@article{MalschLimanWiedmannetal.2018,
  author    = {Malsch, Carolin and Liman, Thomas and Wiedmann, Silke and Siegerink, Bob and Georgakis, Marios K. and Tiedt, Steffen and Endres, Matthias and Heuschmann, Peter U.},
  title     = {Outcome after stroke attributable to baseline factors—the PROSpective Cohort with Incident Stroke (PROSCIS)},
  series = {PLoS ONE},
  volume    = {13},
  journal   = {PLoS ONE},
  number    = {9},
  doi       = {10.1371/journal.pone.0204285},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-177342},
  pages     = {e0204285},
  year      = {2018},
  abstract  = {Background The impact of risk factors on poor outcome after ischemic stroke is well known, but estimating the amount of poor outcome attributable to single factors is challenging in presence of multimorbidity. We aim to compare population attributable risk estimates obtained from different statistical approaches regarding their consistency. We use a real-life data set from the PROSCIS study to identify predictors for mortality and functional impairment one year after first-ever ischemic stroke and quantify their contribution to poor outcome using population attributable risks. Methods The PROSpective Cohort with Incident Stroke (PROSCIS) is a prospective observational hospital-based cohort study of patients after first-ever stroke conducted independently in Berlin (PROSCIS-B) and Munich (PROSCIS-M). The association of baseline factors with poor outcome one year after stroke in PROSCIS-B was analysed using multiple logistic regression analysis and population attributable risks were calculated, which were estimated using sequential population attributable risk based on a multiple generalized additive regression model, doubly robust estimation, as well as using average sequential population attributable risk. Findings were reproduced in an independent validation sample from PROSCIS-M. Results Out of 507 patients with available outcome information after 12 months in PROSCIS-B, 20.5\% suffered from poor outcome. Factors associated with poor outcome were age, pre-stroke physical disability, stroke severity (NIHSS), education, and diabetes mellitus. The order of risk factors ranked by magnitudes of population attributable risk was almost similar for all methods, but population attributable risk estimates varied markedly between the methods. In PROSCIS-M, incidence of poor outcome and distribution of baseline parameters were comparable. The multiple logistic regression model could be reproduced for all predictors, except pre-stroke physical disability. Similar to PROSCIS-B, the order of risk factors ranked by magnitudes of population attributable risk was almost similar for all methods, but magnitudes of population attributable risk differed markedly between the methods. Conclusions Ranking of risk factors by population impact is not affected by the different statistical approaches. Thus, for a rational decision on which risk factor to target in disease interventions, population attributable risk is a supportive tool. However, population attributable risk estimates are difficult to interpret and are not comparable when they origin from studies applying different methodology. The predictors for poor outcome identified in PROSCIS-B have a relevant impact on mortality and functional impairment one year after first-ever ischemic stroke.},
  language  = {en}
}