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INTRODUCTION: Although chronic subdural hematoma (CSDH) is a common neurosurgical disease, there is a lack of algorithms for the treatment of asymptomatic and symptomatic CSDH. The purpose of this article is to describe an algorithm developed using our institutional experience for the treatment of symptomatic CSDH that aims to decrease symptoms and/or hematoma size or to completely resolve both. Our algorithm for treatment of symptomatic CSDH includes subdural drain (SDD) placement via twist-drill craniostomy (TDC) as the first-line treatment, followed by supplemental tissue plasminogen activator (tPA) as second-line treatment, with possible middle meningeal artery embolization (MMAE), followed by craniotomy as the last therapeutic option. This study investigated the efficacy of our institution's algorithm in treating symptomatic CSDH. METHODS: A retrospective study was conducted from 2019 to 2023 identifying patients with CSDH treated with TDC. Electronic medical records were used to gather patient demographics, clinical presentation, radiographic findings, treatment modalities, and clinical outcomes. RESULTS: There were a total of 109 patients with 128 SDD placements. All 109 patients underwent TDC; among them, 37 patients received tPA instillation with three patients requiring craniotomy. Factors including age, gender, race, mechanism of injury, blood thinner usage, Glasgow Coma Scale (GCS), neurologic exam, thickness of CSDH, and midline shift were comparable for all patients regardless of treatment received. The mean number of neomembranes was higher in patients who eventually required craniotomy (4.5) compared to those treated with TDC only (1.8) and TDC+tPA (2.1) (p=0.0035). There was a greater mean hematoma drainage in patients who received tPA instillation without craniotomy (586.7 mL) than those treated with TDC only (293.0 mL) (p<0.0001). Clinical improvement was found in 52/72 patients (72.2%) treated with TDC only, 23/34 patients (67.6%) treated with TDC+tPA only, and 0/3 patients (0.0%) treated with TDC+tPA+craniotomy. Radiographic improvement in mean thickness of CSDH and midline shift, respectively, was found in patients treated with TDC only (p<0.0001; p<0.0001) and TDC+tPA (p<0.0001; p<0.0001) but not in TDC+tPA+craniotomy (p=0.1494; p=0.0762). There were also fewer neomembranes after TDC+tPA treatment only (2.1 vs. 0.5, p<0.0001). Seven patients were readmitted that did not follow the algorithm and only patients treated following the algorithm showed clinical and radiographic improvement. CONCLUSIONS: Using our institutional algorithm, our study demonstrates successful clinical outcomes in treating symptomatic CSDH and recurrent CSDH with minimally invasive therapeutic interventions including SDD via TDC and tPA, thereby minimizing the utilization of more invasive interventions including craniotomy.
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Background Neurological pupil index (NPi) is a novel method of assessing pupillary size and reactivity using pupillometry to reduce human subjectivity. This paper aims to evaluate the use of NPi as a potential prognostic tool in a broad population of neurocritical care patients by observing the correlation between NPi, modified Rankin Scale (mRS), and Glasgow Coma Scale (GCS). Methods Our data was collected from 194 patients in the neurosurgical intensive care unit (ICU) at Arrowhead Regional Medical Center (ARMC), as determined by the power calculation. We utilized the Kolmogorov-Smirnova and Shapiro-Wilk normality tests with Lilliefors significance correction. Pearson product-moment correlation was performed between average final NPi and final GCS. Multi-variate linear regression and analysis of variance (ANOVA) were used to evaluate the association and predictive capabilities of NPi on GCS and discharge mRS. Finally, we evaluated whether age, ethnicity, sex, length of stay (LOS), or discharge location were significantly associated with NPi. Results We observed a significant correlation between final GCS and NPi (r=0.609, p<0.001). Our regression analysis revealed that NPi significantly predicted GCS and mRS scores; however, no associations were found between age, ethnicity, sex, LOS, or discharge location. Limitations of our study include a single institutional study with a lack of disease subtyping and the inability to quantify the predictive ability of NPi. Conclusion The analysis revealed a strong correlation between final GCS and average final NPi. NPi was also able to significantly predict GCS and mRS scores. The correlation between NPi and established methods to determine neurological function, such as mRS and GCS, suggests that NPi can be a good prognostication tool for neurological diseases.