Association of distal radial fracture with comorbidities: model development and validation.

BACKGROUND

To better assess the risk of distal radial fracture in the general population, we need models that take into account a wide range of risk factors other than osteoporosis. The objective was to develop and validate a model for association of patients' characteristics with distal radial fracture that effectively incorporates multifactorial aspects and includes comorbidities.

METHOD: We analyzed data from a large Longitudinal Health Insurance Database between 2000 and 2013. The outcome of the study was the occurrence of distal radial fracture and the predictors were demographic and comorbidity data. Two machine learning models were developed and validated for patients ≥50 (N = 2745) and <50 (N = 1587) years of age.

RESULTS

For patients aged ≥50 years, selected characteristics included sex, age, urbanization level, osteoarthritis, carpal tunnel syndrome, obesity, hyperlipidemia, trigger finger, hypertension, hypothyroidism, diabetes, hyperthyroidism, and rheumatoid arthritis. For patients <50 years old, selected characteristics included age, sex, diabetes mellitus, urbanization level, carpal tunnel syndrome, hyperlipidemia, osteoarthritis, obesity, and hypertension. Accuracy, sensitivity, specificity, area under the curve, and likelihood ratio were 0.77, 0.83, 0.72, 0.77, and 2.92 for age ≥50 years and 0.73, 0.79, 0.67, 0.73, and 2.41 for age <50 years.

CONCLUSION

The study models can serve as reliable screening tools to assess the risk of distal radial fracture in the general population before bone mineral density testing. In addition, they can be integrated into decision support systems to help healthcare providers identify high-risk patients for additional evaluation and education, ultimately improving the quality of care.