妊娠相关因素在预测心血管疾病中的价值:系统综述

IF 2.1 Q3 PERIPHERAL VASCULAR DISEASE
Zhixun Yang , Hendrikus J.A. van Os , Janet M. Kist , Rimke C. Vos , Hedwig M.M. Vos , Niels H. Chavannes , Annelieke H.J. Petrus
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引用次数: 0

摘要

目的妊娠相关因素与心血管疾病(CVD)风险增加相关,可能有助于识别心血管疾病高风险妇女。本研究旨在综述包括妊娠相关因素的CVD预测模型,并评估这些因素对模型性能的影响。方法系统检索spubmed和Embase数据库,直到2023年3月,检索包括妊娠相关因素的CVD预测模型开发或验证的研究报告。使用CHARMS检查表进行数据提取。使用PROBAST评估偏倚风险。结果共纳入7项研究。c指数在0.63 ~ 0.79之间。在四项研究中,加入妊娠相关因素导致c -指数改善,范围从0.0033(95%可信区间[CI]: 0.0022-0.0051)到0.004 (95% CI: 0.002-0.006)。在两项研究中,事件的净再分类改善(NRI)得到改善,范围从0.01 (95% CI: 0.003-0.02)到0.038 (95% CI: 0.003-0.074)。在三项研究中,非事件的NRI得到改善,范围从0.002 (95% CI: 0.0001-0.005)到0.02 (95% CI: 0.001-0.04)。两项研究均显示低偏倚风险和对适用性的低关注。三项研究中按年龄划分的亚组分析表明,年轻女性在模特表现方面的改善更大。结论妊娠相关因素的加入导致CVD预测模型性能的改善有限,年轻女性的改善相对较大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The value of pregnancy-related factors in the prediction of cardiovascular disease: a systematic review

Aims

Pregnancy-related factors are associated with an increased risk of cardiovascular disease (CVD) and may help identify women at high cardiovascular risk. This study aims to provide an overview of prediction models for CVD which included pregnancy-related factors and to evaluate the impact of these factors on model performance.

Methods

PubMed and Embase were systematically searched until March 2023 for studies reporting on the development or validation of prediction models for CVD which included pregnancy-related factors. Data extraction was performed using the CHARMS checklist. Risk of bias was assessed using PROBAST.

Results

Seven studies were included. C-indices ranged between 0.63 and 0.79. Adding pregnancy-related factors resulted in improved C-index in four studies, ranging from 0.0033 (95 % confidence interval [CI]: 0.0022–0.0051) to 0.004 (95 % CI: 0.002–0.006). Net reclassification improvement (NRI) for events was improved in two studies, ranging from 0.01 (95 % CI: 0.003–0.02) to 0.038 (95 % CI: 0.003–0.074). NRI for non-events was improved in three studies, ranging from 0.002 (95 % CI: 0.0001–0.005) to 0.02 (95 % CI: 0.001–0.04). Two studies showed both low risk of bias and low concern regarding applicability. Subgroup analyses by age in three studies indicated larger improvements in model performance in younger women.

Conclusion

Addition of pregnancy-related factors results in limited improvements in performance of CVD prediction models, with relatively larger improvements in younger women.
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