PURPOSE: The purpose of this study was to develop a decision-making process to assess the conditions for predicting gait independence in patients with femoral neck fracture. METHODS: A total of 108 patients were divided into 2 groups on the basis of their walking abilities at discharge for an unrelated illness; an independent (n=55) and dependent group (n=53). Details regarding age, sex, length of hospital therapy, operative procedures, classification of fracture, past history of stroke and fracture were collected from medical records. Body mass index (BMI), knee extension power, maximum walking speed, functional reach test (FRT) and the mini-mental state examination (MMSE) were measured to evaluate motor ability and cognitive status at discharge. Student's t-test and the chi-squared test were used to test for statistical differences between the 2 groups. On multivariate analysis, classification and regression trees (CART) was used to determine the predictive value of those measures that differed significantly between the 2 groups. RESULTS: On bivariable analysis, significant differences were found in nearly all variables, except for BMI and length of hospital therapy. As a result of this analysis, the decision tree, which consists of knee extension power, FRT, MMSE and a past history of stroke, was created. CART analyses showed that when knee extension power was >0.34 kgf/kg, the MMSE score was >13.5; with no past history of stroke, the rate of independent walking at discharge was 93.8%. In contrast, when knee extension power was ≤0.33 kgf/kg, FRT was ≤25.5 cm, the MMSE score was ≤13.5, and the rate of dependent walking at discharge was 100%. CONCLUSIONS: Our findings indicate that the decision tree can be helpful in predicting gait independence in patients with femoral neck fracture.
PURPOSE: The purpose of this study was to develop a decision-making process to assess the conditions for predicting gait independence in patients with femoral neck fracture. METHODS: A total of 108 patients were divided into 2 groups on the basis of their walking abilities at discharge for an unrelated illness; an independent (n=55) and dependent group (n=53). Details regarding age, sex, length of hospital therapy, operative procedures, classification of fracture, past history of stroke and fracture were collected from medical records. Body mass index (BMI), knee extension power, maximum walking speed, functional reach test (FRT) and the mini-mental state examination (MMSE) were measured to evaluate motor ability and cognitive status at discharge. Student's t-test and the chi-squared test were used to test for statistical differences between the 2 groups. On multivariate analysis, classification and regression trees (CART) was used to determine the predictive value of those measures that differed significantly between the 2 groups. RESULTS: On bivariable analysis, significant differences were found in nearly all variables, except for BMI and length of hospital therapy. As a result of this analysis, the decision tree, which consists of knee extension power, FRT, MMSE and a past history of stroke, was created. CART analyses showed that when knee extension power was >0.34 kgf/kg, the MMSE score was >13.5; with no past history of stroke, the rate of independent walking at discharge was 93.8%. In contrast, when knee extension power was ≤0.33 kgf/kg, FRT was ≤25.5 cm, the MMSE score was ≤13.5, and the rate of dependent walking at discharge was 100%. CONCLUSIONS: Our findings indicate that the decision tree can be helpful in predicting gait independence in patients with femoral neck fracture.