Effect of CURES Legislation on Narcotic Prescriptions After Soft-tissue Hand Surgery.

California's Controlled Substance Utilization Review and Evaluation System (CURES) was mandated in 2018 to monitor and limit opiate prescriptions. This study evaluated the effects of this legislation on postoperative opioid prescriptions of patients undergoing soft tissue hand surgery. Patients receiving carpal tunnel release, trigger finger release, and ganglion excisions 18 months prior to and 18 months after CURES were selected. The primary outcome was milligram morphine equivalent (MME) prescribed at the surgical encounter and at first postoperative visit. There were 758 patients in the pre-CURES cohort and 701 patients in the post-CURES cohort. In the pre-CURES cohort, there was 116.9 ± 123.8 MME prescribed post op and 10.2 ± 70.8 at first follow-up, whereas post-CURES had 58.8 ± 68.4 MME and 1.1 ± 14.1 for post-op and first follow-up respectively. Findings of this study indicate state regulations may play a role in reducing narcotic consumption following soft tissue hand surgery. (Journal of Surgical Orthopaedic Advances 33(2):122-124, 2024).

Random forest identifies predictors of discharge destination following total shoulder arthroplasty.

Background: Machine learning algorithms are finding increasing use in prediction of surgical outcomes in orthopedics. Random forest is one of such algorithms popular for its relative ease of application and high predictability. In the process of sample classification, algorithms also generate a list of variables most crucial in the sorting process. Total shoulder arthroplasty (TSA) is a common orthopedic procedure after which most patients are discharged home. The authors hypothesized that random forest algorithm would be able to determine most important variables in prediction of nonhome discharge. Methods: Authors filtered the National Surgical Quality iImprovement Program database for patients undergoing elective TSA (Current Procedural Terminology 23472) between 2008 and 2018. Applied exclusion criteria included avascular necrosis, trauma, rheumatoid arthritis, and other inflammatory arthropathies to only include surgeries performed for primary osteoarthritis. Using Python and the scikit-learn package, various machine learning algorithms including random forest were trained based on the sample patients to predict patients who had nonhome discharge (to facility, nursing home, etc.). List of applied variables were then organized in order of feature importance. The algorithms were evaluated based on area under the curve of the receiver operating characteristic, accuracy, recall, and the F-1 score. Results: Application of inclusion and exclusion criteria yielded 18,883 patients undergoing elective TSA, of whom 1813 patients had nonhome discharge. Random forest outperformed other machine learning algorithms and logistic regression based on American Society of Anesthesiologists (ASA) classification. Random forest ranked age, sex, ASA classification, and functional status as the most important variables with feature importance of 0.340, 0.130, 0.126, and 0.120, respectively. Average age of patients going to facility was 76 years, while average age of patients going home was 68 years. 78.1% of patients going to facility were women, while 52.7% of patients going home were. Among patients with nonhome discharge, 80.3% had ASA scores of 3 or 4, while patients going home had 54% of patients with ASA scores 3 or 4. 10.5% of patients going to facility were considered of partially/totally dependent functional status, whereas 1.3% of patients going home were considered partially or totally dependent (P value < .05 for all). Conclusion: Of various algorithms, random forest best predicted discharge destination following TSA. When using random forest to predict nonhome discharge after TSA, age, gender, ASA scores, and functional status were the most important variables. Two patient groups (home discharge, nonhome discharge) were significantly different when it came to age, gender distribution, ASA scores, and functional status.

The Reoperation, Readmission, and Complication Rates at 30 Days Following Lumbar Decompression for Cauda Equina Syndrome.

Background and objective Cauda equina syndrome (CES) is considered a surgical emergency, and its primary treatment involves decompression of the nerve roots, typically in the form of discectomy or laminectomy. The primary aim of this study was to determine the complication, reoperation, and readmission rates within 30 days of surgical treatment of CES secondary to disc herniation by using the PearlDiver database (PearlDiver Technologies, Colorado Springs, CO). The secondary aim was to assess preoperative risk factors for a higher likelihood of complication occurrence within 30 days of surgery for CES. Methods A total of 524 patients who had undergone lumbar discectomy or laminectomy for CES were identified. The outcome measures were 30-day reoperation rate for revision decompression or lumbar fusion, and 30-day readmissions related to surgery. The patient data collected included medical history and surgical data including the number of levels of discectomy and laminectomy. Results Based on our findings, intraoperative dural tears, valvular heart disease, and fluid and electrolyte abnormalities were significant risk factors for readmission to the hospital within 30 days following surgery for CES. The most common postoperative complications were as follows: visits to the emergency department (63 patients, 12%), surgical site infection (21 patients, 4%), urinary tract infection (14 patients, 3%), and postoperative anemia (11 patients, 2%). Conclusions In the 30-day period following lumbar decompression for cauda equina syndrome, our findings demonstrated an 8% reoperation rate and 17% readmission rate. Although CES is considered an indication for urgent surgery, gaining awareness about reoperation, readmission, and complication rates in the immediate postoperative period may help calibrate expectations and inform medical decision-making.

Risk factors for degenerative, symptomatic rotator cuff tears: a case-control study.

BACKGROUND: Despite the considerable public health burden of rotator cuff tears, there is no consensus on risk factors associated with symptomatic rotator cuff tears. In this study, a large data source was used to identify factors associated with symptomatic rotator cuff tears. We defined cases of rotator cuff tears as those verified by imaging or operative reports and controls as symptomatic shoulders without rotator cuff tears as verified by imaging or operative reports. METHODS: We performed a case-control study of patients with and without symptomatic rotator cuff tears by use of the Vanderbilt University Medical Center de-identified electronic medical record system, the Synthetic Derivative, with records on >2.5 million patients from 1998 to 2017. Cases and controls were confirmed by individual chart review and review of imaging and/or operative notes. A final set of 11 variables were analyzed as potential risk factors for cuff tears: age, sex, body mass index (BMI), race, smoking history, hypertension, depression/anxiety, dyslipidemia, carpal tunnel syndrome, overhead activity, and affected side. Multivariable logistic regression was used to estimate the association between predictor variables and the risk of having a rotator cuff tear. RESULTS: A total of 2738 patients were selected from the Synthetic Derivative, which included 1731 patients with rotator cuff tears and 1007 patients without rotator cuff tears. Compared with individuals without tears, those with rotator cuff tears were more likely to be older (odds ratio [OR], 2.44; 95% confidence interval [CI], 2.12-2.89), to have a higher BMI (OR, 1.45; 95% CI, 1.24-1.69), to be of male sex (OR, 1.56; 95% CI, 1.32-1.85), and to have carpal tunnel syndrome (OR, 1.41; 95% CI, 1.03-1.93). Patients with rotator cuff tears were less likely to have left shoulder symptoms (OR, 0.68; 95% CI, 0.57-0.82) and to have depression/anxiety (OR, 0.77; 95% CI, 0.62-0.95) compared with the control group, which had symptomatic shoulder pain without rotator cuff tears. CONCLUSIONS: In a large imaging and operative report-verified case-control study, we identified advancing age, male sex, higher BMI, and diagnosis of carpal tunnel syndrome as risk factors significantly associated with an increased risk of rotator cuff tears. Left shoulder symptoms and depression/anxiety were less likely to be associated with rotator cuff tears compared with symptomatic shoulders without rotator cuff tears. Contrary to some prior reports in the literature, smoking was not associated with rotator cuff tears.