Prediction model for poor short-term prognosis in patients with chronic subdural hematoma after burr hole drainage: a retrospective cohort study.

Chronic subdural hematoma (CSDH) is a common condition in neurosurgery. With an aging population, there is increasing attention on the prognosis of patients following surgical intervention. We developed a postoperative short-term prognostic prediction model using preoperative clinical indicators, aiming to assist in perioperative medical decision-making and management. The dataset was randomly divided into training and validation cohorts. An mRS score greater than 2 one month after discharge was considered indicative of a poor prognosis. In the training cohort, the least absolute shrinkage and selection operator (LASSO) regression analysis was used for multivariate analysis to identify independent risk factors and construct a prediction nomogram for poor prognosis one month after discharge. The performance of the nomogram was assessed using the Receiver Operating Characteristic (ROC) curve and calibration curve. A Decision Curve Analysis (DCA) was also conducted to determine the net benefit threshold of the prediction model. Among the 505 participants, 18.8% (95/505) had a poor prognosis one month after discharge. The baseline characteristics did not significantly differ between the training cohort and the validation cohort. LASSO regression analysis in the training cohort reduced the predictors to four potential factors. Further multivariate logistic analyses in the training cohort identified four independent predictors: age, admission Glasgow Coma Scale (GCS) score, hemiparesis, and hemoglobin count. These predictors were incorporated into the nomogram prediction model. Internal validation using ROC analysis, calibration curves, and other methods demonstrated a strong correlation between the observed and predicted likelihood of poor prognosis one month after discharge. The visualized nomogram prediction model we developed for short-term postoperative prognosis of chronic subdural hematoma after burr hole drainage aids in predicting short-term outcomes and guiding clinical treatment decisions. Further external validation is needed in the future to confirm its effectiveness.

Exploring the causal association between genetically determined circulating metabolome and hemorrhagic stroke.

Background: Hemorrhagic stroke (HS), a leading cause of death and disability worldwide, has not been clarified in terms of the underlying biomolecular mechanisms of its development. Circulating metabolites have been closely associated with HS in recent years. Therefore, we explored the causal association between circulating metabolomes and HS using Mendelian randomization (MR) analysis and identified the molecular mechanisms of effects. Methods: We assessed the causal relationship between circulating serum metabolites (CSMs) and HS using a bidirectional two-sample MR method supplemented with five ways: weighted median, MR Egger, simple mode, weighted mode, and MR-PRESSO. The Cochran Q-test, MR-Egger intercept test, and MR-PRESSO served for the sensitivity analyses. The Steiger test and reverse MR were used to estimate reverse causality. Metabolic pathway analyses were performed using MetaboAnalyst 5.0, and genetic effects were assessed by linkage disequilibrium score regression. Significant metabolites were further synthesized using meta-analysis, and we used multivariate MR to correct for common confounders. Results: We finally recognized four metabolites, biliverdin (OR 0.62, 95% CI 0.40-0.96, PMVMR = 0.030), linoleate (18. 2n6) (OR 0.20, 95% CI 0.08-0.54, PMVMR = 0.001),1-eicosadienoylglycerophosphocholine* (OR 2.21, 95% CI 1.02-4.76, PMVMR = 0.044),7-alpha-hydroxy-3 -oxo-4-cholestenoate (7-Hoca) (OR 0.27, 95% CI 0.09-0.77, PMVMR = 0.015) with significant causal relation to HS. Conclusion: We demonstrated significant causal associations between circulating serum metabolites and hemorrhagic stroke. Monitoring, diagnosis, and treatment of hemorrhagic stroke by serum metabolites might be a valuable approach.

The MAI score: A novel score to early predict shunt-dependent hydrocephalus in patients with aneurysmal subarachnoid hemorrhage after surgery.

OBJECTIVE: As a chronic complication of aneurysmal subarachnoid hemorrhage(aSAH), Shunt dependent hydrocephalus (SDHC) often leads to severe neurological deficits. At present, risk factors of SDHC after aSAH are being refined. So this study aims to investigate independent risk factors and develop a novel score to identify early the patients who require a permanent shunt. METHOD: Five hundred twenty-four patients treated in the first affiliated hospital of Harbin medical university from March 2019 to March 2021 were analyzed. We collected clinical and radiographic data of patients within 72 h after the ictus. The relevant factors were firstly analyzed by univariate analysis, and the significant factors (p < 0.05) were included in the multivariate logistic regression analysis to obtain the independent risk factors with statistical differences. The MAI score was established based on the contribution of different independent risk factors to the outcome. the new score was validated in another cohort (97 patients with aSAH from April and June 2021). RESULT: We enrolled 524 aneurysm patients and 41(7.82%) patients who underwent ventriculoperitoneal shunt (VPS) after aneurysm treatment. Based on univariate and multivariate analysis, Acute Hydrocephalus (OR 6.498,:95% confidence interval (CI) 1.98-21.33, p = 0.002), Intraventricular hemorrhage (OR 3.55,:95%CI 1.189-10.599, p = 0.023) and Modified Fisher score ≥ 3 (OR 5.846, 95%CI 2.649-12.900, p = 0.001) were independent risk factors. The novel score was assigned according to the contribution of different independent risk factors to the results. The MAI score: Modified Fisher grade ≥ 3 (1 point), Acute Hydrocephalus (1 point), Intraventricular hemorrhage (1 point). In the receiver operating characteristic curve analysis, the area under the curve (AUC) for the MAI score is 0.773 (p < 0.0001, 95%CI 0.686-0.861). Patients scoring 2-3 MAI points showed a 10-fold higher risk for shunt dependency than patients scoring 0-1 MAI points (p < 0.001). We performed internal validation of the MAI scoring system. The scoring system reliably predicted SDHC after aSAH. The AUC of the internal validation was 0.950 (p ＝ 0.002, 95%CI 0.863-1.000). CONCLUSION: We develop a novel score based on univariate and multivariate analysis. The effectiveness of the MAI score has been confirmed in this study, which can more accurately predict SDHC after aASH and can be widely used in clinical practice. Prospective studies are needed for validation in the future.