Developing and validating a risk prediction model for acute care based on frailty syndromes

Soong, Jty; Poots, Alan J; Scott, Shelley A.; Donald, K. W.; Bell, Derek

doi:10.1136/bmjopen-2015-008457

Cited by 54 publications

(77 citation statements)

References 40 publications

(43 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(53) The prevalence and predictive qualities of seven frailty syndromes (i.e., anxiety & depression, functional dependence, falls & fractures, incontinence, mobility problems, pressure ulcers, cognitive impairment [includes delirium, dementia and senility]) based on diagnostic codes abstracted from an administrative data set for hospitals in England have been reported. (54,55) The validity and reliability of approaches largely equating frailty to the presence of particular diagnoses requires additional study.…”

Section: Discussionmentioning

confidence: 99%

A Scoping Review of Frailty and Acute Care in Middle-Aged and Older Individuals with Recommendations for Future Research

et al. 2017

View full text Add to dashboard Cite

There is general agreement that frailty is a state of heightened vulnerability to stressors arising from impairments in multiple systems leading to declines in homeostatic reserve and resiliency, but unresolved issues persist about its detection, underlying pathophysiology, and relationship with aging, disability, and multimorbidity. A particularly challenging area is the relationship between frailty and hospitalization. Based on the deliberations of a 2014 Canadian expert consultation meeting and a scoping review of the relevant literature between 2005 and 2015, this discussion paper presents a review of the current state of knowledge on frailty in the acute care setting, including its prevalence and ability to both predict the occurrence and outcomes of hospitalization. The examination of the available evidence highlighted a number of specific clinical and research topics requiring additional study. We conclude with a series of consensus recommendations regarding future research priorities in this important area.

show abstract

Section: Discussionmentioning

confidence: 99%

A Scoping Review of Frailty and Acute Care in Middle-Aged and Older Individuals with Recommendations for Future Research

et al. 2017

View full text Add to dashboard Cite

show abstract

“…At population level however, describing frailty as an accumulation of deficits is informative. Given that this model is less prescriptive in its construction of frailty, it underpins the majority of the frailty assessments used in large, primary care [29,30] and administrative hospital datasets [28,33].…”

Section: What Is Frailty?mentioning

confidence: 99%

Addressing frailty in patients with breast cancer: A review of the literature

Jauhari

Gannon

Horgan

et al. 2020

European Journal of Surgical Oncology

View full text Add to dashboard Cite

a b s t r a c tVarious studies have documented variation in the management of older patients with breast cancer, and some of this variation stems from different approaches to balancing the expected benefit of different treatments, with the ability of patients to tolerate them. Frailty is an emerging concept that can help to make clinical decisions for older patients more consistent, not least by providing a measure of 'biological' ageing. This would reduce reliance on 'chronological' age, which is not a reliable guide for decisions on the appropriate breast cancer care for older patients.This article examines the potential of frailty assessment to inform on breast cancer treatments. Overall, the current evidence highlights various benefits from implementing comprehensive geriatric assessment and screening for frailty in breast cancer patients. This includes a role in supporting the selection of appropriate therapies and improving physical fitness prior to treatment. However, there are challenges in implementing routine frailty assessments in a breast cancer service. Studies have used a diverse array of frailty assessment instruments, which hampers the generalisability of research findings. Consequently, a number of issues need to be addressed to clearly establish the optimal timing of frailty assessment and the role of geriatric medicine specialists in the breast cancer care pathway.

show abstract

“…In keeping with the national method, nine groups of frailty syndromes (dementia, delirium, mobility problems, falls and fractures, pressure ulcers, incontinence, functional dependence, senility, and anxiety and depression) were coded within International statistical classification of diseases and related health problems: ICD-10 diagnostic coding groups, and within all available diagnostic fields. 6,7,9 Other variables included in the multivariable risk model include age, gender and number of admissions in preceding 12 months, as well as outcomes (Table 1 ).…”

Section: Coding Frailty and Other Variablesmentioning

confidence: 99%

“…We plot the outcomes by risk model deciles to allow for determination of model discrimination. 6 The area under the receiver operating characteristic curve (AUC) was plotted from predicted probabilities to explore predictive power.…”

Section: Risk Modelsmentioning

confidence: 99%

“…4,5 In England, large national studies explored using routinely collected operational data in the form of Hospital Episode Statistics (HES) to quantify clinical frailty, and demonstrate its association with adverse outcomes. 6,7 It is yet unknown how this model could perform at a mesolevel; can the model be used for an intended purpose of risk stratifying patients at the level of care provision, such that hospital managers and clinicians can plan resources to provide optimised care?…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Validating a methodology to measure frailty syndromes at hospital level utilising administrative data

et al. 2020

View full text Add to dashboard Cite

Background Identifying older people with clinical frailty, reliably and at scale, is a research priority. We measured frailty in older people using a novel methodology coding frailty syndromes on routinely collected administrative data, developed on a national English secondary care population, and explored its performance of predicting inpatient mortality and long length of stay at a single acute hospital. Methodology We included patient spells from Secondary User Service (SUS) data for those ≥65 years with attendance to the emergency department or admission to West Middlesex University Hospital between 01 July 2016 to 01 July 2017. We created eight groups of frailty syndromes using diagnostic coding groups. We used descriptive statistics and logistic regression to explore performance of diagnostic coding groups for the above outcomes. Results We included 17,199 patient episodes in the analysis. There was at least one frailty syndrome present in 7,004 (40.7%) patient episodes. The resultant model had moderate discrimination for inpatient mortality (area under the receiver operating characteristic curve (AUC) 0.74; 95% confi dence interval (CI) 0.72-0.76) and upper quartile length of stay (AUC 0.731; 95% CI 0.722-0.741). There was good negative predictive value for inpatient mortality (98.1%). Conclusions Coded frailty syndromes signifi cantly predict outcomes. Model diagnostics suggest the model could be used for screening of elderly patients to optimise their care.

show abstract

Developing and validating a risk prediction model for acute care based on frailty syndromes

Cited by 54 publications

References 40 publications

A Scoping Review of Frailty and Acute Care in Middle-Aged and Older Individuals with Recommendations for Future Research

A Scoping Review of Frailty and Acute Care in Middle-Aged and Older Individuals with Recommendations for Future Research

Addressing frailty in patients with breast cancer: A review of the literature

Validating a methodology to measure frailty syndromes at hospital level utilising administrative data

Contact Info

Product

Resources

About