Readmission for Stroke and Quality of Care
among Patients Hospitalized with Transient
Ischemic Attack (TIA): Findings from Get
With The Guidelines (GWTG)-Stroke
Emily C. O’Brien1, Xin Zhao1, Gregg C. Fonarow2, Eric E. Smith3,
Lee H. Schwamm4, Deepak L. Bhatt5, Ying Xian1, Jeffrey L. Saver2,
Mathew J. Reeves6, Eric D. Peterson1, Adrian F. Hernandez1
1Duke
Clinical Research Institute, Durham, NC; 2Ronald-Reagan UCLA Medical
Center, Los Angeles, CA; 3Hotchkiss Brain Institute, University of Calgary,
Calgary, Alberta, Canada; 4Massachusetts General Hospital, Harvard Medical
School, Boston, MA; 5VA Boston Medical Center, Harvard Medical School,
Boston, MA; 6Michigan State University, East Lansing, MI
Presenter Disclosure Information
Name: Emily O’Brien, PhD
Title: Readmission for Stroke and Quality of Care among
Patients Hospitalized with Transient Ischemic Attack
(TIA): Findings from Get With The Guidelines (GWTG)Stroke
DISCLOSURE INFORMATION:
Disclosures:
The American Heart Association and the American Stroke Association fund Get
With The Guidelines-Stroke. The program has been supported in part by
unrestricted educational grants to the American Heart Association by Pfizer, Inc.,
New York, NY, and the Merck-Schering Plough Partnership (North Wales, PA).
Background
• Transient Ischemic Attack (TIA) is associated with a
markedly elevated risk for ischemic stroke
• Comprehensive risk prediction tools encompassing
readily available variables may enhance
identification of patients at risk for recurrent stroke
• Evidence-based management of TIA may reduce
the risk of recurrent stroke
• The benefit of optimal clinical management in the
context of underlying risk has not been fully
explored
Objectives
1. Estimate risk of one-year admission for
ischemic stroke after TIA hospitalization
2. Assess receipt of quality-of-care metrics
by baseline readmission risk
3. Characterize the association between
quality-of-care metrics provided during
TIA hospitalization and one-year risk of
ischemic stroke readmission
Hypotheses
• Patients with higher baseline ischemic
stroke readmission risk are less likely to
receive evidence-based care
• Receipt of evidence-based care is
associated with lower rates of
readmission for all baseline risk
subgroups
• Data Sources
Methods
– Get With The Guidelines-Stroke


Hospital-based quality improvement initiative
Trained personnel abstract demographic, clinical, and event
information at participating sites
– Probabilistic linkage to Medicare inpatient claims using
indirect identifiers
• Study population
– Starting population: N=108,527 TIA patients from 1326 GWTG sites
– Exclusions:






Not linked to CMS data, or non-index records (N = 9145)
Transferred out, hospice, death, or no documented discharge destination (N =3471)
CMO (N = 435)
Not enrolled in Medicare FFS at hospital discharge (N = 4721)
Admitted after 2008 (N = 22,690)
CMS discharge date after 2008 (N = 173)
– Final study population: N=58,809
GWTG Readmission Risk Score
• Predicted probability of readmission based on
patient baseline characteristics
– Demographics
– Comorbidities
• Cox proportional hazards modeling with
backwards selection (stay criterion of p=0.05)
• Discriminative performance of the model
examined using c-statistics and ROC curves
• Patients categorized into quintiles of predicted
readmission risk
Evidence-Based Care
• Individual
–
–
–
–
–
Antithrombotics by hospital day 2
Anticoagulation for patients with atrial fibrillation
Antithrombotics at discharge
Lipid-lowering medications at discharge
Smoking cessation counseling
• Global
– TIA defect-free care: receipt of all measures for
which the patient was eligible
Outcomes
– Primary

Hospitalization for ischemic stroke
– Secondary

All-cause mortality
Statistical Analysis
• Baseline characteristics compared using Pearson
Chi-squared and Wilcoxon rank sum tests
• Cox proportional hazards model to estimate risk
for ischemic stroke readmission over one year in
derivation cohort, with performance evaluation in
validation cohort
• Censoring at death or loss of Medicare eligibility
• Cox models to estimate association between DFC
and readmission within score quintiles
Baseline Characteristics (%)
• One-year risk of ischemic stroke hospitalization=5.7%
Stroke
Readmission
within 1 year
(N=3,318)
No Stroke
Readmission
within 1 year
(N=54,854)
P-Value
Age, median
80.0
79.0
<.0001
Male gender
39.2
39.0
0.78
Black Race
10.3
7.4
<.0001
Prior stroke
44.6
34.4
<.0001
CAD/Prior MI
37.1
32.8
<.0001
Carotid stenosis
6.0
5.5
0.27
Diabetes
31.7
26.7
<.0001
PVD
6.6
5.2
.0004
Hypertension
81.8
81.2
0.40
Heart Failure
3.4
2.3
<.0001
Smoker
9.5
8.1
0.006
Dyslipidemia
39.3
45.1
<.0001
Variable
In-Hospital Quality Measures (%)
Stroke
Readmission
within 1 year
(N=3,318)
No Stroke
Readmission
within 1 year
(N=54,854)
P-Value
Early Antithrombotics
96.8
96.3
0.28
Discharge Antithrombotics
95.9
95.7
0.71
Anticoagulants for AF
85.9
89.1
0.02
Statin (LDL>100 or ND)
59.4
61.4
0.04
Smoking Cessation
88.4
85.4
0.16
Defect-Free Care†
62.2
63.9
0.04
Variable
† Defect-free
care=receipt of all TIA achievement measures for which the patient was eligible
GWTG 1-Year IS Readmission
Risk Score Model
Variable
Hazard Ratio
95% CI
Age (per 10 year increase)
1.01
1.01, 1.02
White Race
0.79
0.72, 0.86
Atrial Fibrillation/Flutter
1.46
1.34, 1.58
Previous Stroke/TIA
1.45
1.35, 1.55
CAD/Prior MI
1.15
1.07, 1.23
Diabetes Mellitus
1.26
1.16, 1.36
Smoking
1.35
1.19, 1.52
Dyslipidemia
0.87
0.81, 0.93
Arrival Mode (EMS vs. Other)
1.20
1.12, 1.28
Ambulate Independently at Discharge
0.82
0.76, 0.88
C-statistic=0.603
1-Year IS Readmission
Overall 1-year Risk
• Death: 11.8%
• IS Readmission: 5.7%
Observed Risk (%)
25
20
15
IS Readmission
Death
10
5
0
1st
2nd
3rd
4th
5th
GWTG Readmission Risk Score Quintile
*‡ GWTG Readmission for Stroke Risk Score estimated from age, gender, race, history of stroke/TIA, prosthetic
heart valve, CAD/Prior MI, carotid stenosis, diabetes mellitus, PVD, hypertension, HF, smoker, dyslipidemia,
hospital size, hospital type, and geographic region (c-statistic 0.59)
Results
100
90
Early antithrombotics
Antithrombotics at discharge
80
%
Anticoagulation for AF
Statins
70
Smoking Cessation
60
DFC
50
1st
2nd
3rd
4th
5th
GWTG Readmission Risk Score Quintile
*‡ GWTG Readmission for Stroke Risk Score estimated from age, gender, race, history of stroke/TIA, prosthetic heart valve, CAD/Prior
MI, carotid stenosis, diabetes mellitus, PVD, hypertension, HF, smoker, dyslipidemia, hospital size, hospital type, and geographic region
(c-statistic 0.59)
DFC and IS Readmission
HR (95% CI)
1.4
1.0
0.7
Unadjusted
1st
2nd
3rd
4th
GWTG Readmission Risk Quintile
5th
Limitations
• Results may be influenced by residual
confounding
• Information about medication use after discharge
not available
• Percent of TIA patients admitted at each GWTG
hospital not known
• DFC is a composite measure and appears to be
driven largely by the statin use measure in this
population
• Results may not be applicable to broader TIA
patient population
Conclusions
• Patients who were readmitted for ischemic stroke
within one year of TIA had a greater comorbidity
burden than patients who were not readmitted
• TIA patients with a high baseline risk of readmission
for IS are less likely to receive defect-free care than
low-risk patients, largely due to lack of statin
treatment
• Standardized risk assessment and delivery of
optimal inpatient care for TIA may help to reduce
this apparent risk-treatment mismatch
Acknowledgements
• The authors would like to thank the staff and
participants of the GWTG-Stroke Registry for
their important contributions to this work
Thank you
Candidate Risk Score Variables
Age (per 10 year increase)
Female
Race (White vs. Other)
Medical History of Atrial Fibrillation/Flutter
Medical History of Prosthetic Heart Valve
Medical History of Previous Stroke/TIA
Medical History of CAD/Prior MI
Medical History of Carotid Stenosis
Medical History of Diabetes Mellitus
Medical History of PVD
Medical History of Hypertension
Medical History of Smoking
Medical History of Dyslipidemia
Medical History of HF
Academic
Region – NE vs. W
Region – MW vs. W
Region – S vs. W
Risk Treatment Paradox
• Documented for heart failure and acute MI (possibly
improving over time)
• Uncertainty about the risk: benefit ratio in patients at
higher risk who are generally under-represented in
randomized trials
• Information gaps in administrative datasets (i.e., lack
of data on confounding clinical and functional variables
that the clinician must weigh in making clinical
decisions but which are not captured in administrative
databases)
• Should also consider overutilization of nonevidence
based therapies
ABCD2 Score
-Validated for 2, 7, and 90 day IS but not long-term IS
Readmission Risk Prediction
• Meta-analysis of 26 unique risk-prediction
models
• C-statistics: 0.55-0.65
• Two of five found that addition of functional or
social variables improved discrimination
• Limitations include lack of information on
hospital and systems-level factors, do not
assess HRQOL, do not account for
preventable readmissions
JAMA. 2011;306(15):1688-1698