Published online before print August 7, 2008, doi: 10.1161/STROKEAHA.108.524686
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(Stroke. 2008;39:3262.)
© 2008 American Heart Association, Inc.
Original Contributions |
Predictors of Time From Hospital Arrival to Initial Brain-Imaging Among Suspected Stroke Patients
The North Carolina Collaborative Stroke Registry
From the Department of Epidemiology (K.M.R., W.D.R., S.L.H., C.V.M.), School of Public Health, University of North Carolina, Chapel Hill; the Heart Disease & Stroke Prevention Branch (S.L.H.), NC Division of Public Health, Raleigh, N.C.; and the Department of Neurology (C.H.T.), Wake Forest University Baptist Medical Center, Winston-Salem, N.C.
Correspondence to Kathryn M. Rose, PhD, Department of Epidemiology, University of North Carolina at Chapel Hill, 137 E Franklin Street, Suite 306, Chapel Hill, NC 27514. E-mail kathryn_rose{at}unc.edu
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Abstract |
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Background and Purpose— We examined patient demographic and hospital characteristics and clinical predictors of delay time from hospital arrival until CT among 20 374 patients enrolled in the North Carolina Collaborative Stroke Registry (January 2005 to April 2008).
Methods— Delay time was log-transformed in linear regression analyses and dichotomized (
25 minutes, >25 minutes) in logistic regression analyses to correspond to a 1999 National Institute of Neurological Disorders and Stroke guideline.
Results— In multiple linear regression analyses, prehospital delay time, mode of transport, race, gender, presumptive diagnosis, time of day of arrival, weekday versus weekend arrival, and hospital type (defined by Joint Commission Primary Stroke Center certification and teaching status) were significantly associated with CT delay. In analyses of 3549 patients arriving within 2 hours of symptom onset, time of day of arrival and weekday versus weekend arrival were no longer significant. Among patients arriving within 2 hours of symptom onset, the strongest independent predictors of meeting the National Institute of Neurological Disorders and Stroke (NINDS) guideline were arrival by emergency medical services versus other modes of transportation (odds ratio, 95% CI=2.3 [1.9, 2.8]) and a presumptive diagnosis of transient ischemic attack versus unspecified stroke type (odds ratio, 95% CI=0.4 [0.3, 0.5]).
Conclusions— Most patients do not arrive to the hospital in a timely manner and cannot be considered for time-dependent therapies. Among those that do, disparities exist in time to receipt of CT scan, suggesting room for improvement in hospital-level stroke systems of care.
Key Words: stroke • in hospital delay time • computer tomography
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Introduction |
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In the United States approximately 700 000 persons have a stroke annually; one-fourth will die, and associated morbidity and functional limitations among survivors are substantial.1 Time-dependent therapies like tissue plasminogen activator (tPA) can reduce the burden of ischemic stroke–related morbidity and mortality2; however, to maximize benefits and minimize complications, guidelines suggest that tPA be administered within 3 hours from symptom onset.2–4
Recently, the American Heart Association and the American Stroke Association developed a "Stroke Chain of Survival" specifying action areas for maximizing poststroke-functioning.5 Three areas focus on decreasing prehospital delays (symptom recognition, calling emergency medical services [EMS], rapid response by EMS), whereas the fourth focuses on timely diagnosis and treatment after hospitalization. There has been a focus on identifying characteristics associated with prehospital delay6–12 and on steps to reduce prehospital delays13–15; however, correlates of delays in diagnosis and treatment have not been as extensively examined.14,16–19 Receiving a CT scan is a crucial component of a stroke diagnosis and essential before tPA-administration. National Institute of Neurological Disorders and Stroke (NINDS) guidelines established in the 1990s suggest that stroke patients receive an initial CT scan within 25 minutes of hospital arrival20; yet, compliance with this guideline has not been well-addressed. One county-based registry reported that not meeting this NINDS guideline was linked to receipt of tPA; however, it did not examine correlates of receipt of a CT scan within the recommended time guideline.21 We examined patients’ demographic and hospital characteristics, insurance status, and clinical predictors of delay from hospital arrival until initial CT scan (CT delay) among patients presenting with signs and symptoms of a stroke. We also assessed correlates of compliance with the NINDS recommendations for timing from hospital arrival to the initial CT scan.
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Materials and Methods |
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Study Population
The Paul Coverdell National Acute Stroke Registry (PCNASR) established state-based registries to measure, track, and improve the quality of stroke care.22 States collect data on established quality-of-care indicators, including time from hospital arrival to initial brain-imaging. Additional details of the design are published.23,24
The North Carolina Collaborative Stroke Registry (NCCSR) is 1 of 4 states originally funded by the Centers for Disease Control and Prevention (CDC) as a PCNASR. The NCCSR prospectively enrolls stroke cases and collects data concurrent with care. All 108 North Carolina (NC) inpatient hospitals were invited to participate. A card containing 30 stroke care-related questions corresponding to the typical flow of patient care was used; these data were entered into an interactive, web-based database. Staff received training on the online data entry system and instructions on appropriate responses to each data element.
Between January 2005 and April 2008, 46 hospitals enrolled 20 374 suspected stroke patients ages 18 years and older. These hospitals are located in 39 of 100 NC counties (Figure) and comprise 68% of the state’s population.25 Included hospitals account for 61% of all stroke hospital discharges in NC. Our study was limited to 19 259 patients with a presumptive stroke-related admission diagnosis (ischemic stroke, hemorrhagic stroke, transient ischemic attack [TIA], stroke not specified). We excluded patients transferred from another hospital (n=2531), those with missing arrival (n=310), or CT-imaging time (n=722), those with implausible imaging times (n=566) and those with delay times >24 hours (n=13). Our final sample size was 15 117 patients. In analyses of patients arriving within 2 hours of symptom onset, we deleted those with unknown symptom onset time (n=5676) and delay times from symptom onset to hospital arrival of greater than 2 hours (n=5892), leaving in 3549 patients for analyses.
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Defining Delay Time and Predictor Variables
CT delay (hours) was calculated as the time from hospital arrival (ER triage) until initial brain-imaging. We also dichotomized delay time by the NINDS guideline of receipt of a CT scan within 25 minutes of hospital arrival. Demographic characteristics included age (18 to 44, 45 to 64, 65 to 74, 75+), race (white, black, other), and gender. Health insurance status was defined as Medicare, other, and none. Hospital characteristics included teaching or nonteaching status (defined by having a hospital-based residency training program), and whether the facility was a Joint Commission Primary Stroke Center (JCPSC). We combined these 2 variables as a 4-level variable: JCPSC and teaching, JCPSC only, teaching only, and neither JCPSC nor teaching. Patient response characteristics included delay time from symptom onset to hospital arrival (2 hours, >2 hours, unknown onset time), mode of arrival (EMS versus other), time of day of arrival (7:00 AM to 6:59 PM, 7:00 PM to 10:59 PM, 11:00 PM to 6:59 PM) and weekday versus weekend arrival. Patient-related characteristics included prior history of stroke/TIA and ambulation status at arrival (ambulating independently versus not).
Statistical Analysis
CT delay time was not normally distributed. Thus, we present both mean and median delay times and used a nonparametric 1-way analysis of variance with the Kruskall-Wallace test to assess bivariate associations between predictors and CT delay. All significant (P<0.05) variables were included in a multiple linear regression model where CT delay time was log-transformed. Analyses were repeated in the 3549 patients arriving to the hospital within 2 hours after symptom onset.
We also evaluated predictors of receipt of a CT scan within 25 minutes of hospital arrival among patients arriving to the hospital within 2 hours of symptom onset. We first examined bivariate distributions of each variable with the dichotomous delay time variable; significant predictors were entered into a logistic regression model.
Given that patients were clustered within hospitals, there were concerns that standard errors could be underestimated. We repeated selected analyses using a multilevel modeling approach that nested patients within hospitals.26 Our conclusions were essentially unchanged; thus, we present the results from the standard regression analyses. Analyses were conducted using SAS Version 9.1 (SAS Institute, Inc).
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Results |
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Association of Individual Predictors With Delay Time
Table 1 presents the distribution of patients by predictor variables and the mean, median, and intraquartile range (25th to 75th percentile) of delay times from hospital arrival to initial CT. Overall mean time from hospital arrival to initial CT scan was 1.7 hours, whereas the median time was 1.2 hours. Most patients (94%) were 45 years of age or older and 26% were black. Females comprised 54% of registrants and 91% had healthcare coverage. Slightly more than half of patients arrived by EMS. Thirty-eight percent had an unknown symptom onset time and only 23% arrived within 2 hours of symptom onset. Three-fourths of patients arrived on a weekday and between the hours of 7:00 AM and 6:59 PM. The largest group of patients (40%) was treated at hospitals that were nonteaching and not a JCPSC, whereas 23% were treated at JCPSC-teaching institutions. Thirty-six percent of patients had a prior history of stroke or TIA. The most common presumptive diagnoses at admission were ischemic stroke (43%) and TIA (28%); fewer patients were presumed to have a hemorrhagic stroke (9%) or a stroke not specified (20%).
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In bivariate analyses, there were no significant variations in time from hospital arrival to initial CT scan by age, health insurance status, ambulation status at admission, or medical history of stroke or TIA (Table 1). All other predictors are significantly associated with CT delay.
Joint Predictors of CT Delay Time
All significant predictors of CT delay were entered into a multiple linear regression analysis (Table 2). Negative coefficients indicate shorter delay whereas positive coefficients signify delay times longer than those of the referent groups. When all patients were considered, all variables associated with CT delay in the bivariate analyses (P<0.05) were significantly associated with CT delay in the multiple linear regression analysis. Among patients arriving at the hospital within 2 hours of symptom onset (Table 2, last two columns), significant variations in CT delay by weekday/weekend and time of day of arrival did not persist.
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Factors Associated With Receipt of CT Scan Within 25 Minutes of Hospital Arrival
Only 11.5% of all patients received a CT scan within 25 minutes of hospital arrival. However, there was significant variation (P<0.001) in the proportion of patients meeting this NINDS guideline by prehospital delay. Among those arriving within 2 hours of symptom onset, 23.6% received a CT scan in 25 or fewer minutes; this compared to 8.8% of those arriving more than 2 hours after symptom onset and 6.7% of those who had an unknown symptom onset time. We evaluated predictors of receipt of a CT scan within 25 minutes among the 3549 patients arriving to the hospital within 2 hours of symptom onset. There was no significant association between receiving a CT scan within 25 minutes and race, health insurance status, ambulation status, time of day of arrival, or weekday versus weekend arrival. However, gender, age, mode of hospital arrival, hospital type, prior history of stroke, and presumptive admission diagnosis were associated (P<0.05) with receipt of a CT scan within 25 minutes.
Variables significantly associated with receiving a CT scan within 25 minutes were entered into a multivariable logistic regression equation. Odds ratios and 95% CI are presented in Table 3, with odds ratios greater than 1.0 indicative of a higher odds of receiving a CT within 25 minutes than the referent category and odds ratios less than 1.0 being indicative of a lower odds of receiving a CT within 25 minutes. Women were less likely to receive a CT scan within 25 minutes than were men. Compared to patients treated at hospitals that were neither JCPSC nor teaching institutions, those admitted to non-JCPSC–nonteaching facilities were less likely to receive a timely CT scan, whereas those treated at JCPSC-nonteaching facilities were more likely to receive a timely CT scan. Those arriving by EMS were more than twice as likely to receive a CT scan within 25 minutes than those arriving by other modes of transportation. The odds of receiving a timely CT scan given a presumptive diagnosis of TIA were only 40% of that for a person presumed to have an unspecified stroke.
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Supplemental Analyses
Given our prospective data collection approach, we defined stroke based on the presumptive admission diagnosis. However, as most reports define stroke based on ICD-9 discharge codes, we repeated our analyses using ICD-9 discharge codes. Our results were essentially unchanged (data not shown).
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Discussion |
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Among NCCSR patients, delay from symptom onset to hospital arrival was the strongest independent predictor of CT delay. This is consistent with another study reporting longer time to treatment among those with longer prehospital delays.27 Despite a shorter CT delay among those arriving at the hospital within 2 hours of symptom onset, only 23% received a CT scan within 25 minutes per NINDS guidelines.28 To our knowledge, compliance with this NINDS guideline has not previously been reported.
Those arriving by EMS had a CT scan approximately 0.5 hours (30 minutes) earlier than those who did not; this is consistent with earlier reports6,8,29 and may reflect that those with the most severe symptoms or who perceive their symptoms with a sense of urgency30 are more likely to call EMS. Arrival by EMS was an important predictor of shorter CT delay among all patients as well as those arriving within 2 hours of symptom onset, suggesting that the medical care providers perceive a more pressing need to provide immediate attention to this group than for walk-ins regardless of eligibility for time-dependent therapies.
Optimally, shorter CT delays would reflect faster triaging and evaluation by stroke-care teams of those eligible for time-dependent therapies. However, those with the most severe symptoms may seek medical care most quickly after symptoms begin6,31 and are more likely to receive timely attention after hospital arrival.27 This is corroborated by earlier presented data (patients presumed to have a hemorrhagic stroke had the shortest CT delay whereas those presumed to have a TIA had the longest CT delay).
It is noteworthy that presumed TIA cases arriving within 2 hours from symptom onset had the longest median delay until a diagnostic CT scan and were less than half as likely to receive a CT scan within the NINDS time guideline for tPA. This underscores the importance of educational efforts for stroke-care providers which emphasize the importance of timely diagnosis and treatment of all patients presenting with stroke-like symptoms.
In our study, black patients had longer median CT delay times than white patients and women had longer delay times than men; however, delay times did not generally vary by age. Earlier studies report longer delay among women,11,18 blacks32 and the elderly,33 as well as no variation in these groups.33,34
At both teaching and nonteaching hospitals, patients treated at JCPSCs had shorter CT delays than those treated at non-JCPSCs. The establishment of JCPSCs began in 2004; thus, publications on its impact on the quality of stroke care are lacking. Nonetheless, the shorter delay times at JCPSCs are consistent with other reports of the benefits of the establishment of stroke-care teams or systems of care.35–37
Treatment at teaching hospitals was associated with longer CT delays than treatment at nonteaching hospitals, although these differences were smaller at JCPSCs. Hospitals varied by location, characteristics of the communities served, volume of stroke cases, whether they were tertiary care referral centers, and public/private. These data are not currently integrated into our data system; thus, we could not investigate the contribution of these factors to the longer delays at teaching hospitals.
Our study has several limitations. We collected data on the time a CT scan was made, not when it was read, which in some institutions could substantially differ. Also, we did not examine differences in CT delay by socioeconomic status because it was not available on the medical record. We examined variations in the receipt of stroke care by insurance status, which has been used as a proxy for socioeconomic status38 and associated with the quality of health care.39 However, our data did not distinguish between types of insurance demonstrated to impact health outcomes (eg, Medicaid). Patients came from hospitals that agreed to and had resources to participate; thus, our data do not represent all hospitalized stroke patients in NC. Nonetheless, it is reassuring that hospitals from all regions of NC were represented (the Figure) and that participating hospitals discharged more than 60% of all stroke cases.
Approximately 10% of patients were excluded because of missing or implausible time data. This is lower than figures reported in an earlier retrospective study,21,40 but nonetheless supports studies suggesting that greater efforts focused on educating hospital personnel on the importance of accurately documenting time is warranted.41
Our study has several strengths. The NCCSR encourages the collection of patient data concurrent with care. This allowed us to capture how initial clinical impressions and preliminary diagnoses influence the rapidity of diagnosis and treatment. This differs from most quality improvement efforts, which rely on retrospective chart reviews of stroke cases identified by ICD-9 discharge diagnoses.
In summary, prehospital delays and mode of arrival were the strongest predictors of posthospital delays until initial CT scan. As most patients cannot recall their symptom onset or do not arrive at the hospital in a timely manner, most stroke patients cannot be considered for time-dependent therapies. In the NCCSR, among the minority of patients arriving to the hospital within 2 hours of symptom onset, mode of arrival, gender, race, hospital characteristics, and presumptive admission diagnosis were associated with the rapidity with which a CT scan was performed. This suggests areas for improvement in hospital-level stroke systems of care that may increase patient access to time-dependent therapies that can potentially reduce stroke-related morbidity and mortality.
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Acknowledgments |
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We thank participating hospitals and staff.
Sources of Funding
This study was supported by the CDC as a PCNASR via a subcontract with the NC Division of Public Health (Contract # 01-6-2-05).
Disclosures
None.
Received May 2, 2008; accepted May 27, 2008.
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