Stroke unit care in germany: the german stroke registers study group (ADSR)

Data were derived from the German Stroke Registers Study Group (ADSR), a voluntary network of 9 regional registers (Bavaria, Baden-Württemberg, Berlin, Hamburg, Hesse, North Rhine, Northwest Germany, Rhineland-Palatinate, and Schleswig-Holstein), which was founded in 1999 for monitoring quality of acute stroke care in Germany [11]. Data are collected mainly electronically, with predefined completeness and plausibility checks, by the respective quality assurance projects [12]. For example: date of ever treatment must be after the date of admission. Only plausible datasets were evaluated and sent to the data-pooling center. Each regional stroke register sends the complete data set in regular intervals to the data-pooling center (University of Würzburg, 2011-2015) [11]. Participation within a regional register of the ADSR is mandatory for all SUs in Germany certified according to the criteria of the DSG and the SDSH (see below) as well as for all hospitals in several regions [12].

SUs were certified according to criteria of the German Stroke Society (DSG) and the German Stroke Foundation (SDSH) [13] and supported by the LGA InterCert [8]. SUs certified according to these criteria are distinguished into regional SUs and supra-regional SUs, depending on equipment and expertise [6]. Regional SUs have to treat at least 250 patients per year in at least 4 beds [14]. Supra-regional SUs have further demands on structural and procedural requirements [13, 14]. For example, interventional neuroradiology and neurosurgery and all relevant therapeutic and diagnostic facilities for stroke diagnosis, treatment, and management have to be available [6]. Additionally, a minimum of 450 patients per year treated in at least 6 monitoring beds are essential for certification of supra-regional SUs [14]. The minimum number of stroke patients per year was increased up to at least 500 in 2012 [13]. Beside these criteria, certification procedures by the local government of some federal states are also applied (i.e. Baden-Württemberg).

Analyses were restricted to hospitals with a certified SU, according to criteria of the DSG, the SDSH or to regional criteria by the federal states that provided complete information regarding the admitting department. Investigated structural characterisis of participating hospitals providing SU care included: percentage of patients directly admitted to a SU (SU/general ward/intensive care unit/other), percentage of patients admitted directly to a SU (<60%, 60-80%, >80%), year of first certification of the respective SU (2000-2002, 2003-2005, 2006-2008, 2009-2012), and number of stroke and Transient Ischemic Attack (TIA) patients treated per year (<250, 250-500, >500-750, >750).

A set of evidence based patient-oriented quality indicators was defined to measure quality of acute stroke care in participating hospitals. A standardized process was initiated for developing a set of evidence based quality indicators between 2003 and 2006 [11]. The process contained a systematic literature review, external independent validation and the conduction of a prospective pilot study [11]. The quality indicator set is updated on a regular basis every 2-3 years [15]. Target values of quality indicators were defined to indicate good quality of stroke care based on distribution within the data, national recommendations and expert consensus by the working group [15]. For the present analysis, the following set of 11 patient-oriented QIs with defined target values were used with definitions published previously [12]: early antithrombotic therapy; antithrombotic therapy – secondary prevention; anticoagulant therapy; brain imaging in patients with suspected strokes; vascular imaging in IS and TIA; screening for dysphagia; early rehabilitation – physiotherapy/occupational therapy; early rehabilitation – speech therapy; early mobilization; patients who arrived at the hospital within two hours from symptom onset and received brain imaging within one hour; early systemic thrombolytic therapy in eligible patients.

Achievement of QIs was defined by reaching previously defined target values [12]. The proportion of achieved QIs was calculated by the number of QIs fulfilled in patients divided by the number of QIs that a patient could have fulfilled, according to the defined eligibility criteria of the respective QI.

The following regional stroke audits were included in the present analyses: Hesse, Bavaria, Northwest Germany, Baden-Württemberg, North Rhine and the Berlin Stroke Register. Three regional stroke audits (Hamburg, Rhineland-Palatinate, and Schleswig-Holstein) were excluded because of missing or not comparable information on SU admission. Detailed descriptive and univariate analyses were performed to describe the demographic and clinical characteristics of the patients. Multivariable logistic regression analyses were performed to estimate the characteristics of ischemic stroke patients being admitted to a SU including: age (age group: ≤64y, ≥65y and ≤74y, ≥75y and ≤84y, ≥85y); sex; comorbidities (atrial fibrillation, diabetes mellitus, hypertension, previous stroke, hyperlipidemia); time from onset to admission (≤2h, >2-3h, >3-6h, >6-24h, >24h-48h, >48h, unknown); NIHSS (≤3, >4-15, ≥15) [16]; and day of admission (weekday, weekend). Analyses were adjusted for regional audits. The association between hospital characteristics and the achievement of given target values to a defined set of evidence based quality indicators was assessed by Generalized Linear Mixed Models (GLMM). GLMM was used to estimate the p-values and regression coefficients for the differences in the percentage of fulfilled quality indicators regarding the structure of the hospital (percentage of patients being admitted to a SU, year of certification of the respective SU, and the number of stroke and TIA patients treated per year) and individual characteristics (age [categories], sex, stroke classification [ischemic stroke (IS), intracerebral hemorrhage (ICH), Transient Ischemic Attack (TIA), other], day of admission [weekday/weekend], and NIHSS on admission [categories]). The variable hospital was added as a random effect to model the correlations within the hospitals. Analyses were restricted to patients without missing values in the respective variables. The number of missing values ranged from 0.1% in age to 3.8% in NIHSS. Regarding the variables “interval onset to admission” and “day of admission”, no comparable information was provided in one regional stroke register. Therefore, we performed a sensitivity analysis for the multivariable logistic regression analysis including all patients with missing information as own category. Statistical significance was determined at an alpha level of 0.05. Statistical analyses were performed using the SAS 9.3 Software Package, SAS Institute Inc., Cary, NC, USA.

The data pooling was approved by the ethics committee of the Charité-Universitätsmedizin Berlin (EA4/043/10) and is registered by the University of Würzburg (214/11). The identity of the individual patients was completely anonymous. Therefore, no specific informed consent was obtained by patients. The investigators who performed the data analyzes were blinded to hospital identities. These identities are only known by the coordinating center of the respective regional stroke registers [11].