AIMS:Kidney transplantation (KT) improves survival in end-stage renal disease (ESRD); however, the post-transplant fracture risk remains uncertain. This study aimed to compare the fracture incidence between kidney transplant recipients (KTRs) and waitlisted dialysis patients and to examine the association between fracture events and all-cause mortality in each group. MATERIALS AND METHODS:This study included a single-center cohort of 504 KTRs and 423 waitlisted dialysis patients treated at a tertiary hospital in South Korea from 1998 to 2019. Fractures were identified through radiological report review, and mortality data were retrieved from medical records. Multivariate Cox regression models were used to evaluate mortality predictors, including fracture events, as covariates. RESULTS:The fracture incidence did not differ significantly between KTRs (9.1%) and dialysis patients (12.5%). Moreover, fractures were not significantly associated with increased mortality in either group (KTRs: hazard ratio (HR), 2.03; 95% confidence interval (CI), 0.62 - 6.67; p = 0.24; dialysis: HR, 0.97; 95% CI, 0.12 - 8.08; p = 0.98). Age was the strongest mortality predictor. Among KTRs, tacrolimus use was associated with lower mortality compared with cyclosporine (HR, 0.18; 95% CI, 0.05 - 0.66; p = 0.009). CONCLUSION:KTRs have a fracture risk comparable to that of dialysis patients, and fractures were not significantly associated with mortality. The survival benefits of KT are driven by factors other than fracture-related complications. Ongoing, proactive bone health management remains essential in KTRs because transplantation does not eliminate the elevated fracture risk.
OBJECTIVE:This study aimed to investigate the potential of the platelet-to-lymphocyte ratio (PLR) as a predictive biomarker for augmented renal clearance (ARC) in patients undergoing intracerebral hemorrhage (ICH) surgery. MATERIALS AND METHODS:A single-center retrospective cohort study was conducted on 174 patients who underwent ICH surgery. Pearson or Spearman correlation analyses were used to examine the relationship between PLR and baseline characteristics. Restricted cubic spline curves were employed to assess the association between PLR and ARC occurrence. Logistic regression models were applied to identify risk factors for ARC, while the predictive performance of PLR was evaluated using receiver operating characteristic curves. All statistical analyses were performed using IBM SPSS Statistics v.24.0 and R v.4.0.2. RESULTS:Among the 174 patients, 99 (56.9%) developed ARC. The optimal cutoff value for PLR was determined to be 201.31. The AUC of PLR for predicting ARC was 0.69 (95% CI: 0.61 - 0.77), with a sensitivity of 65.3% and specificity of 73.7%. Subgroup analyses revealed that this association was particularly pronounced in patients aged over 55 years, those with a body mass index ≤ 25 kg/m2, an APACHE II score of 10 - 20, and a hemorrhage volume ≤ 30 mL. Integrating PLR into a composite model (APACHE II, SOFA, and GCS) increased the AUC from 0.70 (95% CI: 0.62 - 0.78) to 0.73 (95% CI: 0.65 - 0.81). The addition of PLR to the composite scoring system yielded a category net reclassification improvement of 0.12 (95% CI: 0.00 - 0.25). CONCLUSION:Preoperative low PLR is significantly associated with the early occurrence of ARC and serves as an independent predictor for ARC.
