Development and validation of risk prediction model for identifying 30-day frailty in older inpatients with undernutrition: A multicenter cohort study

Hongpeng Liu; Cheng Li; Jing Jiao; Xinjuan Wu; Minglei Zhu; Xianxiu Wen; Jingfen Jin; Hui Wang; Dongmei Lv; Shengxiu Zhao; Stephen Nicholas; Elizabeth Maitland; Dawei Zhu

doi:10.3389/fnut.2022.1061299

Development and validation of risk prediction model for identifying 30-day frailty in older inpatients with undernutrition: A multicenter cohort study

Front Nutr. 2023 Jan 11:9:1061299. doi: 10.3389/fnut.2022.1061299. eCollection 2022.

Authors

Hongpeng Liu¹, Cheng Li^{2

3}, Jing Jiao⁴, Xinjuan Wu^{4

5}, Minglei Zhu⁶, Xianxiu Wen⁷, Jingfen Jin⁸, Hui Wang⁹, Dongmei Lv¹⁰, Shengxiu Zhao¹¹, Stephen Nicholas^{12

13

14

15}, Elizabeth Maitland¹⁶, Dawei Zhu¹⁷

Affiliations

¹ School of Nursing, Peking University, Beijing, China.
² Department of Orthopaedic Surgery, Beijing Jishuitan Hospital, Fourth Clinical College of Peking University, Beijing, China.
³ Center for Musculoskeletal Surgery (CMSC), Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Charitá-Universitätsmedizin Berlin, Berlin, Germany.
⁴ Department of Nursing, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
⁵ Chinese Nursing Association, Beijing, China.
⁶ Department of Geriatrics, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
⁷ Department of Nursing, Sichuan Provincial People's Hospital, Chengdu, China.
⁸ Department of Nursing, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China.
⁹ Department of Nursing, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
¹⁰ Department of Nursing, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
¹¹ Department of Nursing, Qinghai Provincial People's Hospital, Xining, China.
¹² Australian National Institute of Management and Commerce, Sydney, NSW, Australia.
¹³ School of Economics and School of Management, Tianjin Normal University, Tianjin, China.
¹⁴ Guangdong Institute for International Strategies, Guangdong University of Foreign Studies, Guangzhou, China.
¹⁵ Newcastle Business School, University of Newcastle, University Drive, Newcastle, NSW, Australia.
¹⁶ School of Management, University of Liverpool, Liverpool, United Kingdom.
¹⁷ China Center for Health Development Studies, Peking University, Beijing, China.

Abstract

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 = 2,194) 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.

Clinical trial registration: Chinese Clinical Trial Registry [ChiCTR1800017682].

Keywords: biochemical parameters; frailty; models; older adults; risk assessment; undernutrition.

Grants and funding

The project was funded by China Postdoctoral Science Foundation (Grant numbers 2022TQ0016 and 2022M720298), Beijing Postdoctoral Research Foundation (Grant number 2022-ZZ-040), and Peking Union Medical College Hospital Research Fund (ZC201900516) supported this study but had no role in study design or data collection, analysis, and interpretation or manuscript conception and writing.