Predicting Risk of Atherosclerotic Cardiovascular Disease Using Pooled Cohort Equations in Older Adults With Frailty, Multimorbidity, and Competing Risks.
Predicting Risk of Atherosclerotic Cardiovascular Disease Using Pooled Cohort Equations in Older Adults With Frailty, Multimorbidity, and Competing Risks.
复制标题
DOI：
10.1161/jaha.119.016003
复制
发表时间：
2020-09-15
影响因子：
5.4
通讯作者：
Kim DH
中科院分区：
文献类型：
--
作者：
Nguyen QD;Odden MC;Peralta CA;Kim DH
动脉粥样硬化性心血管疾病（ASCVD）风险的评估对于预防和管理至关重要，但合并队列方程在老年人中的表现尚不清楚。我们评估了这些亚组的合并队列方程和竞争风险的影响。在来自CHS（心血管健康研究）的4249名年龄≥65岁的社区居住成年人中，我们计算了10年严重ASCVD的风险。使用Fried表型确定虚弱。潜在类别分析用于识别使用慢性疾病的多发病模式的个体。我们使用C统计量和校准，通过比较预测的ASCVD风险与使用病因特异性和累积发病率模型估计的风险，根据多发病模式和虚弱状态，评估了区分度。共有917例（21.6%）受试者发生ASCVD事件，706例（16.6%）发生竞争性死亡事件。C统计量在男性中为0.68，在女性中为0.69;与原因特异性和累积发生率估计风险相比，校准效果良好（男性，-0.1%和3.3%;女性，0.6%和1.4%）。潜在类别分析确定了4种模式：轻微疾病、心脏代谢、认知能力低下、肌肉骨骼-肺抑制。在心脏代谢模式中，与男性（7.4%）和女性（6.8%）的累积发病风险相比，ASCVD风险被高估。与病因特异性风险相比，男性（-10.7%）和女性（-8.2%）的风险被低估。误校准主要发生在高预测风险范围内。在该年龄≥65岁的成人队列中，ASCVD预测良好。虽然校准因多发病模式、虚弱和竞争风险而异，但校准错误大多存在于高预测风险范围内，因此不太可能改变一级预防治疗的决策。
Assessment of atherosclerotic cardiovascular disease (ASCVD) risk is crucial for prevention and management, but the performance of the pooled cohort equations in older adults with frailty and multimorbidity is unknown. We evaluated the pooled cohort equations in these subgroups and the impact of competing risks. In 4249 community‐dwelling adults, aged ≥65 years, from the CHS (Cardiovascular Health Study), we calculated 10‐year risk of hard ASCVD. Frailty was determined using the Fried phenotype. Latent class analysis was used to identify individuals with multimorbidity patterns using chronic conditions. We assessed discrimination using the C‐statistic and calibration by comparing predicted ASCVD risks with estimated risk using cause‐specific and cumulative incidence models, by multimorbidity patterns and frailty status. A total of 917 (21.6%) participants had an ASCVD event, and 706 (16.6%) had a competing event of death. C‐statistic was 0.68 in men and 0.69 in women; calibration was good when compared with cause‐specific and cumulative incidence estimated risks (males, −0.1% and 3.3%; females, 0.6% and 1.4%). Latent class analysis identified 4 patterns: minimal disease, cardiometabolic, low cognition, musculoskeletal‐lung depression. In the cardiometabolic pattern, ASCVD risk was overpredicted compared with cumulative incidence risk in men (7.4%) and women (6.8%). Risk was underpredicted in men (−10.7%) and women (−8.2%) with frailty compared with cause‐specific risk. Miscalibration occurred mostly at high predicted risk ranges. ASCVD prediction was good in this cohort of adults aged ≥65 years. Although calibration varied by multimorbidity patterns, frailty, and competing risks, miscalibration was mostly present at high predicted risk ranges and thus less likely to alter decision making for primary prevention therapy.