Abstract
Background
Transient ischemic attack (TIA) carries a high risk of stroke, necessitating immediate evaluation and risk modification. Patients in high-social determinants of health-burden communities often face barriers to rapid outpatient care, while inpatient admission can be resource-intensive and burdensome. We describe outcomes from a rapid TIA inpatient workflow (Rapid TIA) implemented at an urban academic medical center.
Methods
A retrospective single institution observational study of 411 consecutive patients admitted for TIA over 4 years in the Bronx, NY. Rapid TIA had 3 phases: (1) initial neurologic evaluation, (2) hospital admission and expedited implementation of care, and (3) transition to outpatient specialty care. We compared 6 variables related to care delivery, as well as long-term outcomes, in the pre-implementation vs post-implementation groups.
Results
The Rapid TIA program was associated with a significant improvement in overall care delivery measured using a composite process measure score from 3.2 (±1.1) pre-implementation to 3.8 (±1.1) post-implementation (OR 1.63, 95% CI: 1.35, 1.98, P = 0.001). Combined 1 year readmission rates for stroke/TIA, MI, and major bleeding events decreased from 15% (n = 28) in the pre-implementation group to 7% (n = 15) post-implementation (95% CI: 0.19, 0.74, P = 0.004).
Conclusions
Our study demonstrates that a rapid-inpatient TIA management pathway can significantly improve quality care and reduce readmissions. Rapid TIA may serve as a model for TIA care delivery in other underserved communities.
Keywords: stroke risk, quality improvement, neurologic evaluation, healthcare disparity, expedited TIA workflow
Introduction
Transient ischemic attack (TIA) is defined as a sudden, brief focal neurologic deficit often lasting less than 1 hour without infarction on magnetic resonance imaging. 1 The incidence of TIA in the United States is estimated at 1.19 per 1000 person-years based on the Framingham Heart Study. 2 This is likely an underestimation due to the difficulty in symptom recognition by the public, especially in under-served urban or rural communities. 3 Nonetheless, TIA is known as a major risk factor for future stroke, estimated to be 5-12% within the following 90 days.4-6
Rapid post-TIA evaluation and implementation of secondary stroke prevention measures has been shown to decrease the risk of stroke.7,8 While many hospitals admit patients presenting with TIA for stroke workup outside of a defined TIA pathway, several studies including SOS-TIA and RAVEN trials have shown that a rapid outpatient TIA pathway may be a safe alternative.9,10 However, patients living in communities with high local burden of social determinant of health (SDOH) face barriers to completing outpatient evaluation. 11 Therefore, we developed an expedited inpatient TIA pathway (Rapid TIA) aimed at streamlining post-TIA evaluation. We then implemented it in an urban academic medical center serving 1.5 million people in a New York City county with a high local burden of SDOH.
Our hypothesis was that Rapid TIA would improve quality of care for patients presenting with and admitted for TIA, decrease length of stay, and reduce risk of readmission for stroke, myocardial infarction and major bleeding events. We report quality of care delivery as defined by compliance with pre-defined quality measures. Our goal was to objectively describe how a focused, expedited inpatient TIA pathway can serve as a viable alternative to models which focus on rapid outpatient work up.
Methods
Cohort
We performed a pre-and-post observational study of patients consecutively admitted to Montefiore-Einstein, an urban academic medical center in the Bronx, NY, with a diagnosis of TIA from 1/1/2018-12/31/2021. The pre-implementation phase was from 1/1/2018 to 07/2020 and post-implementation phase was from 08/2020 to 12/31/2021. Due to regional changes in management during the height of the COVID-19 pandemic, cases between 03/01/2020 through 05/31/2020 were excluded. Patients were identified using the Internal Classification of Diseases 10th revision (ICD-10) primary code for TIA and matched to the prospective Rapid TIA cohort. All charts were reviewed to confirm that diagnosis met the definition of TIA, and patients were excluded if brain imaging demonstrated an acute infarct, the patient had a prior admission for TIA within 1 year or was under age 18. A total of 493 cases were identified during the study period of which 82 were excluded (Figure 1). Overall, TIA volumes increased in parallel with hospitalization rates for acute stroke as the reach of our comprehensive stroke center grew.
Figure 1.
Inclusion/Exclusion Flow Chart
The final cohort was managed in Research Electronic Data Software (Vanderbilt University, Nashville, TN). Data was extracted using automated extraction tools for basic demographics and then manually reviewed using pre-defined parameters from EPIC Electronic Health Records Software (Epic Systems Corp., Verona Wisconsin). Variable of interests included demographics (age, gender, race, insurance status), clinical comorbidities (diabetes mellitus, hypertension, hyperlipidemia, atrial fibrillation, prior stroke, smoking history), and personalized TIA risk at the time of presentation using the ABCD2 (age, blood pressure, clinical features, duration, diabetes) score.12,13 LDL, hemoglobin A1C, and blood pressure values were recorded as the first value present at the index admission.
Implementation
The aim of the post-implementation phase was to introduce Rapid TIA with the goal of completing a full TIA evaluation and initiate prevention measures within 24 hours of presentation (Supplemental Figure 1). Education and planning with multiple departments took approximately 6 months. These included departments of neurology, emergency medicine, cardiology, social services, rehabilitation, radiology, and hospital logistics. Attendings, house-staff, and physician assistants within departments of neurology and emergency medicine along with operations managers were in-serviced on the Rapid TIA workflow prior to roll out (Supplemental Figure 2). An email list was created with radiology, cardiology and logistics stakeholders to inform them of new patient arrivals and need for expedited evaluation.
Outcomes
To simplify the approach to implementation, the TIA evaluation was split into 3 working phases. The first phase started at the time of emergency room (ER) case recognition, with a neurological specialty evaluation, EKG, initial brain imaging, basic labs, and start of antiplatelet therapy. The second phase was defined by hospital admission specifically to a Neurohospitalist service staffed by a neurology attending and physician assistant and ordering of MRI brain, vessel imaging, TTE, and 24-hour cardiac rhythm monitoring using a pre-defined orderset. The third phase included discharge planning with personalized education, 2-week ambulatory cardiac rhythm monitoring and specialty TIA clinic remote follow up visit at 2 weeks post-discharge (Supplemental Figure 3). The ambulatory cardiac rhythm monitoring and post-discharge visit did not apply to the full TIA completion time window within 24 hours.
Quality of care delivery was defined by 6 process measures and grouped as follows: (1) brain imaging (CT head and MRI brain), (2) vessel imaging (MRA neck or CTA neck or carotid doppler), (3) cardiac workup (EKG and TTE and prolonged rhythm monitoring), (4) laboratory testing (lipid panel and hemoglobin a1c), (5) treatment with antiplatelets or full anti-coagulation, and (6) post-discharge outpatient follow up in specialty TIA clinic (in-person or by telehealth). Process measures 1 through 5 were designed to be implemented within the first 24-hour period, while process 6 within 2 weeks of discharge. However, non-adherence to this timeline did not affect adjudication of the measure. We computed a composite process measure variable ranging from 0 to 6, with 1 point assigned to each of the 6 grouped process measures (Supplemental Table 1). Primary outcomes of interest were adherence to composite of process measures and length of stay. Secondary outcomes included readmission within 1 year at any Montefiore Hospital System hospital for ischemic stroke/TIA, myocardial infarction, and major bleeding events including intracerebral hemorrhage or gastrointestinal bleed.
Statistical Analysis
Continuous variables were reported as means with standard deviations (SD) based on the distribution of the data. Categorical variables were summarized as frequencies and percentages. Differences in baseline characteristics between the pre- and post-implementation groups were assessed using the Student’s t-test or Mann-Whitney U test for continuous variables, and the Chi-square test or Fisher’s exact test for categorical variables.
Time-to-event analyses were conducted using Kaplan-Meier survival curves, with the log-rank test used to compare survival distributions between the pre- and post-implementation groups. The secondary outcome was time to combined readmission for stroke, TIA, major bleeding events, or myocardial infarction (MI) within 365 days of the initial event. Time-to-event was capped at 365 days, and participants who did not experience an event were censored at their last known follow-up.
To evaluate the independent association of baseline characteristics with the primary outcome, a multivariable logistic regression model was employed. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) were calculated for all covariates, including age, sex, race, and key clinical factors such as hypertension, atrial fibrillation, tobacco use, and lipid panel results. The variable of interest, implementation phase (pre- vs post-implementation), was included in the model to assess the impact of the intervention on readmission rates.
All statistical analyses were conducted using R version [4.4.0] (R Foundation for Statistical Computing, Vienna, Austria) and OpenSimplify (MyAnalyst) Clinical Research Tool version [1.2.0]. A two-sided approach was applied to all statistical tests, with significance defined as a P-value <0.05. Inter-rater reliability for data extraction was 97% using a random technique applied to 70 cases. The study was approved by Albert Einstein College of Medicine Institutional Review Board (IRB #2022-14075).
Results
A total of 411 patients were included, 182 in the pre-implementation and 229 in the post-implementation groups. The 2 groups were evenly distributed by age (69 vs 67, P = 0.13), female sex (62% vs 61%), and race (32% vs 33% Black and 16% vs 14% White, P = 0.8). The presence of comorbidities including hypertension, diabetes, atrial fibrillation, heart failure, and tobacco use, were comparable across both groups. Prior stroke was slightly higher in the pre- compared to post-implementation groups (36% vs 31%, P = 0.046). There was no difference in recurrent stroke risk by group as defined by ABCD2 score (Table 1).
Table 1.
Baseline Characteristics of TIA Patients by Implementation Group
| Pre-implementation (N = 182) | Post-implementation (N = 229) | Overall (N = 411) | P-value | |
|---|---|---|---|---|
| Demographics | ||||
| Sex | ||||
| Female | 112 (62 %) | 139 (61 %) | 251 (61 %) | 0.9 |
| Age (years) | ||||
| Mean (SD) | 69 (±15) | 67 (±13) | 67 (±14) | 0.13 |
| Race | 0.8 | |||
| White | 29 (16 %) | 31 (14 %) | 60 (15 %) | |
| Black/African American | 59 (32 %) | 75 (33 %) | 134 (33 %) | |
| Other | 94 (52 %) | 123 (54 %) | 217 (53 %) | |
| Insured | 0.068* | |||
| Yes | 174 (96 %) | 226 (99 %) | 400 (97 %) | |
| Medical history | ||||
| Prior stroke | 66 (36 %) | 62 (27 %) | 128 (31 %) | 0.046 |
| Hypertension | 138 (76 %) | 170 (74 %) | 308 (75 %) | 0.7 |
| Diabetes | 75 (41 %) | 91 (40 %) | 166 (40 %) | 0.8 |
| Atrial fibrillation | 20 (11 %) | 23 (10 %) | 43 (10 %) | 0.8 |
| Heart failure | 9 (5 %) | 13 (6 %) | 22 (5 %) | 0.7 |
| Tobacco use | 75 (41 %) | 89 (39 %) | 164 (40 %) | 0.6 |
| SBP | 150 (±24) | 150 (±25) | 150 (±24) | 0.4 |
| DBP | 84 (±16) | 83 (±15) | 83 (±16) | 0.9 |
| MAP | 110 (±16) | 110 (±15) | 110 (±16) | 0.7 |
| TIA risk (ABCD2 score) | 0.9 | |||
| Low risk (0 – 3) | 64 (35 %) | 77 (34 %) | 141 (34 %) | |
| Moderate risk (4 – 5) | 93 (51 %) | 117 (51%) | 210 (51 %) | |
| High risk (6 – 7) | 25 (14 %) | 35 (15%) | 60 (15 %) | |
Length of stay was 3.3 (±3.0) days in the pre-implementation group and 2.9 (±2.4) days in the post-implementation group (OR 0.95, 95% CI: 0.88, 1.02, P = 0.13). When assessing differences in care implementation, we found significant differences in most process measures of interest. Brain imaging with MRI increased from 83% in the pre-implementation group to 91% post-implementation (P = 0.017). CTH completion rate was not significantly different between both groups. Vessel imaging (MRA, CTA or doppler) improved from 87% to 93% (P = 0.035). Similarly, TTE, EKG, prolonged cardiac monitoring with Holter or implantable recorder, and laboratory assessment (LDL and hemoglobin A1C) were all significantly higher in the post implementation group. Initiation of dual antiplatelet (DAPT) therapy in eligible patients (ABCD2 score ≥4) increased from 28% to 43% (P = 0.004). Follow up in a specialty neurology clinic rose from 41% to 51% (P = 0.043). Overall care delivery measured using the composite process measure score improved significantly from 3.2 (±1.1) to 3.8 (±1.1) post-implementation (OR 1.63, 95% CI: 1.35, 1.98, P = 0.001). Refer to Table 2.
Table 2.
Comparison of Pre- and Post-intervention Outcomes: Unadjusted Results
| Characteristics | Pre | Post | OR1 | 95% CI1 | P-value |
|---|---|---|---|---|---|
| Length of stay (LOS) | 3.3 (±3.0) | 2.9 (±2.4) | 0.95 | 0.88, 1.02 | 0.13 |
| Composite measure | 3.2 (±1.1) | 3.8 (±1.1) | 1.63 | 1.35, 1.98 | <0.001 |
| Stroke/TIA readmission a | 0.033 | ||||
| No (ref) | — | — | |||
| Readmitted | 21 (12 %) | 13 (6 %) | 0.46 | 0.22, 0.94 | |
| MI readmission a | 0.071* | ||||
| No (ref) | — | — | |||
| Readmitted | 2 (1 %) | 0 (0 %) | 0.00 | ||
| Major bleeding events (any) a | 0.019 | ||||
| No (ref) | — | — | |||
| Readmitted | 8 (4 %) | 2 (1 %) | 0.19 | 0.03, 0.78 | |
| Combined readmission | 0.004 | ||||
| No | — | — | |||
| Yes | 28 (15 %) | 15 (7 %) | 0.39 | 0.19, 0.74 |
aWithin 1 year.
Readmission for each of the 3 outcomes of interest reduced significantly in the post-implementation group. Stroke/TIA recurrence decreased from 12% (n = 21) in the pre-implementation group to 6% (n = 13) post-implementation (OR 0.46, 95% CI: 0.22, 0.94, P = 0.033). Combined readmission for stroke/TIA, MI, and major bleeding events decreased from 15% (n = 28) in the pre-implementation group to 7% (n = 15) post-implementation (95%CI: 0.19, 0.74, P = 0.004) (Table 2). This finding remained significant after adjusting for relevant demographic and clinical variables in a multivariable logistic regression model. In the fully adjusted model, the post-implementation phase carried an independent 0.42 lower odds of readmission for the composite endpoint compared to the pre-implementation phase. Overall mortality rate was 0 (Table 3).
Table 3.
Multivariable Logistic Regression Exploring the Association Between Baseline Features and Combined Readmission for Stroke/TIA, Major Bleeding Events, or MI
| Characteristics | OR | 95% CI | P-value |
|---|---|---|---|
| Implementation | |||
| Pre (ref) | — | — | |
| Post | 0.42 | 0.19, 0.88 | 0.024 |
| Age | 0.98 | 0.95, 1.01 | 0.14 |
| Sex (female, ref) | 1.45 | 0.68, 3.10 | 0.3 |
| Race (white, ref) | |||
| Black or African American | 0.37 | 0.11, 1.22 | 0.10 |
| Others | 0.57 | 0.22, 1.63 | 0.3 |
| Hypertension | 0.82 | 0.36, 1.95 | 0.6 |
| Atrial fibrillation | 0.79 | 0.17, 2.71 | 0.7 |
| Heart failure | 0.28 | 0.01, 1.69 | 0.3 |
| Tobacco use | 1.31 | 0.61, 2.78 | 0.5 |
| LDL | 1.00 | 0.99, 1.01 | 0.6 |
| Hemoglobin A1C | 1.10 | 0.93, 1.30 | 0.2 |
| Prior stroke | 2.96 | 1.39, 6.36 | 0.005 |
Abbreviations: CI = Confidence Interval, OR = Odds Ratio, LDL = Low-Density Lipoprotein.
When we compared the time to recurrent stroke/TIA readmission between the pre-implementation and post-implementation groups, log-rank test yielded a P-value of 0.021, indicating a significant difference in time to event within the first year (Figure 2).
Figure 2.
Kaplan-Meier Curve of the Time to Stroke/TIA Readmission Over 1 Year (Pre vs Post Implementation). *y Axis has Been Truncated to Allow Better Visualization of the Change Difference in Groups (Supplemental Figure 4)
Discussion
Even as public awareness of stroke risk factors has increased and healthcare systems have improved their approach to acute stroke management, recognizing the clinical importance of TIA has only recently become evident. 2 We developed the Rapid TIA model for inpatient care to streamline the post-TIA evaluation process in an urban academic medical center serving a population facing significant SDOH challenges. LOS was reduced in the post-implementation group, and more patients were discharged within the first 24 hours but did not meet statistical significance likely due to the power/size of our cohort. Our protocol showed statistically significant improvements in all process measures for stroke workup, treatment, and outpatient follow up as well as a reduction in readmission rates for stroke/TIA, major bleeding events, and MI.
Earlier studies in the approach to improving TIA management emphasized the urgency of outpatient TIA workup. SOS-TIA demonstrated that rapid outpatient follow-up and early initiation of secondary prevention, with round the clock outpatient access led to a marked reduction in risk of recurrent stroke relative to predicted risk based on the ABCD2 score. 9 The EXPRESS study highlighted that by reducing follow-up times, the risk of recurrent stroke was reduced. 14 More recently, RAVEN-TIA demonstrated that this rapid outpatient approach to TIA could provide similar benefit to inpatient admission for patients with TIA or minor stroke. 10
While rapid outpatient work up has been proven effective, the approach is challenging to replicate in neighborhoods with a high burden of SDOH. Jadow et al 15 examined 8 SDOH in New York City, including the primary catchment area of our institution, and found that communities, with higher levels of SDOH had a higher prevalence of stroke. Furthermore, race-ethnic minorities have delayed hospital arrival time after stroke or TIA symptoms. 11 Beyond traditional SDOH, transportation barriers, work needs and family support challenges may create additional barriers to rapid outpatient evaluation. 16
A number of studies have proposed a rapid inpatient approach to TIA workup to improve treatment with DAPT in patient with moderate to high ABCD2 scores.7,8 Lendaris et al compared patients in a population with high burden of SDOH who were admitted for an inpatient TIA workup to those who had an outpatient evaluation and found that patients with moderate to high ABCD2 scores were more likely to be treated with DAPT if they were admitted, emphasizing the importance of an inpatient approach.3,11 Improved approaches to inpatient TIA management have been evaluated in other studies, though limited by a small sample size. Nahab et al 17 developed an accelerated diagnostic pathway for evaluation of TIA and demonstrated reductions in length of stay and cost of hospital encounter, with a comparable risk of recurrent stroke at 90 days. Majidi et al evaluated and compared an inpatient vs outpatient approach to evaluation of patients with TIA or minor stroke. While their study did not show a significant improvement in outcomes in those assigned to an inpatient vs an outpatient evaluation, only 19 patients were included in the inpatient arm. 18 Beyond quality measures alone, the value of education around modifiable stroke risk factors may have contributed to the success of the Rapid TIA program. This was started in the inpatient setting and carried over in the follow up appointment shortly after discharge. Our study was also unique in it's large sample size, and inclusion of a study population with a high burden of SDOH.
There are limitations to our study. The characteristics of our community may not be reflective of other populations. Due to limitations on outside hospital system access, we are only able to access patients readmitted within our own health system. Patients were not called to determine if they were readmitted to an outside hospital system within 1 year of discharge, though we would expect this to influence both groups equally. Additionally, the omission of cases during the height of the COVID-19 pandemic likely led to the disparity in cases between the pre- and post-implementation period. This was unavoidable and likely due to individuals in the population avoiding hospitals despite having symptoms. Lastly, because our study took place at a comprehensive stroke center at a major academic institution, hospitals with lesser resources (e.g. lack of dedicated vascular neurologists or 24/7 MRI), may not see the same benefit in reported outcomes.
Despite prior studies demonstrating that rapid outpatient TIA management may be effective in preventing recurrent events, outpatient workup poses significant barriers to communities with a high-local burden of SDOH. Our study demonstrated that the Rapid TIA pathway significantly improved quality care and reduce readmissions, suggesting a model that could be replicated in other underserved communities.
Supplemental Material
Supplemental Material for Developing a transit-oriented development assessment model based on node-place-ecology for suburban areas of metropolitan cities: A case in Odawara by Weiyao Yang, Wanglin Yan, Lihua Chen, Haichen Wei, and Shuang Gan in The Neurohospitalist.
Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Supplemental Material: Supplemental material for this article is available online.
ORCID iDs
Austin Saline https://orcid.org/0009-0002-7342-0754
Juan Felipe Daza Ovalle https://orcid.org/0009-0002-1182-4075
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Supplementary Materials
Supplemental Material for Developing a transit-oriented development assessment model based on node-place-ecology for suburban areas of metropolitan cities: A case in Odawara by Weiyao Yang, Wanglin Yan, Lihua Chen, Haichen Wei, and Shuang Gan in The Neurohospitalist.