Development and validation of a predictive risk model for frailty in elderly patients with multimorbidity

Risk prediction models for frailty in older adults: A systematic review and critical appraisal

BACKGROUND

Frailty can lead to increased adverse health outcomes in older adults. Risk prediction models for frailty have benefits in guiding the prevention. Studies have increasingly focused on the development of risk prediction models for frailty in older adults. The quality and clinical applicability of these models remain unknown.

OBJECTIVES

To systematically review and critically appraise the current risk prediction models for frailty in older adults.

METHODS

PubMed, Embase, CINAHL, and Cochrane Library were searched from inception to June, 2024 to identify published studies focusing on developing or validating risk prediction models for frailty in older adults. Data extraction was independently conducted by two reviewers based on the checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies. The quality of included models was assessed using the Prediction Model Risk of Bias Assessment Tool.

RESULTS

Of 5421 retrieved studies, 19 studies with 22 risk prediction models for frailty were included. The included models focused on community-dwelling and hospitalized older adults. Logistic regression and machine learning methods were employed to develop risk prediction models. The frequently used predictors were age (77.3 %), cognitive function (31.8 %), self-rated health (27.3 %), sex (22.7 %), activities of daily living (22.7 %), and depression (22.7 %). Internal and external validation were conducted in 17 (77.3 %) and four (18.2 %) models, respectively. Twenty-one (95.5 %) models evaluated model discrimination, with the AUC or c-index ranging from 0.707 to 0.920 in the internal validation and from 0.612 to 0.889 in the external validation. Fifteen (68.2 %) models assessed model calibration using the calibration curve, Hosmer-Lemeshow test, and Brier score and showed good calibration. All risk prediction models had high risk of bias primarily due to problems in the analysis domain and nine (40.9 %) models had high concern regarding applicability.

CONCLUSIONS

Current risk prediction models for frailty in older adults demonstrated poor validation and evaluation. Future research should focus on improving current models to aid their implementation and developing and validating new models with rigorous methodology.

PROSPERO REGISTRATION

CRD42024582883.

Causes of applying Long-Term Care Insurance certification for patients with heart failure with systolic dysfunction

AIM

The number of patients with heart failure with disability and the use of Long-Term Care Insurance (LTCI) has been increasing in the Japanese aging society. This study investigated the main causes of LTCI demands for better care management.

METHODS

We carried out a retrospective study including patients with heart failure with reduced ejection fraction in seven hospitals in Niigata City from 2011 to 2016. Data related to LTCI introduction, such as the main cause and degree of impairments, were collected from official documents for LTCI registration based on the doctor's opinion. Of 3738 patients, 312 were newly eligible for LTCI.

RESULTS

LTCI was introduced due to cardiac and non-cardiac diseases in 49.4% and 50.6% of patients, respectively. Physical impairment was milder in the cardiac group than major non-cardiac groups (Kruskal-Wallis test; P < 0.001, Steel test; cardiac vs cancer: P = 0.027 vs stroke: P < 0.001 vs orthopedic: P = 0.002 vs others: P = 0.041). Only the cancer group had a poorer prognosis than the cardiac group (log-rank test with Bonferroni correction; cardiac vs cancer: P < 0.001 vs stroke: P = 0.282 vs orthopedic disease: P = 0.866 vs others: P = 0.476).

CONCLUSION

Half of the LTCI users were introduced due to non-cardiac diseases in heart failure patients. The cause of LTCI indicating the degree of the patient's disability and prognosis is valuable information to provide favorable care. Geriatr Gerontol Int 2025; ••: ••-••.

Influencing Factors of Frailty in Older Patients With Chronic Heart Failure: Based on Bayesian Network

PURPOSE

Previous research has explored the factors influencing frailty in older patients with chronic heart failure (CHF), but these studies have not revealed the potential network interactions among the related factors. This study aimed to construct a Bayesian network (BN) model of frailty in older patients with CHF, analyze the predictive factors, and explore the internal relationships between these factors.

METHODS

A total of 439 older patients with CHF were selected using a convenience sampling method from September 2023 to March 2024 at the cardiology department of a comprehensive tertiary hospital in Nanchang, Jiangxi, China. Multivariate logistic regression analysis was used to explore the influencing factors of frailty in older patients with CHF. The BN structure was learned using the max-min hill-climbing algorithm, with parameter estimation achieved through maximum likelihood estimation. Netica software was utilized for prediction and diagnosis. The effectiveness of the model was validated using the Receiver Operating Characteristic (ROC) curve.

RESULTS

The prevalence of frailty in older patients with CHF was found to be 53.3%. After using a multivariate logistic regression analysis model that screened the variables, the nutritional risk, physical activity, depression, multimorbidity, grip strength and left atrial diameter were included into the Bayesian network model. The Bayesian network model of frailty related factors in older CHF patients showed that nutritional risk, physical activity, depression, and multimorbidity were directly related to frailty, while grip strength and left atrial diameter were indirectly related.

CONCLUSION

The study results indicated that malnutrition risk, inactivity, depression, and multimorbidity were directly related to frailty, while lower grip strength and a wider left atrial diameter were indirectly related to frailty. Enhancing frailty assessment and implementing measures addressing disease, nutrition, exercise, and psychological well-being are crucial for delaying and potentially reversing the onset and progression of frailty.

Path analysis of predictors of frailty in hospitalised patients with chronic obstructive pulmonary disease

Frailty is highly prevalent in elderly patients with chronic obstructive pulmonary disease (COPD), contributing to poor clinical outcomes and reduced quality of life. To examine the effects of grip strength, CAT score, multimorbidity, GOLD stage, and age on frailty for hospitalised elderly with COPD through path analysis. This cross-sectional study used convenience sampling to select 283 hospitalised patients from March to August 2024. Path analysis explored the direct and indirect effects among grip strength, CAT score, multimorbidity, GOLD stage, and age. Grip strength was measured with a digital dynamometer, CAT score assessed disease impact, multimorbidity was based on patient-reported diagnoses, and GOLD stage was determined by pulmonary function tests. Among 283 hospitalised elderly COPD patients, the prevalence of frailty was 33.92%. The path analysis model showed good fit (χ2/df = 1.170, RMSEA = 0.027, 90% CI = 0.024-0.085, CFI = 0.992, TLI = 0.982, SRMR = 0.051, GFI = 0.981). Grip strength was the strongest predictor of frailty, followed by multimorbidity. The model explained 46.9% of the variance in frailty, with grip strength accounting for 11.2%. Hospitalised elderly patients with COPD who exhibited low grip strength, high CAT score, advanced age, multimorbidity, and a higher GOLD stage were more likely to experience frailty. These findings suggest that interventions aimed at improving grip strength and managing multimorbidity may help alleviate frailty in elderly COPD patients.

Frailty in Hospitalized Older Adults in the Rural Setting and Impact of Mobility on Length of Stay and Discharge Disposition

PURPOSE

To determine the prevalence of frailty among individuals aged ≥65 years with heart failure and/or chronic obstructive pulmonary disease, and if there was a difference in length of stay (LOS) and/or discharge disposition in individuals who mobilized daily versus those who did not.

METHOD

Thirty-five individuals were screened using the FRAIL Scale. A retrospective chart review was performed for participants with frailty, assessing residential location on admission and discharge, LOS, and daily mobilization during hospitalization.

RESULTS

Participants were categorized as robust (n = 1, 2.9%), pre-frail (n = 15, 42.9%), and frail (n = 19, 54.3%). Mean LOS for those who mobilized daily (n = 12) was 2.33 and 4.71 for those who did not mobilize daily (p = 0.028). A lower percentage of participants who mobilized (16.7%) required higher levels of care at discharge versus those who did not mobilize (42.9%, p = 0.067).

CONCLUSION

Early recognition of frailty may provide opportunities to implement mobility programs to decrease LOS and ensure these individuals remain at functional baseline. [Journal of Gerontological Nursing, xx(xx), xx-xx.].

Development and Validation of a Nomogram for Predicting Frailty Risk Among Older Patients With Ischaemic Stroke

AIM

To investigate the risk factors associated with frailty in older patients with ischaemic stroke, develop a nomogram and apply it clinically.

DESIGN

A cross-sectional study.

METHODS

Altogether, 567 patients who experienced ischaemic strokes between March and December 2023 were temporally divided into training (n = 452) and validation (n = 115) sets and dichotomised into frail and non-frail groups using the Tilburg Frailty Indicator scale. In the training set, feature selection was performed using least absolute shrinkage and selection operator regression and random forest recursive feature elimination, followed by nomogram construction using binary logistic regression. Internal validation was performed through bootstrap re-sampling and the validation set was used to assess model generalisability. The receiver operating characteristic curve, Hosmer-Lemeshow test, Brier score, calibration curve, decision curve analysis and clinical impact curve were used to evaluate nomogram performance.

RESULTS

The prevalence of frailty was 58.6%. Marital status, smoking, history of falls (in the preceding year), physical exercise, polypharmacy, albumin levels, activities of daily living, dysphagia and cognitive impairment were predictors in the nomogram. Receiver operating characteristic curve analysis indicated outstanding discrimination of the nomogram. The Hosmer-Lemeshow test, calibration curve and Brier score results confirmed good model consistency and predictive accuracy. The clinical decision and impact curve demonstrated notable clinical utility. This free, dynamic nomogram, created for interactive use and promotion, is available at: https://dongdongshen.shinyapps.io/DynNomapp/.

CONCLUSION

This nomogram may serve as an effective tool for assessing frailty risk in older patients with ischaemic stroke.

RELEVANCE TO CLINICAL PRACTICE

The nomogram in this study may assist healthcare professionals in identifying high-risk patients with frailty and understanding related factors, thereby providing more personalised risk management.

REPORTING METHOD

TRIPOD checklist.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

A dynamic online nomogram for predicting the heterogeneity trajectories of frailty among elderly gastric cancer survivors

BACKGROUND

Frailty is very common among older people with gastric cancer and seriously affects their prognosis. The development of frailty is continuous and dynamic, increasing the difficulty and burden of care.

OBJECTIVES

The aims of this study were to delineate the developmental trajectory of frailty in older people with gastric cancer 1 year after surgery, identify heterogeneous frailty trajectories, and further explore their predictors to construct a nomogram for prediction.

DESIGN

We conducted a prospective longitudinal observation study. Clinical evaluation and data collection were performed at discharge, and at 1, 3, 6, and 12 months.

SETTING AND PARTICIPANTS

This study was conducted in a tertiary hospital and 381 gastric cancer patients over 60 years who underwent radical gastrectomy completed the 1-year follow-up.

METHODS

A growth mixture model (GMM) was used to delineate the frailty trajectories, and identify heterogeneous trajectories. A regression model was performed to determine their predictors and further construct a nomogram based on the predictors. Bootstrap with 1000 resamples was used for internal validation of nomogram, a receiver operating characteristic (ROC) curve to evaluate discrimination, calibration curves to evaluate calibration and decision curve analysis (DCA) to evaluate the clinical value.

RESULTS

GMM identified three classes of frailty trajectories: "frailty improving", "frailty persisting" and "frailty deteriorating". The latter two were referred to as heterogeneous frailty trajectories. Regression analysis showed 8 independent predictors of heterogeneous frailty trajectories and a nomogram was constructed based on these predictors. The area under ROC curve (AUC) of the nomogram was 0.731 (95 % CI = 0.679-0.781), the calibration curves demonstrated that probabilities predicted by the nomogram agreed well with the actual observation with a mean absolute error of 0.025, and the DCA of nomogram indicated that the net benefits were higher than that of the other eight single factors.

CONCLUSIONS

Older gastric cancer patients have heterogeneous frailty trajectories of poor prognosis during one-year postoperative survival. Therefore, early assessment to predict the occurrence of heterogeneous frailty trajectories is essential to improve the outcomes of elderly gastric cancer patients. Scientific and effective frailty interventions should be further explored in the future to improve the prognosis of older gastric cancer patients.

CONTRIBUTION OF THE PAPER STATEMENTS

This study constructed a static and dynamic online nomogram with good discrimination and calibration, which can help to screen high-risk patients, implement preoperative risk stratification easily and promote the rational allocation of medical resources greatly.

REGISTRATION

ClinicalTrials.gov (Number: NCT05982899).

TWEETABLE ABSTRACT

Our findings identified three frailty trajectories and constructed a nomogram to implement preoperative risk stratification and improve patient outcomes.

Development and Validation of a Risk Prediction Model for Frailty in Patients with Chronic Diseases

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. This study recruited 543 patients with chronic diseases, and 237 were included in the development and validation of the predictive model. A total of 57 frailty related indicators were analyzed, encompassing sociodemographic variables, health status, physical measurements, nutritional assessment, physical activity levels, and blood biomarkers. There were 100 cases (42.2%) presenting frailty symptoms. Multivariate logistic regression analysis revealed that gender, age, chronic diseases, Mini Nutritional Assessment score, and Clinical Frailty Scale 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. Altogether, the comprehensive nomogram developed here is a promising and convenient tool for assessing frailty risk in patients with chronic diseases, aiding clinical practitioners in screening high-risk populations.

Types and frequency of social participation and comprehensive frailty among community-dwelling older people in Japan

Although research has established social participation as important for preventing frailty in older people, the association between the type and frequency of social participation and comprehensive frailty remains unclear. This study aimed to reveal the associations between types and frequency of social participation and comprehensive frailty among community-dwelling older people. This was a cross-sectional study conducted in four cities and towns (Inabe City, Nabari City, Odai Town, and Kiho Town) of Mie Prefecture, Japan, among adults who were: (i) aged ≥65 years and (ii) not certified as needing long-term care. We measured comprehensive frailty using the participants' total scores on the Kihon Checklist, developed by Ministry of Health, Labour and Welfare of Japan, which divides frailty status into three categories: robust (0-3 points), prefrail (4-7), and frail (8-25). Types and frequency of social participation were explanatory variables, and ordered logistic regression analysis adjusted for potential confounding factors identified the associations. The frailty status of the 296 participants (age 65-74 years: 44.3 %; female: 74.0 %) was as follows: frail, 21.3 %; prefrail, 40.2 %; and robust, 38.5 %. Lower level of frailty was associated with interaction using smartphones 2-3 times per month, participating in sports ≥4 times per week, participating in local improvement activities several times per year, and engaging in activities for children 2-4 times per month. Social participation among older adults at appropriate frequencies were associated with the lower level of comprehensive frailty. However, future longitudinal studies are needed using populations from more diverse countries or regions and from different cultures.

Frailty Transitions in Older Persons With Lung Function Impairment: A Population-Based Study

BACKGROUND

The aging population and its burden on health care systems warrant early detection of patients at risk of functional decline and mortality. We aimed to assess frailty transitions and its accuracy for mortality prediction in participants with impaired spirometry (Preserved Ratio Impaired Spirometry [PRISm] or chronic obstructive pulmonary disease [COPD]).

METHODS

In participants from the population-based Rotterdam Study (mean age 69.1 ± 8.9 years), we examined whether PRISm (forced expiratory volume in 1 second [FEV1]/forced vital capacity [FVC] ≥ 70% and FEV1 < 80%) or COPD (FEV1/FVC < 70%) affected frailty transitions (progression/recovery between frailty states [robust, prefrailty, and frailty], lost to follow-up, or death) using age-, sex- and smoking state-adjusted multinomial regression models yielding odds ratios (OR). Second, we assessed the diagnostic accuracy of frailty score for predicting mortality in participants with COPD using c-statistics.

RESULTS

Compared to participants with normal spirometry, participants with PRISm were more likely to transit from robust (OR 2.2 [1.2-4.2], p < .05) or prefrailty (OR 2.6 [1.3-5.5], p < .01) toward frailty. Participants with PRISm (OR 0.4 [0.2-0.8], p < .05) and COPD (OR 0.6 [0.4-1.0], NS) were less likely to recover from their frail state, and were more likely to progress from any frailty state toward death (OR between 1.1 and 2.8, p < .01). Accuracy for predicting mortality in participants with COPD significantly improved when adding frailty score to age, sex, and smoking status (90.5 [82.3-89.8] vs 77.9 [67.2-88.6], p < .05).

CONCLUSION

Participants with PRISm or COPD more often developed frailty with poor reversibility. Assessing physical frailty improved risk stratification for participants with impaired spirometry for predicting increased life years.