%0 Journal Article %J Science Direct %D 2013 %T Diagnostic accuracy of a clinical prediction rule (CPR) for identifying patients with recent-onset undifferentiated arthritis who are at a high risk of developing rheumatoid arthritis: A systematic review and meta-analysis %A McNally E %A Keogh, C %A Galvin, R %A Fahey, T %K clinical prediction rule %K Rheumatoid arthritis %K Undifferentiated arthritis %X Objectives The Leiden clinical prediction rule (CPR) was developed in 2007 to predict disease progression in patients with recent-onset undifferentiated arthritis (UA). This systematic review and meta-analysis investigates the predictive ability of the rule at identifying patients who are at a high risk of developing rheumatoid arthritis (RA). Methods A systematic review of the literature search was conducted from 2007 to May 2013 to identify studies that validated the rule. This study adhered to the PRISMA guidelines. The methodological quality of studies was assessed using the QUADAS-2 tool. Pooled sensitivity and specificity values for each of the cut points were generated using a bivariate random-effects model. Heterogeneity was assessed using the variance of logit-transformed sensitivity and specificity. Bayes' theorem was used to calculate post-test probability of progression from UA to RA. Results The search identified four relevant studies, resulting in six data sets (n = 1084). A cut point of ≥9 was identified as the optimal cut point for determining progression to RA. It is associated with a greater pooled specificity (0.99, 95% CI 0.95–1.00) than sensitivity (0.31, 95% CI 0.24–0.37). Using Bayes' theorem, a score of ≥9 points increased the pre-test probability from 40.04% to 93.63%. A less stringent cut-off of ≥8 also identified a significant proportion of patients at risk of RA who have a high likelihood of progressing to RA (LR + 9.5, 95% CI 6.21–14.54). Conclusion A cut point of ≥9 offers an optimal estimate for identifying patients with UA who are at a high risk of developing RA and warrant intervention. However, a number of methodological limitations identified across studies suggest that the results should be interpreted cautiously and that further validation of the Leiden CPR is necessary. %B Science Direct %V 43 %P 498-507 %8 02/2014 %G eng %U http://www.sciencedirect.com/science/article/pii/S0049017213001728 %N 4 %& 498 %R 10.1016/j.semarthrit.2013.08.005 %0 Journal Article %J Ann of Fam Med %D 2014 %T Developing an International Register of Clinical Prediction Rules for Use in Primary Care: A Descriptive Analysis %A Keogh, C %A Wallace, E %A O'Brien, K %A Galvin, R %A Smith, SM %A Lewis, Cliona %A Cummins, Anthony %A Cousins, G %A Dimitrov, B %A Fahey, T %K clinical decision support systems %K clinical prediction rule %K decision aid %K decision making %K primary care %K score card %X Abstract PURPOSE We describe the methodology used to create a register of clinical prediction rules relevant to primary care. We also summarize the rules included in the register according to various characteristics. METHODS To identify relevant articles, we searched the MEDLINE database (PubMed) for the years 1980 to 2009 and supplemented the results with searches of secondary sources (books on clinical prediction rules) and personal resources (eg, experts in the field). The rules described in relevant articles were classified according to their clinical domain, the stage of development, and the clinical setting in which they were studied. RESULTS Our search identified clinical prediction rules reported between 1965 and 2009. The largest share of rules (37.2%) were retrieved from PubMed. The number of published rules increased substantially over the study decades. We included 745 articles in the register; many contained more than 1 clinical prediction rule study (eg, both a derivation study and a validation study), resulting in 989 individual studies. In all, 434 unique rules had gone through derivation; however, only 54.8% had been validated and merely 2.8% had undergone analysis of their impact on either the process or outcome of clinical care. The rules most commonly pertained to cardiovascular disease, respiratory, and musculoskeletal conditions. They had most often been studied in the primary care or emergency department settings. CONCLUSIONS Many clinical prediction rules have been derived, but only about half have been validated and few have been assessed for clinical impact. This lack of thorough evaluation for many rules makes it difficult to retrieve and identify those that are ready for use at the point of patient care. We plan to develop an international web-based register of clinical prediction rules and computer-based clinical decision support systems. %B Ann of Fam Med %V 12 %P 359-366 %8 07/2014 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/25024245 %N 4 %9 Published Article Journal %& 359 %R 10.1370/afm.1640