Development and Validation of a Lifespan Prediction Model in Chinese Adults Aged 65 Years or Older.

Objectives: Previous studies investigated factors associated with mortality. Nevertheless, evidence is limited regarding the determinants of lifespan. We aimed to develop and validate a lifespan prediction model based on the most important predictors.
Design: A prospective cohort study.
Setting and Participants: A total of 23,892 community-living adults aged 65 years or older with confirmed death records between 1998 and 2018 from 23 provinces in China.
Methods: Information including demographic characteristics, lifestyle, functional health, and prevalence of diseases was collected. The risk prediction model was generated using multivariate linear regression, incorporating the most important predictors identified by the Lasso selection method. We used 1000 bootstrap resampling for the internal validation. The model performance was assessed by adjusted R ² , root mean square error (RMSE), mean absolute error (MAE), and intraclass correlation coefficient (ICC).
Results: Twenty-one predictors were included in the final lifespan prediction model. Older adults with longer lifespans were characterized by older age at baseline, female, minority race, living in rural areas, married, with healthier lifestyles and more leisure engagement, better functional status, and absence of diseases. The predicted lifespans were highly consistent with observed lifespans, with an adjusted R ² of 0.893. RMSE was 2.86 (95% CI 2.84-2.88) and MAE was 2.18 (95% CI 2.16-2.20) years. The ICC between observed and predicted lifespans was 0.971 (95% CI 0.971-0.971).
Conclusions and Implications: The lifespan prediction model was validated with good performance, the web-based prediction tool can be easily applied in practical use as it relies on all easily accessible variables.
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