RT info:eu-repo/semantics/article
T1 Comorbidity and osteoporotic fracture: approach through predictive modeling techniques using the OSTEOMED registry
A1 Coco Martín, María Begoña
A1 Leal Vega, Luis
A1 Blázquez Cabrera, José Antonio
A1 Navarro, Amalia
A1 Moro, María Jesús
A1 Arranz García, Francisca
A1 Amérigo, María José
A1 Sosa Henríquez, Manuel
A1 Vázquez, María Ángeles
A1 Montoya, María José
A1 Díaz Curiel, Manuel
A1 Olmos, José Manuel
A1 Pérez Castrillon, José Luis
A1 Filgueira Rubio, José
A1 Sánchez Molini, Pilar
A1 Aguado Caballero, José María
A1 Armengol Sucarrats, Dolors
A1 Calero Bernal, María Luz
A1 de Escalante Yanguas, Begoña
A1 Hernández de Sosa, Nerea
A1 Hernández, José Luis
A1 Jareño Chaumel, Julia
A1 Miranda García, María José
A1 Giner García, Mercedes
A1 Miranda Díaz, Cristina
A1 Cotos Canca, Rafael
A1 Cobeta García, Juan Carlos
A1 Rodero Hernández, Francisco Javier
A1 Tirado Miranda, Raimundo
K1 Osteoporosis · Osteoporotic fractures · Anti-osteoporotic treatment · Comorbidities · Logistic regression · Artificial neural network
AB Purpose To examine the response to anti-osteoporotic treatment, considered as incident fragility fractures after a minimumfollow-up of 1 year, according to sex, age, and number of comorbidities of the patients.Methods For this retrospective observational study, data from baseline and follow-up visits on the number of comorbidities,prescribed anti-osteoporotic treatment and vertebral, humerus or hip fractures in 993 patients from the OSTEOMED registrywere analyzed using logistic regression and an artificial network model.Results Logistic regression showed that the probability of reducing fractures for each anti-osteoporotic treatment consideredwas independent of sex, age, and the number of comorbidities, increasing significantly only in males taking vitaminD (OR = 7.918), patients without comorbidities taking vitamin D (OR = 4.197) and patients with ≥ 3 comorbidities takingcalcium (OR = 9.412). Logistic regression correctly classified 96% of patients (Hosmer–Lemeshow = 0.492) compared withthe artificial neural network model, which correctly classified 95% of patients (AUC = 0.6).Conclusion In general, sex, age and the number of comorbidities did not influence the likelihood that a given anti-osteoporotictreatment improved the risk of incident fragility fractures after 1 year, but this appeared to increase when patients had beentreated with risedronate, strontium or teriparatide. The two models used classified patients similarly, but predicted differentlyin terms of the probability of improvement, with logistic regression being the better fit.
PB Springer EEUU
YR 2022
FD 2022
LK https://uvadoc.uva.es/handle/10324/65419
UL https://uvadoc.uva.es/handle/10324/65419
LA spa
NO Aging Clin Exp Res. 2022 Sep;34(9):1997-2004
NO Producción Científica
DS UVaDOC
RD 27-nov-2024