OBJECTIVE: To evaluate the evidence on comparisons of established cardiovascular risk prediction models and to collect comparative information on their relative prognostic performance. DESIGN: Systematic review of comparative predictive model studies. DATA SOURCES: Medline and screening of citations and references. STUDY SELECTION: Studies examining the relative prognostic performance of at least two major risk models for cardiovascular disease in general populations. DATA EXTRACTION: Information on study design, assessed risk models, and outcomes. We examined the relative performance of the models (discrimination, calibration, and reclassification) and the potential for outcome selection and optimism biases favouring newly introduced models and models developed by the authors. RESULTS: 20 articles including 56 pairwise comparisons of eight models (two variants of the Framingham risk score, the assessing cardiovascular risk to Scottish Intercollegiate Guidelines Network to assign preventative treatment (ASSIGN) score, systematic coronary risk evaluation (SCORE) score, Prospective Cardiovascular Münster (PROCAM) score, QRESEARCH cardiovascular risk (QRISK1 and QRISK2) algorithms, Reynolds risk score) were eligible. Only 10 of 56 comparisons exceeded a 5% relative difference based on the area under the receiver operating characteristic curve. Use of other discrimination, calibration, and reclassification statistics was less consistent. In 32 comparisons, an outcome was used that had been used in the original development of only one of the compared models, and in 25 of these comparisons (78%) the outcome-congruent model had a better area under the receiver operating characteristic curve. Moreover, authors always reported better area under the receiver operating characteristic curves for models that they themselves developed (in five articles on newly introduced models and in three articles on subsequent evaluations). CONCLUSIONS: Several risk prediction models for cardiovascular disease are available and their head to head comparisons would benefit from standardised reporting and formal, consistent statistical comparisons. Outcome selection and optimism biases apparently affect this literature.
OBJECTIVE: To evaluate the evidence on comparisons of established cardiovascular risk prediction models and to collect comparative information on their relative prognostic performance. DESIGN: Systematic review of comparative predictive model studies. DATA SOURCES: Medline and screening of citations and references. STUDY SELECTION: Studies examining the relative prognostic performance of at least two major risk models for cardiovascular disease in general populations. DATA EXTRACTION: Information on study design, assessed risk models, and outcomes. We examined the relative performance of the models (discrimination, calibration, and reclassification) and the potential for outcome selection and optimism biases favouring newly introduced models and models developed by the authors. RESULTS: 20 articles including 56 pairwise comparisons of eight models (two variants of the Framingham risk score, the assessing cardiovascular risk to Scottish Intercollegiate Guidelines Network to assign preventative treatment (ASSIGN) score, systematic coronary risk evaluation (SCORE) score, Prospective Cardiovascular Münster (PROCAM) score, QRESEARCH cardiovascular risk (QRISK1 and QRISK2) algorithms, Reynolds risk score) were eligible. Only 10 of 56 comparisons exceeded a 5% relative difference based on the area under the receiver operating characteristic curve. Use of other discrimination, calibration, and reclassification statistics was less consistent. In 32 comparisons, an outcome was used that had been used in the original development of only one of the compared models, and in 25 of these comparisons (78%) the outcome-congruent model had a better area under the receiver operating characteristic curve. Moreover, authors always reported better area under the receiver operating characteristic curves for models that they themselves developed (in five articles on newly introduced models and in three articles on subsequent evaluations). CONCLUSIONS: Several risk prediction models for cardiovascular disease are available and their head to head comparisons would benefit from standardised reporting and formal, consistent statistical comparisons. Outcome selection and optimism biases apparently affect this literature.
Authors: John M Findlay; Richard S Gillies; Bruno Sgromo; Robert E K Marshall; Mark R Middleton; Nicholas D Maynard Journal: J Gastrointest Surg Date: 2014-04-24 Impact factor: 3.452
Authors: M Iki; Y Fujita; J Tamaki; K Kouda; A Yura; Y Sato; J-S Moon; R Winzenrieth; N Okamoto; N Kurumatani Journal: Osteoporos Int Date: 2015-03-10 Impact factor: 4.507
Authors: Shannon B McKearnan; Julian Wolfson; David M Vock; Gabriela Vazquez-Benitez; Patrick J O'Connor Journal: Am J Epidemiol Date: 2018-06-01 Impact factor: 4.897
Authors: Douglas G Manuel; Meltem Tuna; Carol Bennett; Deirdre Hennessy; Laura Rosella; Claudia Sanmartin; Jack V Tu; Richard Perez; Stacey Fisher; Monica Taljaard Journal: CMAJ Date: 2018-07-23 Impact factor: 8.262
Authors: Benjamin S Wessler; Lana Lai Yh; Whitney Kramer; Michael Cangelosi; Gowri Raman; Jennifer S Lutz; David M Kent Journal: Circ Cardiovasc Qual Outcomes Date: 2015-07-07