Title Navigating the landscape of cardiovascular risk scores: a comparative analysis of eight risk prediction models in a high-risk cohort in Lithuania /
Authors Navickas, Petras ; Lukavičiūtė, Laura ; Glaveckaitė, Sigita ; Baranauskas, Arvydas ; Šatrauskienė, Agnė ; Badarienė, Jolita ; Laucevičius, Aleksandras
DOI 10.3390/jcm13061806
Full Text Download
Is Part of Journal of clinical medicine.. Basel : MDPI AG. 2024, vol. 13, iss. 6, art. no. 1806, p. [1-13].. eISSN 2077-0383
Keywords [eng] cardiovascular risk ; clinical decision-making ; Cohen’s Kappa ; inter-model agreement ; Lithuanian cohort ; risk prediction models ; risk stratification
Abstract [eng] Background: Numerous cardiovascular risk prediction models (RPM) have been developed, however, agreement studies between these models are scarce. We aimed to assess the inter-model agreement between eight RPMs: assessing cardiovascular risk using SIGN, the Australian CVD risk score (AusCVDRisk), the Framingham Risk Score for Hard Coronary Heart Disease, the Multi-Ethnic Study of Atherosclerosis risk score, the Pooled Cohort Equation (PCE), the QRISK3 cardiovascular risk calculator, the Reynolds Risk Score, and Systematic Coronary Risk Evaluation-2 (SCORE2). Methods: A cross-sectional study was conducted on 11,174 40–65-year-old individuals with diagnosed metabolic syndrome from a single tertiary university hospital in Lithuania. Cardiovascular risk was calculated using the eight RPMs, and the results were categorized into high, intermediate, and low-risk groups. Inter-model agreement was quantified using Cohen’s Kappa coefficients. Results: The study revealed significant heterogeneity in risk categorizations with only 1.49% of cases where all models agree on the risk category. SCORE2 predominantly categorized participants as high-risk (67.39%), while the PCE identified the majority as low-risk (62.03%). Cohen’s Kappa coefficients ranged from −0.09 to 0.64, indicating varying degrees of inter-model agreement. Conclusions: The choice of RPM can substantially influence clinical decision-making and patient management. The PCE and AusCVDRisk models exhibited the highest degree of agreement while the SCORE2 model consistently exhibited low agreement with other models.
Published Basel : MDPI AG
Type Journal article
Language English
Publication date 2024
CC license CC license description