Liu Hongpeng, Li Cheng, Jiao Jing, Wu Xinjuan, Zhu Minglei, Wen Xianxiu, Jin Jingfen, Wang Hui, Lv Dongmei, Zhao Shengxiu, Nicholas Stephen, Maitland Elizabeth, Zhu Dawei
School of Nursing, Peking University, Beijing, China.
Department of Orthopaedic Surgery, Beijing Jishuitan Hospital, Fourth Clinical College of Peking University, Beijing, China.
Front Nutr. 2023 Jan 11;9:1061299. doi: 10.3389/fnut.2022.1061299. eCollection 2022.
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.
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 ( = 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, = 648).
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 )], 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.
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.
Chinese Clinical Trial Registry [ChiCTR1800017682].
开发并外部验证一个整合身体因素、心理变量和常规实验室检查参数的衰弱预测模型，以预测营养不良老年患者30天的衰弱风险。
基于一项正在进行的中国老年人群老年综合征调查（SGSE），这项预后研究确定了用于预测营养不良老年患者30天衰弱风险的假定预后指标。使用多变量逻辑回归分析并采用向后剔除法，对预测模型进行内部（自抽样）和外部验证，并通过校准斜率及其C统计量判别能力评估其校准情况。模型推导和模型验证队列数据于2018年10月至2019年2月期间，从中国三级医院住院老年患者的一项前瞻性大规模队列研究中收集。建模推导队列数据（n = 2194）基于包含四川省西南部、北京市北部、青海省西北部、黑龙江省东北部和浙江省东部的SGSE数据，使用来自湖北省的SGSE数据对模型进行外部验证（验证队列，n = 648）。
营养不良老年推导队列中的衰弱发生率在验证队列中分别为13.54%和13.43%。用于估计个体30天衰弱预测风险的最终模型以回归公式呈现：30天衰弱预测风险 = [1/(1 + e )]，其中风险评分 = -0.106 + 0.034×年龄 + 0.796×性别 - 0.361×视力障碍 + 0.373×听力障碍 + 0.408×排尿障碍 - 0.012×日常生活活动能力 + 0.064×抑郁 - 0.139×营养状况 - 0.007×血红蛋白 - 0.034×血清白蛋白 - 0.012×（男性：日常生活活动能力）。推导队列中的曲线下面积（AUC）为0.71，两个队列中模型的判别能力相似，C统计量接近0.7，观察到的风险与预测风险校准良好。
开发并外部验证了一个新的预测模型，该模型量化了营养不良老年患者衰弱的绝对风险。基于身体、心理和生物学变量，该模型提供了一种重要的评估工具，可为营养不良衰弱患者在不同时间提供不同的医疗保健需求。
中国临床试验注册中心[ChiCTR1800017682]
