Frailty measurement in routinely collected data: challenges and benefits
Authors: John Tshon Yit Soong
Journal: The Lancet Healthy Longevity (2021)

A common demographic change linked to population ageing is frailty. Frailty has serious consequences and is associated with increased mortality, functional decline, poor quality of life, high resource utilisation, and increased cost to the health system. Consensus is growing that the consequences of frailty are preventable or could be at least delayed.1Soong JTY Poots AJ Bell D Finding consensus on frailty assessment in acute care through Delphi method.BMJ Open. 2016; 6e012904Crossref PubMed Scopus (12) Google Scholar Robust management of frailty requires timely widespread recognition linked to appropriate interventions to avoid further deterioration and prevention of future decline. However, health systems worldwide struggle to reliably identify patients with this common syndrome. Traditional frailty measurements have their benefits and challenges. Scores based on clinical judgement (eg, Clinical Frailty Scale) are contingent on front-line clinicians recognising frailty when they see it. However, studies into reliability and inter-rater agreement have shown low consistency and accuracy,2Speir A Defining frailty: 'I know it when I see it'.J Thorac Cardiovasc Surg. 2015; 149: 875-876Summary Full Text Full Text PDF PubMed Scopus (6) Google Scholar with additional concerns raised over subjectivity and lack of comprehensiveness. Single measures of physical performance (eg, Timed-get-up-and-Go) or a set of physical characteristics (eg, Frailty Phenotype) are practical, clinically acceptable, and well validated to predict poor outcomes in community-dwelling older people.3Fried LP Tangen CM Walston J et al.Frailty in older adults: evidence for a phenotype.J Gerontol A Biol Sci Med Sci. 2001; 56: M146-M156Crossref PubMed Google Scholar, 4Podsiadlo D Richardson S The timed 'Up & Go': a test of basic functional mobility for frail elderly persons.J Am Geriatr Soc. 1991; 39: 142-148Crossref PubMed Scopus (7969) Google Scholar However, these measures require specialised equipment not readily available in most clinical settings (eg, dynamometer), are not always clinically feasible (eg, for patients with cognitive impairment), and require a manual assessment process that is subject to operator error and is resource intensive, making it difficult to scale to the population level.5Hogan DB Freiheit EA Strain LA et al.Comparing frailty measures in their ability to predict adverse outcome among older residents of assisted living.BMC Geriatr. 2012; 12: 56Crossref PubMed Scopus (68) Google Scholar Given these limitations, there is increasing interest in exploring alternative approaches to assessing frailty that use routinely collected data. These data include administrative data (operations), medical claims, prescriptions, and patient records. Historically, large-scale, high-quality data from patient records have not readily been available. More often registry6Rudd AG Lowe D Hoffman A Irwin P Pearson M Secondary prevention for stroke in the United Kingdom: results from the National Sentinel Audit of Stroke.Age Ageing. 2004; 33: 280-286Crossref PubMed Scopus (54) Google Scholar or administrative7Soong JTY Kaubryte J Liew D et al.Dr Foster global frailty score: an international retrospective observational study developing and validating a risk prediction model for hospitalised older persons from administrative data sets.BMJ Open. 2019; 9e026759Crossref PubMed Scopus (6) Google Scholar data have been used. Risk prediction models using administrative data have sought to differentiate risk by using diagnostic, procedural, and, more recently, prescribing codes. These models have the benefit of using whole population data. Additionally, administrative datasets often have rigorous validation, cleaning, and curation processes to ensure high data quality. Internationally, frailty risk prediction models developed with these datasets have been validated for important outcomes such as mortality, long length of hospital stay, emergency hospital admission, and institutionalisation.7Soong JTY Kaubryte J Liew D et al.Dr Foster global frailty score: an international retrospective observational study developing and validating a risk prediction model for hospitalised older persons from administrative data sets.BMJ Open. 2019; 9e026759Crossref PubMed Scopus (6) Google Scholar They have been used at the population level to describe frailty burden temporally and geospatially.8Soong J Poots AJ Scott S et al.Quantifying the prevalence of frailty in English hospitals.BMJ Open. 2015; 5e008456Crossref PubMed Scopus (58) Google Scholar However, these models have recognised limitations. Administrative datasets mainly rely on data available within structured data fields, which often have standardised predefined possible responses. For frailty risk models, standardised diagnostic codes have often been used. The accuracy and reliability of coding within these fields have been challenged. Additionally, not all variables associated with frailty are captured in these datasets (eg, functional impairment), challenging the comprehensiveness of resulting models. Diagnostic coding often occurs after the health-care episode, which, coupled with the data informatics processes described, can result in a long lead time, challenging the contemporaneousness of the data and their applicability. The Hospital Frailty Risk Score (HFRS) is one such score, with its development originating within the Hospital Episode Statistics inpatient database, a national database containing administrative data for all patients admitted to National Health Service hospitals in England (UK). The risk prediction model used standardised diagnostic codes, namely from the International Classification of Diseases and Related Health Problems, tenth revision. This model has been externally validated in patients with heart failure, undergoing surgery, and requiring critical care, though its methodology has been inconsistently applied. In The Lancet Healthy Longevity, Andrew Street and colleagues report results of a retrospective observational study exploring how to gain maximal utility from standardised diagnostic codes for risk stratification in older individuals by use of the HFRS.9Street A Maynou L Gilbert T Stone T Mason S Conroy S The use of linked routine data to optimise calculation of the Hospital Frailty Risk Score on the basis of previous hospital admissions: a retrospective observational cohort study.Lancet Healthy Longev. 2021; (published online. Feb 23.)https://doi.org/10.1016/S2666-7568(20)30038-6Summary Full Text Full Text PDF Google Scholar Previous studies aiming to apply the HFRS to particular cohorts have had various approaches to using diagnostic data from previous emergency admissions. Using data from 282 091 patients aged 75 years or older who had 675 155 hospital admissions, Street and colleagues constructed multiple forms of the HFRS for each patient, with each form incorporating progressively more previous admissions within a 1-year or 2-year window. The authors then assessed the ability of each form of the HFRS to predict long length of hospital stay (>10 days), in-hospital death, and 30-day readmission. Street and colleagues found that using the diagnostic codes of the index admission and of two previous admissions within the preceding 2 years gave optimal predictive value for long length of hospital stay and in-hospital mortality, and that the use of diagnostic information from additional previous admissions or preceding years did not add significantly to the predictive value. The authors also found that HFRS is not a strong predictor of 30-day emergency re-admission to hospital. Under this optimal construction, for patients with high frailty risk, the probability of long length of hospital stay was 4·3-times higher (4·2 to 4·5), 2·5-times higher (2·4 to 2·6) for in-hospital death, and–1% (−1·2 to −0·5) lower for readmission, compared with patients with low frailty risk. This study allows those who wish to apply the HFRS to their cohorts to be consistent in its application, with knowledge of optimal construction for risk stratification, and prevents additional costs and resources required for obtaining diagnostic information from being incurred to no added benefit. Additional questions remain. HFRS and other risk prediction models are heavily dependent on the quality of data coding within standardised diagnostic fields. However, variation in frequency of coding volume has been described, both within and across countries. Additionally, changes to the frequency of coding volume over time have also been described.7Soong JTY Kaubryte J Liew D et al.Dr Foster global frailty score: an international retrospective observational study developing and validating a risk prediction model for hospitalised older persons from administrative data sets.BMJ Open. 2019; 9e026759Crossref PubMed Scopus (6) Google Scholar Although this variation might represent true differences in frailty burden, it could represent differences and changes to coding quality. What is the minimal level of coding frequency for a risk prediction model? How would one account for the shift in frequency of diagnostic coding over time? How would one manage this variation within an aggregated dataset, such as the one used by Street and colleagues? As these issues plague the use of standardised diagnostic coding, efforts now turn to using unstructured data, such as the free text of medical records.10Kharrazi H Anzaldi LJ Hernandez L et al.The value of unstructured electronic health record data in geriatric syndrome case identification.J Am Geriatr Soc. 2018; 66: 1499-1507Crossref PubMed Scopus (29) Google Scholar Artificial intelligence methods are increasingly being trialled as an alternative or in parallel with structured datasets. But for now, Street and colleagues' study adds valuable insight into the optimal use of diagnostic coding within structured data fields of routinely collected administrative data. I declare no competing interests. The use of linked routine data to optimise calculation of the Hospital Frailty Risk Score on the basis of previous hospital admissions: a retrospective observational cohort studyWe recommend constructing the HFRS with diagnostic information from the current admission and from the previous two admissions in the preceding 2 years. This HFRS form was a powerful predictor of long length of stay and in-hospital mortality, but less so of emergency readmissions. Full-Text PDF Open Access