Yi Li1, Ming Chen1, Houchen Lv1, Pengbin Yin2, Licheng Zhang3, Peifu Tang4. 1. Department of Orthopedics, Chinese PLA General Hospital, Beijing 100853, China; National Clinical Research Center for Orthopedics, Sports Medicine & Rehabilitation, Beijing 100853, China. 2. Department of Orthopedics, Chinese PLA General Hospital, Beijing 100853, China; National Clinical Research Center for Orthopedics, Sports Medicine & Rehabilitation, Beijing 100853, China. Electronic address: yinpengbin@gmail.com. 3. Department of Orthopedics, Chinese PLA General Hospital, Beijing 100853, China; National Clinical Research Center for Orthopedics, Sports Medicine & Rehabilitation, Beijing 100853, China. Electronic address: zhanglcheng218@126.com. 4. Department of Orthopedics, Chinese PLA General Hospital, Beijing 100853, China; National Clinical Research Center for Orthopedics, Sports Medicine & Rehabilitation, Beijing 100853, China. Electronic address: pftang301@163.com.
Abstract
INTRODUCTION: Although several risk stratification models have been developed to predict hip fracture mortality, efforts are still being placed in this area. Our aim is to (1) construct a risk prediction model for long-term mortality after hip fracture utilizing the RSF method and (2) to evaluate the changing effects over time of individual pre- and post-treatment variables on predicting mortality. METHODS: 1330 hip fracture surgical patients were included. Forty-five admission and in-hospital variables were analyzed as potential predictors of all-cause mortality. A random survival forest (RSF) algorithm was applied in predictors identification. Cox regression models were then constructed. Sensitivity analyses and internal validation were performed to assess the performance of each model. C statistics were calculated and model calibrations were further assessed. RESULTS: Our machine-learning RSF algorithm achieved a c statistic of 0.83 for 30-day prediction and 0.75 for 1-year mortality. Additionally, a COX model was also constructed by using the variables selected by RSF, c statistics were shown as 0.75 and 0.72 when applying in 2-year and 4-year mortality prediction. The presence of post-operative complications remained as the strongest risk factor for both short- and long-term mortality. Variables including fracture location, high serum creatinine, age, hypertension, anemia, ASA, hypoproteinemia, abnormal BUN, and RDW became more important as the length of follow-up increased. CONCLUSION: The RSF machine-learning algorithm represents a novel approach to identify important risk factors and a risk stratification models for patients undergoing hip fracture surgery is built through this approach to identify those at high risk of long-term mortality.
INTRODUCTION: Although several risk stratification models have been developed to predict hip fracture mortality, efforts are still being placed in this area. Our aim is to (1) construct a risk prediction model for long-term mortality after hip fracture utilizing the RSF method and (2) to evaluate the changing effects over time of individual pre- and post-treatment variables on predicting mortality. METHODS: 1330 hip fracture surgical patients were included. Forty-five admission and in-hospital variables were analyzed as potential predictors of all-cause mortality. A random survival forest (RSF) algorithm was applied in predictors identification. Cox regression models were then constructed. Sensitivity analyses and internal validation were performed to assess the performance of each model. C statistics were calculated and model calibrations were further assessed. RESULTS: Our machine-learning RSF algorithm achieved a c statistic of 0.83 for 30-day prediction and 0.75 for 1-year mortality. Additionally, a COX model was also constructed by using the variables selected by RSF, c statistics were shown as 0.75 and 0.72 when applying in 2-year and 4-year mortality prediction. The presence of post-operative complications remained as the strongest risk factor for both short- and long-term mortality. Variables including fracture location, high serum creatinine, age, hypertension, anemia, ASA, hypoproteinemia, abnormal BUN, and RDW became more important as the length of follow-up increased. CONCLUSION: The RSF machine-learning algorithm represents a novel approach to identify important risk factors and a risk stratification models for patients undergoing hip fracture surgery is built through this approach to identify those at high risk of long-term mortality.
Authors: Arastoo Nia; Domenik Popp; Georg Thalmann; Fabian Greiner; Natasa Jeremic; Robert Rus; Stefan Hajdu; Harald K Widhalm Journal: Diagnostics (Basel) Date: 2021-03-11