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Background: The coronavirus-2019 (COVID-19) pandemic highlighted the unfortunate reality that many hospitals have insufficient intensive care unit (ICU) capacity to meet massive, unanticipated increases in demand. To drastically increase ICU capacity, NewYork-Presbyterian/Weill Cornell Medical Center modified its existing operating rooms and post-anaesthesia care units during the initial expansion phase to accommodate the surge of critically ill patients. Methods: This retrospective chart review examined patient care in non-standard Expansion ICUs as compared to standard ICUs. We compared clinical data between the two settings to determine whether the expeditious development and deployment of critical care resources during an evolving medical crisis could provide appropriate care. Results: Sixty-six patients were admitted to Expansion ICUs from March 1st to April 30th, 2020 and 343 were admitted to standard ICUs. Most patients were male (70%), White (30%), 45–64 years old (35%), non-smokers (73%), had hypertension (58%), and were hospitalized for a median of 40 days. For patients that died, there was no difference in treatment management, but the Expansion cohort had a higher median ICU length of stay (q = 0.037) and ventilatory length (q = 0.015). The cohorts had similar rates of discharge to home, but the Expansion ICU cohort had higher rates of discharge to a rehabilitation facility and overall lower mortality. Conclusions: We found no significantly worse outcomes for the Expansion ICU cohort compared to the standard ICU cohort at our institution during the COVID-19 pandemic, which demonstrates the feasibility of providing safe and effective care for patients in an Expansion ICU. [ABSTRACT FROM AUTHOR]

The article discusses research which investigated trends in the use of epidural steroid injections to treat back pain before spine surgery. The study assessed the association of preoperative epidural steroid injections with postoperative chronic opioid in patients who underwent anterior cervical discectomy and fusion (ACDF) compared to those who underwent microdiscectomy. Mechanisms behind the association of epidural steroid injections with chronic postoperative opioid use are proposed.

Background: The development of hyponatremia after total joint arthroplasty (TJA) may lead to several adverse events and is associated with prolonged inpatient length of stay as well as increased hospital costs. The purpose of this study was to develop and internally validate machine learning algorithms for predicting hyponatremia after TJA.Methods: A consecutive cohort of 30,703 TJA patients from an institutional registry at a large, tertiary academic hospital were included. A total of 19 potential predictor variables were collected. Hyponatremia was defined as a serum sodium concentration of <135 mEq/L. Five machine learning algorithms were developed using a training set and internally validated using an independent testing set. Algorithm performance was evaluated through discrimination, calibration, decision-curve analysis, and Brier score.Results: The charts of 30,703 patients undergoing TJA were reviewed. Of those patients, 5,480 (17.8%) developed hyponatremia postoperatively. A combination of 6 variables were demonstrated to optimize algorithm prediction: preoperative serum sodium concentration, age, intraoperative blood loss, procedure time, body mass index (BMI), and American Society of Anesthesiologists (ASA) score. Threshold values that were associated with greater hyponatremia risk were a preoperative serum sodium concentration of ≤138 mEq/L, an age of ≥73 years, an ASA score of >2, intraoperative blood loss of >407 mL, a BMI of ≤26 kg/m2, and a procedure time of >111 minutes. The stochastic gradient boosting (SGB) algorithm demonstrated the best performance (c-statistic: 0.75, calibration intercept: -0.02, calibration slope: 1.02, and Brier score: 0.12). This algorithm was turned into a tool that can provide real-time predictions (https://orthoapps.shinyapps.io/Hyponatremia_TJA/).Conclusions: The SGB algorithm demonstrated the best performance for predicting hyponatremia after TJA. The most important factors for predicting hyponatremia were preoperative serum sodium concentration, age, intraoperative blood loss, procedure time, BMI, and ASA score. A real-time hyponatremia risk calculator was developed, but it is imperative to perform external validation of this model prior to using this calculator in clinical practice.Level Of Evidence: Prognostic Level IV. See Instructions for Authors for a complete description of levels of evidence. [ABSTRACT FROM AUTHOR]