Faculty of Medicine, Institute of Clinical Medicine, Vilnius University, 03101 Vilnius, Lithuania.
State Research Institute Centre for Innovative Medicine, 08410 Vilnius, Lithuania.
Medicina (Kaunas). 2024 Sep 16;60(9):1511. doi: 10.3390/medicina60091511.
In the context of female cardiovascular risk categorization, we aimed to assess the inter-model agreement between nine risk prediction models (RPM): the novel Predicting Risk of cardiovascular disease EVENTs (PREVENT) equation, assessing cardiovascular risk using SIGN, the Australian CVD risk score, the Framingham Risk Score for Hard Coronary Heart Disease (FRS-hCHD), 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). A cross-sectional study was conducted on 6527 40-65-year-old women with diagnosed metabolic syndrome from a single tertiary university hospital in Lithuania. Cardiovascular risk was calculated using the nine RPMs, and the results were categorized into high-, intermediate-, and low-risk groups. Inter-model agreement was quantified using Cohen's Kappa coefficients. The study uncovered a significant diversity in risk categorization, with agreement on risk category by all models in only 1.98% of cases. The SCORE2 model primarily classified subjects as high-risk (68.15%), whereas the FRS-hCHD designated the majority as low-risk (94.42%). The range of Cohen's Kappa coefficients (-0.09-0.64) reflects the spectrum of agreement between models. Notably, the PREVENT model demonstrated significant agreement with QRISK3 (κ = 0.55) and PCE (κ = 0.52) but was completely at odds with the SCORE2 (κ = -0.09). Cardiovascular RPM selection plays a pivotal role in influencing clinical decisions and managing patient care. The PREVENT model revealed balanced results, steering clear of the extremes seen in both SCORE2 and FRS-hCHD. The highest concordance was observed between the PREVENT model and both PCE and QRISK3 RPMs. Conversely, the SCORE2 model demonstrated consistently low or negative agreement with other models, highlighting its unique approach to risk categorization. These findings accentuate the need for additional research to assess the predictive accuracy of these models specifically among the Lithuanian female population.
在女性心血管风险分类的背景下，我们旨在评估九个风险预测模型（RPM）之间的模型间一致性：新型心血管疾病事件风险预测（PREVENT）方程，使用 SIGN 评估心血管风险，澳大利亚心血管风险评分，弗雷明汉硬冠状动脉心脏病风险评分（FRS-hCHD），多民族动脉粥样硬化研究风险评分，Pooled Cohort Equation（PCE），QRISK3 心血管风险计算器，Reynolds 风险评分和系统性冠状动脉风险评估-2（SCORE2）。对来自立陶宛一所单一三级大学医院的 6527 名 40-65 岁诊断为代谢综合征的女性进行了一项横断面研究。使用九个 RPM 计算心血管风险，将结果分为高、中、低风险组。使用 Cohen 的 Kappa 系数量化模型间一致性。研究发现，风险分类存在显著差异，所有模型对风险类别的一致率仅为 1.98%。SCORE2 模型主要将受试者归类为高风险（68.15%），而 FRS-hCHD 将大多数归类为低风险（94.42%）。Cohen 的 Kappa 系数范围（-0.09-0.64）反映了模型之间的一致性范围。值得注意的是，PREVENT 模型与 QRISK3（κ=0.55）和 PCE（κ=0.52）具有显著一致性，但与 SCORE2（κ=-0.09）完全不一致。心血管 RPM 的选择对影响临床决策和管理患者护理至关重要。PREVENT 模型的结果平衡，避免了 SCORE2 和 FRS-hCHD 中看到的极端情况。PREVENT 模型与 PCE 和 QRISK3 RPM 之间观察到最高的一致性。相反，SCORE2 模型与其他模型的一致性始终较低或为负，突出了其对风险分类的独特方法。这些发现强调需要进行更多研究来评估这些模型在立陶宛女性人群中的预测准确性。
