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Introduction: Pneumonia is the most common complication after stroke, but our knowledge on risk factors and predictors of stroke-associated pneumonia (SAP) is limited. We sought to evaluate the predictors and outcomes of SAP among acute ischemic stroke (AIS) hospitalizations. Methods: This is a cross-sectional study of the Nationwide Inpatient Sample database from the year 2003 to 2014. We identified adult hospitalizations with AIS using International Classification of Diseases, ninth revision, clinical modification (ICD-9-CM) codes. The SAP was identified by the presence of a secondary diagnosis of hospital-acquired pneumonia and ventilator-associated pneumonia. Multivariable survey logistic regression models were utilized to evaluate the predictors of SAP. Results: Overall, 4,224,924 AIS hospitalizations were identified, of which 149,169 (3.53%) had SAP. The prevalence of SAP decreased from 3.72% in 2003 to 3.17% in 2014 (P<0.0001). Mortality [17.12% vs. 4.77%; adjusted odds ratio (aOR): 1.71; P<0.0001] and morbidity (22.53% vs. 3.28%; aOR: 1.86; P<0.0001) were markedly elevated in SAP group compare to non-SAP group. The significant risk factors of pneumonia among AIS hospitalization were nasogastric tube (aOR: 1.21; P=0.0179), noninvasive mechanical ventilation (aOR: 1.65; P<0.0001), invasive mechanical ventilation (aOR: 4.09; P<0.0001), length of stay between 1 to 2 weeks (aOR: 1.99; P<0.0001), >2 weeks (aOR: 3.90; P<0.0001), hemorrhagic conversion (aOR: 1.17; P=0.0002), and epilepsy (aOR: 1.09; P=0.0009). Other concurrent comorbidities which increased the risk of SAP among AIS patients were acquired immune deficiency syndrome (aOR: 1.88; P<0.0001), alcohol abuse (aOR: 1.60; P=0.0006), deficiency anemia (aOR: 1.26; P<0.0001), heart failure (aOR: 1.62; P<0.0001), pulmonary disease (aOR: 1.73; P<0.0001), diabetes (aOR: 1.29; P=0.0288), electrolyte disorders (aOR: 1.50; P<0.0001), paralysis (aOR: 1.22; P<0.0001), pulmonary circulation disorders (aOR: 1.22; P<0.0001), renal failure (aOR: 1.12; P<0.0001), coagulopathy (aOR: 1.13; P=0.0006), and weight loss (aOR: 1.39; P<0.0001). Conclusion: Our data underline the considerable epidemiological and prognostic impact of SAP in patients with AIS leading to higher mortality, morbidity, length of stay, and hospital cost despite advancements in care.

Introduction At present, there is an emphasis on a multi-modal approach to neuro-prognostication after cardiac arrest using clinical examination, neurophysiologic testing, laboratory biomarkers, and radiological studies. However, this necessitates significant resource utilization and can be challenging in under-resourced clinical settings. Hence, we sought to determine the inter-predictability and correlation of prognostic tests performed in patients after cardiac arrest. Methods Fifty patients were included through neurophysiology laboratory data for this retrospective study. Clinical, radiological and neurophysiological data were collected. Neurophysiological data were re-evaluated by a board-certified neurophysiologist for the purpose of the study. Chi-square testing was used to evaluate the correlation between different diagnostic modalities. Results We found that a non-reactive electroencephalogram (EEG) had a predictive value of 79% for absent bilateral cortical responses (N20) with somatosensory evoked potentials (SSEP). On the other hand, absent bilateral cortical responses N20 had 87% predictive value for a non-reactive EEG. Also, absent cortical responses and non-reactive EEG had predictive values of 78% and 72% for anoxic injury on magnetic resonance imaging (MRI) brain respectively with a non-significant difference on chi-square testing. Individually, absent bilateral N20 SSEP, a non-reactive EEG and anoxic brain injury on MRI studies were highly predictive of poor outcome [modified Rankin scale (mRS) > 4] at hospital discharge. Conclusion Neuroprognostication in a post-cardiac arrest setting is often limited by self-fulfilling prophecy. Given the lack of absolute correlation between different modalities used in post-cardiac arrest patients, the value of the multi-modal approach to neuro-prognostication is highlighted by this study.

Several indexes are used to classify physician burnout, with the Maslach Burnout Inventory currently being the most widely accepted. This index measures physician burnout based on emotional exhaustion, detachment from work, and lack of personal achievement. The overall percentage of physicians with burnout is estimated to be around 40%, but the proportion varies between specialties. Neurology currently has the second-highest rate of burnout and is projected to eventually take the top position. The purpose of this review is to provide a comprehensive overview focusing on the causes and ramifications of burnout and possible strategies for addressing the crisis. Several factors contribute to burnout among neurologist, including psychological trauma associated with patient care and a lack of respect compared to other specialties. Various interventions have been proposed for reducing burnout, and this article explores the feasibility of some of them. Burnout not only impacts the physician but also has adverse effects on the overall quality of patient care and places a strain on the health-care system. Burnout has only recently been recognized and accepted as a health crisis globally, and hence most of the proposed action plans have not been validated. More studies are needed to evaluate the long-term effects of such interventions.

Introduction: Migraine is a chronic disabling neurological disease, with an estimated expense of $15-20 million/year. Several studies with a small number of patients have studied risk factors for migraine such as cardiovascular disorders, stroke, smoking, demographic, and genetic factors but this is the first comprehensive study for evaluation of vascular and nonvascular risk factors. It is important to evaluate all the risk factors that help to prevent the healthcare burden related to migraine. Methodology: We performed a retrospective cross-sectional analysis of the Nationwide Inpatient Sample (NIS) (years 2013-2014) in adult (>18-years old) hospitalizations in the United States. Migraine patients were identified using ICD-9-CM code to determine the demographic characteristics, vascular, and nonvascular risk factors. Univariate analysis was performed using the chi-square test and a multivariate survey logistic regression analysis was performed to identify the prevalence of the risk factors and evaluate the odds of prevalence of risk factors amongst migraine patients compared to nonmigraine patients, respectively. Results: On weighted analysis, after removing missing data of age, gender and race, from years 2013 to 2014, of the total 983,065 (1.74%) migraine patients were identified. We found that younger (median age 48-years vs. 60-years), female (82.1% vs. 58.5%; p<0.0001), white population (76.8% vs. 70.5%; p<0.0001), and privately insured (41.1% vs. 27.4%; p<0.0001) patients were more likely to have migraine than others. Cerebral atherosclerosis, diabetes mellitus, ischemic heart disease, atrial fibrillation, and alcohol abuse were not significantly associated with migraine. Migraineurs had higher odds of having hypertension [odds ratio (OR):