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基于心血管文献的风险算法 (CALIBRA):预测非透析依赖慢性病患者的心血管事件
Luca Neri1, Caterina Lonati2, Jasmine Ion Titapiccolo1
1Clinical and Data Intelligence Systems-Advanced Analytics, Fresenius Medical Care Deutschland GmbH, Vaiano Cremasco, Italy.
Frontiers in nephrology
|September 7, 2023
概括
新的基于文献的心血管风险算法 (CALIBRA) 准确预测慢性病 (CKD) 患者的心血管风险. 卡利布拉的性能优于现有模型,为更好的临床管理提供了更好的风险分层.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 心脏病学 心脏病学
- 生物统计学 生物统计学
背景情况:
- 心血管疾病 (CVD) 是慢性病 (CKD) 患者的主要死亡原因.
- 目前的风险预测模型往往对这一群体不够.
- 准确的心血管疾病风险评估对于有效的CKD临床管理至关重要.
研究的目的:
- 开发和验证一种新的,基于文献的风险预测模型,用于慢性病患者的心血管 (CV) 住院治疗.
- 评估新模型的性能,命名为心血管,基于文献的风险算法 (CALIBRA),与现有的心血管风险得分相比.
主要方法:
- 基于文献的,原始贝斯模型 (CALIBRA) 已被开发,结合了31个传统和CKD特定的风险因素.
- 在两个独立的CKD队列中验证了CALIBRA:FMC NephroCare (EuCliD®) 研究和德国慢性病 (GCKD) 研究.
- 模型的性能使用c统计和校准进行了评估,并与弗雷明汉心脏研究 (FHS),ASCVD和INDANA风险得分进行了比较.
主要成果:
- 在两个验证队列中,CALIBRA表现出良好的区别 (在EuCliD®中AUC为0.79,在GCKD中为0.73).
- 在两个队列中,CALIBRA在FHS,ASCVD和INDANA相比显著提高了准确性 (ΔAUC从-0.04到-0.22不等).
- 该模型即使在缺少数据的情况下也保持了准确性,这表明在现实环境中具有稳定性.
结论:
- 卡利布拉为CKD患者提供准确而强大的心血管风险分层.
- 该模型与已建立的CV风险得分相比,提供了更高的预测准确性.
- 这些发现支持CALIBRA在各种CKD种群和临床环境中的通用性.
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