Literature DB >> 22689714

Predicting the adverse risk of statin treatment: an independent and external validation of Qstatin risk scores in the UK.

Gary S Collins1, Douglas G Altman.   

Abstract

OBJECTIVE: To evaluate the performance of the QStatin scores for predicting the 5-year risk of developing acute renal failure, cataract, liver dysfunction and myopathy in men and women in England and Wales receiving statins.
DESIGN: Prospective cohort study to evaluate the performance of four statin risk prediction models.
SETTING: 364 practices in the UK contributing to The Health Improvement Network database. PARTICIPANTS: 2.2 million patients aged 35-84 years registered with a general practice surgery between 1 January 2002 and 30 June 2008, with 2037 incident cases of acute renal failure, 25 692 incident cataract cases, 14 756 cases of liver dysfunction and 1209 incident cases of myopathy. MAIN OUTCOME MEASURES: First recorded occurrence of acute renal failure, cataract, moderate or severe liver dysfunction and moderate or severe myopathic events as recorded in general practice records.
RESULTS: Results from this independent and external validation of QStatin scores indicate that models predicting the 5-year statin risk of developing acute renal failure, cataracts and myopathy perform well with areas under the receiver operating characteristic curve ranging from 0.73 to 0.87. Calibration plots for the three models also indicated close agreement between observed and predicted risks. Poor performance was observed for the model predicting the 5-year statin risk of developing liver dysfunction with areas under the receiver operating characteristic curve of 0.64 and 0.60 for women and men, respectively.
CONCLUSIONS: QStatin scores for predicting the 5-year statin risk of developing acute renal failure, cataract and myopathy appear to be useful models with good discriminative and calibration properties. The model for predicting the 5-year statin risk of developing liver dysfunction appears to have limited ability to identify high-risk individuals and the authors caution against its use.

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Year:  2012        PMID: 22689714     DOI: 10.1136/heartjnl-2012-302014

Source DB:  PubMed          Journal:  Heart        ISSN: 1355-6037            Impact factor:   5.994


  8 in total

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7.  Sample size considerations for the external validation of a multivariable prognostic model: a resampling study.

Authors:  Gary S Collins; Emmanuel O Ogundimu; Douglas G Altman
Journal:  Stat Med       Date:  2015-11-09       Impact factor: 2.373

8.  Quantifying the impact of different approaches for handling continuous predictors on the performance of a prognostic model.

Authors:  Gary S Collins; Emmanuel O Ogundimu; Jonathan A Cook; Yannick Le Manach; Douglas G Altman
Journal:  Stat Med       Date:  2016-05-18       Impact factor: 2.373

  8 in total

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