The newer classifications of the chiari malformations with clarifications: An anatomical review.

In 1891, Hans Chiari described a group of congenital hindbrain anomalies, which were eventually named after him. He classified these malformations into three types (Chiari malformations I, II, and III), and four years later added the Chiari IV malformation. However, numerous reports across the literature do not seem to fit Chiari's original descriptions of these malformations, so researchers have been encouraged to propose new classifications to encompass these variants (e.g., Chiari 0, Chiari1.5, and Chiari 3.5 malformations). Moreover, there is a continued misunderstanding and misuse of the term "Chiari IV malformation." Therefore, the current review intended to describe anatomical, pathophysiological, and clinical aspects of the newer classifications with clarifications of the Chiari malformations. We reviewed available literature about Chiari malformations and their variants using "PubMed" and "Google Scholar." We also looked into the term Chiari IV, clarifying its original description and citing examples where the term has been used erroneously. References in the reviewed articles were searched manually. Variants of the originally described Chiari malformations are termed Chiari 0, Chiari 1.5, and Chiari 3.5. Each has distinct anatomical characteristics and some of these are extremely rare and incompatible with life (e.g. Chiari 3.5). Chiari IV malformation has been further clarified. Some physicians might be unfamiliar with the newer classifications of Chiari malformations because these conditions are rare or even unique. Furthermore, care is needed in using the term "Chiari IV malformation", which must be consistent with Chiari's original description, i.e. an occipital encephalocele containing supratentorial contents. Clin. Anat. 31:314-322, 2018. © 2018 Wiley Periodicals, Inc.

Endovascular coiling versus neurosurgical clipping for treatment of ruptured and unruptured intracranial aneurysms during pregnancy and postpartum period.

BACKGROUND: Selection of appropriate surgical strategy for the treatment of intracranial aneurysms (IA) during pregnancy requires careful consideration of the potential risks to the mother and fetus. However, limited data guide treatment decisions in these patients. We compared the safety profiles of endovascular coiling (EC) and neurosurgical clipping (NC) performed for the treatment of ruptured and unruptured IA during pregnancy and the postpartum period. METHODS: Pregnancy-related or postpartum hospitalizations undergoing surgical intervention for IA were identified from the Nationwide Readmissions Database 2016-2018. Safety outcomes included periprocedural complications, in-hospital mortality, discharge disposition, and 30-day non-elective readmissions. RESULTS: There were 348 pregnancy-related or postpartum hospitalizations that met the study inclusion criteria (mean±SD age 31.8±5.9 years). Among 168 patients treated for ruptured aneurysms, 115 (68.5%) underwent EC and 53 (31.5%) underwent NC; whereas among 180 patients treated for unruptured aneurysms, 140 (77.8%) underwent EC and 40 (22.2%) underwent NC. There were no statistically significant differences in the baseline characteristics between patients undergoing EC versus NC for either ruptured or unruptured aneurysm groups. The outcomes were statistically comparable between EC and NC for both ruptured and unruptured IA, except for a lower incidence of ischemic stroke in patients undergoing EC for ruptured aneurysms (OR 0.12, 95% CI 0.02 to 0.84). CONCLUSIONS: Most pregnant and postpartum patients are treated with EC for both ruptured and unruptured IA. For treatment of ruptured IA, EC is independently associated with a lower risk of perioperative ischemic stroke, but other in-hospital complications and mortality are comparable between EC and NC.

Early Readmissions After Hospitalization for Posterior Reversible Encephalopathy Syndrome.

OBJECTIVE: To evaluate the frequency, etiologies, and risk factors for 90-day readmissions following hospitalization for PRES. METHODS: Data were obtained from the Nationwide Readmissions Database 2016-2018. Patients with primary diagnosis of PRES, survival to discharge, and known discharge disposition were included. Primary outcome was non-elective readmission within 90 days of discharge. Survival analysis was performed, and independent predictors of readmission were analyzed using multivariable Cox proportional hazards regression. RESULTS: Based on the study inclusion criteria, 6,155 eligible patients were included (mean±SD age: 55.9±17.3 years, female: 71.0%). Non-elective readmission within 90 days of discharge occurred for 1,922 (31.2%) patients. Of these, 617 readmissions were due to PRES-related or neurological etiologies and the remaining 1305 readmissions were due to non-neurological conditions. In multivariable analysis, age was inversely associated with risk of readmission [hazards ratio (HR): 0.92 for every 10 years increase in age, 95% confidence interval (CI): 0.88-0.97]. Patients with diabetes (HR: 1.21, 95% CI: 1.04-1.42), systemic lupus erythematosus (HR: 1.42, 95% CI: 1.03-1.96), acute kidney injury (HR: 1.28, 95% CI: 1.11-1.47) and higher Charlson comorbidity index score (HR: 1.09, 95% CI: 1.06-1.13) were more likely to be readmitted. Further, patients admitted at large bed size hospitals (HR: 1.19, 95% CI: 1.03-1.39), those with longer length of stay (HR: 1.01, 95% CI: 1.00-1.02) and those not discharged to home (HR: 1.33, 95% CI: 1.14-1.55) during the index hospitalization were also at a higher risk for readmission. CONCLUSION: Nearly one-third of patients hospitalized due to PRES are readmitted within 90 days of discharge and about one-third of these readmissions are due to PRES-related or neurological etiologies. Younger age, a higher comorbidity burden, longer length of hospital stay, and discharge disposition other than to home are independently associated with the risk of readmission.

The Neurologic Manifestations of Coronavirus Disease 2019.

The coronavirus disease 2019 (COVID-19) is an ongoing global pandemic that has so far affected 216 countries and more than 5 million individuals worldwide. The infection is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). While pulmonary manifestations are the most common, neurological features are increasingly being recognized as common manifestations of the COVID-19, especially in the cases of severe infection. These include acute cerebrovascular disease, encephalitis, and Guillain-Barre syndrome (GBS). Here, we review the neuropathogenesis of SARS-CoV-2 and the central and peripheral nervous system manifestations of COVID-19.

Depression Reduces Accuracy While Parkinsonism Slows Response Time for Processing Positive Feedback in Patients with Parkinson's Disease with Comorbid Major Depressive Disorder Tested on a Probabilistic Category-Learning Task.

Major depressive disorder (MDD) is the most common non-motor manifestation of Parkinson's disease (PD) affecting 50% of patients. However, little is known about the cognitive correlates of MDD in PD. Using a computer-based cognitive task that dissociates learning from positive and negative feedback, we tested four groups of subjects: (1) patients with PD with comorbid MDD, (2) patients with PD without comorbid MDD, (3) matched patients with MDD alone (without PD), and (4) matched healthy control subjects. Furthermore, we used a mathematical model of decision-making to fit both choice and response time data, allowing us to detect and characterize differences between the groups that are not revealed by cognitive results. The groups did not differ in learning accuracy from negative feedback, but the MDD groups (PD patients with MDD and patients with MDD alone) exhibited a selective impairment in learning accuracy from positive feedback when compared to the non-MDD groups (PD patients without MDD and healthy subjects). However, response time in positive feedback trials in the PD groups (both with and without MDD) was significantly slower than the non-PD groups (MDD and healthy groups). While faster response time usually correlates with poor learning accuracy, it was paradoxical in PD groups, with PD patients with MDD having impaired learning accuracy and PD patients without MDD having intact learning accuracy. Mathematical modeling showed that both MDD groups (PD with MDD and MDD alone) were significantly slower than non-MDD groups in the rate of accumulation of information for stimuli trained by positive feedback, which can lead to lower response accuracy. Conversely, modeling revealed that both PD groups (PD with MDD and PD alone) required more evidence than other groups to make responses, thus leading to slower response times. These results suggest that PD patients with MDD exhibit cognitive profiles with mixed traits characteristic of both MDD and PD, furthering our understanding of both PD and MDD and their often-complex comorbidity. To the best of our knowledge, this is the first study to examine feedback-based learning in PD with MDD while controlling for the effects of PD and MDD.