Global Research on Cognitive Frailty: A Bibliometric and Visual Analysis of Papers Published during 2013–2021

Hui, Zhaozhao; Wang, Xiaoqin; Zhou, Ying; Ren, Xiaohan; Wang, Mingxu

doi:10.3390/ijerph19138170

Cited by 8 publications

(6 citation statements)

References 50 publications

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“…In a previous study, patients with both frailty (FP scale≥3) and MCI (MoCA score [14][15][16][17][18][19][20][21][22][23][24][25] were classified as cognitive frailty (CF), excluding Alzheimer's disease and other dementia types. 18 Following Ruan et al, FP scores of 1-5, along with MCI, were labeled as potential reversible cognitive frailty (PRCF).…”

Section: Determination Of Cognitive Frailtymentioning

confidence: 99%

“…To date, there is no standardized measurement tool for cognitive frailty (CF) 22 . In 2015, Ruan et al introduced a classification system distinguishing two CF subtypes: reversible cognitive impairment (RCF) and potential reversible cognitive frailty (PRCF), characterized by varying degrees of cognitive dysfunction.…”

Section: Introductionmentioning

confidence: 99%

“…To date, there is no standardized measurement tool for cognitive frailty (CF). 22 In 2015, Ruan et al introduced a classification system distinguishing two CF subtypes: reversible cognitive impairment (RCF) and potential reversible cognitive frailty (PRCF), characterized by varying degrees of cognitive dysfunction. RCF involves subjective cognitive decline and/or positive biomarkers linked to physiological factors, while PRCF is primarily characterized by mild cognitive impairment (MCI).…”

Section: Introductionmentioning

confidence: 99%

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Risk factors and a nomogram for predicting cognitive frailty in Chinese patients with lung cancer receiving drug therapy: A single‐center cross‐sectional study

Li,

Wang,

Zhai

et al. 2024

Thoracic Cancer

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BackgroundTo identify independent factors of cognitive frailty (CF) and construct a nomogram to predict cognitive frailty risk in patients with lung cancer receiving drug therapy.MethodsIn this cross‐sectional study, patients with lung cancer undergoing drug therapy from October 2022 to July 2023 were enrolled. The data collected includes general demographic characteristics, clinical data characteristics and assessment of tools for cognitive frailty and other factors. Logistic regression was harnessed to determine the influencing factors, R software was used to establish a nomogram model to predict the risk of cognitive frailty. The enhanced bootstrap method was employed for internal verification of the model. The performance of the nomogram was evaluated by using calibration curves, the area under the receiver operating characteristic curve, and decision curve analysis.ResultsA total of 372 patients were recruited, with a cognitive frailty prevalence of 56.2%. Age, education background, diabetes mellitus, insomnia, sarcopenia, and nutrition status were identified as independent factors. Then, a nomogram model was constructed and patients were classified into high‐ and low‐risk groups with a cutoff value of 0.552. The internal validation results revealed good concordance, calibration and discrimination. The decision curve analysis presented prominent clinical utility.ConclusionsThe prevalence of cognitive frailty was higher in lung cancer patients receiving drug therapy. The nomogram could identify the risk of cognitive frailty intuitively and simply in patients with lung cancer, so as to provide references for early screening and intervention for cognitive frailty at the early phases of drug treatment.

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Section: Determination Of Cognitive Frailtymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Risk factors and a nomogram for predicting cognitive frailty in Chinese patients with lung cancer receiving drug therapy: A single‐center cross‐sectional study

Li,

Wang,

Zhai

et al. 2024

Thoracic Cancer

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“…Sugimoto et al, (2018), following Canevelli & Cesari (2015), suggested that executive function be used as a criterion for distinguishing neurodegenerative disorders (dementias that typically present with memory impairments), from cognitive impairment (CI) related to physical frailty (PF), which more commonly shows executive function decline. Finally, a bibliometric analysis of articles mentioning CF (Hui et al, 2022) yielded 2077 publications since the definition date of 2013, across a wide range of disciplines. Papers were ranked by most frequently mentioned key words: older adult, cognitive impairment, frailty, risk, dementia, prevalence, mortality, health, and Alzheimer’s disease, in that order, with sarcopenia, mortality, prevalence, predictors, and prevention also featured.…”

Section: Introductionmentioning

confidence: 99%

Understanding exogenous factors and biological mechanisms for cognitive frailty: a multidisciplinary scoping review

Holland,

Dravecz,

Owens

et al. 2024

Preprint

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Cognitive frailty (CF) is the conjunction of cognitive impairment without dementia and physical frailty. While predictors of each element are well-researched, mechanisms of their co-occurrence have not been integrated, particularly in terms of relationships between social, psychological, and biological factors. This interdisciplinary scoping review set out to categorise a heterogenous multidisciplinary literature to identify potential pathways and mechanisms of CF, and research gaps. Studies were included if they used the definition of CF OR focused on conjunction of cognitive impairment and frailty, AND excluded studies on specific disease populations, interventions, epidemiology or prediction of mortality. Searches used Web of Science, PubMed and Science Direct. Search terms included "cognitive frailty" OR (("cognitive decline" OR "cognitive impairment") AND (frail*)), with terms to elicit mechanisms, predictors, causes, pathways and risk factors. To ensure inclusion of animal and cell models, keywords such as "behavioural" or "cognitive decline" or "senescence", were added. 206 papers were included. Descriptive analysis provided high-level categorisation of determinants from social and environmental through psychological to biological. Patterns distinguishing CF from Alzheimer's disease were identified and social and psychological moderators and mediators of underlying biological and physiological changes and of trajectories of CF development were suggested as foci for further research.

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“…However, there is no uni ed measurement tool for CF so far [24]. In 2015, Ruan et al introduced a classi cation system, identifying two distinct subtypes of CF, reversible cognitive impairment (RCF) and potential reversible cognitive frailty (PRCF), characterized by differing degrees of cognitive dysfunction.…”

Section: Introductionmentioning

confidence: 99%

Risk factors and a nomogram for predicting cognitive frailty in Chinese patients with lung cancer receiving drug therapy: a single-center cross-sectional study

Li,

Wang,

Zhai

et al. 2023

Preprint

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Purpose To identify independent factors of cognitive frailty and construct a nomogram to predict cognitive frailty risk in patients with lung cancer receiving drug therapy. Methods This is a cross-sectional study that enrolled patients with lung cancer undergoing drug therapy from October 2022 to July 2023. The collected data includes general demographic characteristics, clinical data characteristics and assessment of tools for cognitive frailty and other factors. Logistic regression was harnessed to determine the influencing factors, R software was used to establish a nomogram model to predict the risk of cognitive frailty. The enhanced Bootstrap method was employed for internal verification of the model. The performance of the nomogram was evaluated by using calibration curves, the area under the receiver operating characteristic curve, and decision curve analysis. Results A total of 372 patients were recruited, with a CF prevalence of 56.2%. Age, education background, diabetes Mellitus, insomnia, sarcopenia, and nutrition status were identified as independent factors by multivariate logistic regression analysis. Then, a nomogram model was constructed and patients were classified into high- and low-risk groups with a cutoff value of 0.552. For the entire cohort, the result of Hosmer-Lemeshow good of fit test was χ2 = 3.685 (P = 0.884), the area under the curve was 0.764 (95%CI:0.716–0.812), and the calibration curves presented good concordance with actual observations, which were consistent in both the training set and the validation set. Additionally, decision curve analysis demonstrated that the application of this nomogram could help improve clinical outcomes. Conclusions The prevalence of cognitive frailty was higher in lung cancer patients receiving drug therapy. The nomogram could identify the risk of cognitive frailty intuitively and simply in patients with lung cancer, so as to provide references for early screening and intervention for cognitive frailty at the early phases of drug treatment.

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Global Research on Cognitive Frailty: A Bibliometric and Visual Analysis of Papers Published during 2013–2021

Cited by 8 publications

References 50 publications

Risk factors and a nomogram for predicting cognitive frailty in Chinese patients with lung cancer receiving drug therapy: A single‐center cross‐sectional study

Risk factors and a nomogram for predicting cognitive frailty in Chinese patients with lung cancer receiving drug therapy: A single‐center cross‐sectional study

Understanding exogenous factors and biological mechanisms for cognitive frailty: a multidisciplinary scoping review

Risk factors and a nomogram for predicting cognitive frailty in Chinese patients with lung cancer receiving drug therapy: a single-center cross-sectional study

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