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Acknowledgment of comorbidities could improve fracture prediction algorithms
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An increased awareness of comorbidities and their impact upon fracture risk may help improve upon fracture-prediction algorithms, according to a study published in Bone.
The researchers used the multinational Global Longitudinal Study of Osteoporosis in Women (GLOW) to assess comorbidities’ effect on fracture risk, with baseline questionnaires and occasional follow-up interviews to determine any incidence of clinical fracture and comorbidities. A comorbidity index was compiled, and the impact of this index’s addition to FRAX risk factors was assessed.
According to the study abstract, 3,224 (6.1%) of 52,960 women in the GLOW study developed fractures during a 2-year period. Parkinson’s disease and multiple sclerosis indicate a higher risk of fracture than other comorbidities, the researchers noted. With the exception of hypertension, celiac disease, cancer and high cholesterol, all recorded comorbidities in the GLOW study were significantly associated with fracture risk. Heart disease, osteoarthritis and chronic obstructive pulmonary disease were major predictors of fracture.
Using a fracture prediction algorithm and adding a comorbidity index resulted in an improvement of the fracture prediction model, the authors concluded in the abstract.
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