Inflammatory and Metabolic Biomarkers Associated with Chronic Subdural Hematoma Recurrence: A Single-Center...

Background: Chronic subdural hematoma (CSDH) recurs in 5-20% of patients despite surgical treatment, and readily accessible biomarkers associated with recurrence remain limited. This study evaluated perioperative hematological, biochemical, and metabolic biomarkers associated with CSDH recurrence, focusing on preoperative lactate and the lactate-to-albumin ratio (LAR). Methods: This retrospective single-center cohort study included 163 adults who underwent burr-hole evacuation for CSDH between January 2021 and December 2025. All patients underwent saline irrigation without placement of a postoperative subdural drain, in accordance with the institutional surgical protocol. Clinical, radiological, laboratory, and perioperative arterial blood gas data were analyzed. Recurrence was defined as symptomatic re-expansion requiring repeat surgery within six months. Receiver operating characteristic (ROC) analysis and multivariable logistic regression were used to assess discrimination and associations with recurrence. Results: Postoperative recurrence occurred in 22 patients (13.5%). Recurrence was associated with higher preoperative lactate, LAR, neutrophil-to-lymphocyte ratio, and systemic immune-inflammation index, as well as lower serum albumin. LAR showed moderate discrimination (AUC 0.758, 95% CI 0.652-0.864), while lactate had an AUC of 0.744. Increased LAR (OR 1.42 per 0.1-unit increase, 95% CI 1.15-1.76; p = 0.001), bilateral hematoma (OR 2.91, 95% CI 1.08-7.84; p = 0.035), and mixed-density hematoma (OR 2.64, 95% CI 1.01-6.89; p = 0.047) were independently associated with recurrence in the fitted model. The age × lactate interaction did not reach conventional statistical significance (p = 0.058) and was considered exploratory. Conclusions: Higher preoperative LAR was associated with CSDH recurrence and showed moderate discriminative performance in this cohort. These findings are exploratory and require independent validation before LAR or the combined model can be considered for clinical risk prediction.