Is in-hospital stroke mortality an accurate measure of quality of care?

Risk adjustment of ischemic stroke outcomes for comparing hospital performance: a statement for healthcare professionals from the American Heart Association/American Stroke Association

BACKGROUND AND PURPOSE

Stroke is the fourth-leading cause of death and a leading cause of long-term major disability in the United States. Measuring outcomes after stroke has important policy implications. The primary goals of this consensus statement are to (1) review statistical considerations when evaluating models that define hospital performance in providing stroke care; (2) discuss the benefits, limitations, and potential unintended consequences of using various outcome measures when evaluating the quality of ischemic stroke care at the hospital level; (3) summarize the evidence on the role of specific clinical and administrative variables, including patient preferences, in risk-adjusted models of ischemic stroke outcomes; (4) provide recommendations on the minimum list of variables that should be included in risk adjustment of ischemic stroke outcomes for comparisons of quality at the hospital level; and (5) provide recommendations for further research.

METHODS AND RESULTS

This statement gives an overview of statistical considerations for the evaluation of hospital-level outcomes after stroke and provides a systematic review of the literature for the following outcome measures for ischemic stroke at 30 days: functional outcomes, mortality, and readmissions. Data on outcomes after stroke have primarily involved studies conducted at an individual patient level rather than a hospital level. On the basis of the available information, the following factors should be included in all hospital-level risk-adjustment models: age, sex, stroke severity, comorbid conditions, and vascular risk factors. Because stroke severity is the most important prognostic factor for individual patients and appears to be a significant predictor of hospital-level performance for 30-day mortality, inclusion of a stroke severity measure in risk-adjustment models for 30-day outcome measures is recommended. Risk-adjustment models that do not include stroke severity or other recommended variables must provide comparable classification of hospital performance as models that include these variables. Stroke severity and other variables that are included in risk-adjustment models should be standardized across sites, so that their reliability and accuracy are equivalent. There is a pressing need for research in multiple areas to better identify methods and metrics to evaluate outcomes of stroke care.

CONCLUSIONS

There are a number of important methodological challenges in undertaking risk-adjusted outcome comparisons to assess the quality of stroke care in different hospitals. It is important for stakeholders to recognize these challenges and for there to be a concerted approach to improving the methods for quality assessment and improvement.

The SOAR (Stroke subtype, Oxford Community Stroke Project classification, Age, prestroke modified Rankin) score strongly predicts early outcomes in acute stroke

BACKGROUND

Previous prognostic scoring systems in predicting stroke mortality are complex, require multiple measures that vary with time and failed to produce a simple scoring system.

AIMS/HYPOTHESIS

The study aims to derive and internally validate a stroke prognostic scoring system to predict early mortality and hospital length of stay.

METHODS

Data from a U.K. multicenter stroke register were examined (1997-2010). Using a prior hypothesis based on our and others observations, we selected five patient-related factors (age, gender, stroke subtype, clinical classification, and prestroke disability) as candidate prognostic indicators. An 8-point score was derived based on multiple logistic regression model using four out of five variables. Performance of the model was assessed by plotting the estimated probability of in-hospital death against the actual probability by testing for overfitting (calibration) and area under the curve methods (discrimination).

RESULTS

The total sample consisted of 12,355 acute stroke patients (ischemic stroke 91.0%). The score predicted both in-patient and seven-day mortality. The crude in-patient mortality were 1.57%, 4.02%, 10.65%, 21.41%, 46.60%, 62.72%, and 75.81% for those who scored 0, 1, 2, 3, 4, 5, and 6, respectively. The calibration of the model revealed no evidence of overfitting (estimated overfitting 0.001). The area under the curve values for both in-hospital and seven-day mortality were 0.79. The score predicted length of stay with a higher score was associated with longer median length of stay in those discharged alive and shorter median length of stay in those who died (P for both <0.001).

CONCLUSIONS

A simple 8-point clinical score is highly predictive of acute stroke mortality and length of hospital stay. It could be used as prognostic tool in service planning and also to risk-stratify patients to use these outcomes as markers of stroke care quality across institutions.

A risk score for in-hospital death in patients admitted with ischemic or hemorrhagic stroke

BACKGROUND

We aimed to derive and validate a single risk score for predicting death from ischemic stroke (IS), intracerebral hemorrhage (ICH), and subarachnoid hemorrhage (SAH).

METHODS AND RESULTS

Data from 333 865 stroke patients (IS, 82.4%; ICH, 11.2%; SAH, 2.6%; uncertain type, 3.8%) in the Get With The Guidelines-Stroke database were used. In-hospital mortality varied greatly according to stroke type (IS, 5.5%; ICH, 27.2%; SAH, 25.1%; unknown type, 6.0%; P<0.001). The patients were randomly divided into derivation (60%) and validation (40%) samples. Logistic regression was used to determine the independent predictors of mortality and to assign point scores for a prediction model in the overall population and in the subset with the National Institutes of Health Stroke Scale (NIHSS) recorded (37.1%). The c statistic, a measure of how well the models discriminate the risk of death, was 0.78 in the overall validation sample and 0.86 in the model including NIHSS. The model with NIHSS performed nearly as well in each stroke type as in the overall model including all types (c statistics for IS alone, 0.85; for ICH alone, 0.83; for SAH alone, 0.83; uncertain type alone, 0.86). The calibration of the model was excellent, as demonstrated by plots of observed versus predicted mortality.

CONCLUSIONS

A single prediction score for all stroke types can be used to predict risk of in-hospital death following stroke admission. Incorporation of NIHSS information substantially improves this predictive accuracy.

Risk Score for In-Hospital Ischemic Stroke Mortality Derived and Validated Within the Get With The Guidelines–Stroke Program

Background—

There are few validated models for prediction of in-hospital mortality after ischemic stroke. We used Get With the Guidelines–Stroke Program data to derive and validate prediction models for a patient's risk of in-hospital ischemic stroke mortality.

Methods and Results—

Between October 2001 and December 2007, there were 1036 hospitals that contributed 274 988 ischemic stroke patients to this study. The sample was randomly divided into a derivation (60%) and validation (40%) sample. Logistic regression was used to determine the independent predictors of mortality and to assign point scores for a prediction model. We also separately derived and validated a model in the 109 187 patients (39.7%) with a National Institutes of Health Stroke Scale (NIHSS) score recorded. Model discrimination was quantified by calculating the C statistic from the validation sample. In-hospital mortality was 5.5% overall and 5.2% in the subset in which NIHSS score was recorded. Characteristics associated with in-hospital mortality were age, arrival mode (eg, via ambulance versus other mode), history of atrial fibrillation, previous stroke, previous myocardial infarction, carotid stenosis, diabetes mellitus, peripheral vascular disease, hypertension, history of dyslipidemia, current smoking, and weekend or night admission. The C statistic was 0.72 in the overall validation sample and 0.85 in the model that included NIHSS score. A model with NIHSS score alone provided nearly as good discrimination (C statistic 0.83). Plots of observed versus predicted mortality showed excellent model calibration in the validation sample.

Conclusions—

The Get With the Guidelines–Stroke risk model provides clinicians with a well-validated, practical bedside tool for mortality risk stratification. The NIHSS score provides substantial incremental information on a patient's short-term mortality risk and is the strongest predictor of mortality.

Redirecting patients to improve stroke outcomes: implications of a volume-based approach in one urban market

BACKGROUND

The designation of primary stroke centers may result in patients being redirected from their usual source of care, although there is little evidence that these centers would result in better outcomes or lower costs. An alternative approach could direct patients to hospitals treating greater volumes of stroke patients.

OBJECTIVES

We sought to estimate the effect of hospital stroke volume on patient mortality and costs in a regional hospital market and to analyze the implications of hypothetical volume-based referral policies in that market, including the effects of patient-hospital distance.

METHODS

Using a retrospective cohort, we studied 12,150 Medicare patients admitted for acute stroke to 1 of 29 hospitals in Greater Cleveland during a 7-year period. The primary outcome was risk-adjusted 30-day mortality. Secondary outcomes included log hospital costs and discharge destination. The primary measure of volume was average annual number of stroke patients; patient distance to the nearest hospital was approximated using patient zip code and hospital address data.

RESULTS

Overall 30-day mortality was 14.9%. For each 100-patient increase in hospitals' annual stroke volume, risk-adjusted mortality declined 0.9 percentage points (odds ratio = 0.90; 95% confidence interval = 0.82-0.98; P < 0.02) with no significant difference in hospital costs. For each 1-mile increase in patient distance to nearest hospital, mortality increased 0.6 percentage points (odds ratio = 1.07; 95% confidence interval = 1.03-1.11; P < 0.01). Only 3 of 29 hospitals (10.3%) exceeded the highest plausible threshold (250 strokes/year), redirecting 81.4% of patients for a net reduction in mortality of 0.4%; lower thresholds would redirect fewer patients but have negligible effects on mortality.

CONCLUSIONS

Our findings fail to support redirecting acute stroke patients based on hospital stroke volume.

Factors Associated With In-Hospital Mortality After Administration of Thrombolysis in Acute Ischemic Stroke Patients

Background and Purpose— The prospective trials evaluating the safety and efficacy of intravenous tissue plasminogen activator have generally been conducted at academic medical centers and community hospitals with an institutional commitment to stroke care. Relatively little is known about the safety of this therapy as it is used in the community. We therefore examined outcomes in acute stroke patients treated with thrombolysis using the largest discharge database available in the United States for the years 1999 to 2002.

Methods— Data were derived from the Nationwide Inpatient Sample for the years 1999 to 2002. Using the appropriate International Classification of Disease—Clinical Modification, 9th revision , codes, patients admitted through the emergency room with a primary diagnosis of acute ischemic stroke were selected for analysis. From these patients, those coded as receiving thrombolysis were identified. Multivariate logistic regression was performed on the thrombolysis and nonthrombolysis cohorts to identify independent predictors of in-hospital mortality from among those clinical elements available in the database.

Results— We identified 2594 patients treated with thrombolysis from a group of 248 964 patients admitted through the emergency room with a primary diagnosis of acute ischemic stroke. The thrombolysis cohort had a higher in-hospital mortality rate compared with the nonthrombolysis patients (11.4% versus 6.8%). The rate of intracerebral hemorrhage was 4.4% for the thrombolysis cohort and 0.4% for nonthrombolysis patients. Multivariate logistic regression showed advanced age, Asian/Pacific Islander race, congestive heart failure, and atrial fibrillation/flutter to be independent predictors of in-hospital mortality after thrombolysis. Thrombolysis volume, overall ischemic stroke volume, and teaching status were not significant predictors of in-hospital mortality after thrombolysis.

Conclusions— Thrombolysis, as it is used in the community, has a safety profile that is similar to that observed in the large, prospective clinical trials.

In-hospital stroke mortality, hospital transfers, and referral bias at a rural academic medical center

A chart review of acute stroke (DRG 14) discharges from a rural academic medical center in 1999 was performed because of an observed high in-hospital crude acute stroke mortality rate. An analysis of the results of this chart review demonstrated that the odds ratio of intracerebral hemorrhage in patients who had been transferred from other hospitals was 11.7 compared to intracerebral hemorrhage in nonhospital transfer patients (p < 0.00001). This finding illustrates the potential magnitude and significance of referral bias at a rural academic medical center.