Effect of COVID-19 Pandemic Restructuring on Surgical Volume and Outcomes of Non-COVID Patients Undergoing Surgery

Objectives COVID-19 has caused significant surgical delays as institutions minimize patient exposure to hospital settings and utilization of health care resources. We aimed to assess changes in surgical case mix and outcomes due to restructuring during the pandemic. Methods Patients undergoing surgery at a single tertiary care institution in the Deep South were identified using institutional ACS-NSQIP data. Primary outcome was case mix. Secondary outcomes were post-operative complications. Chi-square, ANOVA, logistic regression, and linear regression were used to compare the control (pre-COVID, Mar 2018-Mar 2020) and case (during COVID, Mar 2020-Mar 2021) groups. Results Overall, there were 6912 patients (control: 4,800 and case: 2112). Patients were 70% white, 29% black, 60% female, and 39% privately insured. Mean BMI was 30.2 (SD = 7.7) with mean age of 58.3 years (SD = 14.8). Most surgeries were with general surgery (48%), inpatient (68%), and elective (83%). On multivariable logistic regression, patients undergoing surgery during the pandemic were more likely to be male (OR: 1.14) and in SIRS (OR: 2.07) or sepsis (OR: 2.28) at the time of surgery. Patients were less likely to have dyspnea with moderate exertion (OR: .75) and were less dependent on others (partially dependent OR: .49 and totally dependent OR: .15). Surgeries were more likely to be outpatient (OR: 1.15) and with neurosurgery (OR: 1.19). On bivariate analysis, there were no differences in post-operative outcomes. Conclusion Surgeries during the COVID-19 pandemic were more often outpatient without differences in post-operative outcomes. Additional analysis is needed to determine the impact of duration of operative delay on surgical outcomes with restructuring focusing more on outpatient surgeries.

25 Operative Delay in Neck of Femur Fracture Management During the COVID 19 Pandemic Lockdown; Reasons and Impact

All NOF patients beyond 24-hour window over the COVID Lockdown period From April 2020 to July 2020 were identified and causes of delay to theatre were stratified. Manual Data collection over a time period of April 2020 to July 2020, when lockdown was introduced. All patients with NOF who had surgery within 24 hours were excluded. Cases not within 24 hours were included. A total of 70 patients were included in the study. Cause for Delay beyond 24 hours included No list space 30, Medically unfit 20, Anticoagulation 18, COVID related 2 needing ventilatory support. Causes of medical Delay include chest infection (not COVID related), High anaesthetic risk, Cardiac, Anaemia, Electrolyte imbalance. Covid19 accounted for 2 % of operative delays of NOF patients. Other causes include medical optimization, theatre efficiency and use of anticoagulation. However, our study proved there is a vital requirement for robust protocols and pathways in the management of NOF especially during a global pandemic. This is important for the reduction of overall Morbidity and Mortality. Local Strategies should be put in place to address these with the importance of a MDT approach including the Anaesthetic and Orthogeriatic team.

Predictive Model of Gait Recovery at One Month after Hip Fracture from a National Cohort of 25,607 Patients: The Hip Fracture Prognosis (HF-Prognosis) Tool

The aim of this study was to develop a predictive model of gait recovery after hip fracture. Data was obtained from a sample of 25,607 patients included in the Spanish National Hip Fracture Registry from 2017 to 2019. The primary outcome was recovery of the baseline level of ambulatory capacity. A logistic regression model was developed using 40% of the sample and the model was validated in the remaining 60% of the sample. The predictors introduced in the model were: age, prefracture gait independence, cognitive impairment, anesthetic risk, fracture type, operative delay, early postoperative mobilization, weight bearing, presence of pressure ulcers and destination at discharge. Five groups of patients or clusters were identified by their predicted probability of recovery, including the most common features of each. A probability threshold of 0.706 in the training set led to an accuracy of the model of 0.64 in the validation set. We present an acceptably accurate predictive model of gait recovery after hip fracture based on the patients’ individual characteristics. This model could aid clinicians to better target programs and interventions in this population.