Objective To derive and validate risk algorithms so that the risks of four clinical outcomes associated with statin use can be estimated for individual patients.
Design Prospective open cohort study using routinely collected data from 368 QResearch general practices in England and Wales to develop the scores. The scores were validated using two separate sets of practices—188 separate QResearch practices and 364 practices contributing to the THIN database.
Subjects In the QResearch derivation cohort 225 922 new users of statins and 1 778 770 non-users of statins were studied. In the QResearch validation cohort 118 372 statin users and 877 812 non-users of statins were studied. In the THIN validation cohort, we studied 282 056 statin users and 1 923 840 non-users of statins were studied.
Methods Cox proportional hazards models in the derivation cohort to derive risk equations. Measures of calibration and discrimination in both validation cohorts.
Outcomes 5-Year risk of moderate/serious myopathic events; moderate/serious liver dysfunction; acute renal failure and cataract.
Results The performance of three of the risk prediction algorithms in the THIN cohort was very good. For example, in women, the algorithm for moderate/serious myopathy explained 42.15% of the variation. The corresponding D statistics was 1.75. The acute renal failure algorithm explained 59.62% of the variation (D statistic=2.49). The cataract algorithm explained 59.14% of the variation (D statistic=2.46). The algorithms to predict moderate/severe liver dysfunction only explained 15.55% of the variation (D statistics=0.89). The performance of each algorithm was similar for both sexes when tested on the QResearch validation cohort.
Conclusions The algorithms to predict acute renal failure, moderate/serious myopathy and cataract could be used to identify patients at increased risk of these adverse effects enabling patients to be monitored more closely. Further research is needed to develop a better algorithm to predict liver dysfunction.
- Lipid lowering
- risk stratification
- general practice
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Data Sharing statement The patient level data from the QResearch and THIN databases are specifically licensed according to the governance framework for each database. See http://www.qresearch.org for further details.
Algorithms and software The algorithms presented in this paper will be released as OpenSource Software under the GNU lesser GPL v3. The web calculator can be found at http://www.qintervention.org.
Funding The project was undertaken by ClinRisk Ltd. There was no external funding. Other Funders: work undertaken by ClinRisk Ltd.
Competing interests JHC is professor of clinical epidemiology at the University of Nottingham and co-director of QResearch—a not-for-profit organisation, which is a joint partnership between the University of Nottingham and EMIS (leading commercial supplier of IT for 60% of general practices in the UK). JHC is also director of ClinRisk Ltd which produces software to ensure the reliable and updatable implementation of clinical risk algorithms within clinical computer systems to help improve patient care. CC is associate professor of medical statistics at the University of Nottingham and a consultant statistician for ClinRisk Ltd. This work and any views expressed within it are solely those of the co-authors and not of any affiliated bodies or organisations.
Ethics approval This study was conducted with the approval of the Trent Multi-Centre Ethics Committee as part of the QResearch approval process.
Provenance and peer review Not commissioned; externally peer reviewed.
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