Junfei Guo1,2, Jun Di1, Xian Gao3, Junpu Zha1, Xiuli Wang4, Zhiqian Wang5, Qingxian Wang5, Zhiyong Hou1,2,6, Yingze Zhang1,2,6,7. 1. Department of Orthopaedic Surgery, Third Hospital of Hebei Medical University, Shijiazhuang, China. 2. Orthopaedic Research Institute of Hebei Province, Shijiazhuang, China. 3. Medical Department, First Hospital of Hebei Medical University, Shijiazhuang, China. 4. Department of Anesthesiology, Third Hospital of Hebei Medical University, Shijiazhuang, China. 5. Department of Geriatric Orthopedics, Third Hospital of Hebei Medical University, Shijiazhuang, China. 6. NHC Key Laboratory of Intelligent Orthopaedic Equipment (The Third Hospital of Hebei Medical University), Shijiazhuang, China. 7. Chinese Academy of Engineering, Beijing, China.
Abstract
INTRODUCTION: Preoperative risk assessment can predict adverse outcomes following hip fracture surgery, helping with decision-making and management strategies. Several risk adjustment models based on coded comorbidities such as Charlson Comorbidity Index (CCI), modified Elixhauser's Comorbidity Measure (mECM), and modified frailty index (mFI-5) are currently prevalent for orthopedic patients, but there is no consensus regarding which is optimal. The primary purpose was to identify the risk factors of CCI, mECM, and mFI-5, as well as patient characteristics for predicting (1) 1-month, 3-month, 1-year, and 2-year mortality, (2) perioperative complications, and (3) extended length of stay (LOS) following hip fractured surgery. The secondary aim was to compare the best-performing comorbidity index combined with characteristics identified in terms of their discriminative ability for adverse outcomes. METHODS: We retrospectively reviewed 3,379 consecutive patients presenting with intertrochanteric fractures at our Level I trauma center from 2013 to 2018. After eliminated by exclusion criteria, 2,241 patients undergoing hip fracture surgery by PFNA, with age ≥65 years, were included. Three main multivariate logistic regression models were constructed. Cox proportional hazards models were used to calculate hazard ratios for mortality. A base model included age, BMI, surgical delay, anesthesia type, hemoglobin record at admission, and American Society of Anesthesiologists grade (ASA) also was constructed and assessed. RESULTS: Base model + mECM outperformed other models for the occurrence of major complications including severe complications, cardiac complications, and pulmonary complications [the area under the receiver operating characteristic curve (AUC), 0.647; 95% CI, 0.616-0.677; AUC, 0.637; 95% CI, 0.610-0.664; AUC, 0.679; 95% CI, 0.642-0.715, respectively], while base model + CCI provided better prediction of minor complications of neurological complications and hematological complications (AUC, 0.659; 95% CI, 0.609, 0.709; AUC, 0.658; 95% CI, 0.635, 0.680). In addition, BMI, surgical delay, anesthesia type, and ASA were found highly relevant to extended LOS. Age-group (with a 10-year interval) was indicated to be mostly associated with all-cause mortality with fully adjusted hazard ratio of 1.35 and 95% CI range 1.20-1.51. CONCLUSIONS: In comparison with mFI-5 and CCI, mECM so far may be the best comorbidity index combined with the base model for predicting major complications following hip fracture. The base model already achieved good discrimination for all-cause mortality and extended LOS, further addition of risk adjustment indices led to only 1% increase in the amount of variation explained.
INTRODUCTION: Preoperative risk assessment can predict adverse outcomes following hip fracture surgery, helping with decision-making and management strategies. Several risk adjustment models based on coded comorbidities such as Charlson Comorbidity Index (CCI), modified Elixhauser's Comorbidity Measure (mECM), and modified frailty index (mFI-5) are currently prevalent for orthopedic patients, but there is no consensus regarding which is optimal. The primary purpose was to identify the risk factors of CCI, mECM, and mFI-5, as well as patient characteristics for predicting (1) 1-month, 3-month, 1-year, and 2-year mortality, (2) perioperative complications, and (3) extended length of stay (LOS) following hip fractured surgery. The secondary aim was to compare the best-performing comorbidity index combined with characteristics identified in terms of their discriminative ability for adverse outcomes. METHODS: We retrospectively reviewed 3,379 consecutive patients presenting with intertrochanteric fractures at our Level I trauma center from 2013 to 2018. After eliminated by exclusion criteria, 2,241 patients undergoing hip fracture surgery by PFNA, with age ≥65 years, were included. Three main multivariate logistic regression models were constructed. Cox proportional hazards models were used to calculate hazard ratios for mortality. A base model included age, BMI, surgical delay, anesthesia type, hemoglobin record at admission, and American Society of Anesthesiologists grade (ASA) also was constructed and assessed. RESULTS: Base model + mECM outperformed other models for the occurrence of major complications including severe complications, cardiac complications, and pulmonary complications [the area under the receiver operating characteristic curve (AUC), 0.647; 95% CI, 0.616-0.677; AUC, 0.637; 95% CI, 0.610-0.664; AUC, 0.679; 95% CI, 0.642-0.715, respectively], while base model + CCI provided better prediction of minor complications of neurological complications and hematological complications (AUC, 0.659; 95% CI, 0.609, 0.709; AUC, 0.658; 95% CI, 0.635, 0.680). In addition, BMI, surgical delay, anesthesia type, and ASA were found highly relevant to extended LOS. Age-group (with a 10-year interval) was indicated to be mostly associated with all-cause mortality with fully adjusted hazard ratio of 1.35 and 95% CI range 1.20-1.51. CONCLUSIONS: In comparison with mFI-5 and CCI, mECM so far may be the best comorbidity index combined with the base model for predicting major complications following hip fracture. The base model already achieved good discrimination for all-cause mortality and extended LOS, further addition of risk adjustment indices led to only 1% increase in the amount of variation explained.
Authors: Ronald Man Yeung Wong; Yao Zu; Wai Wang Chau; Chi Yin Tso; Wing Hong Liu; Raymond Wai Kit Ng; Simon Kwoon Ho Chow; Wing Hoi Cheung; Ning Tang; Kevin Ki Wai Ho Journal: Sci Rep Date: 2022-03-19 Impact factor: 4.379