A Nomogram for Predicting 30-day Mortality in Elderly Patients Undergoing Hemiarthroplasty for Femoral Neck Fractures

Abstract

Introduction: Femoral neck fractures in the elderly are increasingly common as a result of a growing geriatric population with 1-year mortality rates approaching 35%. While preoperative medical optimization and early time to surgery have reduced morbidity and mortality, patients with numerous medical comorbidities remain high risk for death in the perioperative period. Identifying those with greatest risk with a scoring system or nomogram may assist multidisciplinary teams in reducing mortality following hemiarthroplasty. Purpose: Identify predictors of 30-day mortality in elderly patients who underwent hemiarthroplasty for a femoral neck fracture to generate a predictive nomogram to determine the probability of post-operative mortality. Methods: Retrospective evaluation using data from the ACS-NSQIP database from 2005 to 2014 with CPT code 27125 for hip hemiarthroplasty. Multiple factors including demographics and comorbidities were compared in patients who experienced 30-day mortality and those who did not. T-test and chi-square tests were used to analyze data and a multivariate model was generated using logistic regression. Results: Advanced age (odds ratio (OR) 1.04), underweight BMI (OR 1.55), male sex (OR 1.80), reduced functional status (OR 2.04), heart failure within 30 days prior to surgery (OR 2.22), American Society of Anesthesiologists grade > 2 (OR > 2.50), disseminated cancer (OR 3.43) were all found to have statistically significant odds ratios for 30-day mortality following hemiarthroplasty. Conclusion: A tool based on easily identifiable risk factors, demographics, and comorbidities was developed that can help predict elderly patients who will experience mortality within 30 days of following hemiarthroplasty. In addition to identifying high risk patients, the nomogram can serve as a counseling tool for physicians to use with patients and their families to assist with better understanding of perioperative mortality risk.