%0 Journal Article %J Clinical Epidemiology %D 2015 %T A simplified approach to the pooled analysis of calibration of clinical prediction rules for systematic reviews of validation studies %A Dimitrov, B %A Motterlini, N %A Fahey, T %K clinical prediction rules %K derivation %K Meta-analysis %K primary care %K validation %X Objective: Estimating calibration performance of clinical prediction rules (CPRs) in systematic reviews of validation studies is not possible when predicted values are neither published nor accessible or sufficient or no individual participant or patient data are available. Our aims were to describe a simplified approach for outcomes prediction and calibration assessment and evaluate its functionality and validity. Study design and methods: Methodological study of systematic reviews of validation studies of CPRs: a) ABCD2 rule for prediction of 7 day stroke; and b) CRB-65 rule for prediction of 30 day mortality. Predicted outcomes in a sample validation study were computed by CPR distribution patterns (“derivation model”). As confirmation, a logistic regression model (with derivation study coefficients) was applied to CPR-based dummy variables in the validation study. Meta-analysis of validation studies provided pooled estimates of “predicted:observed” risk ratios (RRs), 95% confidence intervals (CIs), and indexes of heterogeneity (I2) on forest plots (fixed and random effects models), with and without adjustment of intercepts. The above approach was also applied to the CRB-65 rule. Results: Our simplified method, applied to ABCD2 rule in three risk strata (low, 0–3; intermediate, 4–5; high, 6–7 points), indicated that predictions are identical to those computed by univariate, CPR-based logistic regression model. Discrimination was good (c-statistics =0.61–0.82), however, calibration in some studies was low. In such cases with miscalibration, the under-prediction (RRs =0.73–0.91, 95% CIs 0.41–1.48) could be further corrected by intercept adjustment to account for incidence differences. An improvement of both heterogeneities and P-values (Hosmer-Lemeshow goodness-of-fit test) was observed. Better calibration and improved pooled RRs (0.90–1.06), with narrower 95% CIs (0.57–1.41) were achieved. Conclusion: Our results have an immediate clinical implication in situations when predicted outcomes in CPR validation studies are lacking or deficient by describing how such predictions can be obtained by everyone using the derivation study alone, without any need for highly specialized knowledge or sophisticated statistics. %B Clinical Epidemiology %V 7 %P 267-280 %8 04/2015 %G eng %U http://www.dovepress.com/articles.php?article_id=21355 %9 Published Journal Article %& 267 %R http://dx.doi.org/10.2147/CLEP.S67632 %0 Journal Article %J eGEMS %D 2015 %T A Multi-step Maturity Model for the implementation of Electronic and Computable Diagnostic Clinical Prediction Rules (eCPRs) %A Corrigan, D %A McDonnell, R %A Zarabzadeh, A %A Fahey, T %K clinical prediction rules %K Evidence Based Medicine %K Health Information Technology %K Learning Health System %K Research Translation %X Introduction: The use of Clinical Prediction Rules (CPRs) has been advocated as one way of implementing actionable evidence-based rules in clinical practice. The current highly manual nature of deriving CPRs makes them difficult to use and maintain. Addressing the known limitations of CPRs requires implementing more flexible and dynamic models of CPR development. We describe the application of Information and Communication Technology (ICT) to provide a platform for the derivation and dissemination of CPRs derived through analysis and continual learning from electronic patient data. Model Components: We propose a multistep maturity model for constructing electronic and computable CPRs (eCPRs). The model has six levels – from the lowest level of CPR maturity (literaturebased CPRs) to a fully electronic and computable service-oriented model of CPRs that are sensitive to specific demographic patient populations. We describe examples of implementations of the core model components – focusing on CPR representation, interoperability, electronic dissemination, CPR learning, and user interface requirements. Conclusion: The traditional focus on derivation and narrow validation of CPRs has severely limited their wider acceptance. The evolution and maturity model described here outlines a progression toward eCPRs consistent with the vision of a learning health system (LHS) – using central repositories of CPR knowledge, accessible open standards, and generalizable models to avoid repetition of previous work. This is useful for developing more ambitious strategies to address limitations of the traditional CPR development life cycle. The model described here is a starting point for promoting discussion about what a more dynamic CPR development process should look like. %B eGEMS %V 3 %G eng %U http://repository.academyhealth.org/egems/vol3/iss2/8/ %N 2 %R 10.13063/2327-9214.1153 %0 Journal Article %J Clinical Evidence (Online) %D 2010 %T Clinical prediction rules in primary care: what can be done to maximise their implementation? %A Keogh, C %A Fahey, T %K clinical practice %K clinical prediction rules %X Clinical prediction rules (CPRs) have become more prevalent in the published literature in recent years. Known by an array ofsynonymous terms including risk score, scorecard, algorithm, guide, and model, CPRs are clinical tools that quantify the contribution ofa patient’s history, physical examination, and diagnostic tests to stratify patients in terms of the probability of having a specific target disorder. Outcomes of CPRs can be presented as diagnosis, prognosis, referral, or treatment. Although not designed to replace clinical knowledge and experience, CPRs do offer a way to assist with the overall diagnostic and prognostic process.[1] Despite the value of these clinical tools, relatively few CPRs have been quantified and their utility validated. One CPR that has gained widespread acceptance is the Centor score,[2] which is based on four clinical features (tonsillar exudate, tender cervical anterior adenopathy, history of fever, and absence of cough) and is used to identify patients with group A beta-haemolytic streptococcal throat infections. What can be done to expedite implementation of other CPRs into routine primary care? %B Clinical Evidence (Online) %8 10/2010 %G eng %U http://clinicalevidence.bmj.com/x/set/static/ebm/learn/678151.html %0 Journal Article %J British Journal of General Practice %D 2014 %T Clinical prediction rules in practice:review of clinical guidelines and survey of GPs %A Plüddemann, A %A Wallace, E %A Bankhead, Clare %A Keogh, C %A Van der Windt, D %A Lasserson, D %A Galvin, R %A Moschetti, I %A Kearley, K %A O'Brien, K %A Sanders, S %A Mallett, S %A Malanda, U %A Thompson, M %A Fahey, T %A Stevens, R %K clinical guidelines %K clinical prediction rules %K survey %X Abstract Background The publication of clinical prediction rules (CPRs) studies has risen significantly. It is unclear if this reflects increasing usage of these tools in clinical practice or how this may vary across clinical areas. Aim To review clinical guidelines in selected areas and survey GPs in order to explore CPR usefulness in the opinion of experts and use at the point of care. Design and setting A review of clinical guidelines and survey of UK GPs. Method Clinical guidelines in eight clinical domains with published CPRs were reviewed for recommendations to use CPRs including primary prevention of cardiovascular disease, transient ischaemic attack (TIA) and stroke, diabetes mellitus, fracture risk assessment in osteoporosis, lower limb fractures, breast cancer, depression, and acute infections in childhood. An online survey of 401 UK GPs was also conducted. Results Guideline review: Of 7637 records screened by title and/or abstract, 243 clinical guidelines met inclusion criteria. CPRs were most commonly recommended in guidelines regarding primary prevention of cardiovascular disease (67%) and depression (67%). There was little consensus across various clinical guidelines as to which CPR to use preferentially. Survey: Of 401 responders to the GP survey, most were aware of and applied named CPRs in the clinical areas of cardiovascular disease and depression. The commonest reasons for using CPRs were to guide management and conform to local policy requirements. Conclusion GPs use CPRs to guide management but also to comply with local policy requirements. Future research could focus on which clinical areas clinicians would most benefit from CPRs and promoting the use of robust, externally validated CPRs. %B British Journal of General Practice %V 64 %P 233-243 %8 01/2014 %G eng %U http://bjgp.org/content/64/621/e233.full %N 621 %9 Published Journal Article %& 233 %R 10.3399/bjgp14X677860