Geisinger Health System.
Circ J. 2018 Feb 23;82(3):724-731. doi: 10.1253/circj.CJ-17-0670. Epub 2018 Jan 18.
Prediction models such as the Seattle Heart Failure Model (SHFM) can help guide management of heart failure (HF) patients, but the SHFM has not been validated in the office environment. This retrospective cohort study assessed the predictive performance of the SHFM among patients with new or pre-existing HF in the context of an office visit.Methods and Results:SHFM elements were ascertained through electronic medical records at an office visit. The primary outcome was all-cause mortality. A "warranty period" for the baseline SHFM risk estimate was sought by examining predictive performance over time through a series of landmark analyses. Discrimination and calibration were estimated according to the proposed warranty period. Low- and high-risk thresholds were proposed based on the distribution of SHFM estimates. Among 26,851 HF patients, 14,380 (54%) died over a mean 4.7-year follow-up period. The SHFM lost predictive performance over time, with C=0.69 and C<0.65 within 3 and beyond 12 months from baseline respectively. The diminishing predictive value was attributed to modifiable SHFM elements. Discrimination (C=0.66) and calibration for 12-month mortality were acceptable. A low-risk threshold of ∼5% mortality risk within 12 months reflects the 10% of HF patients in the office setting with the lowest risk.
The SHFM has utility in the office environment.
预测模型,如西雅图心力衰竭模型(SHFM),可以帮助指导心力衰竭(HF)患者的管理,但该模型尚未在办公环境中得到验证。本回顾性队列研究评估了 SHFM 在办公室就诊时新诊断或既往存在 HF 的患者中的预测性能。
通过电子病历在办公室就诊时确定 SHFM 元素。主要结局是全因死亡率。通过一系列里程碑分析,随着时间的推移,通过检查预测性能,寻求基线 SHFM 风险估计的“保修期”。根据提出的保修期估计了区分度和校准度。在 26851 例 HF 患者中,14380 例(54%)在平均 4.7 年的随访期间死亡。SHFM 的预测性能随着时间的推移而下降,C=0.69 和 C<0.65 分别在基线后 3 个月和 12 个月内。预测价值的降低归因于可修改的 SHFM 元素。12 个月死亡率的区分度(C=0.66)和校准度可接受。12 个月内死亡率风险<5%的低风险阈值反映了办公室环境中 HF 患者中风险最低的 10%。
SHFM 在办公环境中具有实用性。