Modelo Predictivo de la No Unión de Tibia

Resumen

Objetivos Estimar un modelo predictivo para la no-unión en pacientes que presentan fractura de tibia.

Materiales y Métodos Estudio de cohorte retrospectivo, en pacientes con fractura de tibia operadas entre 2012 y 2018, con un mínimo de 12 meses de seguimiento, excluyendo amputaciones traumáticas. Realizamos un modelo de regresión logística con 13 variables descritas en la literatura. Se descartaron las variables estadísticamente no significativas y las que no causaban efecto de confusión. Se evaluó la bondad de ajuste mediante el test de Hosmer-Lemeshow y la discriminación del modelo con la curva ROC.

Resultados Se incluyeron 411 fracturas de tibia, las variables estadísticamente significativas fueron: exposición ósea OR = 2,57(IC:1,15–5,75, p = 0,022), diabetes OR = 3,29(IC:1,37–7,91, p = 0,008) y uso de tutor externo OR = 1,77(IC:0,81–3,85), el que tuvo efecto de confusión. La bondad de ajuste demostró que los datos se ajustan adecuadamente al modelo (p = 0,35). La curva ROC demuestra un 70,91% de poder discriminatorio. Al evaluar aisladamente las fracturas expuestas, no hubo asociación estadísticamente significativa con ninguna variable.

Discusión Al evaluar el modelo, obtuvimos una asociación estadísticamente significativa entre: no unión, exposición ósea, diabetes y uso de tutor externo, información concordante con la literatura. Al estudiar el subgrupo de fracturas expuestas, las demás variables son estadísticamente no significativas. Eso refleja que la exposición ósea es la variable que confiere mayor riesgo. El seguimiento adecuado de esos pacientes es fundamental dado este alto riesgo de evolucionar con no-unión.

Conclusión En nuestra serie, la exposición ósea es el factor de riesgo más importante para presentar no unión de tibia.

Abstract

Objectives Estimate a predictive model for non-union in patients presenting with a tibial fracture.

Materials and Methods Retrospective cohort study in patients with tibia fractures operated between 2012 and 2018, with a minimum follow-up of 12 months, excluding traumatic amputations. We performed a multivariate logistic regression model with 13 variables described in the literature. The variables that were statistically non-significant and those variables that do not cause confusion, were discarded. Goodness of fit was evaluated using the Hosmer-Lemeshow test and the discrimination of the model with the ROC curve.

Results 411 tibial fractures were included, the statistically significant variables were: bone exposure OR = 2.57(CI:1.15–5.75, p = 0.022), diabetes OR = 3.29(CI:1.37–7.91, p = 0.008) and use of external fixation OR = 1.77(CI:0.81–3.85), being included in the model because of its confounding effect. Goodness of fit demonstrates that the data fit the model adequately(p = 0.35). The ROC curve demonstrates 70.91% discriminatory power. When evaluating the exposed fractures in isolation, there was no statistically significant association with any variable.

Discussion When evaluating the model, we obtained a statistically significant association between non-union, bone exposure, diabetes and use of external fixation, being consistent with the literature. When studying the subset of exposed fractures, the other variables are statistically non-significant. This reflects that bone exposure is the variable that confers the greatest risk. Proper follow-up of these patients is essential given this high risk of evolving with non-union.

Conclusion In our series, bone exposure is the most important risk factor for presenting tibial non-union

Palabras Clave

fractura de tibia - modelo predictivo - no unión de tibia

Keywords

predictive model - tibial fracture - tibial non union

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