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September 06, 2011; 77 (10) Articles

Factors influencing in-hospital mortality and morbidity in patients treated on a stroke unit

H.-C. Koennecke, W. Belz, D. Berfelde, M. Endres, S. Fitzek, F. Hamilton, P. Kreitsch, B.-M. Mackert, D.G. Nabavi, C.H. Nolte, W. Pöhls, I. Schmehl, B. Schmitz, M. von Brevern, G. Walter, P.U. Heuschmann, For the Berlin Stroke Register Investigators
First published August 24, 2011, DOI: https://doi.org/10.1212/WNL.0b013e31822dc795
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Abstract

Objective: To determine the extent that demographics, clinical characteristics, comorbidities, and complications contribute to the risk of in-hospital mortality and morbidity in acute stroke.

Methods: Data of consecutive patients admitted to 14 stroke units cooperating within the Berlin Stroke Register were analyzed. The association of demographics, clinical characteristics, comorbidities, and complications with the risk of in-hospital death and poor outcome at discharge was assessed, and independent attributable risks were calculated, applying average sequential attributable fractions.

Results: In a 3-year period, 16,518 consecutive patients with ischemic or hemorrhagic stroke were documented. In-hospital mortality was 5.4%, and 45.7% had a poor outcome (modifed Rankin Scale score ≥3). In patients with length of stay (LOS) ≤7 days, 37.5% of in-hospital deaths were attributed to stroke severity, 23.1% to sociodemographics (age and prestroke disability), and 28.9% to increased intracranial pressure (iICP) and other complications. In those with LOS >7 days, age and stroke severity accounted for 44.1%, whereas pneumonia (12.2%), other complications (12.6%), and iICP (8.3%) contributed to one-third of in-hospital deaths. For poor outcome, attributable risks were similar for prestroke disability, stroke severity, pneumonia, and other complications regardless of the patient's LOS.

Conclusions: Approximately two-thirds of early death and poor outcome in acute stroke is attributed to nonmodifiable predictors, whereas main modifiable factors are early complications such as iICP, pneumonia, or other complications, on which stroke unit treatment should focus to further improve the prognosis of acute stroke.

GLOSSARY

ADSR=
Arbeitsgemeinschaft Deutscher Schlaganfall Register;
AF=
attributable fraction;
BSR=
Berlin Stroke Register;
ICH=
intracranial hemorrhage;
iICP=
increased intracranial pressure;
IQR=
interquartile range;
IS=
ischemic stroke;
mRS=
modified Rankin Scale;
NIHSS=
National Institutes of Health Stroke Scale

Despite advances in acute stroke management, the prognosis for a significant proportion of patients with acute ischemic or hemorrhagic stroke is still poor with 30-day case fatality rates in Western societies ranging from 12% to 22%.1,2 Treatment of patients with acute stroke on dedicated wards (stroke units) has been proven to be of major significance for the overall improvement of long-term stroke prognosis.3 In addition, in-hospital case fatality and outcome can be significantly improved by stroke unit treatment.4 Nonetheless, between 3% and 11% of patients die during hospitalization.4,5 To what specific extent potentially modifiable factors (neurologic and medical complications) and unmodifiable characteristics (demographic and clinical parameters and comorbidities) have an independent impact on short-term mortality and morbidity of patients treated on stroke units has not been previously investigated. We therefore sought to determine the attributable impact of several parameters on in-hospital mortality and morbidity in consecutive patients treated in stroke units from a well-defined urban region.

METHODS

All data were collected within the Berlin Stroke Register (BSR), a network of all 14 stroke units in the city of Berlin. These units serve a source population of 3.4 million inhabitants, of which, based on incidence data from the Erlangen Stroke Project,6 approximately 10,000 subjects per year have a first-ever or recurrent stroke or TIA. During the study period an estimated 80% of patients with acute strokes were admitted to one of the 14 stroke units, which are all part of a neurologic department and treat patients by applying an interdisciplinary approach, directed by neurologists. As a member of the German Stroke Registers Study Group (Arbeitsgemeinschaft Deutscher Schlaganfall Register [ADSR]), the BSR applies a standardized dataset for patient documentation as described previously.7 In brief, this dataset comprises sociodemographics; prehospital and intrahospital management; patient's clinical and functional status prestroke, on admission, and at discharge; comorbidities; diagnostic tests; complications; and treatment. Documentation of these parameters is computer-based in all participating hospitals and incumbent on the treating physician, with implemented completeness and plausibility checks.

In Berlin, emergency medical services are predominantly provided by the Berlin Fire Department. Based on an agreement between the BSR and the Berlin Fire Department, the vast majority of patients with acute stroke get transferred to one of the BSR stroke units, without other criteria being applied for patient selection.

Study population and data selection.

All patients with an acute stroke within 7 days before admission were documented. Patients with TIA or subarachnoid hemorrhage were excluded from the present analyses. Beyond these, no other criteria (e.g., age or severity of stroke) were applied to define eligibility for the present study. Because patients with intracerebral hemorrhage (ICH) often get admitted to an intensive care unit with neurosurgical affiliation, the proportion of these patients is underrepresented in our cohort compared with the distribution of stroke subtypes among unselected stroke populations.1

Definitions of variables.

In the present analyses, the influence of the following variables on early stroke outcome was assessed: age (categorized as ≤65, 65–74, 75–84, and ≥85 years); gender; prestroke functional status (independent vs dependent at home vs dependent at institution); stroke subtype (ischemic stroke [IS] vs ICH); comorbidities (hypertension [documented systolic blood pressure of ≥140 mm Hg or diastolic blood pressure of ≥90 mm Hg more than 48 hours after admission or history of treated hypertension], diabetes mellitus [pathologic elevated fasting blood glucose level or history of diabetes], atrial fibrillation [documented by standard EKG or Holter monitoring], or previous stroke [evidence for acute neurologic deficit >24 hours before current event]); severity of the neurologic deficit according to the German version8 of the National Institutes of Health Stroke Scale (NIHSS), categorized a priori as <5, 5–15, 16–25, and >25 points based on previous studies9; functional impairment according to the German version8 of the modified Rankin Scale (mRS), categorized a priori into good (mRS score 0–2) and poor (mRS score 3–6) outcome; and complications. The latter comprised pneumonia (clinical or diagnostic findings of pulmonary infection or hospital-acquired pneumonia only), increased intracranial pressure (iICP) (imaging evidence of mass effect or brain shift syndrome with clinical deterioration), and other complications (a single checkbox variable comprising all other complications that require diagnostic or treatment procedures, such as recurrent stroke, seizure, urinary tract infection, or deep venous thrombosis with or without pulmonary embolism).

Statistical analyses.

To estimate crude and adjusted odds ratios and resulting 95% confidence intervals of potential predictors for in-hospital death and poor outcome, univariate and multivariable logistic regression analyses were performed with in-hospital death or poor outcome as binary outcome. In multivariable analyses, the independent effect of age, sex, comorbidities, stroke severity, stroke subtype, living conditions prestroke, and complications during hospital stay was assessed. For variable selection, we used the stepwise backward selection method with likelihood ratio test in SPSS. To investigate variations in the impact of investigated variables by length of observation, terms of interaction between age, sex, stroke severity, living conditions prestroke, early complications, and length of hospital stay (LOS) (≤7 days and >7 days) were assessed by adding terms of interaction to the multivariable model and testing the resulting coefficients by the likelihood ratio test. In contrast to the approach for model choice in the main analysis, for detection of significant interaction terms by the likelihood ratio test we used the method ENTER, which means that for each possible interaction we compared the model including all main factors with the model including all main factors plus the corresponding interaction term. These analyses revealed interaction terms between LOS and stroke severity (p < 0.001), LOS and sex (p = 0.007), LOS and pneumonia (p < 0.001), LOS and iICP (p = 0.011), and LOS and other complications (p < 0.001) for the risk of in-hospital death and between LOS and stroke severity (p < 0.001), LOS and age (p < 0.001), LOS and sex (p = 0.011), LOS and iICP (p < 0.001), and LOS and other complications (p = 0.017) for the risk of poor outcome at discharge. Thus, further analyses were presented after stratification for LOS. To determine the relevance of factors influencing early mortality and morbidity within the whole population of stroke patients, attributable fractions (AFs) were calculated. The AF for a factor is defined as the proportion by which a disease occurrence (here mortality and morbidity) is reduced if the whole population attains the same risk of disease as the unexposed individuals. The AF considers both the individual association and the exposure frequency and thereby allows estimation of the relevance of a risk factor for the outcome in a population. For estimating attributable risk proportions of in-hospital death and poor outcome, the method of average sequential attributable fractions10 was used for implementing the principle of attributable fractions in a multivariable setting. This approach ensures that adjusted AFs for several risk factors never add up to a value greater than 100%,11 the total AF allocated to a block of special risk factors is equal to the sum of allocated fractions for the factors constituting the block,10 and the values for the attributable fractions of the single factors and with this the aggregate value for all risk factors considered are independent from the order of risk factor removal from the model, i.e., independent of how the factors are numbered.10 All variables included in the model were dichotomized based on a priori defined cutoff values. Documentation of all variables was complete except for prestroke functional status, NIHSS scores, and hypertension, which were voluntary to be documented in the first years of the study; the number of missing values for these variables ranged between 10.8% for NIHSS and 13.8% for hypertension. A series of sensitivity analyses were performed to assess potential selection biases by missingness; for these purposes, the cumulative number of neurologic deficits (limb paresis, aphasia, dysarthria, and disturbances of consciousness) was defined as proxy for stroke severity instead of the NIHSS as used in previous analyses.12 In addition, missing categories of hypertension and prestroke functional status were included as their own variable. All tests were 2-tailed, and statistical significance was determined at an α level of 0.05. Statistical analyses were performed using the SAS macro average AF by Rückinger et al.11 and the SPSS 16.0 Data Mining Statistical Analysis software package.

Standard protocol approvals, registrations, and patient consents.

Participation of hospitals in programs for quality assurance of acute clinical care is mandatory in Germany. Because the identity of individual patients is completely anonymous in our dataset, no specific informed consent was required from patients or relatives. Hospital identities were only known to the coordination office of the BSR at the Board of Physicians Berlin (Ärztekammer Berlin) and the data pooling center at the Bavarian Permanent Working Party for Quality Assurance in Munich, whereas the center performing the present analyses was blinded.

RESULTS

During the 3-year study period (January 1, 2007, through December 31, 2009) data from 20,677 patients were documented. Of these, 16,518 with an IS or ICH; 4,023 with a TIA, 86 with a subarachnoid hemorrhage, and 50 with an undetermined stroke were excluded. Some centers did not start data collection until April 2007; thus, the actual number of documented strokes for this year was slightly lower than epidemiologically expected. The main characteristics of the patients included in the present analyses are shown in table 1. An IS was diagnosed in more than 90% of patients, and clinical stroke severity was moderate to severe (NIHSS score ≥5) in more than half of patients. The median LOS was 8 days (interquartile range [IQR] 5–12 days); 46.0% were discharged within 7 days after admission (median LOS 5 days, IQR 3–6 days). In patients discharged after 7 days, median LOS was 11 days (IQR 9–16 days). Approximately 80% of the subjects remained free of complications. The overall in-hospital mortality was 5.4% (n = 896; 7.6% in those with LOS ≤7 days and 3.6% among those discharged after 7 days). At discharge, 45.7% (n = 7554) had a poor outcome (mRS score ≥3; 35.2% with LOS ≤7 days and 54.7% with LOS >7 days), of whom 6,658 were alive at discharge.

View this table:
Table 1

Characteristics of the study population (n = 16,518)

Univariate analyses.

In univariate analyses (table 2), a higher risk of in-hospital death was observed for older age, female sex, prestroke disability, stroke severity, ICH, atrial fibrillation, and any complication. An increased risk of poor outcome was observed for older age, female sex, prestroke disability, stroke severity, ICH, hypertension, previous stroke, atrial fibrillation, diabetes, and any complication.

View this table:
Table 2

Univariate analyses: Frequency of in-hospital death and poor outcome at discharge

Multivariable analyses.

Multivariable analyses were stratified for LOS because of the differences in the impact of variables on in-hospital mortality and morbidity (table 3). Age, prestroke disability, stroke severity, ICH, iICP, and other complications were independent predictors of in-hospital death in those with LOS ≤7 days, whereas age, female sex, stroke severity, and complications independently predicted death in patients with LOS >7 days. Poor outcome at discharge was associated with age, prestroke disability, ICH, diabetes, hypertension, atrial fibrillation, previous stroke, stroke severity, and complications in those with LOS ≤7 days, whereas age, prestroke disability, diabetes, stroke severity, ICH, pneumonia, and other complications contributed to poor outcome in patients with LOS >7 days (table 3).

View this table:
Table 3

Multivariable analysis: Predictors of in-hospital death and poor outcome at discharge stratified by length of stay in hospitala

Attributable risks.

In patients with LOS ≤7 days, more than one-third of in-hospital deaths were attributed to stroke severity, more than 20% to demographic characteristics, and more than one-fourth to iICP or other complications. In those with LOS >7 days, the effects of age, stroke severity, and iICP were less, but pneumonia contributed to 12.2% of in-hospital deaths. Attributable risk for poor outcome was similar for prestroke disability, ICH, diabetes, and stroke severity in patients with LOS ≤7 and >7 days, whereas older age had a lesser effect in patients with LOS ≥7 days (table 4). Modifiable factors (pneumonia, iICP, and other complications) had the greatest relative attributable impact on mortality in those with LOS >7 days (33.1%), whereas for patients with shorter LOS as well as both LOS strata regarding poor outcome, the effect of these factors ranged between 13% and 29%. Overall, more variability was explained by factors included in the current models for risk of in-hospital death compared with poor outcome at discharge. Inclusion of the cumulative number of neurologic deficits instead of NIHSS score as well as inclusion of missing values of prestroke functional status and hypertension as their own categories within a set of predefined sensitivity analyses did not substantially influence the observed association in multivariable analyses or the attributable risks of death or poor outcome (data not shown). In addition, restricting analyses to patients with ischemic stroke did not change the attributable risk estimates substantially (data not shown).

View this table:
Table 4

Attributable risks of in-hospital death and poor outcome at discharge, stratified for length of stay in hospital

DISCUSSION

Previous studies have demonstrated that several factors independently increase the risk of death or poor outcome in acute stroke.2,12,–,20 Our study, however, differs from these investigations in several ways. First, data were exclusively collected from patients treated on a neurologic stroke unit, which might improve the reliability of clinical data and enhance the homogeneity of the sample; second, our study is the first to investigate the independent attributable impact of specific parameters already known to be of significance for acute stroke mortality and outcome; third, we estimated for the first time the association between different factors and functional outcome at discharge.

A prior study reporting data from stroke registers in Germany comprising departments of neurology, internal medicine, and geriatric medicine found that approximately 50% of all in-hospital deaths after ischemic stroke might be attributed to serious complications.12 This figure is slightly higher compared with our estimates. However, in this analysis conventional methods to calculate attributable risks that may overestimate the potential population impact of specific factors were applied, because these methods were not specifically designed for a multivariable setting and factors influencing risk of in-hospital death after stroke are not acting independently.21,22 Therefore, in our present analyses, we used sequential attributable fractions to estimate the attributable risk of death and poor outcome, because this method has the advantage of not adding up the contribution of different risk factors to more than 100%.11

Our study has shown that among patients with a shorter LOS, between 60% and 70% of early death and poor outcome is attributed to nonmodifiable predictors, of which stroke severity on admission, age, and prestroke disability have the largest impact. In contrast, modifiable factors are of major importance for in-hospital death in patients staying longer in the hospital, accounting for almost 39% related to the total of explained events (table 4). In particular, pneumonia affected in-hospital mortality in this cohort. Given that most stroke-related cases of pneumonia are believed to result from aspiration,23 the necessity to screen acute stroke patients more frequently for dysphagia is emphasized. Against this background, future research may prove the concept of prophylactic antibiotic treatment24 in subjects at high risk for developing stroke-related pneumonia. However, valid and reliable clinical scores predicting pneumonia after stroke for frequent use in daily clinical practice are lacking. With respect to in-hospital mortality and regardless of the LOS, other complications were of similar importance and accounted for 12%–14% of deaths. Although not specified in our study, it can be assumed that these complications comprised those entities reported most frequently, such as urinary tract infection, deep venous thrombosis, myocardial infarction, and congestive heart failure.23 Of note, in patients with a longer LOS, less than 50% of the risk of poor outcome could be attributed to the parameters assessed in our study, suggesting that other undocumented factors (e.g., heart failure, depression, and cognitive impairment) are important determinants of functional outcome.

Acute stroke case fatality and outcome, although subject to bias in many aspects, may be used as a quality indicator for acute stroke treatment.7 Furthermore, in-hospital case fatality may be an important determinant to measure early stroke mortality in general, given that most relevant clinical decisions are usually made in the first few days after hospital admission. One might argue that short-term outcome may not be a suitable instrument for measuring the long-term prognosis of stroke. A previous study, however, demonstrated that similar variables are associated with 7-day and 1-year stroke case fatality.2 Likewise, the level of functional disability at discharge after acute stroke is a strong predictor of long-term disability in stroke.25,26 Thus, factors influencing short-term outcome, as identified in our study, can be used as reliable predictors of prognosis after stroke.

There are some limitations of our study that need comments. First, there was no differentiation of complications other than pneumonia and iICP. This confinement of the ADSR dataset resulted from the findings of a previous study identifying both of these complications as the most important for in-hospital mortality.12 However, because our cohort can be considered to be representative of unselected stroke patients, the frequency of complications other than pneumonia and iICP can be assumed to coincide with the frequencies from other studies focused on stroke-related complications.23 Second, documentation of stroke patients was incomplete in some centers during the first 4 months of our study period. This was caused by delayed implementation of software for computer-based documentation in the respective hospitals, in which consecutive documentation was started immediately thereafter. Hence, any selection bias due to a delayed start of documentation is rather unlikely. Third, because of the smaller numbers of individuals with more severe stroke (NIHSS score >15 points), confidence intervals are inevitably wide in this subgroup. Nevertheless, even odds ratios at the lower margin still demonstrate a strong effect of stroke severity on outcome.

It is claimed that much of the improvement in stroke outcomes in recent decades is largely attributed to reducing and treating complications more effectively.27 There is, however, a relative paucity of adequate research in this field. In this context and despite the somewhat disenchanting fact that two-thirds of the parameters determining acute stroke outcome are currently nonmodifiable, the extent to which treatable complications contribute to the risk of early death and poor outcome is of critical importance to focus future research and treatment strategies.

AUTHOR CONTRIBUTIONS

Dr. Koennecke: drafting/revising the manuscript, study concept or design, analysis or interpretation of data, and study supervision. Dr. Belz: analysis or interpretation of data and acquisition of data. Dr. Berfelde: analysis or interpretation of data and acquisition of data. Dr. Endres: study concept or design, analysis or interpretation of data, and obtaining funding. Dr. Fitzek: drafting/revising the manuscript and acquisition of data. Dr. Hamilton: analysis or interpretation of data and acquisition of data. Dr. Kreitsch: drafting/revising the manuscript and acquisition of data. Dr. Mackert: drafting/revising the manuscript and acquisition of data. Dr. Nabavi: drafting/revising the manuscript, study concept or design, analysis or interpretation of data, acquisition of data, and study supervision. Dr. Nolte: study concept or design, analysis or interpretation of data, and study supervision. Dr. Pöhls: drafting/revising the manuscript, acquisition of data, and study supervision. Dr. Schmehl: analysis or interpretation of data and acquisition of data. Dr. Schmitz: drafting/revising the manuscript and analysis or interpretation of data. Dr. von Brevern: drafting/revising the manuscript, contribution of vital reagents/tools/patients, and acquisition of data. Dr. Walter: drafting/revising the manuscript, study concept or design, contribution of vital reagents/tools/patients, and acquisition of data. Dr. Heuschmann: drafting/revising the manuscript, analysis or interpretation of data, and statistical analysis.

COINVESTIGATOR

C. Markl-Vieto Estrada, Referat Qualitätsmanagement, Ärztekammer Berlin.

DISCLOSURE

Dr. Koennecke has received speaker honoraria from ev3 medical, Medtronic, Inc., UCB, and Boehringer Ingelheim. Dr. Belz and Dr. Berfelde report no disclosures. Dr. Endres has served on scientific advisory boards for the Center of Stroke Research Berlin, Boehringer Ingelheim, sanofi-aventis, and Merck Sharpe & Dohme; has received funding for travel and/or speaker honoraria from Trommsdorff, GlaxoSmithKline, Bristol-Myers Squibb, AstraZeneca, Boehringer Ingelheim, Pfizer Inc, Berlin-Chemie, sanofi-aventis, Bayer Schering Pharma, Takeda Pharmaceutical Company Limited, Desitin Pharmaceuticals, GmbH, and Novartis; serves on the editorial board of Stroke, the Journal of Cerebral Blood Flow Metabolism, Cerebrovascular Disease, the Journal of Molecular Medicine, and Neuropsychopharmacology; treats stroke patients in Department of Neurology with neurointensive care and stroke unit (50% effort); and has received research support from Novartis, AstraZeneca, BMBF, DFG, EU, and the Volkswagen Foundation. Dr. Fitzek, Dr. Hamilton, and Dr. Kreitsch report no disclosures. Dr. Mackert has received funding for travel from Boehringer Ingelheim, Bayer Schering Pharma, and Merck Serono. Dr. Nabavi has received speaker honoraria from Boehringer Ingelheim, sanofi-aventis, and Trommsdorff. Dr. Nolte has received speaker honoraria from Boehringer Ingelheim and research support from the German Ministry of Research and Education. Dr. Pöhls reports no disclosures. Dr. Schmehl has received speaker honoraria from Boehringer Ingelheim and Novartis. Dr. Schmitz has served on scientific advisory boards for Pfizer Inc, GlaxoSmithKline, Novartis, Janssen, Desitin Pharmaceuticals, GmbH, and UCB; has received funding for travel and/or speaker honoraria from Pfizer Inc, GlaxoSmithKline, UCB, Desitin Pharmaceuticals, GmbH, and Janssen; serves on the editorial boards of Epilepsy and Behaviour and Zeitschrift für Epileptologie; serves on speakers' bureaus for GlaxoSmithKline, Pfizer Inc, Janssen, Desitin Pharmaceuticals, GmbH, Novartis, sanofi-aventis, UCB, Desitin Pharmaceuticals, GmbH, and Eisai Inc.; receives publishing royalties from Elsevier, Cambridge University Press, Springer, and Thieme; and has received research support from Pfizer Inc, GlaxoSmithKline, Novartis, Desitin Pharmaceuticals, GmbH, Janssen, sanofi-aventis, UCB. DFG, and Deutsche Gesellschaft für Epileptologie. Dr. von Brevern and Dr. Walter report no disclosures. Dr. Heuschmann receives research support from the EU, the German Ministry of Research and Education, University of Erlangen, German Stroke Foundation, and the Stanley Thomas Johnson Foundation.

ACKNOWLEDGMENT

The authors thank Uwe Malzahn, Charité–Universitätsmedizin Berlin, for data analysis, the Ärztekammer Berlin for continuous support of the BSR, Professor Hermanek and Dr. Burmeister from the Bavarian Permanent Working Party for Quality Assurance for reliable management of pooled BSR data, all patients and their families, and the health care professionals in the contributing hospitals.

Footnotes

  • Study funding: Data analyses were funded by the German Ministry of Research and Education within the Center for Stroke Research Berlin.

  • Received November 11, 2010.
  • Accepted May 2, 2011.

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