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In individuals with underlying chronic liver disease (CLD), hepatitis E virus (HEV) infection is a potential trigger of acute‐on‐chronic liver failure. In this systematic review, seven electronic databases were searched. Pooled incidence rates with 95% confidence intervals (95% CIs) were calculated by the Freeman–Tukey double arcsine transformation method. The association between death or liver failure and HEV superinfection in CLD patients was estimated by the odds ratios (OR) with a 95% CI. A total of 18 studies from 5 countries were eligible for systematic review. The prevalence of acute HEV infection in hospitalized CLD patients with clinical manifestations of hepatitis was 13.6%, which was significantly higher than that in CLD patients from the community (pooled prevalence 1.1%). The overall rates of liver failure and mortality in CLD patients with HEV superinfection were 35.8% (95% CI: 26.7%–45.6%) and 14.3% (95% CI: 10.6%–18.5%), respectively, with the rates in cirrhotic patients being approximately 2‐fold and 4‐fold higher than those in noncirrhotic patients, respectively. The risks of liver failure (OR = 5.5, 95% CI: 1.5–20.1) and mortality (OR = 5.0, 95% CI: 1.9–13.3) were significantly higher in CLD patients with HEV superinfection than in those without HEV superinfection. HEV testing in hospitalized CLD patients is necessary due to the high prevalence of HEV infection observed in hospitalized CLD patients. HEV superinfection could accelerate disease progression in patients with underlying CLD and increase mortality in these patients. HEV vaccination is appropriate for patients with pre‐existing CLD. [ABSTRACT FROM AUTHOR]

This prospective study investigated and analyzed the clinical characteristics and prognosis of paroxysmal sympathetic hyperactivity (PSH) in patients with severe nontraumatic brain injury. Patients presenting with severe nontraumatic brain injury with PSH from July 2018 to June 2019 were enrolled. A PSH assessment measure ≥ 8 points was used as the criterion for PSH. Clinical data, indicators related to PSH, treatment effects and the prognosis were prospectively collected and analyzed. A total of 220 patients with severe nontraumatic brain injury were analyzed, and PSH occurred in 8 patients (3.6%). The primary neurological diseases included acute cerebral infarction, anti-N-methyl-D-aspartate receptor encephalitis, hypoxic encephalopathy and acute disseminated encephalitis. The Glasgow Coma Scale score was lower than 8 in the 8 patients with PSH. Seven of these eight patients had a Glasgow outcome scale (GOS) score of 3 or less than 3, and one patient had a GOS of 5 after 6 months. The medicines that effectively controlled PSH included dexmedetomidine, clonazepam, midazolam and diazepam. Although the incidence was lower for nontraumatic brain injury complicated with PSH than for traumatic brain injury, patients with PSH had a more severe disease state and poorer prognoses. Dexmedetomidine might effectively control PSH. [ABSTRACT FROM AUTHOR]

• Clinical risk scores help predict functional outcomes for patients with stroke. • MR images contain crucial information related to ischemic lesions. • Deep learning techniques can extract imaging biomarkers from MR images. • MR imaging biomarkers enhance the predictive ability of existing risk scores. Clinical risk scores are essential for predicting outcomes in stroke patients. The advancements in deep learning (DL) techniques provide opportunities to develop prediction applications using magnetic resonance (MR) images. We aimed to develop an MR-based DL imaging biomarker for predicting outcomes in acute ischemic stroke (AIS) and evaluate its additional benefit to current risk scores. This study included 3338 AIS patients. We trained a DL model using deep neural network architectures on MR images and radiomics to predict poor functional outcomes at three months post-stroke. The DL model generated a DL score, which served as the DL imaging biomarker. We compared the predictive performance of this biomarker to five risk scores on a holdout test set. Additionally, we assessed whether incorporating the imaging biomarker into the risk scores improved the predictive performance. The DL imaging biomarker achieved an area under the receiver operating characteristic curve (AUC) of 0.788. The AUCs of the five studied risk scores were 0.789, 0.793, 0.804, 0.810, and 0.826, respectively. The imaging biomarker's predictive performance was comparable to four of the risk scores but inferior to one (p = 0.038). Adding the imaging biomarker to the risk scores improved the AUCs (p- values) to 0.831 (0.003), 0.825 (0.001), 0.834 (0.003), 0.836 (0.003), and 0.839 (0.177), respectively. The net reclassification improvement and integrated discrimination improvement indices also showed significant improvements (all p < 0.001). Using DL techniques to create an MR-based imaging biomarker is feasible and enhances the predictive ability of current risk scores. [ABSTRACT FROM AUTHOR]

Intracerebral hemorrhage (ICH) is a devastating stroke type that causes high mortality rates and severe disability among survivors. Many prognostic models are available for prognosticating patients with ICH. This study aimed to investigate whether clinical narratives can improve the performance for predicting functional outcomes after ICH. This study used data from the hospital stroke registry and electronic health records. The study population (n = 1363) was randomly divided into a training set (75%, n = 1023) and a holdout test set (25%, n = 340). Five risk scores for ICH were used as baseline prognostic models. Using natural language processing (NLP), text-based markers were generated from the clinical narratives of the training set through machine learning (ML) and deep learning (DL) approaches. The primary outcome was a poor functional outcome (modified Rankin Scale score of 3 to 6) at hospital discharge. The predictive performance was compared between the baseline models and models enhanced by incorporating the text-based markers using the holdout test set. The enhanced prognostic models outperformed the baseline models, regardless of whether ML or DL approaches were used. The areas under the receiver operating characteristic curve (AUCs) of the baseline models were between 0.760 and 0.892. Adding the text-based marker to the baseline models significantly increased the model discrimination, with AUCs ranging from 0.861 to 0.914. The net reclassification improvement and integrated discrimination improvement indices also showed significant improvements. Using NLP to extract textual information from clinical narratives could improve the predictive performance of all baseline prognostic models for ICH. • Prognostic models help clinical decision-making in intracerebral hemorrhage. • Natural language processing could extract textual information from clinical text. • Textual information could improve the performance of existing prognostic models. [ABSTRACT FROM AUTHOR]

Objective To explore the characteristics of electroencephalogram (EEG) in patients with anti-N-methyl- d -aspartate receptor (anti-NMDAR) encephalitis. Methods Anti-NMDAR encephalitis patients admitted to the Department of Neurology between January 2012 and June 2016 were enrolled. All patients underwent electroencephalogram (EEG) at least once in the disease peak stage, and received tumor screening, symptomatic therapy, and immunotherapy. Patients received outcome evaluation every 6 months after the immunotherapy, and modified Rankin scale (mRS) 0–2 was defined as favorable outcome. Results This study enrolled 62 cases of anti-NMDAR encephalitis patients, including 29 males (46.8%) and 33 females (53.2%). The patient ages were between 10 and 59 (mean 26.3 ± 11.3) years. A total of 93 instances of EEG monitoring were performed on 62 patients. At the peak stage, EEG presentations showed 61 cases (98.4%) were abnormal, cranial MRI showed 29 cases (46.8%) were abnormal among all 62 patients. The main presentations of abnormal EEG were diffuse slowing (25 cases, 40.3%), epileptiform discharges (11 cases, 17.7%), extreme delta brush (EDB) (10 cases, 16.1%), polymorphic delta rhythm (6 cases, 9.7%), focal slowing (5 cases, 8.1%), and diffuse beta activities (4 cases, 6.5%). Patients with normal background, epileptiform discharges, polymorphic delta rhythm, and diffuse beta activity in EEG all had favorable long-term outcome. Conclusions The majority of anti-NMDAR encephalitis patients had abnormal EEG. EEG could sensitively reflect the abnormal brain functions of patients and could assist with early clinical diagnosis and prognosis prediction. Significance Diffuse slowing was the most common presentation on the EEG in patients with anti-NMDAR encephalitis. The EEG pattern of normal background, epileptiform discharges, polymorphic delta rhythm, and diffuse beta activities at the peak stage might suggest favorable long-term prognosis. [ABSTRACT FROM AUTHOR]