Emergency Medical Services Compliance With Prehospital Stroke Quality Metrics Is Associated With Faster Stroke Evaluation and Treatment.

BACKGROUND: Emergency medical services (EMS) is an important link in the stroke chain of recovery. Various prehospital quality metrics have been proposed for prehospital stroke care, but their individual impact is uncertain. We sought to measure associations between EMS quality metrics and downstream stroke care. METHODS: This is a retrospective analysis of a cohort of EMS-transported stroke patients assembled through a linkage between Michigan's EMS and stroke registries. We used multivariable regression to quantify the independent associations between EMS quality metric compliance (dispatch within 90 seconds of 911 call, prehospital stroke screen documentation [Prehospital stroke scale], glucose check, last known well time, maintenance of scene times ≤15 minutes, hospital prenotification, and intravenous line placement) and shorter door-to-CT times (door-to-CT ≤25), accounting for EMS recognition, age, sex, race, stroke subtype, severity, and duration of symptoms. We then developed a simple EMS quality score based on metrics associated with early CT and examined its associations with hospital stroke evaluation times, treatment, and patient outcomes. RESULTS: Five thousand seven hundred seven EMS-transported stroke cases were linked to prehospital records from January 2018 through June 2019. In multivariable analysis, prehospital stroke scale documentation (adjusted odds ratio, 1.4 [1.2-1.6]), glucose check (1.3 [1.1-1.6]), on-scene time ≤15 minutes (1.6 [1.4-1.9]), hospital prenotification ([2.0 [1.4-2.9]), and intravenous line placement (1.8 [1.5-2.1]) were independently associated with a door-to-CT ≤25 minutes. A 5-point quality score (1 point for each element) was therefore developed. In multivariable analysis, a 1-point higher EMS quality score was associated with a shorter time from EMS contact to CT (-9.2 [-10.6 to -7.8] minutes; P<0.001) and thrombolysis (-4.3 [-6.4 to -2.2] minutes; P<0.001), and higher odds of discharge to home (adjusted odds ratio, 1.1 [1.0-1.2]; P=0.002). CONCLUSIONS: Five EMS actions recommended by national guidelines were associated with rapid CT imaging. A simple quality score derived from these measures was also associated with faster stroke evaluation, greater odds of reperfusion treatment, and discharge to home.

Emergency Medical Services Stroke Care Performance Variability in Michigan: Analysis of a Statewide Linked Stroke Registry.

Background Emergency medical services (EMS) compliance with recommended prehospital care for patients with acute stroke is inconsistent; however, sources of variability in compliance are not well understood. The current analysis utilizes a linkage between a statewide stroke registry and EMS information system data to explore patient and EMS agency-level contributions to variability in prehospital care. Methods and Results This is a retrospective analysis of a cohort of confirmed stroke cases transported by EMS to hospitals participating in a statewide stroke registry. Using EMS information system data, the authors quantified EMS compliance with 6 performance measures derived from national guidelines for prehospital stroke care: prehospital stroke scale performance, glucose check, stroke recognition, on-scene time ≤15 minutes, time last known well documentation, and hospital prenotification. Multilevel multivariable logistic regression analysis was then used to examine associations between patient-level demographic and clinical characteristics and EMS compliance while accounting for and quantifying the variation attributable to agency of transport and recipient hospital. Over an 18-month period, EMS and stroke registry records were linked for 5707 EMS-transported stroke cases. Compliance ranged from 24% of cases for last known well documentation to 82% for documentation of a glucose check. The other measures were documented in approximately half of cases. Older age, higher National Institutes of Health Stroke Scale, and earlier presentation were associated with more compliant prehospital care. EMS agencies accounted for more than half of the variation in EMS prehospital stroke scale documentation and last known well documentation and 27% of variation in glucose check but <10% of stroke recognition and prenotification variability. Conclusions EMS stroke care remains highly variable across different performance measures and EMS agencies. EMS agency and electronic medical record type are important sources of variability in compliance with key prehospital performance metrics for stroke.

A Comparison of Probabilistic and Deterministic Match Strategies for Linking Prehospital and in-Hospital Stroke Registry Data.

BACKGROUND: Understanding and improving EMS stroke care requires linking data from both the prehospital and hospital settings. In the US, such data is collected in separate de-identified registries that cannot be directly linked due to lack of a common, unique patient identifier. In the absence of unique patient identifiers two common approaches to linking databases are deterministic matching, which uses combinations of non-unique matching variables to define matches, and probabilistic matching, which generates estimates of match probability based on the degree of similarity between records. This analysis seeks to compare these two approaches for matching EMS and stroke registry data. METHODS: Stroke cases transported by EMS to Michigan hospitals participating in the Michigan Coverdell Acute Stroke Registry were linked to records from Michigan's EMS Information System (MI-EMSIS) between January 2018 and June 2019. Destination hospital, date-of-service, patient age, date-of-birth, and sex were used to perform deterministic and probabilistic linkages. Match rates and representativeness of the matched samples were compared between the two matching strategies. Multivariable logistic regression was used to identify characteristics associated with successful matching. RESULTS: During the 18-month study period there were 8,828 EMS transported confirmed stroke cases in the registry and 620,907 EMS transports to 38 Coverdell registry-participating hospitals. The probabilistic match linked 5985 (67.7%) strokes to EMS records; the deterministic match linked 4012 (45.5%). Within each strategy the characteristics of matched and unmatched cases were similar, with the exception that deterministically matched cases were less likely to be older than 89 (adjusted odds ratio [aOR]=0.3), white (aOR=0.8), and more likely to have subarachnoid hemorrhage (aOR=1.4) than unmatched cases. CONCLUSION: Probabilistic matching resulted in higher match rates and a more representative sample of EMS transported strokes, suggesting it may be superior in assessing EMS stroke care compared to a deterministic approach.

Impact of Missing Stroke Severity Data on the Accuracy of Hospital Ischemic Stroke Mortality Profiling.

BACKGROUND: The Centers for Medicare and Medicaid Services have proposed 30-day ischemic stroke risk-standardized mortality rates that include adjustment for stroke severity using the National Institute of Health Stroke Scale (NIHSS), which is often undocumented. We used simulations to quantify the effect of missing NIHSS data on the accuracy of hospital-level ischemic stroke risk-standardized mortality rate profiling for 100 hypothetical hospitals with different case volumes. METHODS AND RESULTS: We generated simulated data sets of patients with NIHSS scores and other predictors of 30-day mortality based on empirical analysis of data from 7654 patients with ischemic stroke in the Michigan Stroke Registry. We assigned and rank-ordered a true (known) 30-day mortality rate to each hospital in the simulated data sets of N=100 hospitals with either low (100 patients with stroke), medium (300), or high (500) case volumes. We then estimated and rank-ordered 30-day risk-standardized mortality rates for the N=100 hospitals in each simulated data set using hierarchical logistic regression models. In each data set, we systematically varied the rate of missing NIHSS data and whether missing NIHSS data was independent (missing completely at random) or dependent (missing not at random) on the NIHSS score. With no missing NIHSS data, the Spearman correlation between the true hospital performance rank order assigned during data set generation and the estimated 30-day risk-standardized mortality rate rank order was 0.72, 0.88, and 0.91 in low, medium, and high volume hospitals, respectively. However, this fell to as low as 0.50, 0.71, and 0.79 as missing NIHSS data reached 70%. CONCLUSIONS: Missing NIHSS data had substantial detrimental effects on the accuracy of profiling of ischemic stroke mortality, especially in lower volume hospitals. Even with complete NIHSS documentation, significant limitations in ischemic stroke mortality profiling remain.

Quantifying Selection Bias in National Institute of Health Stroke Scale Data Documented in an Acute Stroke Registry.

BACKGROUND: As a measure of stroke severity, the National Institutes of Health Stroke Scale (NIHSS) is an important predictor of patient- and hospital-level outcomes, yet is often undocumented. The purpose of this study is to quantify and correct for potential selection bias in observed NIHSS data. METHODS AND RESULTS: Data were obtained from the Michigan Stroke Registry and included 10 262 patients with ischemic stroke aged ≥65 years discharged from 23 hospitals from 2009 to 2012, of which 74.6% of patients had documented NIHSS. We estimated models predicting NIHSS documentation and NIHSS score and used the Heckman selection model to estimate a correlation coefficient (ρ) between the 2 model error terms, which quantifies the degree of selection bias in the documentation of NIHSS. The Heckman model found modest, but significant, selection bias (ρ=0.19; 95% confidence interval: 0.09, 0.29; P<0.001), indicating that because NIHSS score increased (ie, strokes were more severe), the probability of documentation also increased. We also estimated a selection bias-corrected population mean NIHSS score of 4.8, which was substantially lower than the observed mean NIHSS score of 7.4. Evidence of selection bias was also identified using hospital-level analysis, where increased NIHSS documentation was correlated with lower mean NIHSS scores (r=-0.39; P<0.001). CONCLUSIONS: We demonstrate modest, but important, selection bias in documented NIHSS data, which are missing more often in patients with less severe stroke. The population mean NIHSS score was overestimated by >2 points, which could significantly alter the risk profile of hospitals treating patients with ischemic stroke and subsequent hospital risk-adjusted outcomes.

Characteristics and Outcomes of Stroke Patients Transferred to Hospitals Participating in the Michigan Coverdell Acute Stroke Registry.

BACKGROUND: Interhospital transfer of acute stroke patients is becoming increasingly important as regional stroke systems of care continue to evolve. We describe the characteristics and outcomes of stroke cases transferred to hospitals participating in the Michigan Coverdell Stroke Registry. METHODS AND RESULTS: Thirty-six hospitals participated in the Michigan registry during 2009 to 2011. Transfer patients were transferred from another hospital either acutely or after admission. Multivariable logistic regression was used to determine predictors of transfer and the independent association between transfer and in-hospital mortality and complications. Of 16 202 acute stroke admissions, 19.1% were transferred. Independent predictors of being transferred included younger age, hemorrhagic stroke, and higher stroke severity, but having a past history of stroke decreased the likelihood of being transferred. Transferred cases had higher in-hospital mortality (12.0% versus 6.4%; P<0.001) compared with regular admissions and were more likely to suffer complications (18.4% versus 12.8%; P<0.001). These differences remained after adjustment for confounding variables (adjusted odds ratio for mortality =1.32, 95% confidence interval 1.12, 1.56; adjusted odds ratio for complications =1.39, 95% confidence interval 1.22, 1.58). Among ischemic stroke, elevated odds of poor outcomes among transferred patients remained after adjustment for stroke severity. CONCLUSIONS: Transferred patients represent a complex admixture of patient characteristics that result in higher risks of poor outcomes. Our results suggest that it is prudent to account for patient transfer status when comparing hospital outcomes and that stroke registries need to expand their data collection capacity to provide a better understanding of the relative benefits and risks of transferring patients.

Assessment of the completeness and accuracy of case ascertainment in the Michigan Stroke Registry.

BACKGROUND: Accurate case ascertainment is essential for clinical registries to be valid and representative. We assessed case ascertainment in the Michigan Stroke Registry by linking to a statewide hospital discharge database (Michigan Inpatient Database [MIDB]). METHODS AND RESULTS: In 2009, all ischemic stroke cases submitted by 30 registry hospitals were linked to ischemic stroke discharges (International Classification of Diseases, Ninth Revision code 433.x1, 434.x1, or 436) in the MIDB. Databases were linked using hospital, age, sex, and admission date. The MIDB was regarded as the gold standard. To assess completeness, we calculated the percent difference between the number of cases entered in the registry relative to the MIDB. To quantify accuracy, we defined sensitivity as the proportion of cases identified in the MIDB that were matched to the registry and positive predictive value as the proportion of cases identified in the registry that were matched to the MIDB. Before data linkage, 4 hospitals were known to be using a case sampling approach. The remaining 26 registry hospitals submitted 21% fewer cases (n=3403) than were found in the MIDB (n=4340). The overall sensitivity was 68.8% (95% confidence interval, 76.4%-79.3%), and positive predictive value was 87.7% (95% confidence interval, 87.4%-89.8%). The sensitivity of case ascertainment was significantly lower in teaching hospitals and primary stroke centers but was higher in the sites that used prospective case ascertainment methods. CONCLUSIONS: Among registry hospitals, these results revealed relatively high levels of completeness and accuracy. Matching registry data to hospital discharge data identified hospitals that changed their case ascertainment method to a case sampling approach. This study illustrates the value of monitoring case ascertainment in stroke registries using external data sources.

Hospital variation in thrombolysis times among patients with acute ischemic stroke: the contributions of door-to-imaging time and imaging-to-needle time.

IMPORTANCE: Given the limited time window available for treatment with tissue plasminogen activator (tPA) in patients with acute ischemic stroke, guidelines recommend door-to-imaging time (DIT) within 25 minutes of hospital arrival and door-to-needle (DTN) time within 60 minutes for patients with acute ischemic stroke. Despite improvements in DITs, DTN times for tPA treatment in patients with acute ischemic stroke remain suboptimal. OBJECTIVES: To examine the contributions of DIT and imaging-to-needle (ITN) time to delays in timely delivery of tPA to patients with acute ischemic stroke and to assess between-hospital variation in DTN times. DESIGN, SETTING, AND PARTICIPANTS: A cohort analysis of 1193 patients having acute ischemic stroke treated with intravenous tPA between January 2009 and December 2012. Multilevel linear regression models included random effects for 25 Michigan hospitals participating in the Paul Coverdell National Acute Stroke Registry. MAIN OUTCOMES AND MEASURES: The primary outcome was a continuous measure of DTN time, in minutes, from emergency department arrival to thrombolytic delivery. RESULTS: The mean age was 68.1 years, the median National Institutes of Health Stroke Scale score was 11.0 (interquartile range, 6-17), 51.4% were female, and 37.5% were of nonwhite race/ethnicity.The mean (SD) DTN time was 82.9 (35.4) minutes, the mean (SD) DIT was 22.8 (15.9) minutes, and the mean (SD) ITN time was 60.1 (32.3) minutes. Most patients (68.4%) had DIT within 25 minutes, while 28.7% had DTN time within 60 minutes. Hospital variation accounted for 12.7% of variability in DTN times. Neither annual stroke volume nor primary stroke center designation was a significant predictor of shorter DTN time. Patient factors (age, sex, race/ethnicity, arrival mode, onset-to-arrival time, and stroke severity) explained 15.4% of the between-hospital variation in DTN times. After adjustment for patient-level factors, DIT explained 10.8% of the variation in hospital risk-adjusted DTN times, while ITN time explained 64.6%. CONCLUSIONS AND RELEVANCE: Compared with DIT, ITN time is a much greater source of variability in hospital DTN times and is a more common contributor to delays in timely tPA therapy for acute ischemic stroke. More attention is needed to determine systems changes that can decrease ITN time for patients with acute ischemic stroke.

Compliance with the stroke education performance measure in the Michigan Paul Coverdell National Acute Stroke Registry.

BACKGROUND AND PURPOSE: Stroke education, 1 of 8 endorsed stroke performance measures, consists of 5 specific subcomponents: risk factors, stroke warning signs, emergency medical service activation, physician follow-up, and discharge medications. We identified predictors of stroke education performance measure compliance in the Michigan Paul Coverdell National Acute Stroke Registry. METHODS: Data were collected on 9609 acute stroke admissions to 20 registry hospitals during 2008 and 2009. Predictors of measure compliance (delivery of all 5 subcomponents) were determined using multivariable logistic regression. RESULTS: Overall compliance with the stroke education measure was 61.8% (hospital-level compliance ranged between 16% and 93%). Compliance with individual subcomponents were risk factors (65.5%), stroke warning signs (68.9%), emergency medical service activation (66.8%), physician follow-up (92.9%), and discharge medications (91.5%). Age, gender, stroke subtype, prestroke ambulation, discharge destination, and hospital size were all significant independent predictors of compliance. Stroke education was delivered less often to patients who were ≥ 70 years of age, nonambulatory prestroke, not discharged to home, had transient ischemic attack, or hemorrhagic stroke. CONCLUSIONS: Only 60% of patients received stroke education consistent with the endorsed performance measures. Strategies to increase stroke education, including the impact of incorporating stroke-specific education measures into hospital care protocols, should be explored.