Development and validation of risk prediction model for identifying 30-day frailty in older inpatients with undernutrition: A multicenter cohort study - Liu, Hongpeng; Li, Cheng; Jiao, Jing; Wu, Xinjuan; Zhu, Minglei; Wen, Xianxiu; Jin, Jingfen; Wang, Hui; Lv, Dongmei; Zhao, Shengxiu; Nicholas, Stephen; Maitland, Elizabeth; et al. (Frontiers in Nutrition, 2023)
Objective: To develop and externally validate a frailty prediction model integrating physical factors, psychological variables and routine laboratory test parameters to predict the 30-day frailty risk in older adults with undernutrition. Methods: Based on an ongoing survey of geriatrics syndrome in elder adults across China (SGSE), this prognostic study identified the putative prognostic indicators for predicting the 30-day frailty risk of older adults with undernutrition. Using multivariable logistic regression analysis with backward elimination, the predictive model was subjected to internal (bootstrap) and external validation, and its calibration was evaluated by the calibration slope and its C statistic discriminative ability. The model derivation and model validation cohorts were collected between October 2018 and February 2019 from a prospective, large-scale cohort study of hospitalized older adults in tertiary hospitals in China. The modeling derivation cohort data (n=2194) were based on the SGSE data comprising southwest Sichuan Province, northern Beijing municipality, northwest Qinghai Province, northeast Heilongjiang Province, and eastern Zhejiang Province, with SGSE data from Hubei Province used to externally validate the model (validation cohort, n=648). Results: The incidence of frailty in the older undernutrition derivation cohort was 13.54% and 13.43% in the validation cohort. The final model developed to estimate the individual predicted risk of 30-day frailty was presented as a regression formula: predicted risk of 30-day frailty = (1/(1+e−riskscore)), where riskscore = −0.106＋0.034 × age＋0.796 × sex −0.361 × vision dysfunction＋0.373 × hearing dysfunction＋0.408 × urination dysfunction − 0.012 × ADL＋0.064 × depression − 0.139 × nutritional status − 0.007 × hemoglobin − 0.034 × serum albumin − 0.012 ×(male: ADL). Area under the curve (AUC) of 0.71 in the derivation cohort, and discrimination of the model were similar in both cohorts, with a C statistic of nearly 0.7, with excellent calibration of observed and predicted risks. Conclusion: A new prediction model that quantifies the absolute risk of frailty of older patients suffering from undernutrition was developed and externally validated. Based on physical, psychological, and biological variables, the model provides an important assessment tool to provide different healthcare needs at different times for undernutrition frailty patients.