Dynamic Statistical Model for Predicting the Risk of Death Among Older Chinese People, Using Longitudinal Repeated Measures of the Frailty Index: A Prospective Cohort Study

Abstract

Background: Frailty is a common characteristic of the elderly with the aging process. We aimed to develop and validate a dynamic statistical prediction model to calculate the risk of death in people aged ≥65 years using longitudinal frailty index (FI). Methods: One training dataset and three validation datasets from the Chinese Longitudinal Healthy Longevity Survey were used in our study: 9,748 people aged ≥65 years recruited in 2002 (Training Dataset); 7,459 people aged ≥65 years recruited in 2005 (Validation Dataset 1); 9,093 people aged ≥80 years recruited in 1998 (Validation Dataset 2); and 6,368 people aged ≥80 years recruited in 2000 (Validation Dataset 3). We used 35 health deficits to construct the FI. Joint model was used to build dynamic prediction model considering both baseline covariates and longitudinal FI. Areas under time-dependent receiver-operating characteristics curves (AUCs) were employed to assess the discrimination power. Findings: Our dynamic prediction model built on longitudinal FI, age, residence, and sex. The dynamic prediction model showed good discrimination in the Training Dataset and Validation Dataset 1 (AUCs: 0.7368 to 0.8279), and moderate discrimination in Validation Datasets 2 and 3 (AUCs: 0.6526 to 0.7356). Interpretation: The dynamic prediction model was able to update predictions of the risk of death as updated measurements of FI became available, which was a supportive tool for determining the risk of death in individuals, and for clinical decision-making. Funding Statement: This work was supported by grants from the National Key RD Special Clinical Research in Health Industry in Shanghai [20184Y0054]; Shanghai Sail Program[19YF1459200]; Shanghai Pujiang Program[18PJC116], National Natural Science Foundation[71804186], Young Talent Support Project[17-JCJQ-QT-029]; Beijing Science and Technology New Star [2018011]. Declaration of Interests: The authors have declared that there is no conflict of interest. Ethics Approval Statement: The Committee on Ethics of Medicine of Navy Medical University reviewed and approved ethics for this study. A written consent was obtained from each of all CLHLS participants.