RÉSUMÉ
Objective:To explore the risk factors associated with tibia fractures in children with congenital anterolateral bowing of the tibia (ALBT).Methods:A retrospective analysis was conducted on data from 87 children diagnosed with ALBT at the Children's Hospital of Hunan Province from January 2012 to January 2020. The collected data included age at initial diagnosis, affected limb side, whether there was a concomitant type I neurofibromatosis, whether there was a concomitant fibular pseudoarthrosis, whether there was concomitant ankle joint deformity, whether there was bone cystic change in the region of tibial bowing deformity, location of the apex of the bowing deformity, diameter of the tibial bowing deformity on the affected side, diameter on the healthy side in the same plane as the tibial bowing deformity, angle of lateral bending deformity of the tibia, angle of anterior bending deformity of the tibia, occurrence of tibia fracture, history of trauma before fracture, location of fracture, and age at the time of fracture. The follow-up endpoint was January 2023. Receiver operating characteristic (ROC) curves were used to determine the optimal cutoff values for the angles of lateral and anterior bending deformity of the tibia and the ratio of cross-sectional areas. The correlation between the above factors and tibial fractures in children was analyzed by single factor survival analysis, and the indicators with statistical significance were included in multivariate Cox proportional risk regression analysis to determine the risk factors for tibial fractures in children with ALBT.Results:Of the 87 children diagnosed with ALBT, the median age at initial diagnosis was 14.0 months (range, 1-93 months), with 42 males and 45 females, 44 left-sided and 43 right-sided cases. The median follow-up time for non-fracture cases was 42.0 months (range, 1-124 months). At the last follow-up, 43 children had experienced fractures, while 44 had not. The average time to fracture-free survival was 70.3 months, the median fracture-free survival time was 55.0 months, and the median survival time without fractures was 42.0 months. The ROC curve results indicated a cutoff value of 25.55° for the lateral bending angle of the tibia and 32.63° for the anterior bending angle of the tibia, with no statistically significant significance for the cross-sectional area ratio [AUC=0.54, 95% CI (0.42, 0.66), P=0.530]. Single-factor analysis of fracture-free survival suggested that there were statistically significant differences in the intergroup fracture-free survival rates of four factors: lateral bending angle of the tibia (χ 2=7.06, P=0.008), anterior bending angle of the tibia (χ 2=8.96, P=0.003), history of trauma (χ 2=18.26, P<0.001), and tibial bone cystic change (χ 2=4.30, P=0.038). The results of the multivariate Cox proportional hazards regression analysis showed that a lateral bending angle of the tibia≥25.55° ( HR=2.73, P=0.007), tibial bone cystic change ( HR=2.35, P=0.018), and history of trauma ( HR=2.65, P=0.004) were all positively correlated with fractures. Conclusion:The main risk factors for tibia fractures in children with ALBT include trauma, tibial bowing deformity with concomitant bone cystic change, and lateral bending angle of the tibia≥25.55°.
RÉSUMÉ
IntroductionDespite limitations on the use of cycle threshold (CT) values for individual patient care, population distributions of CT values may be useful indicators of local outbreaks. MethodsSpecimens from the greater El Paso area were processed in the Dascena COVID-19 Laboratory. Daily median CT value, daily transmission rate R(t), daily count of COVID-19 hospitalizations, daily change in percent positivity, and rolling averages of these features were plotted over time. Two-way scatterplots and linear regression evaluated possible associations between daily median CT and outbreak measures. Cross-correlation plots determined whether a time delay existed between changes in the daily median CT value and measure of community disease dynamics. ResultsDaily median CT was negatively correlated with the daily R(t), the daily COVID-19 hospitalization count (with a time delay), and the daily change in percent positivity among testing samples. Despite visual trends suggesting time delays in the plots for median CT and outbreak measures, a statistically significant delay was only detected between changes in median CT and COVID-19 hospitalization count. ConclusionsThis study adds to the literature by analyzing samples collected from an entire geographical area, and contextualizing the results with other research investigating population CT values.