No abstract

This first population-based stroke register in Norway revealed incidence rates of stroke similar to other Scandinavian countries, and comparison between other European countries did not indicate regional variations within Western Europe.

Background and Purpose-The validity of hospital discharge diagnoses is essential in improving stroke surveillance and estimating healthcare costs of stroke. The aim of this study was to assess sensitivity, positive predictive value, and accuracy of discharge diagnoses compared with a stroke register. Methods-A record linkage was made between a population-based stroke register and the discharge records of the hospital serving the population of the stroke register (nϭ70 000). The stroke register (including patients aged 15 and older and with no upper age limit), applied here as a "gold standard," was used to estimate sensitivity, positive predictive value, and accuracy of the discharge diagnoses classification. The length of stay in hospital by stroke patients was measured. Diseases,, codes 430 to 438.9, first admission) lead to a substantial overestimation of stroke in the target population. Restricting the retrieval to acute stroke diagnoses (ICD-9 codes 430, 431, 434, and 436) gave an incidence estimate closer to the "true" incidence rate in the stroke register. Selecting ICD-9 codes 430 to 438 of cerebrovascular diseases gave the highest sensitivity (86%). The highest positive predictive value (68%) was achieved by selecting acute stroke diagnoses (ICD-9 codes 430, 431, 434, and 436), at the expense of a lower sensitivity (81%). Accuracy of ICD codes 430 to 438.9 (nϭ678) revealed the highest proportion of incident strokes identified by the acute stroke diagnoses (ICD-9 codes 430, 431, 434, and 436). Seventy-four percent of hospital discharge diagnoses classified as first-ever stroke kept the original diagnosis. Only 4.6% of the discharge diagnoses were classified as nonstroke diagnoses after validation. The estimation of length of stay in the hospital was improved by selection of acute stroke diagnoses from hospital discharge data (ICD-9 codes 430, 431, 434, and 436), which gave the same estimate of length of stay, a median of 8 days (2.5 percentileϭ0 and 97.5 percentileϭ56), compared with a median of 8 days (2.5 percentileϭ0 and 97.5 percentileϭ51) based on the stroke register. Conclusions-Hospital discharge data may overestimate stroke incidence and underestimate the length of stay in the hospital, unless selection routines of hospital discharge diagnoses are restricted to acute stroke diagnoses (ICD-9 codes 430, 431, 434, and 436). If supplemented by a validation procedure, including estimates of sensitivity, positive predictive value, and accuracy, hospital discharge data may provide valid information on hospital-based stroke incidence and lead to better allocation of health resources. Distinguishing subtypes of stroke from hospital discharge diagnoses should not be performed unless coding practices are improved. (Stroke. 1999;30:56-60.) Results-Identifying cerebrovascular diseases by hospital discharge diagnoses (International Classification of

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.

Use of DOACs for anticoagulation in atrial fibrillation became more prevalent between 2010 and 2015 in Norway, at the expense of warfarin.