Promising Use of Big Data to Increase the Efficiency and Comprehensiveness of Stroke Outcomes Research

Twenty Years of Get With The Guidelines-Stroke: Celebrating Past Successes, Lessons Learned, and Future Challenges

The Get With The Guidelines-Stroke program which, began 20 years ago, is one of the largest and most important nationally representative disease registries in the United States. Its importance to the stroke community can be gauged by its sustained growth and widespread dissemination of findings that demonstrate sustained increases in both the quality of care and patient outcomes over time. The objectives of this narrative review are to provide a brief history of Get With The Guidelines-Stroke, summarize its major successes and impact, and highlight lessons learned. Looking to the next 20 years, we discuss potential challenges and opportunities for the program.

Use of machine learning techniques for identifying ischemic stroke instead of the rule-based methods: a nationwide population-based study

BACKGROUND

Many studies have evaluated stroke using claims data; most of these studies have defined ischemic stroke using an operational definition following the rule-based method. Rule-based methods tend to overestimate the number of patients with ischemic stroke.

OBJECTIVES

We aimed to identify an appropriate algorithm for identifying stroke by applying machine learning (ML) techniques to analyze the claims data.

METHODS

We obtained the data from the Korean National Health Insurance Service database, which is linked to the Ilsan Hospital database (n = 30,897). The performance of prediction models (extreme gradient boosting [XGBoost] or gated recurrent unit [GRU]) was evaluated using the area under the receiver operating characteristic curve (AUROC), the area under precision-recall curve (AUPRC), and calibration curve.

RESULTS

In total, 30,897 patients were enrolled in this study, 3145 of whom (10.18%) had ischemic stroke. XGBoost, a tree-based ML technique, had the AUROC was 94.46% and AUPRC was 92.80%. GRU showed the highest accuracy (99.81%), precision (99.92%) and recall (99.69%).

CONCLUSIONS

We proposed recurrent neural network-based deep learning techniques to improve stroke phenotyping. This can be expected to produce rapid and more accurate results than the rule-based methods.

Care Transition Interventions to Improve Stroke Outcomes: Evidence Gaps in Underserved and Minority Populations

In many countries hospital length of stay after an acute stroke admission is typically just a few days, therefore, most of a person's recovery from stroke occurs in the community. Care transitions, which occur when there is a change in, or handoff between 2 different care settings or providers, represent an especially vulnerable period for patients and caregivers. For some patients with stroke the return home is associated with substantial practical, psychosocial, and health-related challenges leading to substantial burden for the individual and caregiver. Underserved and minority populations, because of their exposure to poor environmental, social, and economic conditions, as well as structural racism and discrimination, are especially vulnerable to the problems of complicated care transitions which in turn, can negatively impact stroke recovery. Overall, there remain significant unanswered questions about how to promote optimal recovery in the post-acute care period, particularly for those from underserved communities. Evidence is limited on how best to support patients after they have returned home where they are required to navigate the chronic stages of stroke with little direct support from health professionals.

Towards better reporting of the Proportion of Days Covered method in cardiovascular medication adherence: A scoping review and new tool TEN-SPIDERS

Although medication adherence is commonly measured in electronic datasets using the proportion of days covered (PDC), no standardized approach is used to calculate and report this measure. We conducted a scoping review to understand the approaches taken to calculate and report the PDC for cardiovascular medicines to develop improved guidance for researchers using this measure. After prespecifying methods in a registered protocol, we searched Ovid Medline, Embase, Scopus, CINAHL Plus and grey literature (1 July 2012 to 14 December 2020) for articles containing the terms “proportion of days covered” and “cardiovascular medicine”, or synonyms and subject headings. Of the 523 articles identified, 316 were reviewed in full and 76 were included (93% observational studies; 47% from the USA; 2 grey literature articles). In 45 articles (59%), the PDC was measured from the first dispensing/claim date. Good adherence was defined as 80% PDC in 61 articles, 56% of which contained a rationale for selecting this threshold. The following parameters, important for deriving the PDC, were often not reported/unclear: switching (53%), early refills (45%), in‐hospital supplies (45%), presupply (28%) and survival (7%). Of the 46 articles where dosing information was unavailable, 59% reported how doses were imputed. To improve the transparent and systematic reporting of the PDC, we propose the TEN‐SPIDERS tool, covering the following PDC parameters: Threshold, Eligibility criteria, Numerator and denominator, Survival, Presupply, In‐hospital supplies, Dosing, Early Refills, and Switching. Use of this tool will standardize reporting of the PDC to facilitate reliable comparisons of medication adherence estimates between studies.

The Allure of Big Data to Improve Stroke Outcomes: Review of Current Literature

PURPOSE OF REVIEW

To critically appraise literature on recent advances and methods using "big data" to evaluate stroke outcomes and associated factors.

RECENT FINDINGS

Recent big data studies provided new evidence on the incidence of stroke outcomes, and important emerging predictors of these outcomes. Main highlights included the identification of COVID-19 infection and exposure to a low-dose particulate matter as emerging predictors of mortality post-stroke. Demographic (age, sex) and geographical (rural vs. urban) disparities in outcomes were also identified. There was a surge in methodological (e.g., machine learning and validation) studies aimed at maximizing the efficiency of big data for improving the prediction of stroke outcomes. However, considerable delays remain between data generation and publication. Big data are driving rapid innovations in research of stroke outcomes, generating novel evidence for bridging practice gaps. Opportunity exists to harness big data to drive real-time improvements in stroke outcomes.

Incidence and Outcomes of Hemorrhagic Stroke among Adults in Spain (2016-2018) According to Sex: A Retrospective, Cohort, Observational, Propensity Score Matched Study

(1) Background: We aim to analyze sex differences in the incidence, clinical characteristics and in-hospital outcomes of hemorrhagic stroke (HS) in Spain (2016–2018) using the National Hospital Discharge Database. (2) Methods: Retrospective, cohort, observational study. We estimated the incidence of HS in men and women. We analyzed comorbidity, treatments, procedures, and hospital outcomes. We matched each woman with a man by age, type of HS and medical conditions using propensity score matching. (3) Results: HS was coded in 57,227 patients aged ≥18 years (44.3% women). Overall, men showed higher incidence rates (57.3/10⁵ vs. 43.0/10⁵; p < 0.001; IRR = 1.60; 95% CI: 1.38–1.83). Women suffered more subarachnoid hemorrhages (25.2% vs. 14.6%), whereas men more often had intracerebral hemorrhages (55.7% vs. 54.1%). In-hospital mortality (IHM) was higher for intracerebral hemorrhage in both men and women. Women underwent decompressive craniectomy less often than men (5.0% vs. 6.2%; p < 0.001). After matching, IHM among women was higher (29.0% vs. 23.7%; p < 0.001). Increments in age, comorbidity and use of anticoagulants and antiplatelet agents prior to hospitalization were associated were higher IHM, and decompressive craniectomy was associated with lower IHM in both sexes. After multivariable adjustment, women had higher IHM (OR = 1.23; 95% CI: 1.18–1.28). (4) Conclusion: Men had higher incidence rates of HS than women. Women less often underwent decompressive craniectomy. IHM was higher among women admitted for HS than among men.

Sex-related disparities in the incidence and outcomes of hemorrhagic stroke among type 2 diabetes patients: a propensity score matching analysis using the Spanish National Hospital Discharge Database for the period 2016-18

Background

To analyze incidence, use of therapeutic procedures, use of oral anticoagulants (OACs) and antiplatelet agents prior to hospitalization, and in-hospital outcomes among patients who were hospitalized with hemorrhagic stroke (HS) according to the presence of type 2 diabetes mellitus (T2DM) in Spain (2016–2018) and to assess the role of sex differences among those with T2DM.

Methods

Using the Spanish National Hospital Discharge Database we estimated the incidence of HS hospitalizations in men and women aged ≥ 35 years with and without T2DM. Propensity score matching (PSM) was used to compare population subgroups according to sex and the presence of T2DM.

Results

HS was coded in 31,425 men and 24,975 women, of whom 11,915 (21.12%) had T2DM. The adjusted incidence of HS was significantly higher in patients with T2DM (both sexes) than in non-T2DM individuals (IRR 1.15; 95% CI 1.12–1.17). The incidence of HS was higher in men with T2DM than in T2DM women (adjusted IRR 1.60; 95% CI 1.57–1.63). After PSM, men and women with T2DM have significantly less frequently received decompressive craniectomy than those without T2DM. In-hospital mortality (IHM) was higher among T2DM women than matched non-T2DM women (32.89% vs 30.83%; p = 0.037), with no differences among men. Decompressive craniectomy was significantly more common in men than in matched women with T2DM (5.81% vs. 3.33%; p < 0.001). IHM was higher among T2DM women than T2DM men (32.89% vs. 28.28%; p < 0.001). After adjusting for confounders with multivariable logistic regression, women with T2DM had a 18% higher mortality risk than T2DM men (OR 1.18; 95% CI 1.07–1.29). Use of OACs and antiplatelet agents prior to hospitalization were associated to higher IHM in men and women with and without T2DM.

Conclusions

T2DM is associated with a higher incidence of HS and with less frequent use of decompressive craniectomy in both sexes, but with higher IHM only among women. Sex differences were detected in T2DM patients who had experienced HS, with higher incidence rates, more frequent decompressive craniectomy, and lower IHM in men than in women.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-021-01334-2.

Trends in acute ischemic stroke treatments and mortality in the United States from 2012 to 2018

OBJECTIVE

The establishment of mechanical thrombectomy (MT) as a first-line treatment for select patients with acute ischemic stroke (AIS) and the expansion of stroke systems of care have been major advancements in the care of patients with AIS. In this study, the authors aimed to identify temporal trends in the usage of tissue-type plasminogen activator (tPA) and MT within the AIS population from 2012 to 2018, and the relationship to mortality.

METHODS

Using a nationwide private health insurance database, 117,834 patients who presented with a primary AIS between 2012 and 2018 in the United States were identified. The authors evaluated temporal trends in tPA and MT usage and clinical outcomes stratified by treatment and age using descriptive statistics.

RESULTS

Among patients presenting with AIS in this population, the mean age was 69.1 years (SD ± 12.3 years), and 51.7% were female. Between 2012 and 2018, the use of tPA and MT increased significantly (tPA, 6.3% to 11.8%, p < 0.0001; MT, 1.6% to 5.7%, p < 0.0001). Mortality at 90 days decreased significantly in the overall AIS population (8.7% to 6.7%, p < 0.0001). The largest reduction in 90-day mortality was seen in patients treated with MT (21.4% to 14.1%, p = 0.0414) versus tPA (11.8% to 7.0%, p < 0.0001) versus no treatment (8.3% to 6.3%, p < 0.0001). Age-standardized mortality at 90 days decreased significantly only in patients aged 71-80 years (11.4% to 7.8%, p < 0.0001) and > 81 years (17.8% to 11.6%, p < 0.0001). Mortality at 90 days stagnated in patients aged 18 to 50 years (3.0% to 2.2%, p = 0.4919), 51 to 60 years (3.8% to 3.9%, p = 0.7632), and 61 to 70 years (5.5% to 5.2%, p = 0.2448).

CONCLUSIONS

From 2012 to 2018, use of tPA and MT increased significantly, irrespective of age, while mortality decreased in the entire AIS population. The most dramatic decrease in mortality was seen in the MT-treated population. Age-standardized mortality improved only in patients older than 70 years, with no change in younger patients.

Big data registries in spine surgery research: the lurking dangers

Spine surgery research has improved considerably over the last few decades. Its' most recent growth is in large part due to the mounting increase in studies conducted using national databases and registries. With easy access to a large number of patients, the benefit of these registries has become evident. However, as with any research, this type of data must be used responsibly with the appropriate strengths and limitations kept in mind. Inappropriate use of these registries continues to be a growing concern as potentially false or inaccurate conclusions can adversely impact clinical practice. It is, therefore, the author and the readers' responsibility to acknowledge and understand the limitations of this type of data. Knowledge of methodological requirements in the use and analyses of registry data is essential to ensuring quality evidence with proper interpretation.

Automating Stroke Data Extraction From Free-Text Radiology Reports Using Natural Language Processing: Instrument Validation Study

BACKGROUND

Diagnostic neurovascular imaging data are important in stroke research, but obtaining these data typically requires laborious manual chart reviews.

OBJECTIVE

We aimed to determine the accuracy of a natural language processing (NLP) approach to extract information on the presence and location of vascular occlusions as well as other stroke-related attributes based on free-text reports.

METHODS

From the full reports of 1320 consecutive computed tomography (CT), CT angiography, and CT perfusion scans of the head and neck performed at a tertiary stroke center between October 2017 and January 2019, we manually extracted data on the presence of proximal large vessel occlusion (primary outcome), as well as distal vessel occlusion, ischemia, hemorrhage, Alberta stroke program early CT score (ASPECTS), and collateral status (secondary outcomes). Reports were randomly split into training (n=921) and validation (n=399) sets, and attributes were extracted using rule-based NLP. We reported the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the overall accuracy of the NLP approach relative to the manually extracted data.

RESULTS

The overall prevalence of large vessel occlusion was 12.2%. In the training sample, the NLP approach identified this attribute with an overall accuracy of 97.3% (95.5% sensitivity, 98.1% specificity, 84.1% PPV, and 99.4% NPV). In the validation set, the overall accuracy was 95.2% (90.0% sensitivity, 97.4% specificity, 76.3% PPV, and 98.5% NPV). The accuracy of identifying distal or basilar occlusion as well as hemorrhage was also high, but there were limitations in identifying cerebral ischemia, ASPECTS, and collateral status.

CONCLUSIONS

NLP may improve the efficiency of large-scale imaging data collection for stroke surveillance and research.

Linking Australian Stroke Clinical Registry data with Australian government Medicare and medication dispensing claims data and the potential for bias

OBJECTIVE

We aim to report the accuracy of linking data from a non-government-held clinical quality registry to national claims data and identify associated sources of systematic bias.

METHODS

Patients with stroke or transient ischaemic attack admitted to hospitals participating in the Australian Stroke Clinical Registry (AuSCR) were linked with Medicare and medication dispensings through the Australian Medicare enrolment file (MEF). The proportion of registrants in the datasets was calculated and factors associated with a non-merge assessed using multivariable analyses.

RESULTS

A total of 17,980 AuSCR registrants (January 2010 - July 2014) were submitted for linkage (median age 76 years; 46% female; 67% ischaemic stroke); the proportion merged was 97% MEF, 93% Medicare and 95% medication dispensings. Data from registrants born in Asia were less likely to link with the MEF (adjusted Odds Ratio [aOR]: 0.20; 95%Confidence Interval [CI]: 0.15, 0.27). Data for those aged 85-plus compared to those under 65 years were less likely to merge with Medicare (aOR 0.25; 95%CI:0.21, 0.30) but more likely to merge with dispensing claims data (aOR: 2.15 (95%CI:1.71, 2.69). Implications for public health: Linkage between the AuSCR, a national clinical quality registry and Commonwealth datasets was achieved and potential sources of bias were identified.

Data Sharing Under the General Data Protection Regulation: Time to Harmonize Law and Research Ethics?

Supplemental Digital Content is available in the text.

The General Data Protection Regulation (GDPR) became binding law in the European Union Member States in 2018, as a step toward harmonizing personal data protection legislation in the European Union. The Regulation governs almost all types of personal data processing, hence, also, those pertaining to biomedical research. The purpose of this article is to highlight the main practical issues related to data and biological sample sharing that biomedical researchers face regularly, and to specify how these are addressed in the context of GDPR, after consulting with ethics/legal experts. We identify areas in which clarifications of the GDPR are needed, particularly those related to consent requirements by study participants. Amendments should target the following: (1) restricting exceptions based on national laws and increasing harmonization, (2) confirming the concept of broad consent, and (3) defining a roadmap for secondary use of data. These changes will be achieved by acknowledged learned societies in the field taking the lead in preparing a document giving guidance for the optimal interpretation of the GDPR, which will be finalized following a period of commenting by a broad multistakeholder audience. In parallel, promoting engagement and education of the public in the relevant issues (such as different consent types or residual risk for re-identification), on both local/national and international levels, is considered critical for advancement. We hope that this article will open this broad discussion involving all major stakeholders, toward optimizing the GDPR and allowing a harmonized transnational research approach.

Validation of ICD-10-CM Diagnosis Codes for Identification of Patients with Acute Hemorrhagic Stroke in a National Health Insurance Claims Database

Purpose

The performance of the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes for identifying acute hemorrhagic stroke in Taiwan’s National Health Insurance claims database has not been assessed. This study aimed to construct and validate the case definitions for acute hemorrhagic stroke based on ICD-10-CM diagnostic codes.

Patients and Methods

From January 2018 to December 2019, all inpatient records with ICD-10-CM code of I60 or I61 in any field of the discharge diagnoses were retrieved from the hospitalization claims data and all hospitalizations with a final diagnosis of subarachnoid hemorrhage (SAH) or intracerebral hemorrhage (ICH) were identified from the stroke registry databases. The clinical diagnosis in the stroke registry was treated as the reference standard. For hospitalizations not recorded in the stroke registry, manual review of the medical records and images was done to ascertain the diagnosis. The positive predictive value (PPV) and sensitivity of various case definitions for acute hemorrhagic stroke were estimated.

Results

Among the 983 hospitalizations, 860, 111, and 12 were determined to be true-positive, false-positive, and false-negative episodes of acute hemorrhagic stroke, respectively. The PPV and sensitivity of the ICD-10-CM codes of I60 or I61 for identifying acute hemorrhagic stroke were 88.6% and 98.6%, respectively. The PPV increased to 98.2%, whereas the sensitivity decreased to 93.1% when acute hemorrhagic stroke was defined as hospitalizations in which the primary diagnosis field contained I60 or I61. Hemorrhagic transformation of ischemic stroke and concomitant cerebrovascular diseases other than SAH or ICH were the main reasons for a false-positive and false-negative diagnosis of acute hemorrhagic stroke, respectively.

Conclusion

This study demonstrated the performance of ICD-10-CM codes for identifying acute hemorrhagic stroke and may offer a reference for future claims-based stroke studies.

Performance of ICD-10-CM Diagnosis Codes for Identifying Acute Ischemic Stroke in a National Health Insurance Claims Database

Purpose

The validity of the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) coding for the identification of acute ischemic stroke (AIS) in Taiwan’s National Health Insurance claims database has not been investigated. This study aimed to construct and validate the case definition algorithms for AIS based on ICD-10-CM diagnostic codes.

Patients and Methods

This study identified all hospitalizations with ICD-10-CM code of I63* in any position of the discharge diagnoses from the inpatient claims database and all patients with a final diagnosis of AIS from the stroke registry between Jan 2018 and Dec 2019. Hospitalizations in the claims data that could be successfully linked to those in the registry data were regarded as true episodes of AIS. Otherwise, their electronic medical records and images were manually reviewed to ascertain whether they were true episodes of AIS. Using the true episodes of AIS as the reference standard, the positive predictive value (PPV) and sensitivity of various case definition algorithms for AIS were calculated.

Results

A total of 1227 hospitalizations were successfully linked. Among the 155 hospitalizations that could not be linked, 54 were determined to be true episodes of AIS. Using ICD-10-CM code of I63* in any position of the discharge diagnoses to identify AIS yielded a PPV and sensitivity of 92.7% and 99.4%, respectively. The PPV increased to 99.8% with >12% decrease in the sensitivity when AIS was restricted to those with I63* as the primary diagnosis. When AIS was defined to be I63* as the primary, first secondary, or second secondary diagnosis, both PPV and sensitivity were greater than 97%.

Conclusion

This study demonstrated the validity of various case definition algorithms for AIS based on ICD-10-CM coding and can provide a reference for future claims-based stroke research.

Assuming one dose per day yields a similar estimate of medication adherence in patients with stroke: An exploratory analysis using linked registry data

PURPOSE

Prescribed daily dose (PDD), the number of doses prescribed to be taken per day, is used to calculate medication adherence using pharmacy claims data. PDD can be substituted by (i) one dose per day (1DD), (ii) an estimate based on the 75th percentile of days taken by patients to refill a script (PDD₇₅ ) or (iii) the World Health Organization's defined daily dose (DDD). We aimed to compare these approaches for estimating the duration covered by medications and whether this affects calculated 1-year adherence to antihypertensive medications post-stroke.

METHODS

We conducted a retrospective review of prospective cohort data from the ongoing Australian Stroke Clinical Registry linked with pharmacy claims data. Adherence was calculated as the proportion of days covered (PDC) for 1DD, PDD₇₅ and DDD. Differences were assessed using Wilcoxon rank-sum tests.

RESULTS

Among 12 628 eligible patients with stroke, 10 057 (80%) were prescribed antihypertensive medications in the year after hospital discharge (78.2% aged ≥65 years, 45.2% female). Overall, the 75th percentile of patient time until next medication refill was 39 days. The greatest variations in dose regimens, estimated using person- and dose-level refill times, were for beta blockers (11.4% taking two tablets/day). There were comparable levels of adherence between 1DD and the PDD₇₅ (median PDC 91.0% vs 91.2%; P = 0.70), but adherence was slightly higher using DDD (92.3%; both P < 0.001). However, this would represent a clinically nonsignificant difference.

CONCLUSION

Adherence to antihypertensive medications shows similar estimates across standard measures of dosage in patients during the first year after an acute stroke.

Home-Time as a Surrogate Measure for Functional Outcome After Stroke: A Validation Study

Purpose

Home-time has been found to correlate well with modified Rankin Scale (mRS) scores in patients with stroke. This study aimed to determine its correlations in patients with different types of stroke at various time points after stroke in a non-Western population.

Methods

This study used linked data from multi-center stroke registry databases and a nationwide claims database of health insurance. Functional outcomes as measured with the modified Rankin Scale were obtained from the registry databases and home-time was derived from the claims database. Spearman correlation coefficients were used to assess the correlation between home-time and mRS scores. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of home-time in predicting good functional outcome.

Results

This study included 7959 patients hospitalized for stroke or transient ischemic attack (TIA), for whom mRS scores were available in 6809, 6694, and 4330 patients at 90, 180, and 365 days, respectively. Home-time was highly correlated with mRS scores at the three time-points in patients with ischemic (Spearman's rho -0.69 to -0.83) or hemorrhagic (Spearman's rho -0.86 to -0.88) stroke, but the correlation was only weak to moderate in those with TIA (Spearman's rho -0.32 to -0.58). Home-time predicted good functional outcome with excellent discrimination in patients with ischemic (AUCs >0.8) or hemorrhagic (AUCs >0.9) stroke but less so in those with TIA (AUCs >0.7).

Conclusion

Home-time was highly correlated with mRS scores and showed excellent discrimination in predicting good functional outcome in patients with ischemic or hemorrhagic stroke. Home-time could serve as a valid surrogate measure for functional outcome after stroke.

Deriving a Passive Surveillance Stroke Severity Indicator From Routinely Collected Administrative Data: The PaSSV Indicator

BACKGROUND

Adjusting for stroke severity is crucial for stroke outcomes research. However, this information is not available in administrative healthcare data. We aimed to derive an indicator of baseline stroke severity using these data.

METHODS AND RESULTS

We identified patients with stroke enrolled in a population-based registry in Ontario, Canada, and used the Canadian Neurological Scale (CNS), documented in the registry, as a measure of stroke severity. We derived an estimated CNS from a linear regression model in which we regressed the observed CNS on predictor variables: age, sex, arrival by ambulance, interhospital transfer, mechanical ventilation, and an emergency department triage score. The effect of stroke severity on the estimated hazard ratios for 30-day mortality was determined in 3 Cox-proportional hazards models with (1) no CNS, (2) observed CNS, and (3) estimated CNS, all adjusted for age, sex, Charlson index, and stroke type. We assessed model discrimination using C statistics. To assess for construct validity, we repeated these analyses in a subset of patients with documented National Institute of Health Stroke Scale and in a cohort of patients with stroke external to the registry. We derived the estimated stroke severity in 41 481 patients (48.7% female, median age of 75 years [interquartile range, 64- 83]). The magnitude of the association between stroke severity and mortality was similar for the observed and estimated CNS. The discriminative ability of the Cox-proportional hazards models to predict mortality was highest when the observed CNS was included (C statistic, 0.82 [95% CI, 0.81-0.82]), moderate with estimated CNS (0.76 [0.75-0.76]), and lowest without CNS (0.69 [0.69-0.70]. Our findings were replicated with the National Institute of Health Stroke Scale and in the external cohort.

CONCLUSIONS

We derived an estimated measure of stroke severity using administrative data. This can be applied for risk adjustment in population-based stroke outcomes research and in assessments of health system performance.

Establishment of an internationally agreed minimum data set for acute telestroke

INTRODUCTION

Globally, the use of telestroke programmes for acute care is expanding. Currently, a standardised set of variables for enabling reliable international comparisons of telestroke programmes does not exist. The aim of the study was to establish a consensus-based, minimum dataset for acute telestroke to enable the reliable comparison of programmes, clinical management and patient outcomes.

METHODS

An initial scoping review of variables was conducted, supplemented by reaching out to colleagues leading some of these programmes in different countries. An international expert panel of clinicians, researchers and managers (n = 20) from the Australasia Pacific region, USA, UK and Europe was convened. A modified-Delphi technique was used to achieve consensus via online questionnaires, teleconferences and email.

RESULTS

Overall, 533 variables were initially identified and harmonised into 159 variables for the expert panel to review. The final dataset included 110 variables covering three themes (service configuration, consultations, patient information) and 12 categories: (1) details about telestroke network/programme (n = 12), (2) details about initiating hospital (n = 10), (3) telestroke consultation (n = 17), (4) patient characteristics (n = 7), (5) presentation to hospital (n = 5), (6) general clinical care within first 24 hours (n = 10), (7) thrombolysis treatment (n = 10), (8) endovascular treatment (n = 13), (9) neurosurgery treatment (n = 8), (10) processes of care beyond 24 hours (n = 7), (11) discharge information (n = 5), (12) post-discharge and follow-up data (n = 6).

DISCUSSION

The acute telestroke minimum dataset provides a recommended set of variables to systematically evaluate acute telestroke programmes in different countries. Adoption is recommended for new and existing services.