Comorbidity and osteoporotic fracture: approach through predictive modeling techniques using the OSTEOMED registry

Autor: Coco Martín, María Begoña, Leal Vega, Luis, Blázquez Cabrera, José Antonio, Navarro, Amalia, Moro, María Jesús, Arranz García, Francisca, Amérigo, María José, Sosa Henríquez, Manuel, Vázquez, María Ángeles, Montoya, María José, Díaz Curiel, Manuel, Olmos, José Manuel, Pérez Castrillon, José Luis, Filgueira Rubio, José, Sánchez Molini, Pilar, Aguado Caballero, José María, Armengol Sucarrats, Dolors, Calero Bernal, María Luz, Escalante Yanguas, Begoña de, Hernández de Sosa, Nerea, Hernández, José Luis, Jareño Chaumel, Julia, Miranda García, María José, Giner García, Mercedes, Miranda Díaz, Cristina, Cotos Canca, Rafael, Cobeta García, Juan Carlos, Rodero Hernández, Francisco Javier, Tirado Miranda, Raimundo
Přispěvatelé: Universidad de Cantabria
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Aging Clin Exp Res . 2022 Sep;34(9):1997-2004
UVaDOC. Repositorio Documental de la Universidad de Valladolid
instname
DOI: 10.1007/s40520-022-02129-5
Popis: Producción Científica
Purpose: To examine the response to anti-osteoporotic treatment, considered as incident fragility fractures after a minimum follow-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 registry were analyzed using logistic regression and an artificial network model. Results: Logistic regression showed that the probability of reducing fractures for each anti-osteoporotic treatment considered was independent of sex, age, and the number of comorbidities, increasing significantly only in males taking vitamin D (OR = 7.918), patients without comorbidities taking vitamin D (OR = 4.197) and patients with ≥ 3 comorbidities taking calcium (OR = 9.412). Logistic regression correctly classified 96% of patients (Hosmer–Lemeshow = 0.492) compared with the 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-osteoporotic treatment improved the risk of incident fragility fractures after 1 year, but this appeared to increase when patients had been treated with risedronate, strontium or teriparatide. The two models used classified patients similarly, but predicted differently in terms of the probability of improvement, with logistic regression being the better fit.
Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCLE
Databáze: OpenAIRE
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