Department of Primary Care and Population Health, University College London, London, United Kingdom; Research Team for Social Participation and Community Health, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan.
Research Team for Social Participation and Community Health, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan.
J Am Med Dir Assoc. 2018 Sep;19(9):797-800.e2. doi: 10.1016/j.jamda.2018.05.012. Epub 2018 Jul 3.
To explore comparability of Kihon Checklist (KCL) and Kaigo-Yobo Checklist (KYCL) to Frailty Index (FI) in predicting risks of long-term care insurance (LTCI) certification and/or mortality over 3 years.
Prospective cohort study.
1023 Japanese community-dwelling older adults from the Kusatsu Longitudinal Study of Aging and Health.
Frailty status was quantified at baseline using KCL, KYCL, and 32-deficit and 68-deficit FI. Relationships of the measures were examined using Spearman rank correlation coefficients. Cox regression models examined the risk of new certification of LTCI or mortality according to KCL, KYCL, and FI. Predictive abilities of KCL and KYCL were compared with FI using area under the receiver operating characteristic curve (AUC), C statistics, net reclassification improvement (NRI), and integrated discrimination improvement (IDI).
Mean age was 74.7 years and 57.6% were women. KCL and KYCL were significantly correlated to 32-FI (r = 0.60 and 0.36, respectively) and to 68-FI (r = 0.88 and 0.61, respectively). During the follow-up period, 92 participants (9%) were newly certified for LTCI or died. Fully adjusted Cox models showed that higher KCL, KYCL, 32-FI, and 68-FI were all significantly associated with elevated risks [hazard ratio (HR) = 1.03, 95% CI = 1.01-1.04, P < .001; HR = 1.04, 95% CI = 1.02-1.05, P < .001; HR = 1.03, 95% CI = 1.01-1.05, P = .001; HR = 1.04, 95% CI = 1.02-1.06, P < .001, respectively, per 1/100 increase of max score]. AUC and C-statistics of KCL and KYCL were not different statistically from those of 32-FI and 68-FI. Predictive abilities of KCL were superior to 32-FI in NRI and IDI but inferior to 68-FI in category-free NRI, and those of KYCL were superior to 32-FI in IDI but inferior to 68-FI in NRI.
Although KCL and KYCL include smaller numbers of items than standard FI, both tools were shown to be highly correlated with FI, significant predictors of LTCI certification and/or mortality, and compatible to FI in the risk prediction.
探讨基本清单(KCL)和看护者清单(KYCL)与衰弱指数(FI)在预测长期护理保险(LTCI)认证和/或 3 年以上死亡率风险方面的可比性。
前瞻性队列研究。
来自草津老龄化与健康纵向研究的 1023 名日本社区居住的老年人。
使用 KCL、KYCL、32 项和 68 项缺陷 FI 在基线时量化衰弱状况。使用 Spearman 秩相关系数检查措施之间的关系。Cox 回归模型根据 KCL、KYCL 和 FI 检查新认证 LTCI 或死亡的风险。使用接收器工作特征曲线(AUC)下面积、C 统计量、净重新分类改善(NRI)和综合判别改善(IDI)比较 KCL 和 KYCL 的预测能力。
平均年龄为 74.7 岁,57.6%为女性。KCL 和 KYCL 与 32-FI(r=0.60 和 0.36)和 68-FI(r=0.88 和 0.61)均显著相关。在随访期间,92 名参与者(9%)新获得 LTCI 认证或死亡。完全调整的 Cox 模型显示,较高的 KCL、KYCL、32-FI 和 68-FI 与风险升高显著相关[风险比(HR)=1.03,95%CI=1.01-1.04,P<0.001;HR=1.04,95%CI=1.02-1.05,P<0.001;HR=1.03,95%CI=1.01-1.05,P=0.001;HR=1.04,95%CI=1.02-1.06,P<0.001,每增加 1/100 最大得分]。KCL 和 KYCL 的 AUC 和 C 统计量在统计学上与 32-FI 和 68-FI 没有差异。KCL 的预测能力在 NRI 和 IDI 方面优于 32-FI,但在无类别 NRI 方面劣于 68-FI,KYCL 的预测能力在 IDI 方面优于 32-FI,但在 NRI 方面劣于 68-FI。
尽管 KCL 和 KYCL 比标准 FI 包含的项目少,但这两种工具都与 FI 高度相关,是 LTCI 认证和/或死亡率的重要预测指标,在风险预测方面与 FI 具有兼容性。