An early predictive model of frailty for older inpatients according to nutritional risk: protocol for a cohort study in China

Liu, Hongpeng; Jiao, Jing; Zhu, Minglei; Wen, Xiaozhong; Jin, Jingfen; Wang, Hui; Lv, Dongmei; Zhao, Shengxiu; Wei, Chen; Wu, Xinjuan; Xu, Tao

doi:10.1186/s12877-021-02396-3

Cited by 3 publications

(4 citation statements)

References 70 publications

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“…With the rapid development of health science and the global economy, the life span of the world's population is increasing ( 1 – 3 ). By 2050, it is estimated that 16% of the world's population would be aged 65 years and older ( 4 ), with 80% of those living in low- and middle-income countries ( 5 ).…”

Section: Introductionmentioning

confidence: 99%

Nutritional Status According to the Short-Form Mini Nutritional Assessment (MNA-SF) and Clinical Characteristics as Predictors of Length of Stay, Mortality, and Readmissions Among Older Inpatients in China: A National Study

et al. 2022

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BackgroundStudies are scarce in China that explore the association of nutritional status, measured using the Short-Form Mini Nutritional Assessment (MNA-SF) and biochemical data, on adverse clinical outcomes among older inpatients. In this study, we aimed to determine the prevalence of malnutrition in tertiary hospitals of China and the associations between malnutrition and adverse clinical outcomes.MethodsThis prospective study involved 5,516 older inpatients (mean age 72.47 ± 5.77 years) hospitalized in tertiary hospitals between October 2018 and February 2019. The tertiary hospitals refer to the hospital with more than 500 beds and can provide complex medical care services. The MNA-SF was used to assess nutritional status. Multiple logistic regression and negative binomial regression were used to analyze the relationship between nutritional parameters and risk of hospital length of stay (LoS), mortality, and rehospitalization.ResultsWe found that 46.19% of hospitalized patients had malnutrition or malnutrition risk, according to the MNA-SF. Death occurred in 3.45% of patients. MNA-SF scores 0–7 (odds ratio [OR] 5.738, 95% confidence interval [CI] 3.473 to 9.48) were associated with a six-fold higher likelihood of death, and scores 8–11 (OR 3.283, 95% CI 2.126–5.069) with a three-fold higher likelihood of death, compared with MNA-SF scores 12–14 in the logistic regression model, after adjusting for potential confounders. A low MNA-SF score of 0–7 (regression coefficient 0.2807, 95% CI 0.0294–0.5320; P < 0.05) and a score of 8–11 (0.2574, 95% CI 0.0863–0.4285; P < 0.01) was associated with a significantly higher (28.07 and 25.74%, respectively) likelihood of increased LoS, compared with MNA-SF score 12–14. MNA-SF scores 0–7 (OR 1.393, 95% CI 1.052–1.843) and 8–11 (OR 1.356, 95% CI 1.124–1.636) were associated with a nearly 1.5-fold higher likelihood of 90-day readmission compared with MNA-SF scores 12–14 in the logistic regression model. Moreover, hemoglobin level, female sex, education level, former smoking, BMI 24–27.9 kg/m2, age 75 years and above, and current alcohol consumption were the main factors influencing clinical outcomes in this population.ConclusionsMalnutrition increases the risk of hospital LoS, mortality, and 90-day readmission. The use of nutritional assessment tools in all hospitalized patients in China is needed. The MNA-SF combined with hemoglobin level may be used to identify older inpatients with a high risk of adverse clinical outcomes. These findings may have important implications for the planning of hospital services.

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Section: Introductionmentioning

confidence: 99%

Nutritional Status According to the Short-Form Mini Nutritional Assessment (MNA-SF) and Clinical Characteristics as Predictors of Length of Stay, Mortality, and Readmissions Among Older Inpatients in China: A National Study

et al. 2022

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“…Compared to our study, the FI values were considered as "independent values" used to predict the log-likelihood of dying or not. Liu et al 42 developed a prediction model of frailty among older inpatients with nutritional risks. The dependent variable was binary (no frailty and frailty).…”

Section: Discussionmentioning

confidence: 99%

Modeling the frailty phenotype by using biochemical markers measured in blood samples and multiple regression analysis

ZAMUDIO,

Garcia,

Palomo

et al. 2024

Preprint

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Background Human frailty syndrome is a complex phenotypic trait and the result of multiple sources of influence. Its assessment is not a simple task; however, it is important to preventing disability in adult people. However, it requires retrieval of clinical data and calculations that are time-consuming, which may be unfeasible in routine clinical practice. We propose a diagnostic tool based on multiple regression analysis and aimed to have a preliminary assessment of the frailty condition in senior adults. Because it depends on blood sample analyzes, our predictive functions are less invasive, expensive, time consuming, and less physically and psychologically discomforting. Method Data came from the Toledo Study for Healthy Aging, a prospective cohort study designed for evaluating the determinants, and consequences, of the frailty syndrome in older people living in the province of Toledo, Spain. The Garcia´s Frailty Trait Scale (FTS12) was considered as the dependent variable in the statistical analyzes. Forty-six traits assessed from blood samples were included as independent variables to be tested. They included 15 variables from hemograms, 19 variables from biochemical profiles, and 12 extra biochemical characteristics (not included in standard biochemical profiles). The stepwise regression method was used in deciding which explanatory variables were to be included in the predictive models. Results Two predictive functions of the observed frailty for each gender were obtained: a “full model” and a “reduced model”. A range of 0.615 to 0.649 of moderately high Pearson's correlations was recorded between observed and predicted FTS12 values across the four equations. The correlation between all functions of risk factors and FTS12 was larger than 0.4 and highly significant in all four study cases. With single risk factors, the highest correlation coefficient was 0.283 (p-value < 0.0001). Conclusions The frailty syndrome can be moderately well predicted by using biochemical information. However, full models showed a narrow superior predictive capability. Albumin was present in all functions of risk factors selected by the modeling. It showed to be a risk factor that helps in predicting the frailty phenotype. We expect that exploring Albumin’s potential as part of a predictive equation might help to design anti-aging therapeutic measures.

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“…Creatine Kinase (CK) [18] , Albumin [17] , and Testosterone [19] ) are all associated with frailty. Some existing research has focused on speci c groups of patients with particular chronic conditions [20][21][22] or has only concentrated on speci c physiological indicators [23] , lacking a comprehensive and reliable predictive model.…”

Section: Methodsmentioning

confidence: 99%

“…Previous studies have con rmed that sociodemographic factors (such as age [7] , gender [8] , marital status [9] , income [10] , education [11] , and place of residence [12] ); behavioral factors (such as smoking [13] , drinking [14] , exercise [15] , and diet [16] ); and biomarkers (such as Interleukin-6 (IL-6) [17] ,Creatine Kinase (CK) [18] , Albumin [17] , and Testosterone [19] ) are all associated with frailty. Some existing research has focused on speci c groups of patients with particular chronic conditions [20][21][22] or has only concentrated on speci c physiological indicators [23] , lacking a comprehensive and reliable predictive model.This study aims to construct and validate a comprehensive frailty prediction model speci cally designed for patients with chronic diseases, covering multiple dimensions of indicators and factors. By integrating various data from sociodemographic characteristics, lifestyle factors, clinical indicators, and other aspects, we endeavor to establish a comprehensive model aimed at effectively predicting the risk of frailty among patients with chronic diseases.…”

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confidence: 99%

Development and validation of a risk prediction model for frailty in patients with chronic diseases

wang,

xu,

cao

et al. 2023

Preprint

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Background The occurrence rate of frailty is high among patients with chronic diseases. However, the assessment of frailty among these patients is still far from being a routine part of clinical practice. The aim of this study is to develop a validated predictive model for assessing frailty risk in patients with chronic illnesses. Methods This study utilized survey data from elderly chronic disease patients (aged ≥ 60 years) at a tertiary hospital in China between 2022 and 2023. A total of 57 indicators were analyzed, encompassing sociodemographic variables, health status, physical measurements, nutritional assessment, physical activity levels, and blood biomarkers. The research cohort was randomly divided into training and validation sets at a ratio of 70–30%. Employing LASSO regression analysis, the study selected the optimal predictive factors based on univariate analysis. Logistic regression models were applied to investigate factors associated with frailty in chronic disease patients. A nomogram was constructed to establish the predictive model. The predictive performance of the model was evaluated using the area under the receiver operating characteristic curve, calibration curves, and decision curve analysis. Results This study recruited a total of 543 patients with chronic diseases, among which 237 were included in the development and validation of the predictive model. There were 100 cases (42.2%) presenting frailty symptoms. Multivariate logistic regression analysis revealed that gender, age, chronic diseases, Mini Nutritional Assessment (MNA) score, and Clinical Frailty Scale (CFS) score were predictive factors for frailty in chronic disease patients. Utilizing these factors, a nomogram model demonstrated good consistency and accuracy. The AUC values for the predictive model and validation set were 0.946 and 0.945, respectively. Calibration curves, ROC, and DCA indicated the nomogram had favorable predictive performance. Conclusions The comprehensive nomogram developed in this study is a promising and convenient tool for assessing frailty risk in patients with chronic diseases, aiding clinical practitioners in screening high-risk populations. Registration: ChICTR2300068076 (first recruitment date was 2022/07/06)

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An early predictive model of frailty for older inpatients according to nutritional risk: protocol for a cohort study in China

Cited by 3 publications

References 70 publications

Nutritional Status According to the Short-Form Mini Nutritional Assessment (MNA-SF) and Clinical Characteristics as Predictors of Length of Stay, Mortality, and Readmissions Among Older Inpatients in China: A National Study

Nutritional Status According to the Short-Form Mini Nutritional Assessment (MNA-SF) and Clinical Characteristics as Predictors of Length of Stay, Mortality, and Readmissions Among Older Inpatients in China: A National Study

Modeling the frailty phenotype by using biochemical markers measured in blood samples and multiple regression analysis

Development and validation of a risk prediction model for frailty in patients with chronic diseases

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