Background: The incidence of fractures averages 20 per 1000 hemodialysis patient years at risk. This study sought to design and evaluate the utility of a simple prediction rule for fractures in dialysis patients using only standard demographical and biochemical information.
Methods: 1777 prevalent hemodialysis patients of the Austrian dialysis and transplant database who had an evaluation of fractures in 2004 and 2005 were included into analysis. Validation of the prediction rule model by a test set was performed using three different resampling techniques, the split sample approach, a 100-fold cross validation and a 100x bootstrap. Calibration of the model was performed visually by comparing the observed to the expected number of outcomes in each category and by calculating the Hosmer and Lemeshow goodness-of-fit statistic.
Results: A multivariable logistic regression model built on clinical expertise yielded a discrimination of c=0.73 (AUC of ROC). Further reduction of the covariables to age and sex as the only predictive variables did not result in loss of discrimination (c=0.71) and at the same time provided adequate calibration (p=0.69). The probability of fractures (PF) occurring within the next year of hemodialysis can be calculated from our prediction model as, PF=e(-6.25+0.4*age(in decades)-0.93(if male))/1+e(-6.25+0.4*age(in decades)-0.93(if male)), e.g., a 70-year-old male would have a fracture probability of 0.01 or 1%, a female 3%. The optimism derived by all resampling techniques was between 1% and 2% suggesting adequate generalizability of the prediction rule.
Conclusion: A sufficient and parsimonious prediction rule for fractures in hemodialysis patients consists of the independent variables age and sex.