Implications of Large Language Models for Quality and Efficiency of Neurologic Care: Emerging Issues in Neurology.

Large language models (LLMs) are advanced artificial intelligence (AI) systems that excel in recognizing and generating human-like language, possibly serving as valuable tools for neurology-related information tasks. Although LLMs have shown remarkable potential in various areas, their performance in the dynamic environment of daily clinical practice remains uncertain. This article outlines multiple limitations and challenges of using LLMs in clinical settings that need to be addressed, including limited clinical reasoning, variable reliability and accuracy, reproducibility bias, self-serving bias, sponsorship bias, and potential for exacerbating health care disparities. These challenges are further compounded by practical business considerations and infrastructure requirements, including associated costs. To overcome these hurdles and harness the potential of LLMs effectively, this article includes considerations for health care organizations, researchers, and neurologists contemplating the use of LLMs in clinical practice. It is essential for health care organizations to cultivate a culture that welcomes AI solutions and aligns them seamlessly with health care operations. Clear objectives and business plans should guide the selection of AI solutions, ensuring they meet organizational needs and budget considerations. Engaging both clinical and nonclinical stakeholders can help secure necessary resources, foster trust, and ensure the long-term sustainability of AI implementations. Testing, validation, training, and ongoing monitoring are pivotal for successful integration. For neurologists, safeguarding patient data privacy is paramount. Seeking guidance from institutional information technology resources for informed, compliant decisions, and remaining vigilant against biases in LLM outputs are essential practices in responsible and unbiased utilization of AI tools. In research, obtaining institutional review board approval is crucial when dealing with patient data, even if deidentified, to ensure ethical use. Compliance with established guidelines like SPIRIT-AI, MI-CLAIM, and CONSORT-AI is necessary to maintain consistency and mitigate biases in AI research. In summary, the integration of LLMs into clinical neurology offers immense promise while presenting formidable challenges. Awareness of these considerations is vital for harnessing the potential of AI in neurologic care effectively and enhancing patient care quality and safety. The article serves as a guide for health care organizations, researchers, and neurologists navigating this transformative landscape.

Healthcare utilization impacts of an eConsult program for headache at an academic medical center.

INTRODUCTION: Interprofessional consultations ("eConsults") can reduce healthcare utilization. However, the impact of eConsults on healthcare utilization remains poorly characterized among patients with headache. METHODS: We performed a retrospective, 1:1 matched cohort study comparing patients evaluated for headache via eConsult request or in-person referral at the Mount Sinai Health System in New York. Groups were matched on clinical and demographic characteristics. Our primary outcome was one or more outpatient headache-related encounters in 6 months following referral date. Secondary outcomes included one or more all-cause outpatient neurology and headache-related emergency department (ED) encounters during the same period. We used univariable and multivariable logistic regression to model associations between independent variables and outcomes. RESULTS: We identified 74 patients with headache eConsults who were matched to 74 patients with in-person referrals. Patients in the eConsult group were less likely to achieve the primary outcome (29.7% vs 62.2%, P < 0.0001) or have an all-cause outpatient neurology encounter (33.8% vs 79.7%, P < 0.0001) than patients in the comparison group. Both groups did not significantly differ by headache-related ED encounters. In multivariable analyses, patients in the eConsult group had significantly lower odds of having one or more headache-related or all-cause neurology encounters than patients in the comparison group (odds ratio (OR) 0.3, 95% confidence interval (CI) 0.1-0.6; OR 0.1, 95% CI 0.1-0.3, respectively). DISCUSSION: In comparison to in-person referrals, eConsult requests for headache were associated with reduced likelihood of outpatient neurology encounters in the short-term but not with differential use of headache-related ED encounters. Larger-scale, prospective studies should validate our findings and assess patient outcomes.

Latent class analysis of eHealth behaviors among adults with epilepsy.

OBJECTIVE: The objective of this study was to determine the proportions of uptake and factors associated with electronic health (eHealth) behaviors among adults with epilepsy. METHODS: The 2013, 2015, and 2017 National Health Interview Surveys were analyzed. We assessed the proportions of use of five domains of eHealth in those with epilepsy: looked up health information on the internet, filled a prescription on the internet, scheduled a medical appointment on the internet, communicated with a health care provider via email, and used chat groups to learn about health topics. Multivariate logistic regressions were conducted to identify factors associated with any eHealth behaviors among those with active epilepsy. Latent class analysis was performed to identify underlying patterns of eHealth activity. Survey participants were classified into three discrete classes: (1) frequent, (2) infrequent, and (3) nonusers of eHealth. Multinomial logistic regression was performed to identify factors associated with frequency of eHealth use. RESULTS: There were 1770 adults with epilepsy, of whom 65.87% had at least one eHealth behavior in the prior year. By domain, 62.61% looked up health information on the internet, 15.81% filled a prescription on the internet, 14.95% scheduled a medical appointment on the internet, 17.20% communicated with a health care provider via email, and 8.27% used chat groups to learn about health topics. Among those with active epilepsy, female sex, more frequent computer usage, and internet usage were associated with any eHealth behavior. Female sex and frequent computer use were associated with frequent eHealth use as compared to nonusers. SIGNIFICANCE: A majority of persons with epilepsy were found to use at least one form of eHealth. Various technological and demographic factors were associated with eHealth behaviors. Individuals with lower eHealth behaviors should be provided with targeted interventions that address barriers to the adoption of these technologies.

Trends in the Utilization of Teleneurology and Other Healthcare Resources Prior to and During the COVID-19 Pandemic in an Urban, Tertiary Health System.

BACKGROUND: Patient groups traditionally affected by health disparities were less likely to use video teleneurology (TN) care during the initial COVID-19 pandemic surge in the United States. Whether this asymmetry persisted later in the pandemic or was accompanied with a loss of access to care remains unknown. METHODS: We conducted a retrospective cohort study using patient data from a multicenter healthcare system in New York City. We identified all established pediatric or adult neurology patients with at least two prior outpatient visits between June 16th, 2019 and March 15th, 2020 using our electronic medical record. For this established pre-COVID cohort, we identified telephone, in-person, video TN or emergency department visits and hospital admissions for any cause between March 16th and December 15th, 2020 ("COVID period"). We determined clinical, sociodemographic, income, and visit characteristics. Our primary outcome was video TN utilization, and our main secondary outcome was loss to follow-up during the COVID period. We used multivariable logistic regression to model the relationship between patient-level characteristics and both outcomes. RESULTS: We identified 23,714 unique visits during the COVID period, which corresponded to 14,170 established patients from our institutional Neurology clinics during the pre-COVID period. In our cohort, 4,944 (34.9%) utilized TN and 4,997 (35.3%) were entirely lost to follow-up during the COVID period. In the adjusted regression analysis, Black or African-American race [adjusted odds ratio (aOR) 0.60, 97.5%CI 0.52-0.70], non-English preferred language (aOR 0.49, 97.5%CI 0.39-0.61), Medicaid insurance (aOR 0.50, 97.5%CI 0.44-0.57), and Medicare insurance (aOR 0.73, 97.5%CI 0.65-0.83) had decreased odds of TN utilization. Older age (aOR 0.98, 97.5%CI 0.98-0.99), female sex (aOR 0.90 97.5%CI 0.83-0.99), and Medicaid insurance (aOR 0.78, 0.68-0.90) were associated with decreased odds of loss to follow-up. CONCLUSION: In the first 9 months of the COVID-19 pandemic, we found sociodemographic patterns in TN utilization that were similar to those found very early in the pandemic. However, these sociodemographic characteristics were not associated with loss to follow-up, suggesting that lack of TN utilization may not have coincided with loss of access to care.

Treating High-Risk TIA and Minor Stroke Patients With Dual Antiplatelet Therapy: A National Survey of Emergency Medicine Physicians.

BACKGROUND: Treatment with aspirin plus clopidogrel, dual antiplatelet therapy (DAPT), within 24 hours of high-risk transient ischemic attack (TIA) or minor stroke symptoms to eligible patients is recommended by national guidelines. Whether or not this treatment has been adopted by emergency medicine (EM) physicians is uncertain. METHODS: We conducted an online survey of EM physicians in the United States. The survey consisted of 13 multiple choice questions regarding physician characteristics, practice settings, and usual approach to TIA and minor stroke treatment. We report participant characteristics and use chi-squared tests to compare between groups. RESULTS: We included 162 participants in the final study analysis. 103 participants (64%) were in practice for >5 years and 96 (59%) were at nonacademic centers; all were EM board-certified or board-eligible. Only 9 (6%) participants reported that they would start DAPT for minor stroke and 8 (5%) reported that they would start DAPT after high-risk TIA. Aspirin alone was the selected treatment by 81 (50%) participants for minor stroke patients who presented within 24 hours of symptom onset and were not candidates for thrombolysis. For minor stroke, 69 (43%) participants indicated that they would defer medical management to consultants or another team. Similarly, 75 (46%) of participants chose aspirin alone to treat high-risk TIA; 74 (46%) reported they would defer medical management after TIA to consultants or another team. CONCLUSION: In a survey of EM physicians, we found that the reported rate of DAPT treatment for eligible patients with high-risk TIA and minor stroke was low.

Teleneurology Expansion in Response to the COVID-19 Outbreak at a Tertiary Health System in New York City.

OBJECTIVE: To assess the implementation of teleneurology (TN), including patient and clinician experiences, during the coronavirus respiratory disease 2019 (COVID-19) pandemic. METHODS: We studied synchronous (video visit) and asynchronous (store-and-forward, patient-portal evaluation, remote monitoring) TN utilization in the Mount Sinai Health System Neurology Department in New York, 2 months before and after the start of our department's response to the pandemic in mid-March 2020. Weekly division meetings enabled ongoing assessments and analysis of barriers and facilitators according to the Consolidated Framework for Implementation Research and the Expert Recommendations for Implementing Change models. We used postvisit surveys of clinicians (from April 13 to May 15, 2020) and patients (from May 11 to 15, 2020) to determine technology platforms used, and TN experience and acceptability, using Likert scales (1 = very poor/unlikely to 5 = very good/likely). RESULTS: Over the 4-month period, 117 TN clinicians (n = 14 subspecialties) conducted 4,225 TN visits with 3,717 patients (52 pre- vs 4,173 post-COVID-19). No asynchronous TN services were delivered. Post-COVID-19, the number of TN clinicians, subspecialties performing TN, and visits increased by 963%, 133%, and 7,925%, respectively. Mean acceptability among patients and clinicians was 4.7 (SD 0.6) and 3.4 (SD 1.6), respectively. Most video visits were completed using Epic MyChart (78.5%) and Zoom (8.1%). TN implementation facilitators included Medicare geographic restriction waivers, development of clinician educational materials, and MyChart outreach programs for patients experiencing technical difficulties. CONCLUSIONS: A significant expansion of TN utilization accompanied the COVID-19 response. Patients found TN more acceptable than did clinicians. Proactive application of an implementation framework facilitated rapid and effective TN expansion.

Predicting adult neuroscience intensive care unit admission from emergency department triage using a retrospective, tabular-free text machine learning approach.

Early admission to the neurosciences intensive care unit (NSICU) is associated with improved patient outcomes. Natural language processing offers new possibilities for mining free text in electronic health record data. We sought to develop a machine learning model using both tabular and free text data to identify patients requiring NSICU admission shortly after arrival to the emergency department (ED). We conducted a single-center, retrospective cohort study of adult patients at the Mount Sinai Hospital, an academic medical center in New York City. All patients presenting to our institutional ED between January 2014 and December 2018 were included. Structured (tabular) demographic, clinical, bed movement record data, and free text data from triage notes were extracted from our institutional data warehouse. A machine learning model was trained to predict likelihood of NSICU admission at 30 min from arrival to the ED. We identified 412,858 patients presenting to the ED over the study period, of whom 1900 (0.5%) were admitted to the NSICU. The daily median number of ED presentations was 231 (IQR 200-256) and the median time from ED presentation to the decision for NSICU admission was 169 min (IQR 80-324). A model trained only with text data had an area under the receiver-operating curve (AUC) of 0.90 (95% confidence interval (CI) 0.87-0.91). A structured data-only model had an AUC of 0.92 (95% CI 0.91-0.94). A combined model trained on structured and text data had an AUC of 0.93 (95% CI 0.92-0.95). At a false positive rate of 1:100 (99% specificity), the combined model was 58% sensitive for identifying NSICU admission. A machine learning model using structured and free text data can predict NSICU admission soon after ED arrival. This may potentially improve ED and NSICU resource allocation. Further studies should validate our findings.

Comparative analysis, applications, and interpretation of electronic health record-based stroke phenotyping methods.

BACKGROUND: Accurate identification of acute ischemic stroke (AIS) patient cohorts is essential for a wide range of clinical investigations. Automated phenotyping methods that leverage electronic health records (EHRs) represent a fundamentally new approach cohort identification without current laborious and ungeneralizable generation of phenotyping algorithms. We systematically compared and evaluated the ability of machine learning algorithms and case-control combinations to phenotype acute ischemic stroke patients using data from an EHR. MATERIALS AND METHODS: Using structured patient data from the EHR at a tertiary-care hospital system, we built and evaluated machine learning models to identify patients with AIS based on 75 different case-control and classifier combinations. We then estimated the prevalence of AIS patients across the EHR. Finally, we externally validated the ability of the models to detect AIS patients without AIS diagnosis codes using the UK Biobank. RESULTS: Across all models, we found that the mean AUROC for detecting AIS was 0.963 ± 0.0520 and average precision score 0.790 ± 0.196 with minimal feature processing. Classifiers trained with cases with AIS diagnosis codes and controls with no cerebrovascular disease codes had the best average F1 score (0.832 ± 0.0383). In the external validation, we found that the top probabilities from a model-predicted AIS cohort were significantly enriched for AIS patients without AIS diagnosis codes (60-150 fold over expected). CONCLUSIONS: Our findings support machine learning algorithms as a generalizable way to accurately identify AIS patients without using process-intensive manual feature curation. When a set of AIS patients is unavailable, diagnosis codes may be used to train classifier models.

Emergent Large Vessel Occlusion Stroke During New York City's COVID-19 Outbreak: Clinical Characteristics and Paraclinical Findings.

BACKGROUND AND PURPOSE: The 2019 novel coronavirus outbreak and its associated disease (coronavirus disease 2019 [COVID-19]) have created a worldwide pandemic. Early data suggest higher rate of ischemic stroke in severe COVID-19 infection. We evaluated whether a relationship exists between emergent large vessel occlusion (ELVO) and the ongoing COVID-19 outbreak. METHODS: This is a retrospective, observational case series. Data were collected from all patients who presented with ELVO to the Mount Sinai Health System Hospitals across New York City during the peak 3 weeks of hospitalization and death from COVID-19. Patients' demographic, comorbid conditions, cardiovascular risk factors, COVID-19 disease status, and clinical presentation were extracted from the electronic medical record. Comparison was made between COVID-19 positive and negative cohorts. The incidence of ELVO stroke was compared with the pre-COVID period. RESULTS: Forty-five consecutive ELVO patients presented during the observation period. Fifty-three percent of patients tested positive for COVID-19. Total patients' mean (±SD) age was 66 (±17). Patients with COVID-19 were significantly younger than patients without COVID-19, 59±13 versus 74±17 (odds ratio [95% CI], 0.94 [0.81-0.98]; P=0.004). Seventy-five percent of patients with COVID-19 were male compared with 43% of patients without COVID-19 (odds ratio [95% CI], 3.99 [1.12-14.17]; P=0.032). Patients with COVID-19 were less likely to be White (8% versus 38% [odds ratio (95% CI), 0.15 (0.04-0.81); P=0.027]). In comparison to a similar time duration before the COVID-19 outbreak, a 2-fold increase in the total number of ELVO was observed (estimate: 0.78 [95% CI, 0.47-1.08], P≤0.0001). CONCLUSIONS: More than half of the ELVO stroke patients during the peak time of the New York City's COVID-19 outbreak were COVID-19 positive, and those patients with COVID-19 were younger, more likely to be male, and less likely to be White. Our findings also suggest an increase in the incidence of ELVO stroke during the peak of the COVID-19 outbreak.

History of Stroke Is Independently Associated With In-Hospital Death in Patients With COVID-19.

BACKGROUND AND PURPOSE: In December 2019, an outbreak of severe acute respiratory syndrome coronavirus causing coronavirus disease 2019 (COVID-19) occurred in China, and evolved into a worldwide pandemic. It remains unclear whether the history of cerebrovascular disease is associated with in-hospital death in patients with COVID-19. METHODS: We conducted a retrospective, multicenter cohort study at Mount Sinai Health System in New York City. Using our institutional data warehouse, we identified all adult patients who were admitted to the hospital between March 1, 2020 and May 1, 2020 and had a positive nasopharyngeal swab polymerase chain reaction test for severe acute respiratory syndrome coronavirus in the emergency department. Using our institutional electronic health record, we extracted clinical characteristics of the cohort, including age, sex, and comorbidities. Using multivariable logistic regression to control for medical comorbidities, we modeled the relationship between history of stroke and all-cause, in-hospital death. RESULTS: We identified 3248 patients, of whom 387 (11.9%) had a history of stroke. Compared with patients without history of stroke, patients with a history of stroke were significantly older, and were significantly more likely to have a history of all medical comorbidities except for obesity, which was more prevalent in patients without a history of stroke. Compared with patients without history of stroke, patients with a history of stroke had higher in-hospital death rates during the study period (48.6% versus 31.7%, P<0.001). In the multivariable analysis, history of stroke (adjusted odds ratio, 1.28 [95% CI, 1.01-1.63]) was significantly associated with in-hospital death. CONCLUSIONS: We found that history of stroke was associated with in-hospital death among hospitalized patients with COVID-19. Further studies should confirm these results.

A Multilevel Analysis of Surgical Category and Individual Patient-Level Risk Factors for Postoperative Stroke.

BACKGROUND AND PURPOSE: Many studies supporting the association between specific surgical procedure categories and postoperative stroke (POS) do not account for differences in patient-level characteristics between and within surgical categories. The risk of POS after high-risk procedure categories remains unknown after adjusting for such differences in patient-level characteristics. METHODS: Using inpatients in the American College of Surgeons National Surgical Quality Initiative Program database, we conducted a retrospective cohort study between January 1, 2000, and December 31, 2010. Our primary outcome was POS within 30 days of surgery. We characterized the relationship between surgical- and individual patient-level factors and POS by using multivariable, multilevel logistic regression that accounted for clustering of patient-level factors with surgical categories. RESULTS: We identified 729 886 patients, 2703 (0.3%) of whom developed POS. Dependent functional status (odds ratio [OR]: 4.11, 95% confidence interval [95% CI]: 3.60-4.69), history of stroke (OR: 2.35, 95%CI: 2.06-2.69) or transient ischemic attack (OR: 2.49 95%CI: 2.19-2.83), active smoking (OR: 1.20, 95%CI: 1.08-1.32), hypertension (OR: 2.11, 95%CI: 2.19-2.82), chronic obstructive pulmonary disease (OR: 1.39 95%CI: 1.21-1.59), and acute renal failure (OR: 2.35, 95%CI: 1.85-2.99) were significantly associated with POS. After adjusting for clustering, patients who underwent cardiac (OR: 11.25, 95%CI: 8.52-14.87), vascular (OR: 4.75, 95%CI: 3.88-5.82), neurological (OR: 4.60, 95%CI: 3.48-6.08), and general surgery (OR: 1.40, 95%CI: 1.15-1.70) had significantly greater odds of POS compared to patients undergoing other types of surgical procedures. CONCLUSIONS: Vascular, cardiac, and neurological surgery remained strongly associated with POS in an analysis accounting for the association between patient-level factors and surgical categories.

Geographic Analysis of Mobile Stroke Unit Treatment in a Dense Urban Area: The New York City METRONOME Registry.

Background Mobile stroke units (MSUs) reduce time to intravenous thrombolysis in acute ischemic stroke. Whether this advantage exists in densely populated urban areas with many proximate hospitals is unclear. Methods and Results We evaluated patients from the METRONOME (Metropolitan New York Mobile Stroke) registry with suspected acute ischemic stroke who were transported by a bi-institutional MSU operating in Manhattan, New York, from October 2016 to September 2017. The comparison group included patients transported to our hospitals via conventional ambulance for acute ischemic stroke during the same hours of MSU operation (Monday to Friday, 9 am to 5 pm). Our exposure was MSU care, and our primary outcome was dispatch-to-thrombolysis time. We estimated mean differences in the primary outcome between both groups, adjusting for clinical, demographic, and geographic factors, including numbers of nearby designated stroke centers and population density. We identified 66 patients treated or transported by MSU and 19 patients transported by conventional ambulance. Patients receiving MSU care had significantly shorter dispatch-to-thrombolysis time than patients receiving conventional care (mean: 61.2 versus 91.6 minutes; P=0.001). Compared with patients receiving conventional care, patients receiving MSU care were significantly more likely to be picked up closer to a higher mean number of designated stroke centers in a 2.0-mile radius (4.8 versus 2.7, P=0.002). In multivariable analysis, MSU care was associated with a mean decrease in dispatch-to-thrombolysis time of 29.7 minutes (95% CI, 6.9-52.5) compared with conventional care. Conclusions In a densely populated urban area with a high number of intermediary stroke centers, MSU care was associated with substantially quicker time to thrombolysis compared with conventional ambulance care.

Neurological Dashboards and Consultation Turnaround Time at an Academic Medical Center.

BACKGROUND: Neurologists perform a significant amount of consultative work. Aggregative electronic health record (EHR) dashboards may help to reduce consultation turnaround time (TAT) which may reflect time spent interfacing with the EHR. OBJECTIVES: This study was aimed to measure the difference in TAT before and after the implementation of a neurological dashboard. METHODS: We retrospectively studied a neurological dashboard in a read-only, web-based, clinical data review platform at an academic medical center that was separate from our institutional EHR. Using our EHR, we identified all distinct initial neurological consultations at our institution that were completed in the 5 months before, 5 months after, and 12 months after the dashboard go-live in December 2017. Using log data, we determined total dashboard users, unique page hits, patient-chart accesses, and user departments at 5 months after go-live. We calculated TAT as the difference in time between the placement of the consultation order and completion of the consultation note in the EHR. RESULTS: By April 30th in 2018, we identified 269 unique users, 684 dashboard page hits (median hits/user 1.0, interquartile range [IQR] = 1.0), and 510 unique patient-chart accesses. In 5 months before the go-live, 1,434 neurology consultations were completed with a median TAT of 2.0 hours (IQR = 2.5) which was significantly longer than during 5 months after the go-live, with 1,672 neurology consultations completed with a median TAT of 1.8 hours (IQR = 2.2; p = 0.001). Over the following 7 months, 2,160 consultations were completed and median TAT remained unchanged at 1.8 hours (IQR = 2.5). CONCLUSION: At a large academic institution, we found a significant decrease in inpatient consult TAT 5 and 12 months after the implementation of a neurological dashboard. Further study is necessary to investigate the cognitive and operational effects of aggregative dashboards in neurology and to optimize their use.

Associations between cerebrovascular risk factors and parkinson disease.

OBJECTIVE: To determine whether cerebrovascular risk factors are associated with subsequent diagnoses of Parkinson disease, and whether these associations are similar in magnitude to those with subsequent diagnoses of Alzheimer disease. METHODS: This was a retrospective cohort study using claims data from a 5% random sample of Medicare beneficiaries from 2008 to 2015. The exposures were stroke, atrial fibrillation, coronary disease, hyperlipidemia, hypertension, sleep apnea, diabetes mellitus, heart failure, peripheral vascular disease, chronic kidney disease, chronic obstructive pulmonary disease, valvular heart disease, tobacco use, and alcohol abuse. The primary outcome was a new diagnosis of idiopathic Parkinson disease. The secondary outcome was a new diagnosis of Alzheimer disease. Marginal structural Cox models adjusting for time-dependent confounding were used to characterize the association between exposures and outcomes. We also evaluated the association between cerebrovascular risk factors and subsequent renal colic (negative control). RESULTS: Among 1,035,536 Medicare beneficiaries followed for a mean of 5.2 years, 15,531 (1.5%) participants were diagnosed with Parkinson disease and 81,974 (7.9%) were diagnosed with Alzheimer disease. Most evaluated cerebrovascular risk factors, including prior stroke (hazard ratio = 1.55; 95% confidence interval = 1.39-1.72), were associated with the subsequent diagnosis of Parkinson disease. The magnitudes of these associations were similar, but attenuated, to the associations between cerebrovascular risk factors and Alzheimer disease. Confirming the validity of our analytical model, most cerebrovascular risk factors were not associated with the subsequent diagnosis of renal colic. INTERPRETATION: Cerebrovascular risk factors are associated with Parkinson disease, an effect comparable to their association with Alzheimer disease. ANN NEUROL 2019;86:572-581.

Impact of Patient Language on Emergency Medical Service Use and Prenotification for Acute Ischemic Stroke.

BACKGROUND AND PURPOSE: Use of emergency medical services (EMS) is associated with decreased door-to-needle time in acute ischemic stroke (AIS). Whether patient language affects EMS utilization and prenotification in AIS has been understudied. We sought to characterize EMS use and prenotification by patient language among intravenous tissue plasminogen activator (IV-tPA) tissue plasminogen (IV-tPA) treated patients at a single center with a large Spanish-speaking patient population. METHODS: We performed a retrospective analysis of all patients who received IV-tPA in our emergency department between July 2011 and June 2016. Baseline characteristics, EMS use, and prenotification were compared between English- and Spanish-speaking patients. Logistic regression was used to measure the association between patient language and EMS use. RESULTS: Of 391 patients who received IV-tPA, 208 (53%) primarily spoke English and 174 (45%) primarily spoke Spanish. Demographic and clinical factors including National Institutes of Health Stroke Scale (NIHSS) did not differ between language groups. Emergency medical services use was higher among Spanish-speaking patients (82% vs 70%; P < .01). Prenotification did not differ by language (61% vs 63%; P = .8). In a multivariable model adjusted for age, sex, and NIHSS, Spanish speakers remained more likely to use EMS (odds ratio: 1.8, 95% confidence interval: 1.1-3.0). CONCLUSION: Emergency medical services usage was higher in Spanish speakers compared to English speakers among AIS patients treated with IV-tPA; however, prenotification rates did not differ. Future studies should evaluate differences in EMS utilization according to primary language and ethnicity.

Ischemic Stroke After Emergency Department Discharge for Symptoms of Transient Neurological Attack.

BACKGROUND AND PURPOSE: The significance of transient neurological attack (TNA) symptoms is unclear. We sought to determine the risk of ischemic stroke after discharge from the emergency department (ED) with a diagnosis consistent with symptoms of TNA. METHODS: Using administrative claims data, we identified patients discharged from EDs in New York between 2006 and 2012 with a primary discharge diagnosis of a TNA symptom, defined as altered mental status, generalized weakness, and sensory changes. The primary outcome was ischemic stroke. We used Kaplan-Meier survival statistics to calculate cumulative rates, and Cox regression to compare stroke risk after TNA versus after transient ischemic attack (TIA; positive control) or renal colic (negative control) while adjusting for demographics and vascular risk factors. RESULTS: Of 499 369 patients diagnosed with a TNA symptom and discharged from the ED, 7756 were hospitalized for ischemic stroke over a period of 4.7 (±1.9) years. At 90 days, the cumulative stroke rate was 0.29% (95% confidence interval [CI]: 0.28%-0.31%) after TNA symptoms versus 2.08% (95% CI: 1.89%-2.28%) after TIA and 0.03% (95% CI: 0.02%-0.04%) after renal colic. The hazard ratio (HR) of stroke was higher after TNA than after renal colic (HR: 2.13; 95% CI: 1.90-2.40) but significantly lower than after TIA (HR: 0.47; 95% CI: 0.44-0.50). Compared to TIA, TNA was less strongly associated with stroke among patients under 60 years of age compared to those over 60. CONCLUSIONS: Patients discharged from the ED with TNA symptoms faced a higher risk of ischemic stroke than patients with renal colic, but the magnitude of stroke risk was low, particularly compared to TIA.

Risk of seizures and status epilepticus in older patients with liver disease.

OBJECTIVE: Seizures can be provoked by systemic diseases associated with metabolic derangements, but the association between liver disease and seizures remains unclear. METHODS: We performed a retrospective cohort study using inpatient and outpatient claims between 2008 and 2015 from a nationally representative 5% sample of Medicare beneficiaries. The primary exposure variable was cirrhosis, and the secondary exposure was mild, noncirrhotic liver disease. The primary outcome was seizure, and the secondary outcome was status epilepticus. Diagnoses were ascertained using validated International Classification of Diseases, Ninth Edition, Clinical Modification codes. Survival statistics were used to calculate incidence rates, and Cox proportional hazards models were used to examine the association between exposures and outcomes while adjusting for seizure risk factors. RESULTS: Among 1 782 402 beneficiaries, we identified 10 393 (0.6%) beneficiaries with cirrhosis and 19 557 (1.1%) with mild, noncirrhotic liver disease. Individuals with liver disease were older and had more seizure risk factors than those without liver disease. Over 4.6 ± 2.2 years of follow-up, 49 843 (2.8%) individuals were diagnosed with seizures and 25 patients (0.001%) were diagnosed with status epilepticus. Cirrhosis was not associated with seizures (hazard ratio [HR] = 1.1, 95% confidence interval [CI] = 1.0-1.3), but there was an association with status epilepticus (HR = 1.9, 95% CI = 1.3-2.8). Mild liver disease was not associated with a higher risk of seizures (HR = 0.8, 95% CI = 0.6-0.9) or status epilepticus (HR = 1.1, 95% CI = 0.7-1.5). SIGNIFICANCE: In a large, population-based cohort, we found an association between cirrhosis and status epilepticus, but no overall association between liver disease and seizures.

Clinical Information Systems Integration in New York City's First Mobile Stroke Unit.

BACKGROUND: Mobile stroke units (MSUs) reduce time to thrombolytic therapy in acute ischemic stroke. These units are widely used, but the clinical information systems underlying MSU operations are understudied. OBJECTIVE: The first MSU on the East Coast of the United States was established at New York Presbyterian Hospital (NYP) in October 2016. We describe our program's 7-month pilot, focusing on the integration of our hospital's clinical information systems into our MSU to support patient care and research efforts. METHODS: NYP's MSU was staffed by two paramedics, one radiology technologist, and a vascular neurologist. The unit was equipped with four laptop computers and networking infrastructure enabling all staff to access the hospital intranet and clinical applications during operating hours. A telephone-based registration procedure registered patients from the field into our admit/discharge/transfer system, which interfaced with the institutional electronic health record (EHR). We developed and implemented a computerized physician order entry set in our EHR with prefilled values to permit quick ordering of medications, imaging, and laboratory testing. We also developed and implemented a structured clinician note to facilitate care documentation and clinical data extraction. RESULTS: Our MSU began operating on October 3, 2016. As of April 27, 2017, the MSU transported 49 patients, of whom 16 received tissue plasminogen activator (t-PA). Zero technical problems impacting patient care were reported around registration, order entry, or intranet access. Two onboard network failures occurred, resulting in computed tomography scanner malfunctions, although no patients became ineligible for time-sensitive treatment as a result. Thirteen (26.5%) clinical notes contained at least one incomplete time field. CONCLUSION: The main technical challenges encountered during the integration of our hospital's clinical information systems into our MSU were onboard network failures and incomplete clinical documentation. Future studies are necessary to determine whether such integrative efforts improve MSU care quality, and which enhancements to information systems will optimize clinical care and research efforts.

Modeling the Impact of Interhospital Transfer Network Design on Stroke Outcomes in a Large City.

BACKGROUND AND PURPOSE: We sought to model the effects of interhospital transfer network design on endovascular therapy eligibility and clinical outcomes of stroke because of large-vessel occlusion for the residents of a large city. METHODS: We modeled 3 transfer network designs for New York City. In model A, patients were transferred from spoke hospitals to the closest hub hospitals with endovascular capabilities irrespective of hospital affiliation. In model B, which was considered the base case, patients were transferred to the closest affiliated hub hospitals. In model C, patients were transferred to the closest affiliated hospitals, and transfer times were adjusted to reflect full implementation of streamlined transfer protocols. Using Monte Carlo methods, we simulated the distributions of endovascular therapy eligibility and good functional outcomes (modified Rankin Scale score, 0-2) in these models. RESULTS: In our models, 200 patients (interquartile range [IQR], 168-227) with a stroke amenable to endovascular therapy present to New York City spoke hospitals each year. Transferring patients to the closest hub hospital irrespective of affiliation (model A) resulted in 4 (IQR, 1-9) additional patients being eligible for endovascular therapy and an additional 1 (IQR, 0-2) patient achieving functional independence. Transferring patients only to affiliated hospitals while simulating full implementation of streamlined transfer protocols (model C) resulted in 17 (IQR, 3-41) additional patients being eligible for endovascular therapy and 3 (IQR, 1-8) additional patients achieving functional independence. CONCLUSIONS: Optimizing acute stroke transfer networks resulted in clinically small changes in population-level stroke outcomes in a dense, urban area.