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

Abstract

ObjectivesPopulation ageing may result in increased comorbidity, functional dependence and poor quality of life. Mechanisms and pathophysiology underlying frailty have not been fully elucidated, thus absolute consensus on an operational definition for frailty is lacking. Frailty scores in the acute medical care setting have poor predictive power for clinically relevant outcomes. We explore the utility of frailty syndromes (as recommended by national guidelines) as a risk prediction model for the elderly in the acute care setting.SettingEnglish Secondary Care emergency admissions to National Health Service (NHS) acute providers.ParticipantsThere were N=2 099 252 patients over 65 years with emergency admission to NHS acute providers from 01/01/2012 to 31/12/2012 included in the analysis.Primary and secondary outcome measuresOutcomes investigated include inpatient mortality, 30-day emergency readmission and institutionalisation. We used pseudorandom numbers to split patients into train (60%) and test (40%). Receiver operator characteristic (ROC) curves and ordering the patients by deciles of predicted risk was used to assess model performance. Using English Hospital Episode Statistics (HES) data, we built multivariable logistic regression models with independent variables based on frailty syndromes (10th revision International Statistical Classification of Diseases, Injuries and Causes of Death (ICD-10) coding), demographics and previous hospital utilisation. Patients included were those >65 years with emergency admission to acute provider in England (2012).ResultsFrailty syndrome models exhibited ROC scores of 0.624–0.659 for inpatient mortality, 0.63–0.654 for institutionalisation and 0.57–0.63 for 30-day emergency readmission.ConclusionsFrailty syndromes are a valid predictor of outcomes relevant to acute care. The models predictive power is in keeping with other scores in the literature, but is a simple, clinically relevant and potentially more acceptable measurement for use in the acute care setting. Predictive powers of the score are not sufficient for clinical use.

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

“…(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.…”

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

“…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].…”

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

“…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 ).…”

“…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.…”

“…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?…”