RESUMO
BACKGROUND: The association of low-density lipoprotein cholesterol (LDL-C) and lymphocyte counts with the development of deep vein thrombosis (DVT) has been demonstrated in many fields but remains lacking in open wedge high tibial osteotomy (OWHTO). This study aimed to assess the predictive value of LDL-C to lymphocyte count ratio (LLR) in screening for postoperative new-onset DVT. METHODS: Clinical data were retrospectively collected from patients who underwent OWHTO between June 2018 and May 2023. The limited restricted cubic spline (RCS) was conducted to evaluate the nonlinear relationship between LLR and the risk of postoperative new-onset DVT. The receiver operating characteristic (ROC) curves were plotted and the predictive value of biomarkers was assessed. After adjusting for intergroup confounders by propensity score matching, the univariate logistic regression was applied to assess the association between LLR and DVT. RESULTS: 1293 eligible patients were included. RCS analysis showed a linear positive correlation between LLR and the risk of DVT (P for overall = 0.008). We identified LLR had an area under the curve of 0.607, accuracy of 74.3%, sensitivity of 38.5%, and specificity of 80.7%, and LLR > 1.75 was independently associated with a 1.45-fold risk of DVT (95% CI: 1.01-2.08, P = 0.045). Furthermore, significant heterogeneities were observed in the subgroups of age, BMI, diabetes mellitus, hypertension, Kellgren-Lawrence grade, the American Society of Anesthesiologists (ASA) score, and intraoperative osteotomy correction size. CONCLUSION: LLR is a valuable biomarker for predicting postoperative new-onset DVT in patients with OWHTO, and routine screening is expected to yield positive benefits.
RESUMO
Background: As the worldwide population ages, the population receiving open wedge high tibial osteotomy (OWHTO) is growing, and surgical site infection (SSI) is a rare but fatal surgical complication. This study aimed to identify risk factors independently associated with SSI following OWHTO and develop a predictive nomogram. Methods: Clinical data of patients who received OWHTO and followed up for more than 12 months in our hospital were retrospectively reviewed. Multivariable logistic regression was performed to determine independent risk factors for SSI and to construct predictive nomograms. The study further illustrated the predictive performance of the model by using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results: A total of 1294 eligible patients were included in the study. Multivariate analysis revealed tobacco consumption (OR=3.44, p=0.010), osteotomy size ≥12 mm (OR=3.3, p=0.015), the use of allogeneic bone or artificial bone graft substitutes (allogeneic bone vs none, OR=4.08, p=0.037; artificial bone vs none, OR=5.16, p=0.047), Kellgren-Lawrence (K-L) grade IV (OR=2.5, p=0.046), systemic immune-inflammation index (SII) >423.62 (OR=6.2, p<0.001), high-sensitivity C-reactive protein (HCRP) >2.6 mg/L (OR=2.42, p=0.044), and a higher level of fasting blood glucose (FBG) (OR=1.32, p=0.022) were the independent predictors of SSI. The cutoff score of the model was 148, with a sensitivity of 76.0% and specificity of 81.0%. The concordance index (C-index) and Brier score of the nomogram were 0.856 and 0.017, and the corrected values after 1000 bootstrapping validations were 0.820 and 0.018, respectively. Furthermore, the ROC curve, calibration curve, and DCA exhibited excellent predictive accuracy and clinical applicability of the model. Conclusion: This study developed a dynamic nomogram based on seven predictors, which allowed surgeons to individualize risk stratification of patients and intervene promptly to reduce SSI rates.