Indications for Direct Laryngoscopic Examination of Vocal Cord Function Prior to Anterior Cervical Surgery

Recurrent laryngeal nerve palsy (RLNP) is among the most common complications in both thyroid surgeries and anterior approaches to the cervical spine, having both a diverse etiology and presentation. Most bilateral paresis, with subsequent devastating impact on patients, are due to failure to recognize unilateral recurrent laryngeal nerve paralysis and, although rare, are entirely preventable with appropriate history and screening. Recurrent laryngeal nerve palsy has been shown to present asymptomatically in as high as 32% of cases, which yields limitations on exclusively screening with physical examination. Based on the available literature, diagnosis of unilateral RLNP is the critical factor in preventing the occurrence of bilateral RLNP as the surgeon may elect to operate on the injured side to prevent bilateral paresis. Analysis of incidence rates shows postoperative development of unilateral RLNP is 13.1 (95% confidence interval [CI]: 6.1-28.1) and 13.90 (95% CI: 6.6-29.3) times more likely in anterior spine and thyroid surgery, respectively, in comparison with intubation. Currently, there is no consensus on when to order a preoperative laryngoscopic examination prior to anterior cervical spine surgery. The importance of patient history should be emphasized, as it is the basis for indications of preoperative laryngoscopy. Efforts to minimize postoperative complications must be made, especially when considering the rising rate of cervical fusion. This study presents a systematic review of the literature defining key causes of RLNP, with a probability-based protocol to indicate direct laryngoscopy prior to anterior cervical surgery as a screening tool in the prevention of bilateral RLNP

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