Hospital Factors Associated With Interhospital Transfer Destination for Stroke in the Northeast United States

Differences in Interfacility Transfer from Emergency Department and Inpatient Services for Inpatient Neurologic Care

Introduction

Interhospital transfer is an important mechanism for improving access to specialized neurologic care but there are large gaps in our understanding of interhospital transfer for the management of non-stroke-related neurologic disease.

Methods

This observational study included consecutive patients admitted to an adult academic general neurology service via interhospital transfer from July 1, 2015 to July 1, 2017. Characteristics of the referring hospital and transferred patients were obtained through the American Hospital Association Directory, a hospital transfer database maintained by the accepting hospital, and the electronic medical record. The analyses used descriptive statistics to examine the cohort overall and compare characteristics of patients transferred from an emergency department and inpatient service.

Results

504 patients were admitted via interhospital transfer during the study period. Of these, 395 patients (78.4%) were transferred because the referring hospital lacked capability, and 139 patients (27.6%) were transferred from an emergency department as opposed to inpatient service. Seizures was the most common diagnosis (23.8%). Patients who were transferred from an emergency department had a higher proportion covered by Medicaid (44.6%) than those transferred from an inpatient service (28.8%) and had a shorter median length of stay (3 days; IQR 2-7 vs 7 days; IQR 4-12).

Conclusions

The majority of observed interhospital non-stroke neurologic transfers occurred to improve access to specialized neurological care for patients, though patients transferred from the ED, as opposed to an inpatient service, had lower health care utilization, and this will be important to consider when developing systems of care and in future research.

A proposed method for identifying Interfacility transfers in Medicare claims data

OBJECTIVE

To develop a method of consistently identifying interfacility transfers (IFTs) in Medicare Claims using patients with ST-Elevation Myocardial Infarction (STEMI) as an example.

DATA SOURCES/STUDY SETTING

100% Medicare inpatient and outpatient Standard Analytic Files and 5% Carrier Files, 2011-2020.

STUDY DESIGN

Observational, cross-sectional comparison of patient characteristics between proposed and existing methods.

DATA COLLECTION/EXTRACTION METHODS

We limited to patients aged 65+ with STEMI diagnosis using both proposed and existing methods.

PRINCIPAL FINDINGS

We identified 62,668 more IFTs using the proposed method (86,128 versus 23,460). A separately billable interfacility ambulance trip was found for more IFTs using the proposed than existing method (86% vs. 79%). Compared with the existing method, transferred patients under the proposed method were more likely to live in rural (p < 0.001) and lower income (p < 0.001) counties and were located farther away from emergency departments, trauma centers, and intensive care units (p < 0.001).

CONCLUSIONS

Identifying transferred patients based on two consecutive inpatient claims results in an undercount of IFTs and under-represents rural and low-income patients.

Racial and ethnic disparities in emergency department transfers to public hospitals

OBJECTIVE

To examine racial/ethnic differences in emergency department (ED) transfers to public hospitals and factors explaining these differences.

DATA SOURCES AND STUDY SETTING

ED and inpatient data from the Healthcare Cost and Utilization Project for Florida (2010-2019); American Hospital Association Annual Survey (2009-2018).

STUDY DESIGN

Logistic regression examined race/ethnicity and payer on the likelihood of transfer to a public hospital among transferred ED patients. The base model was controlled for patient and hospital characteristics and year fixed effects. Models II and III added urbanicity and hospital referral region (HRR), respectively. Model IV used hospital fixed effects, which compares patients within the same hospital. Models V and VI stratified Model IV by payer and condition, respectively. Conditions were classified as emergency care sensitive conditions (ECSCs), where transfer is protocolized, and non-ECSCs. We reported marginal effects at the means.

DATA COLLECTION/EXTRACTION METHODS

We examined 1,265,588 adult ED patients transferred from 187 hospitals.

PRINCIPAL FINDINGS

Black patients were more likely to be transferred to public hospitals compared with White patients in all models except ECSC patients within the same initial hospital (except trauma). Black patients were 0.5-1.3 percentage points (pp) more likely to be transferred to public hospitals than White patients in the same hospital with the same payer. In the base model, Hispanic patients were more likely to be transferred to public hospitals compared with White patients, but this difference reversed after controlling for HRR. Hispanic patients were - 0.6 pp to -1.2 pp less likely to be transferred to public hospitals than White patients in the same hospital with the same payer.

CONCLUSIONS

Large population-level differences in whether ED patients of different races/ethnicities were transferred to public hospitals were largely explained by hospital market and the initial hospital, suggesting that they may play a larger role in explaining differences in transfer to public hospitals, compared with other external factors.

Pediatric Patient Insurance Status and Regionalization of Admissions

BACKGROUND

Pediatric hospital care is becoming increasingly regionalized, and previous data have suggested that insurance may be associated with transfer. The aims of the study are to describe regionalization of pediatric care and density of the interhospital transfer network and to determine whether these varied by insurance status.

METHODS

Using the New York State ED Database and State Inpatient Database from 2016, we identified all pediatric patients and calculated regionalization indices (RI) and network density, overall and stratified by insurance. Regionalization indices are based on the likelihood of a patient completing care at the initial hospital. Network density is the proportion of actual transfers compared with the number of potential hospital transfer connections. Both were calculated using the standard State ED Database/State Inpatient Database transfer definition and in a sensitivity analysis, excluding the disposition code requirement.

RESULTS

We identified 1,595,566 pediatric visits (emergency department [ED] or inpatient) in New York in 2016; 7548 (0.5%) were transferred and 7374 transferred visits had eligible insurance status (Medicaid, private, uninsured). Of the transfers, 24% were from ED to ED with discharge, 28% from ED to ED with admission, 31% from ED to inpatient, 16% from inpatient to inpatient, and 1.2% from inpatient to ED. The overall RI was 0.25 (95% confidence interval [95% CI], 0.20-0.31). The overall weighted RI was 0.09 (95% CI, 0.06-0.12) and was 0.09 (95% CI, 0.06-0.13) for Medicaid-insured patients, 0.08 (95% CI, 0.05-0.11) for privately insured patients, and 0.08 (95% CI, 0.05-0.11) for patients without insurance. The overall network density was 0.018 (95% CI, 0.017-0.020). Network density was higher, and transfer rates were lower, for patients with Medicaid insurance as compared with private insurance.

CONCLUSIONS

We found significant regionalization of pediatric emergency care. Although there was not material variation by insurance in regionalization, there was variation in network density and transfer rates. Additional work is needed to understand factors affecting transfer decisions and how these patterns might vary by state.

Insurance-Based Disparities in Stroke Center Access in California: A Network Science Approach

BACKGROUND

Our objectives were to determine whether there is an association between ischemic stroke patient insurance and likelihood of transfer overall and to a stroke center and whether hospital cluster modified the association between insurance and likelihood of stroke center transfer.

METHODS

This retrospective network analysis of California data included every nonfederal hospital ischemic stroke admission from 2010 to 2017. Transfers from an emergency department to another hospital were categorized based on whether the patient was discharged from a stroke center (primary or comprehensive). We used logistic regression models to examine the relationship between insurance (private, Medicare, Medicaid, uninsured) and odds of (1) any transfer among patients initially presenting to nonstroke center hospital emergency departments and (2) transfer to a stroke center among transferred patients. We used a network clustering method to identify clusters of hospitals closely connected through transfers. Within each cluster, we quantified the difference between insurance groups with the highest and lowest proportion of transfers discharged from a stroke center.

RESULTS

Of 332 995 total ischemic stroke encounters, 51% were female, 70% were ≥65 years, and 3.5% were transferred from the initial emergency department. Of 52 316 presenting to a nonstroke center, 3466 (7.1%) were transferred. Relative to privately insured patients, there were lower odds of transfer and of transfer to a stroke center among all groups (Medicare odds ratio, 0.24 [95% CI, 0.22-0.26] and 0.59 [95% CI, 0.50-0.71], Medicaid odds ratio, 0.26 [95% CI, 0.23-0.29] and odds ratio, 0.49 [95% CI, 0.38-0.62], uninsured odds ratio, 0.75 [95% CI, 0.63-0.89], and 0.72 [95% CI, 0.6-0.8], respectively). Among the 14 identified hospital clusters, insurance-based disparities in transfer varied and the lowest performing cluster (also the largest; n=2364 transfers) fully explained the insurance-based disparity in odds of stroke center transfer.

CONCLUSIONS

Uninsured patients had less stroke center access through transfer than patients with insurance. This difference was largely explained by patterns in 1 particular hospital cluster.

Association of hospital centrality in inter-hospital patient-sharing networks with patient mortality and length of stay

OBJECTIVE

The interdependence of hospitals is underappreciated in patient outcomes studies. We used a network science approach to foreground this interdependence. Specifically, within two large state-based interhospital networks, we examined the relationship of a hospital's network position with in-hospital mortality and length of stay.

METHODS

We constructed interhospital network graphs using data from the Healthcare Cost and Utilization Project and the American Hospital Association Annual Survey for Florida (2014) and California (2011). The exposure of interest was hospital centrality, defined as weighted degree (sum of all ties to a given hospital from other hospitals). The outcomes were in-hospital mortality and length of stay with sub-analyses for four acute medical conditions: pneumonia, heart failure, ischemic stroke, myocardial infarction. We compared outcomes for each quartile of hospital centrality relative to the most central quartile (Q4), independent of patient- and hospital-level characteristics, in this retrospective cross-sectional study.

RESULTS

The inpatient cohorts had 1,246,169 patients in Florida and 1,415,728 in California. Compared to Florida's central hospitals which had an overall mortality 1.60%, peripheral hospitals had higher in-hospital mortality (1.97%, adjusted OR (95%CI): Q1 1.61 (1.37, 1.89), p<0.001). Hospitals in the middle quartiles had lower in-hospital mortality compared to central hospitals (%, adjusted OR (95% CI): Q2 1.39%, 0.79 (0.70, 0.89), p<0.001; Q3 1.33%, 0.78 (0.70, 0.87), p<0.001). Peripheral hospitals had longer lengths of stay (adjusted incidence rate ratio (95% CI): Q1 2.47 (2.44, 2.50), p<0.001). These findings were replicated in California, and in patients with heart failure and pneumonia in Florida. These results show a u-shaped distribution of outcomes based on hospital network centrality quartile.

CONCLUSIONS

The position of hospitals within an inter-hospital network is associated with patient outcomes. Specifically, hospitals located in the peripheral or central positions may be most vulnerable to diminished quality outcomes due to the network. Results should be replicated with deeper clinical data.

A Conceptual Framework for Optimizing the Equity of Hospital-Based Emergency Care: The Structure of Hospital Transfer Networks

Policy Points Current pay-for-performance and other payment policies ignore hospital transfers for emergency conditions, which may exacerbate disparities. No conceptual framework currently exists that offers a patient-centered, population-based perspective for the structure of hospital transfer networks. The hospital transfer network equity-quality framework highlights the external and internal factors that determine the structure of hospital transfer networks, including structural inequity and racism.

CONTEXT

Emergency care includes two key components: initial stabilization and transfer to a higher level of care. Significant work has focused on ensuring that local facilities can stabilize patients. However, less is understood about transfers for definitive care. To better understand how transfer network structure impacts population health and equity in emergency care, we proposea conceptual framework, the hospital transfer network equity-quality model (NET-EQUITY). NET-EQUITY can help optimize population outcomes, decrease disparities, and enhance planning by supporting a framework for understanding emergency department transfers.

METHODS

To develop the NET-EQUITY framework, we synthesized work on health systems and quality of health care (Donabedian, the Institute of Medicine, Ferlie, and Shortell) and the research framework of the National Institute on Minority Health and Health Disparities with legal and empirical research.

FINDINGS

The central thesis of our framework is that the structure of hospital transfer networks influences patient outcomes, as defined by the Institute of Medicine, which includes equity. The structure of hospital transfer networks is shaped by internal and external factors. The four main external factors are the regulatory, economic environment, provider, and sociocultural and physical/built environment. These environments all implicate issues of equity that are important to understand to foster an equitable population-based system of emergency care. The framework highlights external and internal factors that determine the structure of hospital transfer networks, including structural racism and inequity.

CONCLUSIONS

The NET-EQUITY framework provides a patient-centered, equity-focused framework for understanding the health of populations and how the structure of hospital transfer networks can influence the quality of care that patients receive.

Evaluation of factors associated with interhospital transfers to pediatric and adult tertiary level of care: A study of acute neurological disease cases

INTRODUCTION

Patient referrals to tertiary level of care neurological services are often potentially avoidable and result in inferior clinical outcomes. To decrease transfer burden, stakeholders should acquire a comprehensive perception of specialty referral process dynamics. We identified associations between patient sociodemographic data, disease category and hospital characteristics and avoidable transfers, and differentiated factors underscoring informed decision making as essential care management aspects.

MATERIALS AND METHODS

We completed a retrospective observational study. The inclusion criteria were pediatric and adult patients with neurological diagnosis referred to our tertiary care hospital. The primary outcome was potentially avoidable transfers, which included patients discharged after 24 hours from admission without requiring neurosurgery, neuro-intervention, or specialized diagnostic methodologies and consult in non-neurologic specialties during their hospital stay. Variables included demographics, disease category, health insurance and referring hospital characteristics.

RESULTS

Patient referrals resulted in 1615 potentially avoidable transfers. A direct correlation between increasing referral trends and unwarranted transfers was observed for dementia, spondylosis and trauma conversely, migraine, neuro-ophthalmic disease and seizure disorders showed an increase in unwarranted transfers with decreasing referral trends. The age group over 90 years (OR, 3.71), seizure disorders (OR, 4.16), migraine (OR, 12.50) and neuro-ophthalmic disease (OR, 25.31) significantly associated with higher probability of avoidable transfers. Disparities between pediatric and adult transfer cases were identified for discrete diagnoses. Hospital teaching status but not hospital size showed significant associations with potentially avoidable transfers.

CONCLUSIONS

Neurological dysfunctions with overlapping clinical symptomatology in ageing patients have higher probability of unwarranted transfers. In pediatric patients, disease categories with complex symptomatology requiring sophisticated workup show greater likelihood of unwarranted transfers. Future transfer avoidance recommendations include implementation of measures that assist astute disorder assessment at the referring hospital such as specialized diagnostic modalities and teleconsultation. Additional moderators include after-hours specialty expertise provision and advanced directives education.

Stroke-related length of hospitalization trends and in-hospital mortality in Peru

Background

Peru faces challenges to provide adequate care to stroke patients. Length of hospitalization and in-hospital mortality are two well-known indicators of stroke care. We aimed to describe the length of stay (LOS) of stroke in Peru, and to assess in-hospital mortality risk due to stroke, and subtypes.

Methods

This retrospective cohort study used hospitalization registries coding with ICD-10 from 2002 to 2017 (N = 98,605) provided by the Ministry of Health; in-hospital mortality was available for 2016-2017 (N = 6,566). Stroke cases aged ≥35 years were divided into subarachnoid hemorrhage (I60), intracerebral hemorrhage (I61), cerebral infarction (I63), and stroke not specified as hemorrhage or infarction (I64). Data included stroke LOS and in-hospital mortality; socio-demographic and clinical variables. We fitted a region- and hospital level-stratified Weibull proportional hazard model to assess the in-hospital mortality.

Results

The median LOS was 7 days (IQR: 4-13). Hemorrhagic strokes had median LOS longer than ischemic strokes and stroke not specified as hemorrhage or infarction (P = <0.001). The case fatality rate (CFR) of patients with stroke was 11.5% (95% CI [10-12%]). Subarachnoid hemorrhage (HR = 2.45; 95% CI [1.91-3.14]), intracerebral hemorrhage (HR = 1.95; 95% CI [1.55-2.46]), and stroke not specified as hemorrhage or infarction (HR = 1.45; 95% CI [1.16-1.81]) were associated with higher in-hospital mortality risk in comparison to ischemic strokes.

Discussion

Between 2002 and 2017, LOS due to stroke has not changed in Peru in stroke patients discharged alive. Hemorrhagic cases had the longest LOS and highest in-hospital mortality risk during 2016 and 2017. The findings of our study seem to be consistent with a previous study carried out in Peru and similar to that of HIC and LMIC, also there is an increased median LOS in stroke cases managed in specialized centers. Likewise, LOS seems to depend on the type of stroke, where ischemic stroke cases have the lowest LOS. Peru needs to improve access to stroke care.

Comparison of Factors Associated With Direct Versus Transferred-in Admission to Government-Designated Regional Centers Between Acute Ischemic Stroke and Myocardial Infarction in Korea

BACKGROUND

There has been no comparison of the determinants of admission route between acute ischemic stroke (AIS) and acute myocardial infarction (AMI). We examined whether factors associated with direct versus transferred-in admission to regional cardiocerebrovascular centers (RCVCs) differed between AIS and AMI.

METHODS

Using a nationwide RCVC registry, we identified consecutive patients presenting with AMI and AIS between July 2016 and December 2018. We explored factors associated with direct admission to RCVCs in patients with AIS and AMI and examined whether those associations differed between AIS and AMI, including interaction terms between each factor and disease type in multivariable models. To explore the influence of emergency medical service (EMS) paramedics on hospital selection, stratified analyses according to use of EMS were also performed.

RESULTS

Among the 17,897 and 8,927 AIS and AMI patients, 66.6% and 48.2% were directly admitted to RCVCs, respectively. Multivariable analysis showed that previous coronary heart disease, prehospital awareness, higher education level, and EMS use increased the odds of direct admission to RCVCs, but the odds ratio (OR) was different between AIS and AMI (for the first 3 factors, AMI > AIS; for EMS use, AMI < AIS). EMS use was the single most important factor for both AIS and AMI (OR, 4.72 vs. 3.90). Hypertension and hyperlipidemia increased, while living alone decreased the odds of direct admission only in AMI; additionally, age (65-74 years), previous stroke, and presentation during non-working hours increased the odds only in AIS. EMS use weakened the associations between direct admission and most factors in both AIS and AMI.

CONCLUSIONS

Various patient factors were differentially associated with direct admission to RCVCs between AIS and AMI. Public education for symptom awareness and use of EMS is essential in optimizing the transportation and hospitalization of patients with AMI and AIS.

Influence of Hospital Characteristics on Hospital Transfer Destinations for Patients With Stroke

BACKGROUND

Patients with stroke are frequently transferred between hospitals. This may have implications on the quality of care received by patients; however, it is not well understood how the characteristics of sending and receiving hospitals affect the likelihood of a transfer event. Our objective was to identify hospital characteristics associated with sending and receiving patients with stroke.

METHODS

Using a comprehensive statewide administrative dataset, including all 78 Massachusetts hospitals, we identified all transfers of patients with ischemic stroke between October 2007 and September 2015 for this observational study. Hospital variables included reputation (US News and World Report ranking), capability (stroke center status, annual stroke volume, and trauma center designation), and institutional affiliation. We included network variables to control for the structure of hospital-to-hospital transfers. We used relational event modeling to account for complex temporal and relational dependencies associated with transfers. This method decomposes a series of patient transfers into a sequence of decisions characterized by transfer initiations and destinations, modeling them using a discrete-choice framework.

RESULTS

Among 73 114 ischemic stroke admissions there were 7189 (9.8%) transfers during the study period. After accounting for travel time between hospitals and structural network characteristics, factors associated with increased likelihood of being a receiving hospital (in descending order of relative effect size) included shared hospital affiliation (5.8× higher), teaching hospital status (4.2× higher), stroke center status (4.3× and 3.8× higher when of the same or higher status), and hospitals of the same or higher reputational ranking (1.5× higher).

CONCLUSIONS

After accounting for distance and structural network characteristics, in descending order of importance, shared hospital affiliation, hospital capabilities, and hospital reputation were important factor in determining transfer destination of patients with stroke. This study provides a starting point for future research exploring how relational coordination between hospitals may ensure optimized allocation of patients with stroke for maximal patient benefit.

The relationship between stroke system organization and disparities in access to stroke center care in California

Background

There are significant racial and ethnic disparities in receipt of reperfusion interventions for acute ischemic stroke. Our objective was to determine whether there are disparities in access to stroke center care by race or ethnicity that help explain differences in reperfusion therapy and to understand whether interhospital patient transfer plays a role in improving access.

Methods

Using statewide administrating data including all emergency department and hospital discharges in California from 2010 to 2017, we identified all acute ischemic stroke patients. Primary outcomes of interest included presentation to primary or comprehensive stroke center (PSC or CSC), interhospital transfer, discharge from PSC or CSC, and discharge from CSC alone. We used hierarchical logistic regression modeling to identify the relationship between patient- and hospital-level characteristics and outcomes of interest.

Results

Of 336,247 ischemic stroke patients, 55.4% were non-Hispanic White, 19.6% Hispanic, 10.6% non-Hispanic Asian/Pacific Islander, and 10.3% non-Hispanic Black. There was no difference in initial presentation to stroke center hospitals between groups. However, adjusted odds of reperfusion intervention, interhospital transfer and discharge from CSC did vary by race and ethnicity. Adjusted odds of interhospital transfer were lower among Hispanic (odds ratio [OR] 0.94, 95% confidence interval [CI] 0.89 to 0.98) and non-Hispanic Asian/Pacific Islander patients (OR 0.84, 95% CI 0.79 to 0.90) and odds of discharge from a CSC were lower for Hispanic (OR 0.91, 95% CI 0.85 to 0.97) and non-Hispanic Black patients (OR 0.74, 95% CI 0.67 to 0.81).

Conclusions

There are racial and ethnic disparities in reperfusion intervention receipt among stroke patients in California. Stroke system of care design, hospital resources, and transfer patterns may contribute to this disparity.

Association of Hospital Telestroke Adoption With Changes in Initial Hospital Presentation and Transfers Among Patients With Stroke and Transient Ischemic Attacks

IMPORTANCE

It has been proposed that the implementation of telestroke services (a web-based approach to using video telecommunication to treat patients with stroke before hospital admission) changes where patients with stroke symptoms receive care, but this proposal has not been rigorously assessed.

OBJECTIVE

To assess whether the implementation of telestroke services is associated with changes in where and how patients initially present with stroke symptoms, in their decision to be transferred to another hospital, and which hospitals they are transferred to.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study compared changes in stroke systems of care between a sample of 593 US hospitals that adopted telestroke during the period from 2009 to 2016 but were not comprehensive stroke centers, major teaching hospitals, or thrombectomy-capable hospitals vs 593 matched control hospitals without telestroke based on rural location, critical access hospital status, bed size, primary stroke center status, presence of hospital alternatives in the community, hospital stroke volume, census region, and ownership. With the use of data on 100% of Medicare fee-for-service beneficiaries, all stroke and transient ischemic attack admissions from 2008 to 2018 were identified.

EXPOSURES

For each hospital pair (telestroke plus matched control), the telestroke hospital's implementation date and difference-in-differences approach were used to quantify the association between telestroke implementation and changes in care from 2 years before implementation to 2 years after implementation. Models also controlled for differences in observed patient characteristics.

MAIN OUTCOMES AND MEASURES

Hospital stroke volume, patients' ambulance transport distance to initial hospital, hospital case mix, interhospital transfer proportion, and size of the receiving hospital for transferred patients.

RESULTS

Of the 669 telestroke hospitals and 2143 potential control hospitals, 593 hospital pairs were matched; in each category, 261 hospitals (44.0%) were located in a rural area, 179 (30.2%) were primary stroke centers, and 130 (21.9%) were critical access hospitals. The changes in the preimplementation to postimplementation period were similar at telestroke and control hospitals in mean annual stroke volume (telestroke hospitals, decreased from 79.6 to 76.3 patients; control hospitals, decreased from 78.8 to 75.5 patients [-3.3 patients per year for both; difference-in-differences, 0.009; P ≥ .99]). Similarly, no differences were seen in ambulance transport distance, case mix, interhospital transfers, or bed size of receiving hospitals among transferred patients.

CONCLUSIONS AND RELEVANCE

This study suggests that, across a national sample of hospitals implementing telestroke, no association between telestroke adoption and changes in stroke systems of care were found.

Reperfusion Treatment and Stroke Outcomes in Hospitals With Telestroke Capacity

Importance

Telestroke is increasingly used in hospital emergency departments, but there has been limited research on its impact on treatment and outcomes.

Objective

To describe differences in care patterns and outcomes among patients with acute ischemic stroke who present to hospitals with and without telestroke capacity.

Design, Setting, and Participants

Patients with acute ischemic stroke who first presented to hospitals with telestroke capacity were matched with patients who presented to control hospitals without telestroke capacity. All traditional Medicare beneficiaries with a primary diagnosis of acute ischemic stroke (approximately 2.5 million) who presented to a hospital between January 2008 and June 2017 were considered. Matching was based on sociodemographic and clinical characteristics, hospital characteristics, and month and year of admission. Hospitals included short-term acute care and critical access hospitals in the US without local stroke expertise. In 643 hospitals with telestroke capacity, there were 76 636 patients with stroke who were matched 1:1 to patients at similar hospitals without telestroke capacity. Data were analyzed in July 2020.

Main Outcomes and Measures

Receipt of reperfusion treatment through thrombolysis with alteplase or thrombectomy, mortality at 30 days from admission, spending through 90 days from admission, and functional status as measured by days spent living in the community after discharge.

Results

In the final sample of 153 272 patients, 88 386 (57.7%) were female, and the mean (SD) age was 78.8 (10.4) years. Patients cared for at telestroke hospitals had higher rates of reperfusion treatment compared with those cared for at control hospitals (6.8% vs 6.0%; difference, 0.78 percentage points; 95% CI, 0.54-1.03; P < .001) and lower 30-day mortality (13.1% vs 13.6%; difference, 0.50 percentage points; 95% CI, 0.17-0.83, P = .003). There were no differences in days spent living in the community following discharge or in spending. Increases in reperfusion treatment were largest in the lowest-volume hospitals, among rural residents, and among patients 85 years and older.

Conclusions and Relevance

Patients with ischemic stroke treated at hospitals with telestroke capacity were more likely to receive reperfusion treatment and have lower 30-day mortality.

Strategy for reliable identification of ischaemic stroke, thrombolytics and thrombectomy in large administrative databases

Background

Administrative data are frequently used in stroke research. Ensuring accurate identification of patients who had an ischaemic stroke, and those receiving thrombolysis and endovascular thrombectomy (EVT) is critical to ensure representativeness and generalisability. We examined differences in patient samples based on mode of identification, and propose a strategy for future patient and procedure identification in large administrative databases.

Methods

We used non-public administrative data from the state of California to identify all patients who had an ischaemic stroke discharged from an emergency department (ED) or inpatient hospitalisation from 2010 to 2017 based on International Classification of Disease (ICD-9) (2010–2015), ICD-10 (2015–2017) and Medicare Severity-Diagnosis-related Group (MS-DRG) discharge codes. We identified patients with interhospital transfers, patients receiving thrombolytics and patients treated with EVT based on ICD, Current Procedural Terminology (CPT) and MS-DRG codes. We determined what proportion of these transfers and procedures would have been identified with ICD versus MS-DRG discharge codes.

Results

Of 365 099 ischaemic stroke encounters, most (87.70%) had both a stroke-related ICD-9 or ICD-10 code and stroke-related MS-DRG code; 12.28% had only an ICD-9 or ICD-10 code and 0.02% had only an MS-DRG code. Nearly all transfers (99.99%) were identified using ICD codes. We identified 32 433 thrombolytic-treated patients (8.9% of total) using ICD, CPT and MS-DRG codes; the combination of ICD and CPT codes identified nearly all (98%). We identified 7691 patients treated with EVT (2.1% of total) using ICD and MS-DRG codes; both MS-DRG and ICD-9/ICD-10 codes were necessary because ICD codes alone missed 13.2% of EVTs. CPT codes only pertain to outpatient/ED patients and are not useful for EVT identification.

Conclusions

ICD-9/ICD-10 diagnosis codes capture nearly all ischaemic stroke encounters and transfers, while the combination of ICD-9/ICD-10 and CPT codes are adequate for identifying thrombolytic treatment in administrative datasets. However, MS-DRG codes are necessary in addition to ICD codes for identifying EVT, likely due to favourable reimbursement for EVT-related MS-DRG codes incentivising accurate coding.

Rural Stroke Patients Have Higher Mortality: An Improvement Opportunity for Rural Emergency Medical Services Systems

PURPOSE

Early recognition and prompt prehospital care is a cornerstone of acute stroke treatment. Residents of rural areas have worse access to stroke services than urban residents. The purpose of this study was to (1) describe US trends in rural-urban stroke mortality and (2) identify possible factors associated with rural-urban stroke case-fatality disparities.

METHODS

This study was a nationwide retrospective cohort study of stroke admissions. The primary exposure was rurality of patient's residence. The primary outcome was death during hospital encounter. The secondary outcome was discharge to a care facility or home healthcare. Univariable and multivariable logistic regressions estimated the odds of mortality by subject rurality among stroke subjects.

FINDINGS

Rural stroke subjects had higher mortality than nonrural counterparts (18.6% rural vs 16.9% nonrural). After adjustment for patient and hospital factors, patient rurality was associated with increased odds of mortality (aOR = 1.11; 95% CI: 1.06-1.15; P < .001). For the secondary outcome of discharge to home, rural stroke subjects were less likely to be discharged to a care facility than nonrural stroke visits (aOR 0.94; 95% CI: 0.91-0.97; P < .001). Results were similar after adjusting for thrombolytics administration and transfer status.

CONCLUSIONS

Rural stroke patients have higher mortality than their urban counterparts likely due to their increased burden of chronic disease, lower health literacy, and reduced access to prompt prehospital care. There may be an opportunity for emergency medical services systems to assist in increasing stroke awareness for both patients and clinicians and to establish response patterns to expedite emergency care.

Hospital Factors Associated With Interhospital Transfer Destination for Stroke in the Northeast United States

Background

We aimed to determine if there is an association between hospital quality and the likelihood of a given hospital being a preferred transfer destination for stroke patients.

Methods and Results

Data from Medicare claims identified acute ischemic stroke transferred between 394 northeast US hospitals from 2007 to 2011. Hospitals were categorized as transferring (n=136), retaining (n=241), or receiving (n=17) hospitals based on the proportion of acute ischemic stroke encounters transferred or received. We identified all 6409 potential dyads of sending and receiving hospitals, and categorized dyads as connected if ≥5 patients were transferred between the hospitals annually (n=82). We used logistic regression to identify hospital characteristics associated with establishing a connected dyad, exploring the effect of adjusting for different quality measures and outcomes. We also adjusted for driving distance between hospitals, receiving hospital stroke volume, and the number of hospitals in the receiving hospital referral region. The odds of establishing a transfer connection increased when rate of alteplase administration increased at the receiving hospital or decreased at the sending hospital, however this finding did not hold after applying a potential strategy to adjust for clustering. Receiving hospital performance on 90‐day home time was not associated with likelihood of transfer connection.

Conclusions

Among northeast US hospitals, we found that differences in hospital quality, specifically higher levels of alteplase administration, may be associated with increased likelihood of being a transfer destination. Further research is needed to better understand acute ischemic stroke transfer patterns to optimize stroke transfer systems.