Clinical Neurology and Neurosurgery最新文献

Objective: 8-30 % of patients who present with aneurysmal subarachnoid hemorrhage (aSAH) have multiple intracranial aneurysms (MIA). Although prompt treatment to secure ruptured aneurysms (RA) is standard of care, there is no clear consensus regarding whether incidental unruptured aneurysms (UA) should be treated during the same procedural time as the RA. This study aims to examine the effect of treatment of UA during the same procedural time as treatment for the RA (concurrent treatment) versus delaying the treatment of an UA after discharging the patient (delayed treatment).

Methods: This is a retrospective review of the medical records of patients with the diagnosis of aSAH and MIA admitted to a single neurocritical care unit between 2013 and 2021, and who underwent treatment of at least 1 aneurysm during the index hospitalization. Data was divided in 2 groups: concurrent treatment (2 or more aneurysms treated), and delayed treatment (1 aneurysm treated). Clinical and radiological data including demographic characteristics, modified Fisher Scale (mFS), treatment modality (clipping or endovascular), ventriculoperitoneal shunt (VPS) rates, surgical/procedural complications, delayed cerebral ischemia (DCI), length of stay (LOS), modified Rankin Score (mRS) and type of insurance of the patients during the hospitalization were collected.

Results: We identified 109 patients with aSAH and MIA, who fit criteria. The median age was 58 (48-67) years old. 91 were female (83.5 %). A total of 287 aneurysms were found, 109 were ruptured. 64 patients underwent treatment of a single aneurysm (delayed treatment group), and 45 patients underwent treatment of 2 or more aneurysms (concurrent treatment group). mFS were similar in both groups (p=.56). Clipping (52.3 %) was the treatment modality most frequently used. No significant differences in surgical/procedural complications (p=.54) or VPS (p=.91) rates were seen among the 2 groups. No significant differences in delayed cerebral ischemia rates were seen (p=.85) There were no significant differences between the mRS at discharge (mRS 0-3 v 4-6 (p=.78)), LOS in the ICU (12 vs 13 (p=.58) days) and LOS in the hospital (16 vs 14.5 (p=.95) days) between the delayed and concurrent treatment groups respectively.

Conclusions: No difference in functional status at discharge was observed between delayed treatment versus concurrent treatment. Treatment of most or all surgically amenable aneurysms, at the time when the RA is being treated, does not increase the risk of DCI or poor outcomes at discharge.

Background: Accurate intraoperative tissue diagnostics could impact on decision making regarding the extent of resection (EOR) during brain tumor surgery. Stimulated Raman histology (SRH) is a label-free optical imaging method that uses different biochemical properties of tissue to generate a hematoxylin-eosin-like image and, in combination with an artificial intelligence-based image classifier, offers the opportunity to obtain rapid intraoperative tissue diagnoses.

Objective: The goal of this study was to report on our initial experience with SRH to evaluate its accuracy in comparison to final tissue diagnosis.

Materials & methods: We evaluated 70 consecutive adult cases with brain tumors. We compared results of the three different SRH classifier (diagnostic, molecular and tumor/non-tumor) to the respective final histopathological result. Similarly, we evaluated the isocitrate dehydrogenase (IDH) mutations in 18 patients using SRH. Lastly, we compared SRH results of samples taken from the tumor margins with early postoperative MRI. Prediction accuracy was evaluated by logistic regression and Receiver Operator Curve (ROC) analysis.

Results: We included 19 gliomas, 9 metastases, 22 meningiomas and 14 other tumor entities. Regarding accuracy of intraoperative SRH predictions, regression analysis showed an Area Under the Curve (AUC) of 0.77 (95 % C.I. 0.64-0.89, p = 0.0008), suggesting agreement of predictions with final diagnosis. For specific tumor entities, variable accuracies were observed: The highest accuracy was obtained for meningiomas followed by high-grade glioma. IDH mutations were predicted with an AUC of 0.93 (95 % C.I. 0.88-0.98; p < 0.0001). The SRH examination of tissue samples from tumor margins corresponded with postoperative MRI in 4 out of 5 cases.

Conclusion: Our initial experience with SRH shows that this novel imaging technique is a promising approach to obtain rapid intraoperative tissue diagnosis to guide surgical decision making based on histology and cell-density. With further refinement of AI-based automated image classification and a better integration into the surgical workflow, prediction accuracy and reliability could be improved.

Objectives: Stroke is the second leading cause of death and the third leading cause of disability globally, so monitoring inflammation and nutritional levels is essential for the secondary prevention. The impact of the Prognostic Nutritional Index (PNI) on tumor and perioperative outcomes has been demonstrated as an optimal combination of immune and nutritional indicators. However, the role of PNI on hospitalized outcomes in stroke patients remains unknown. This study aimed to investigate the clinical predictive value of PNI on hospitalized outcomes in stroke patients.

Materials and methods: In this study, stroke cases in the Medical Information Mart for Intensive Care IV database were analyzed using two-sample comparisons, proportional hazards model, subgroup analyses, and ROC analyses, and a nomogram was constructed.

Results: 1795 stroke cases were included in this study, including 1537 in the survival group and 258 in the death group. The results showed that PNI was higher in the survival group than in the death group (43.98±0.21 vs. 36.09±0.49, P=0.001). The optimal regression equation obtained after screening variables using COX stepwise regression included age, GCS score, hypertension, PNI, leukocytes, and PT (C-index=0.730). The optimal regression equation showed that each increase in the PNI value was associated with a 6.6 % reduction in patient mortality, holding all other factors constant (HR 0.934, 95 %CI 0.914-0.954, P<0.008). Subgroup analyses showed that the Optimum regression equation was more effective in predicting hospitalized mortality in Hemorrhagic Stroke than in Ischemic Stroke (C-index: 0.803 vs. 0.703). ROC analysis revealed that the cut-off value of PNI for predicting hospital mortality in stroke patients was 37.45. The Kaplan-Meier curves clearly show that patients with PNI>37.45 have a higher survival rate than the low PNI group.

Conclusions: Higher PNI is associated with better hospitalization outcomes for stroke patients. PNI can be used as a supplement to existing indicators, which helps predict the survival of stroke inpatients and provides reference value for clinical treatment.