Atsushi Endo1, Heather J Baer2,3,4, Masashi Nagao5, Michael J Weaver6. 1. Harvard T.H. Chan School of Public Health, Boston, MA. 2. Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA. 3. Harvard Medical School, Boston, MA. 4. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA. 5. Department of Orthopedic Surgery, Juntendo University School of Medicine, Tokyo, Japan. 6. Harvard Orthopaedic Trauma Service, Brigham and Women's Hospital and Massachusetts General Hospital, Boston, MA.
Abstract
OBJECTIVES: Mortality in elderly patients after the surgical treatment of hip fractures remains high. Although individual clinical risk factors have been widely studied, there has been limited research on prediction models in this population. The purpose of this study was to develop a prediction model for in-hospital mortality after hip fracture surgery and to evaluate the performance of this model. METHODS: Using the National Inpatient Sample database from 2012 to 2013, we collected data on 535,475 patients older than 50 years who had hip fracture surgery. Patient characteristics, surgery-specific factors, and Elixhauser comorbidities were used as candidate variables. The patients were randomly divided into training and testing cohorts. The Lasso (least absolute shrinkage and selection operator) method was used to select predictor variables, and points were assigned to each variable based on its coefficient. RESULTS: We identified 8 essential predictors (age, timing of surgery, male sex, congestive heart failure, pulmonary circulation disease, renal failure, weight loss, and fluid and electrolyte disorders) for mortality, with a maximum prediction score of 20. The model's area under the curve was 0.74, and the Hosmer-Lemeshow test P value was 0.59 on the testing set. With the application of cutoff values (scores 0-5, 6-9, and 10-20), the observed in-hospital postoperative mortality was 0.6%, 2.5%, and 7.5%, respectively. CONCLUSIONS: We built a simple prediction model with 8 essential clinical factors that predict in-hospital mortality after hip fracture surgery. This model may assist in counseling patients and families and measuring hospital quality of care. LEVEL OF EVIDENCE: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.
OBJECTIVES: Mortality in elderly patients after the surgical treatment of hip fractures remains high. Although individual clinical risk factors have been widely studied, there has been limited research on prediction models in this population. The purpose of this study was to develop a prediction model for in-hospital mortality after hip fracture surgery and to evaluate the performance of this model. METHODS: Using the National Inpatient Sample database from 2012 to 2013, we collected data on 535,475 patients older than 50 years who had hip fracture surgery. Patient characteristics, surgery-specific factors, and Elixhauser comorbidities were used as candidate variables. The patients were randomly divided into training and testing cohorts. The Lasso (least absolute shrinkage and selection operator) method was used to select predictor variables, and points were assigned to each variable based on its coefficient. RESULTS: We identified 8 essential predictors (age, timing of surgery, male sex, congestive heart failure, pulmonary circulation disease, renal failure, weight loss, and fluid and electrolyte disorders) for mortality, with a maximum prediction score of 20. The model's area under the curve was 0.74, and the Hosmer-Lemeshow test P value was 0.59 on the testing set. With the application of cutoff values (scores 0-5, 6-9, and 10-20), the observed in-hospital postoperative mortality was 0.6%, 2.5%, and 7.5%, respectively. CONCLUSIONS: We built a simple prediction model with 8 essential clinical factors that predict in-hospital mortality after hip fracture surgery. This model may assist in counseling patients and families and measuring hospital quality of care. LEVEL OF EVIDENCE: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.
Authors: Mark Alan Fontana; Stephen Lyman; Gourab K Sarker; Douglas E Padgett; Catherine H MacLean Journal: Clin Orthop Relat Res Date: 2019-06 Impact factor: 4.176
Authors: Vikas Kumar; Christopher Roche; Steven Overman; Ryan Simovitch; Pierre-Henri Flurin; Thomas Wright; Joseph Zuckerman; Howard Routman; Ankur Teredesai Journal: Clin Orthop Relat Res Date: 2020-10 Impact factor: 4.755