sutured on the proximal end of the long head of the biceps tendon before fixation of tendon tissue. Anteroposterior radiographs were performed immediately postoperatively, at 1 week, and at 3 months. Bead migration was measured, and preoperative PROMs were compared with those at latest follow-up. Results: Of 115 patients, 94 (82%) were available for final follow-up. IS fixation yielded the least tendon migration with no difference between the open and arthroscopic approaches (4.31 vs 5.04 mm; P = .70). Fixation with 1 suture anchor demonstrated significantly greater migration than that achieved with an IS at both 1 week (6.47 vs 0.1 mm, 6.47 vs 1.75 mm, P < .001;) and 3 months (14.76 vs 4.31 mm, 14.76 vs 5.04 mm, P < .001) postoperatively. Two-suture anchor fixation yielded significantly greater migration than IS fixation at 1 week (7.02 vs 0.1 mm, P < .001; 7.02 vs 1.75 mm, P = .003) but not 3 months postoperatively (8.06 vs 4.31 mm, P = .10; 8.06 vs 5.04 mm, P = .07). Four patients with suture anchor fixation (3 patients in the OSPBT 1 SSSA group, 9.4%, and 1 patient in the ASPBT 2 SSSAs group, 3.8%) developed a Popeye deformity, whereas no Popeye deformities occurred in the IS groups. Mean 3-month bead migration in patients with and without a Popeye deformity was 60.8 and 11.2 mm, respectively ( P < .0001). PROMs did not differ among groups at final follow-up. Conclusion: Interference screw fixation yielded the least tendon migration whether achieved arthroscopically or open. The available data indicated that fixation with 1 SSSA but not 2 SSSAs resulted in significantly greater migration than that achieved with an IS. Despite variations in tendon migration, PROMs were similar among all groups. When SSSAs are used, tendon migration may be minimized by using ≥2 anchors.

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.

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.

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.