Annually, as many as 250,000 Americans may experience a transient ischemic attack (TIA); the annual worldwide incidence of TIA may exceed one million [
Identifying TIA patients at risk for a stroke is an important component of targeting preventive therapies. In the present study, we develop a risk-standardized model to (i) quantify the contribution of demographic and clinical factors to stroke risk and (ii) to identify TIA patients at high-risk for stroke. The model is also suitable for comparing stroke readmission rates across hospitals to gain insight into how hospitalization may affect stroke rates in the TIA patient population.
The data used in the present study were retrospectively gathered from 155 United States hospitals in 20 states managed by a single healthcare provider (Hospital Corporation of America, Nashville, TN, USA). The hospitals studied are primarily urban, community-based facilities that accept all admissions; there are no membership or insurance requirements to obtain care at any of the facilities from which we gathered data. The hospitals have a mean licensed-bed size of 223 beds. Clinical data are centrally warehoused for all hospitals studied and undergo quality control and data-integrity assurances. From the data warehouse, all TIA visits were gathered for the five calendar years from 2004 through 2008, including inpatient hospitalizations and emergency department (ED) visits. Prior to obtaining the data, it was deidentified in a Health Insurance Portability and Accountability Act (HIPAA)-compliant manner. This study was approved by the Austin Multi-Institutional Review Board (AMIRB, Austin, TX, USA).
TIA visits were identified using International Classification of Diseases (ICD)-9-CM codes, which are a standardized set of codes used to classify diseases, symptoms, complaints, and other causes of injury or disease that are published by the World Health Organization. TIA visits were taken as those containing the ICD-9 code 435 anywhere in the patient’s visit record; the code 435 includes transient cerebral ischemias “with transient focal neurological signs and symptoms resulting from insufficiency of the basilar, carotid and vertebral arteries.” In most cases (85%), the code 435 was the first or second diagnosis code in the patient’s record, suggesting that this was the primary reason for admission. The data describing each admission included (i) unique patient and facility identifiers to track readmissions across facilities; (ii) demographic information including age, gender, and race; (iii) length of stay; (iv) all diagnostic and procedural ICD-9 codes recorded during the course of hospitalization. The Joint Commission Primary Stroke Center (PSC) certification status of each hospital was also collected; hospitals were classified as PSC certified over the entire study period if the facility maintained active PSC certification for the final year of the study period. Thirty-eight (23%) of the hospital facilities in our dataset were categorized as PSCs.
To determine stroke readmission cases, an “index visit” was defined as a patient’s first hospital visit in which TIA was reported. Index visits that failed to meet the following quality controls and assurances were removed. TIA visits that occurred within 30 days of the endpoint of the study period or that included a trauma diagnosis (ICD-9 800–959) were removed. To remove confounding by earlier admissions, TIA visits were removed when the patient was hospitalized within the previous 30 days. Given the large size of the dataset, it was not possible to gather detailed information about the etiology of the TIA, the diagnostics (e.g., CT imaging) used, the secondary preventive strategies implemented (e.g., anticoagulants), or other clinical details. Also, we are unable to determine if visits containing both TIA and stroke codes were the result of a TIA that progressed to a stroke during hospitalization or whether there was an initial diagnosis (TIA) that was later amended (stroke). To account for this, patients were excluded if their index visit (or any previous visit) included a report of cerebrovascular disease (ICD-9 430–438).
Descriptive statistics were used to describe the demographic characteristics of the TIA patients. The percent of TIA cases that were subsequently readmitted for stroke was used to estimate the overall risk of stroke in patients with a history of TIA. The cumulative distribution of readmission probabilities was plotted over time, and by considering a decreasing pool of susceptible TIA patients.
A risk-standardized, hierarchical regression model was created to identify demographic and clinical variables associated with increased risk for ischemic stroke in patients with a history of TIA and to quantify the contribution of identified variables to overall risk for stroke. Hierarchical regression is commonly used in healthcare statistics to account for clustering in patient populations [
In the hierarchical model, the outcome (response) variable was a binary variable indicating the occurrence of an ischemic stroke readmission within 30 days of discharge from the index TIA visit. The following variables were considered as candidates for inclusion in the model: patient age, patient gender, patient race, geographic region (hospital zip code), the index visit’s length of stay, the patient’s comorbidities and complications (other ICD-9 codes noted in the visit record), and the hospital’s PSC status. Patient age and length of stay were treated as continuous variables, race and geographical region were treated as categorical variables, and all other variables were binary indicators. The set of ICD-9 codes representing comorbidities and complications that were considered in the model selection process were those identified as significantly independently associated with stroke readmissions (Fisher’s exact test,
During the study period, there were 85,641 TIA visits across all of the facilities studied. After removing cases with previous cerebrovascular disease, trauma, or recent previous admissions (see Section
Demographics of TIA patients.
Total, |
57,585 (100) |
Male, |
23,752 (41) |
Age at index visit, mean (Med.) | 70.6 (72) |
Age at readmission, mean (Med.) | 72.6 (75) |
White, |
43,385 (75) |
Black, |
6,341 (11) |
Hispanic, |
5,241 (9) |
Asian, |
625 (1) |
Other/unknown, |
1,993 (4) |
The percent of TIA cases that were later readmitted for an ischemic stroke provides insight into the overall risk of ischemic stroke in patients with a history of TIA. Over the study period, 649 (1.1%) TIA cases were readmitted for an ischemic stroke within 30 days of discharge and 1,256 (2.6%) TIA patients were readmitted for an ischemic stroke within one year of discharge (Table
Stroke incidence in patients with history of TIA.
2-day | 7-day | 30-day | 90-day | 1-year | |
---|---|---|---|---|---|
|
|
|
|
| |
All-cause, |
556 (1.0) | 2,544 (4.3) | 6,405 (11.1) | 11,359 (20.3) | 19,159 (40.4) |
Acute ischemic stroke, |
63 (0.1) | 328 (0.6) | 649 (1.1) | 945 (1.7) | 1,256 (2.6) |
*When computing X-day readmission rates, we did not include patients whose index visit was within X days of the final date in our dataset.
The daily incidence of stroke was studied to determine if stroke risk after TIA changes over time. The cumulative distribution of readmission probabilities over the 30-days after a TIA increases rapidly in the short term and plateaus over the remainder of the 30-day time frame (Figure
(a) Cumulative percentage of patients readmitted for ischemic stroke within the 30-day window after discharge from TIA hospitalization. (b) Daily probabilities of stroke readmission.
Among all 30-day readmission visits from TIA patients, cerebrovascular disease was the most common reason for readmission (19%) in patients with history of TIA; hypertensive (16%), other heart disease (15%), metabolic disorders (14%), and pulmonary infections and diseases (10%) were other common reasons for readmit visits (Table
Common comorbid conditions in readmit visits.
Reason for 30-day |
|
---|---|
Readmission after TIA | |
Cerebrovascular disease* | 1211 (19) |
Hypertensive disease† | 1056 (16) |
Other heart disease‡ | 952 (15) |
Metabolic disorders§ | 872 (14) |
Pulmonary infections and disease |
645 (10) |
*430–438.
†401–403.
‡415, 416, 424–429.
§244, 250, 253, 263, 272–278.
A risk-standardized, hierarchical regression model was created to comprehensively study the contribution of specific factors to a TIA patient’s overall stroke risk. The final hierarchical regression model included 5 comorbid conditions in addition to gender, age, and length of stay (Table
Risk factors for ischemic stroke.
Variable | Coefficient | Odds ratio | 95% Confidence interval |
---|---|---|---|
Intercept | −6.16 | ||
Demographics | |||
Male | 0.24 | 1.27 | (1.08–1.50) |
Age | 0.02 | 1.02 | (1.01–1.03) |
Length of stay | −0.06 | 0.94 | (0.91–0.98) |
Comorbidities (ICD-9) | |||
Cardiovascular and blood-related | |||
Other peripheral vascular disease (443) | 0.61 | 1.83 | (1.33–2.52) |
Hypertensive chronic kidney disease (403) | 0.53 | 1.69 | (1.19–2.41) |
Old myocardial infarction (412) | 0.28 | 1.33 | (0.98–1.79) |
Essential hypertension (401) | 0.13 | 1.14 | (0.96–1.37) |
Metabolic disorders | |||
Diabetes mellitus (250) | 0.16 | 1.17 | (0.98–1.41) |
Four of the five identified comorbid variables were known cardiovascular or circulatory conditions; the remaining comorbid condition was diabetes. The odds-ratios suggested that other peripheral vascular disease (OR (95% CI): 1.83(1.33–2.52)) and hypertensive chronic kidney disease (OR (95% CI): 1.69(1.19–2.41)) significantly contributed to individual risk, whereas the other comorbid conditions did not. The unadjusted probability of readmission was significantly higher in men than in women (1.02% versus 1.28%,
The model was also suitable for comparing readmission rates across hospitals because it controls for differences in the patient populations in the local catchment areas. The distribution of risk-adjusted hospital-specific readmission rates had a mean of 1.1% and standard deviation of 0.07% (Figure
Distribution of risk-standardized rates of readmission for ischemic stroke.
This retrospective, population-based study used 57,585 TIA cases to comprehensively analyze the causes of and risk factors for stroke following a hospitalization for TIA. The present study used regression models that were consistent with American Heart Association standards for patient-centric statistical models [
Previous studies have considered TIA as a harbinger of an impending ischemic stroke and have aimed to detect demographic, clinical, or lifestyle factors significantly associated with risk for stroke occurrence after TIA [
The 30-day stroke incidence of 11 per 1,000 patients is lower than reported elsewhere [
As expected, the model suggests that cardiovascular comorbidities are the major risk factors for stroke in patients with a history of TIA. In this sense, our model is consistent with existing systems such as the ABCD [
Our model has an important advantage over existing stroke-risk scores: after suitable risk adjustment, our model permits among-hospital comparisons of the stroke rates within each hospital’s respective TIA patient populations. Remarkably, such a comparison suggested that the stroke rates following TIA index visits did not appreciably vary among hospitals. The coefficient of variation,
Interestingly, hospitals with PSC certification were not associated with significantly lower 30-day stroke readmission rates in their TIA patient populations. This observation may provide insight into to the status and efficacy of current treatment protocols for TIA. Performance benchmarks for PSCs include acute and long-term drug management of cardiovascular disease, stroke education, and assessment for rehabilitation and postacute care [
The homogeneity in readmission outcomes and the lack of significance conferred by PSC certification suggests opportunities for innovation in the treatment of TIA. For example, admitting TIA patients identified as a high stroke risk for 24–48 hours (the timeframe in which 25% of stroke readmissions occur) may lead to better prognosis for TIA patients. However, greater focus on postacute care may provide the best opportunity to reduce stroke rates in TIA patients. Several studies have suggested that patient management after hospitalization for cerebrovascular events is critically important in reducing the rates of readmission and recurrence of secondary cerebrovascular events [
We note a few practical limitations regarding our dataset. First, our data is administrative in nature, which is becoming increasingly common in cerebrovascular disease studies [
This study highlights the importance of cardiovascular comorbid conditions in increasing an individual’s risk for a 30-day readmissions following a TIA episode, especially for acute ischemic strokes. Our results suggest that managing these conditions in the period immediately after TIA, both in the acute and postacute settings, should be the primary focus of prevention efforts. Perhaps most importantly, our results speak about a need for renewed effort towards developing improved treatment options for TIA patients identified as high-risk for a subsequent stroke.
This work was supported by funding from the St. David’s Community Health Foundation and Impact Fund.
The authors wish to acknowledge Kacie Kleja, MS, for the significant time and effort put forth to gather the dataset used herein. The results of this study are the sole responsibility of the authors and do not necessarily represent the views of the Impact Fund or the St. David’s Community Health Foundation.