Formal and informal prediction of recurrent stroke and myocardial infarction after stroke: a systematic review and evaluation of clinical prediction models in a new cohortReportar como inadecuado




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BMC Medicine

, 12:58

First Online: 04 April 2014Received: 21 November 2013Accepted: 28 February 2014DOI: 10.1186-1741-7015-12-58

Cite this article as: Thompson, D.D., Murray, G.D., Dennis, M. et al. BMC Med 2014 12: 58. doi:10.1186-1741-7015-12-58

Abstract

BackgroundThe objective of this study was to: 1 systematically review the reporting and methods used in the development of clinical prediction models for recurrent stroke or myocardial infarction MI after ischemic stroke; 2 to meta-analyze their external performance; and 3 to compare clinical prediction models to informal clinicians’ prediction in the Edinburgh Stroke Study ESS.

MethodsWe searched Medline, EMBASE, reference lists and forward citations of relevant articles from 1980 to 19 April 2013. We included articles which developed multivariable clinical prediction models for the prediction of recurrent stroke and-or MI following ischemic stroke. We extracted information to assess aspects of model development as well as metrics of performance to determine predictive ability. Model quality was assessed against a pre-defined set of criteria. We used random-effects meta-analysis to pool performance metrics.

ResultsWe identified twelve model development studies and eleven evaluation studies. Investigators often did not report effective sample size, regression coefficients, handling of missing data; typically categorized continuous predictors; and used data dependent methods to build models. A meta-analysis of the area under the receiver operating characteristic curve AUROCC was possible for the Essen Stroke Risk Score ESRS and for the Stroke Prognosis Instrument II SPI-II; the pooled AUROCCs were 0.60 95% CI 0.59 to 0.62 and 0.62 95% CI 0.60 to 0.64, respectively. An evaluation among minor stroke patients in the ESS demonstrated that clinicians discriminated poorly between those with and those without recurrent events and that this was similar to clinical prediction models.

ConclusionsThe available models for recurrent stroke discriminate poorly between patients with and without a recurrent stroke or MI after stroke. Models had a similar discrimination to informal clinicians- predictions. Formal prediction may be improved by addressing commonly encountered methodological problems.

KeywordsSystematic review Meta-analysis Stroke Prediction Statistical modelling Evaluation Development AbbreviationsAUROCCarea under the receiver operating characteristic curve

CIconfidence interval

EPVevents per variable

ESRSESSEN Stroke Risk Score

ESSThe Edinburgh Stroke Study

IQRinterquartile range

MImyocardial infarction

PIprediction interval

RRE-90Recurrence Risk Estimator at 90 days

SPI-IIStroke Prognosis Instrument II

TIAtransient ischemic attack.

Electronic supplementary materialThe online version of this article doi:10.1186-1741-7015-12-58 contains supplementary material, which is available to authorized users.

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Autor: Douglas D Thompson - Gordon D Murray - Martin Dennis - Cathie LM Sudlow - William N Whiteley

Fuente: https://link.springer.com/







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