Cohn, Matthew R.

Background: Approximately 90% of patients who undergo arthroscopic rotator cuff repair (RCR) are satisfied with their pain levels and function after surgery. However, a subset of patients experience continued symptoms that warrant revision surgery. Preoperative risk factors for RCR failure requiring revision surgery have not been clearly defined. Purpose: To (1) determine the rate of RCR failure requiring revision surgery and (2) identify risk factors for revision surgery, which will help surgeons to determine patients who are at the greatest risk for RCR failure. Study Design: Systematic review and meta-analysis; Level of evidence, 4. Methods: A systematic review and meta-analysis in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were performed. The initial search resulted in 3158 titles, and 533 full-text articles were assessed for eligibility. A total of 10 studies met the following inclusion criteria: (1) human clinical studies, (2) arthroscopic RCR, (3) original clinical research, and (4) evaluation of preoperative risk factors for revision. Results: After a full-text review, a total of 16 risk factors were recorded and analyzed across 10 studies. Corticosteroid injection was the most consistent risk factor for revision surgery, reaching statistical significance in 4 of 4 studies, followed by workers’ compensation status (2/3 studies). Patients with corticosteroid injections had a pooled increased risk of revision surgery by 47% (odds ratio, 1.44 [95% CI, 1.36-1.52]). Patients with workers’ compensation had a pooled increased risk of revision surgery by 133% (odds ratio, 2.33 [95% CI, 2.09-2.60]). Age, smoking status, diabetes, and obesity were found to be risk factors in half of the analyzed studies. Conclusion: Corticosteroid injections, regardless of the frequency of injections, and workers’ compensation status were found to be significant risk factors across the literature based on qualitative analysis and pooled analysis. Surgeons should determine ideal candidates for arthroscopic RCR by accounting for corticosteroid injection history, regardless of the frequency, and insurance status of the patient.

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.

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.

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