Adherence to American Heart Association/American Stroke Association Clinical Performance Measures in a Peruvian Neurological Reference Institute

BACKGROUND

Little is known about adherence to American Heart Association/American Stroke Association (AHA/ASA) stroke performance measures in developing countries like Peru.

AIMS

We assessed adherence and determined factors associated with adherence to the AHA/ASA stroke performance measures at a reference center for neurological diseases in Lima, Peru.

METHODS

We conducted a retrospective chart review of 150 stroke patients admitted to the Neurological Institute of Neurological Science from 2014 to 2016 to ascertain adherence to 15 different AHA/ASA stroke performance measures. Adherence was measured as a simple proportion, with both single and composite measures. Associations were analyzed with nonparametric statistics and multivariate logistic regression.

RESULTS

Mean adherence to AHA/ASA stroke performance measures was 47%. We observed a statistically significant relationship between adherence to ischemic stroke performance measures and being married (OR = 3.78, 95% CI: 1.05-13.55), as well as an inverse relationship with an onset of symptoms of greater than 4.5 h prior to arrival at the hospital compared to those with ≤ 4.5 h (OR = 0.14, 95% CI: 0.02-0.97). Compared to patients with a lower National Institutes of Health Stroke Scale (NIHSS) score (<13), those with a score of ≥13 were less likely to have good adherence (OR = 0.11, 95% CI: 0.04-0.31).

CONCLUSIONS

The mean composite measure of adherence to internationally recognized standards of stroke management in our Peruvian institution was below the level needed for an achievement award by AHA/ASA. An intervention targeted toward stroke prevention and training could lead to improved outcomes of stroke patients in Peru.

Consensus for prevention and management of coronavirus disease 2019 (COVID-19) for neurologists

Coronavirus disease 2019 (COVID-19) has become a pandemic disease globally. Although COVID-19 directly invades lungs, it also involves the nervous system. Therefore, patients with nervous system involvement as the presenting symptoms in the early stage of infection may easily be misdiagnosed and their treatment delayed. They become silent contagious sources or 'virus spreaders'. In order to help neurologists to better understand the occurrence, development and prognosis, we have developed this consensus of prevention and management of COVID-19. It can also assist other healthcare providers to be familiar with and recognise COVID-19 in their evaluation of patients in the clinic and hospital environment.

Legal Liability Associated With rtPA Administration and Surrogate Decision Makers

Stroke is one of the most common conditions neurologists treat in emergency situations. This article examines the issues of surrogate decision makers and the physician's potential legal liability in the context of the administration or nonadministration of recombinant tissue plasminogen activator (rtPA) in a common emergency department scenario.

Levels of diagnostic certainty for nonepileptic attack disorder in the neuropsychiatry setting

Nonepileptic attack disorder (NEAD) is a medical condition commonly seen in neuropsychiatry services, often as a differential diagnosis of other neuropsychiatric conditions. Recommendations by the International League Against Epilepsy (ILAE) Nonepileptic Seizures Task Force propose a four-level hierarchical approach to the diagnosis of NEAD, based on history, witnessed event, and electroencephalographic (EEG) investigation. We set out to provide the first description of the diagnostic levels of patients with NEAD at a specialist neuropsychiatry clinic. Comprehensive clinical data from 148 consecutive patients with NEAD attending the specialist Neuropsychiatry Clinic run by a single Consultant in Behavioral Neurology were retrospectively reviewed. Patients with NEAD were primarily referred to neuropsychiatry by Consultant Neurologists (n = 94; 63.5%). The majority of patients were female (n = 108; 73.0%), with a disease duration of 7.9 years (standard deviation: 10.4). Anxiety was the most common comorbidity (n = 43; 26.7%). Categorization of patients according to the ILAE Nonepileptic Seizures Task Force criteria was mainly based on clinical features and EEG findings, as only 7 (4.7%) patients had attacks witnessed by a specialist. The largest diagnostic categories were 'possible' (n = 54; 36.5%) and 'clinically established' (n = 40; 27.0%), followed by 'documented' (n = 12; 8.1%) and 'probable' (n = 5; 3.4%). In 125 patients (84.4%), EEGs were performed. Selective serotonin reuptake inhibitors were the most frequently prescribed psychotropic medications (n = 48; 32.4%); 89 patients (60.1%) received behavioral therapy. There were no differences in pharmacological or behavioral management strategies across the patients categorized under different diagnostic levels. Patients with NEAD seen within neuropsychiatry settings are mainly assigned 'possible' and 'clinically established' levels of diagnostic certainty. Difficulty in capturing typical clinical events witnessed by an experienced clinician while on video-EEG can limit the clinical application of the 'documented' diagnostic level. If appropriate, active interventions can be implemented irrespective of diagnostic levels to minimize delays in the neuropsychiatric care pathways.

Neurocritical Care Coding for Neurologists

Coding specifies the work performed when providing patient care. Critical care services mostly use code 99291, and other codes specify additional time and procedures. Current Procedural Terminology defines critically ill as "a high probability of imminent or life-threatening deterioration in the patient's condition," a condition necessary for use of the critical care code. A patient may be critically ill for neurologic reasons even when stable from a cardiorespiratory status. Rules govern who can use these codes, whether they can be used by more than one physician, the locations where the code may be used, and what services are included and excluded. Physicians need to document the medical necessity of visits and nature of critical illness or high-risk medical decision making because auditors may not understand the nature of serious neurologic illness.

Deep Learning-Assisted Diagnosis of Cerebral Aneurysms Using the HeadXNet Model

IMPORTANCE

Deep learning has the potential to augment clinician performance in medical imaging interpretation and reduce time to diagnosis through automated segmentation. Few studies to date have explored this topic.

OBJECTIVE

To develop and apply a neural network segmentation model (the HeadXNet model) capable of generating precise voxel-by-voxel predictions of intracranial aneurysms on head computed tomographic angiography (CTA) imaging to augment clinicians' intracranial aneurysm diagnostic performance.

DESIGN, SETTING, AND PARTICIPANTS

In this diagnostic study, a 3-dimensional convolutional neural network architecture was developed using a training set of 611 head CTA examinations to generate aneurysm segmentations. Segmentation outputs from this support model on a test set of 115 examinations were provided to clinicians. Between August 13, 2018, and October 4, 2018, 8 clinicians diagnosed the presence of aneurysm on the test set, both with and without model augmentation, in a crossover design using randomized order and a 14-day washout period. Head and neck examinations performed between January 3, 2003, and May 31, 2017, at a single academic medical center were used to train, validate, and test the model. Examinations positive for aneurysm had at least 1 clinically significant, nonruptured intracranial aneurysm. Examinations with hemorrhage, ruptured aneurysm, posttraumatic or infectious pseudoaneurysm, arteriovenous malformation, surgical clips, coils, catheters, or other surgical hardware were excluded. All other CTA examinations were considered controls.

MAIN OUTCOMES AND MEASURES

Sensitivity, specificity, accuracy, time, and interrater agreement were measured. Metrics for clinician performance with and without model augmentation were compared.

RESULTS

The data set contained 818 examinations from 662 unique patients with 328 CTA examinations (40.1%) containing at least 1 intracranial aneurysm and 490 examinations (59.9%) without intracranial aneurysms. The 8 clinicians reading the test set ranged in experience from 2 to 12 years. Augmenting clinicians with artificial intelligence-produced segmentation predictions resulted in clinicians achieving statistically significant improvements in sensitivity, accuracy, and interrater agreement when compared with no augmentation. The clinicians' mean sensitivity increased by 0.059 (95% CI, 0.028-0.091; adjusted P = .01), mean accuracy increased by 0.038 (95% CI, 0.014-0.062; adjusted P = .02), and mean interrater agreement (Fleiss κ) increased by 0.060, from 0.799 to 0.859 (adjusted P = .05). There was no statistically significant change in mean specificity (0.016; 95% CI, -0.010 to 0.041; adjusted P = .16) and time to diagnosis (5.71 seconds; 95% CI, 7.22-18.63 seconds; adjusted P = .19).

CONCLUSIONS AND RELEVANCE

The deep learning model developed successfully detected clinically significant intracranial aneurysms on CTA. This suggests that integration of an artificial intelligence-assisted diagnostic model may augment clinician performance with dependable and accurate predictions and thereby optimize patient care.

Improving knowledge translation of clinical practice guidelines for epilepsy

BACKGROUND

Clinical practice guidelines (CPGs) have the potential to improve quality of care. However, implementation of CPGs into the clinical care of people with epilepsy is less than optimal. This study aimed to examine barriers and facilitators to the use of CPGs for the care of people with epilepsy.

METHODS

A cross-sectional survey of Canadian neurologists was conducted to evaluate CPG use, barriers and facilitators of CPG use, and factors associated with CPG use among neurologists. The barriers and facilitators of CPG use among neurologists that manage people with epilepsy were compared with those who do not.

RESULTS

Of 311 responders (response rate = 38.7%), 78.7% indicated that they manage people with epilepsy. Neurologists that manage people with epilepsy did not differ from those who do not with regard to demographic characteristics nor in the proportion that report using CPGs in their clinical practice. The barriers and facilitators of CPG use were largely similar between neurologist that do and do not manage people with epilepsy; except applicability of CPGs tended to be less commonly endorsed as a barrier to CPG use by those who manage people with epilepsy compared with those who do not.

CONCLUSIONS

This study suggests that knowledge, applicability, motivation, resources, and targeting of CPGs to appropriate audience are barriers and facilitators of CPG use among neurologists who manage people with epilepsy. The similarity between barriers and facilitators of CPG use among neurologists who manage people with epilepsy compared with those who do not provides support for the use of a knowledge translation (KT) strategy tailored to these barriers and facilitators of CPG use, and targeted towards neurologists. Implementation of epilepsy CPGs has the potential to improve the quality of care for people with epilepsy.

Comparison of conventional and modern methods in determining ischemic stroke etiology by general and stroke neurologists

Background/aim

This study aimed to investigate the consistency between stroke and general neurologists in subtype assignment using the Trial of ORG-10172 in Acute Stroke Treatment (TOAST) and Causative Classification of Stroke (CCS) systems.

Materials and methods

Fifty consecutive acute ischemic stroke patients admitted to the stroke unit were recruited. Patients were classified by two stroke and two general neurologists, each from different medical centers, according to TOAST followed by the CCS. Each neurologist was assessed for consistency and compliance in pairs. Concordance among all four neurologists was investigated and evaluated using the kappa (ĸ) value.

Results

The kappa (ĸ) value of diagnostic compliance between stroke neurologists was 0.61 (95% CI: 0.45–0.77) for TOAST and 0.78 (95% CI: 0.62–0.94) for CSS-5. The kappa (ĸ) value was 0.64 (95% CI: 0.48–0.80) for TOAST and 0.75 (95% CI: 0.60–0.91) for CCS-5 for general neurologists. Compliance was moderate [ĸ: 0.59 (95% CI: 0.52–0.65)] for TOAST and was strong [ĸ: 0.75 (95% CI: 0.68–0.81)] for CCS-5 for all 4 neurologists. ‘Cardioembolism’ (91.04%) had the highest compliance in both systems. The frequency of the group with ‘undetermined etiologies’ was less in the CCS (26%) compared to TOAST.

Conclusion

The CCS system improved compliance in both stroke and general neurologists compared with TOAST. This suggests that the automatic, evidence-based, easily reproducible CCS system was superior to the TOAST system.

Assessments of neurocognitive and behavioral function in the mucopolysaccharidoses

The mucopolysaccharidoses (MPS) are a group of rare, inherited lysosomal storage disorders in which accumulation of glycosaminoglycans (GAGs) leads to progressive tissue and organ dysfunction. In addition to a variety of somatic signs and symptoms, patients with rapidly progressing MPS I (Hurler), II, III, and VII can present with significant neurological manifestations, including impaired cognitive abilities, difficulties in language and speech, behavioral abnormalities, sleep problems, and/or seizures. Neurological symptoms have a substantial impact on the quality of life of MPS patients and their families. Due to the progressive nature of cognitive impairment in these MPS patients, neurocognitive function is a sensitive indicator of disease progression, and a relevant outcome when testing efficacy of therapies for these disorders. In order to effectively manage and develop therapies that address neurological manifestations of MPS, it is important to use appropriate neurocognitive assessment tools that are sensitive to changes in neurocognitive function in MPS patients. This review discusses expert opinions on key issues and considerations for effective neurocognitive testing in MPS patients. In addition, it describes the neurocognitive assessment tools that have been used in clinical practice for these patients. The content of this review is based on existing literature and information from a meeting of international experts with extensive experience in managing and treating MPS disorders.

Introduction to Magnetic Resonance Imaging for Neurologists

PURPOSE OF REVIEW

In neuroradiology, highly sophisticated methods such as MRI are implemented to investigate different entities of the central nervous system and to acquire miscellaneous images where tissues display varying degrees of characteristic signal intensity or brightness. Compared to x-ray, CT, and ultrasound, MRI produces clearer images of tissues, body fluids, and fat. The basics of MRI may be unknown to neurologists; this article introduces MRI physics, techniques, and interpretation guidelines.

RECENT FINDINGS

This article discusses the basics of MRI to provide clinicians with the scientific underpinning of MRI technology and to help them better understand image features and improve their diagnosis and differential diagnosis by combining MRI characteristics with their knowledge of pathology and neurology.

SUMMARY

This article will help neurologists deepen their knowledge and understanding of MRI by introducing the basics of MRI physics, technology, image acquisition, protocols, and image interpretation.