Preoperative CT parameters to predict tibiofibular syndesmosis injury associated with ankle fracture: a propensity score-matched analysis.

BACKGROUND: Untreated ankle fractures with concomitant tibiofibular syndesmosis injury often lead to postoperative pain and early traumatic arthritis. CT has advantages in the preoperative diagnosis of combined ankle injuries. However, a few studies have investigated the best preoperative CT parameters to predict tibiofibular syndesmosis injuries associated with ankle fractures. This study aimed to identify and evaluate the optimal preoperative CT parameters for predicting tibiofibular syndesmosis injuries associated with ankle fractures. METHODS: We retrospectively analyzed 129 patients who underwent preoperative CT of an ankle fracture treated between January 2016 and April 2022 at a tertiary A hospital. All patients underwent open reduction and internal fixation and intraoperative stability testing. Based on the Cotton test, the patients were divided into the stable group (n = 83, 64.3%) and unstable group (n = 46, 35.7%). After 1:1 propensity score matching, the general conditions, anterior tibiofibular distance (TFD), posterior TFD, maximum TFD, tibiofibular syndesmosis area, sagittal fracture angle, Angle-A, and Angle-B were compared between the stable and unstable groups. RESULTS: The propensity score-matched cohort comprised 82 patients. There were no significant differences between the stable and unstable groups in sex, age, affected side, operation interval, injury mechanism, Lauge-Hansen classification, sagittal fracture angle, and Angle-A (all P > 0.05). Compared with the stable group, the unstable group had a significantly greater aTFD, pTFD, maxTFD, and area (all P < 0.05). PTFD, maxTFD, and area were positively correlated with joint instability. Angle-B was smaller in the unstable group (57.13°) than the stable group (65.56°). ROC analysis showed that Area (AUC 0.711) and maxTFD (AUC 0.707) had the highest diagnostic efficacy. CONCLUSION: MaxTFD and Area were the best predictive parameters; a larger Area was associated with a higher likelihood of instability of the tibiofibular syndesmosis after ankle fracture fixation.

Estimation of conditional and marginal odds ratios using the prognostic score.

Introduced by Hansen in 2008, the prognostic score (PGS) has been presented as 'the prognostic analogue of the propensity score' (PPS). PPS-based methods are intended to estimate marginal effects. Most previous studies evaluated the performance of existing PGS-based methods (adjustment, stratification and matching using the PGS) in situations in which the theoretical conditional and marginal effects are equal (i.e., collapsible situations). To support the use of PGS framework as an alternative to the PPS framework, applied researchers must have reliable information about the type of treatment effect estimated by each method. We propose four new PGS-based methods, each developed to estimate a specific type of treatment effect. We evaluated the ability of existing and new PGS-based methods to estimate the conditional treatment effect (CTE), the (marginal) average treatment effect on the whole population (ATE), and the (marginal) average treatment effect on the treated population (ATT), when the odds ratio (a non-collapsible estimator) is the measure of interest. The performance of PGS-based methods was assessed by Monte Carlo simulations and compared with PPS-based methods and multivariate regression analysis. Existing PGS-based methods did not allow for estimating the ATE and showed unacceptable performance when the proportion of exposed subjects was large. When estimating marginal effects, PPS-based methods were too conservative, whereas the new PGS-based methods performed better with low prevalence of exposure, and had coverages closer to the nominal value. When estimating CTE, the new PGS-based methods performed as well as traditional multivariate regression. Copyright © 2016 John Wiley & Sons, Ltd.