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      How accurate is the reporting of stroke in hospital discharge data? A pilot validation study using a population-based stroke registry as control

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          Abstract

          Population-based stroke registries can provide valid stroke incidence because they ensure exhaustiveness of case ascertainment. However, their results are difficult to extrapolate because they cover a small population. The French Hospital Discharge Database (FHDDB), which routinely collects administrative data, could be a useful tool for providing data on the nationwide burden of stroke. The aim of our pilot study was to assess the validity of stroke diagnosis reported in the FHDDB. All records of patients with a diagnosis of stroke between 2004 and 2008 were retrieved from the FHDDB of Dijon Teaching Hospital. The Dijon Stroke Registry was considered as the gold standard. The sensitivity, positive predictive value (PPV), and weighted kappa were calculated. The Dijon Stroke Registry identified 811 patients with a stroke, among whom 186 were missed by the FHDDB and thus considered false-negatives. The FHDDB identified 903 patients discharged following a stroke including 625 true-positives confirmed by the registry and 278 false-positives. The overall sensitivity and PPV of the FHDDB for the diagnosis of stroke were, respectively, 77.1 % (95 % CI 74.2–80) and 69.2 % (95 % CI 66.1–72.2). For cardioembolic and lacunar strokes, the FHDDB yielded higher PPVs (respectively 86.7 and 84.6 %; p < 0.0001) than those of other stroke subtypes. The PPV but not sensitivity significantly increased over the years ( p < 0.0001). Agreement with the stroke registry was moderate (kappa 52.8; 95 % CI 46.8–58.9). The FHDDB-based stroke diagnosis showed moderate validity compared with the Dijon Stroke Registry as the gold standard. However, its accuracy (PPV) increased with time and was higher for some stroke subtypes.

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          Most cited references21

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          Measuring diagnoses: ICD code accuracy.

          To examine potential sources of errors at each step of the described inpatient International Classification of Diseases (ICD) coding process. The use of disease codes from the ICD has expanded from classifying morbidity and mortality information for statistical purposes to diverse sets of applications in research, health care policy, and health care finance. By describing a brief history of ICD coding, detailing the process for assigning codes, identifying where errors can be introduced into the process, and reviewing methods for examining code accuracy, we help code users more systematically evaluate code accuracy for their particular applications. We summarize the inpatient ICD diagnostic coding process from patient admission to diagnostic code assignment. We examine potential sources of errors at each step and offer code users a tool for systematically evaluating code accuracy. Main error sources along the "patient trajectory" include amount and quality of information at admission, communication among patients and providers, the clinician's knowledge and experience with the illness, and the clinician's attention to detail. Main error sources along the "paper trail" include variance in the electronic and written records, coder training and experience, facility quality-control efforts, and unintentional and intentional coder errors, such as misspecification, unbundling, and upcoding. By clearly specifying the code assignment process and heightening their awareness of potential error sources, code users can better evaluate the applicability and limitations of codes for their particular situations. ICD codes can then be used in the most appropriate ways.
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            The validation of the Finnish Hospital Discharge Register and Causes of Death Register data on stroke diagnoses.

            Administrative registers, like hospital discharge registers and causes of death registers are used for the monitoring of disease incidences and in the follow-up studies. Obtaining reliable results requires that the diagnoses in these registers are correct and the coverage of the registers is high. The purpose of this study was to evaluate the validity of the Finnish hospital discharge registers and causes of death registers stroke diagnoses against the population-based FINSTROKE register. All first stroke events from the hospital discharge registers and causes of death registers from the areas covered by the FINSTROKE register were obtained for years 1993-1998 and linked to the FINSTROKE register. The sensitivity and positive predictive values were calculated. A total of 3633 stroke events, 767 fatal and 2866 non-fatal strokes, were included in the registers. The sensitivity for all first stroke events was 85%, for fatal strokes 86% and for non-fatal strokes 85%. The positive predictive values for all first strokes was 86%, for fatal strokes 92% and for non-fatal strokes 85%. The sensitivity as well as the positive predictive values for subarachnoid haemorrhage and intracerebral haemorrhage was higher than for cerebral infarctions. There were no marked differences in the sensitivity or positive predictive values between men and women. The sensitivity and the positive predictive values of the Finnish hospital discharge registers and causes of death registers are fairly good. Finnish administrative registers can be used for the monitoring of stroke incidence, but the number of cerebral infarctions should be interpreted with caution.
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              Hospital volume and stroke outcome: does it matter?

              Although hospital-outcome relationships have been explored for a variety of procedures and interventions, little is known about the association between annual stroke admission volumes and stroke mortality. Our aim was to determine whether facility type and hospital volume was associated with stroke mortality. All hospital admissions for ischemic stroke were identified from the Hospital Morbidity database (HMDB) from April 2003 to March 2004. The HMDB is a national database that contains patient-level sociodemographic, diagnostic, procedural, and administrative information across Canada. Ischemic stroke was identified through patient's principal diagnosis recorded using the International Classification of Diseases (9 and 10). Multivariable analysis was performed with generalized estimating equations with adjustment for demographic characteristics, provider specialty, facility type, hospital volume, and clustering of observations at institutions. Overall, 26,676 patients with ischemic stroke were admitted to 606 hospitals. Seven-day stroke mortality was 7.6% and mortality at discharge was 15.6%. Adverse outcomes were more frequent in patients treated in low-volume facilities ( 200 strokes patients/year) (for 7-day mortality: 9.5 vs 7.3%, p < 0.001; 9.5 vs 6.0%, p < 0.001; for discharge mortality: 18.2 vs 15.2%, p < 0.001; 18.2 vs 12.8%, p < 0.001). The difference persisted after multivariable adjustment or when hospital volume was divided into quartiles. High annual hospital volume was consistently associated with lower stroke mortality. Our study encourages further research to determine whether this is due to differences in case mix, more organized care in high-volume facilities, or differences in the performance or in the processes of care among facilities.
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                Author and article information

                Contributors
                maurice.giroud@chu-dijon.fr
                catherine.quantin@chu-dijon.fr
                Journal
                J Neurol
                J. Neurol
                Journal of Neurology
                Springer-Verlag (Berlin/Heidelberg )
                0340-5354
                1432-1459
                18 October 2012
                18 October 2012
                February 2013
                : 260
                : 2
                : 605-613
                Affiliations
                [ ]Stroke Registry of Dijon, EA 4184, University Hospital and Faculty of Medicine of Dijon, STIC-Santé, University of Burgundy, Dijon, France
                [ ]Département d’Informatique Médicale, University Hospital of Dijon, Dijon, France
                [ ]INSERM U666, University of Burgundy, Dijon, France
                [ ]CIC, CHU de Grenoble, La Tronche, France
                [ ]Service de Neurologie, CHU Dijon, BP 77908, 21079 Dijon CEDEX, France
                Article
                6686
                10.1007/s00415-012-6686-0
                3566387
                23076827
                b2855e89-167c-4401-a002-457a57ff4a91
                © The Author(s) 2012
                History
                : 24 April 2012
                : 21 September 2012
                : 22 September 2012
                Categories
                Original Communication
                Custom metadata
                © Springer-Verlag Berlin Heidelberg 2013

                Neurology
                administrative data,hospital discharge data,registry,stroke,validation
                Neurology
                administrative data, hospital discharge data, registry, stroke, validation

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