Development and validation of a hospital frailty risk score focusing on older people in acute care settings using electronic hospital records; an observational study

s published since July 2011. We identified 264 relevant publications where the primary analysis involved the use of HES APC data, and a further 130 papers where HES data had been linked to cohorts created in other datasets. The annual number of publications using HES APC data has increased from 2 in 1993 to 88 in 2015. Published studies using HES APC data have covered a diverse range of topics. They have explored the incidence of conditions across regions and over time. They have also examined cross-sectional and longitudinal patterns of treatment by organization, including comparing NHS and privately contracted providers or regions, both from descriptive and analytical perspectives. Regional comparisons have included evaluating the impact of clinical evidence or guidelines as well as health care policies. They have examined the outcome of medical as well as surgical therapies (such as survival, short-term postoperative mortality, complications, reoperation and hospital readmissions), with some seeking to identify factors that are associated with these outcomes, in terms of both patient characteristics and organizational factors such as surgical volume or day of week. Methodological studies include creating coding frameworks, applying comorbidity scores, developing risk prediction models and using look-back methods to impute missing data items. Many high profile routinely produced reports on the quality of secondary care are based on HES APC data. These include hospital mortality monitoring reports produced by NHS Digital and commercial organizations, and research reports by independent think-tanks and Royal Medical Colleges. Strengths and weaknesses

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Data Resource Profile: Hospital Episode Statistics Admitted Patient Care (HES APC).

Background Frailty is a key determinant of health status and outcomes of health care interventions in older adults that is not readily measured in Medicare data. This study aimed to develop and validate a claims-based frailty index (CFI). Methods We used data from Medicare Current Beneficiary Survey 2006 (development sample: n = 5,593) and 2011 (validation sample: n = 4,424). A CFI was developed using the 2006 claims data to approximate a survey-based frailty index (SFI) calculated from the 2006 survey data as a reference standard. We compared CFI to combined comorbidity index (CCI) in the ability to predict death, disability, recurrent falls, and health care utilization in 2007. As validation, we calculated a CFI using the 2011 claims data to predict these outcomes in 2012. Results The CFI was correlated with SFI (correlation coefficient: 0.60). In the development sample, CFI was similar to CCI in predicting mortality (C statistic: 0.77 vs. 0.78), but better than CCI for disability, mobility impairment, and recurrent falls (C statistic: 0.62-0.66 vs. 0.56-0.60). Although both indices similarly explained the variation in hospital days, CFI outperformed CCI in explaining the variation in skilled nursing facility days. Adding CFI to age, sex, and CCI improved prediction. In the validation sample, CFI and CCI performed similarly for mortality (C statistic: 0.71 vs. 0.72). Other results were comparable to those from the development sample. Conclusion A novel frailty index can measure the risk for adverse health outcomes that is not otherwise quantified using demographic characteristics and traditional comorbidity measures in Medicare data.

Measuring Frailty in Medicare Data: Development and Validation of a Claims-Based Frailty Index.

Objective to validate STOPPFrail, a list of explicit criteria for potentially inappropriate medication (PIM) use in frail older adults with limited life expectancy. Design a Delphi consensus survey of an expert panel comprising academic geriatricians, clinical pharmacologists, palliative care physicians, old age psychiatrists, general practitioners and clinical pharmacists. Setting Ireland. Subjects seventeen panellists. Methods STOPPFrail criteria were initially created by the authors based on clinical experience and literature appraisal. Criteria were organised according to the physiological system; each criterion accompanied by an explanation. Using Delphi consensus methodology, panellists ranked their agreement with each criterion on a 5-point Likert scale and provided written feedback. Criteria with a median Likert response of 4/5 (agree/strongly agree) and a 25th centile of ≥4 were included in the final list. Results all panellists completed three Delphi rounds. Thirty criteria were proposed, 27 were accepted. The first two criteria suggest deprescribing medications without indication or where compliance is poor. The remaining 25 criteria include lipid-lowering therapies, alpha-blockers for hypertension, anti-platelets, neuroleptics, memantine, proton-pump inhibitors, H2-receptor antagonists, anti-spasmodic agents, theophylline, leukotriene antagonists, calcium supplements, bone anti-resorptive therapy, selective oestrogen receptor modulators, non-steroidal anti-inflammatories, corticosteroids, 5-alpha-reductase inhibitors, alpha-1-selective blockers, muscarinic antagonists, oral diabetic agents, ACE-inhibitors, angiotensin receptor blockers, systemic oestrogens, multivitamins, nutritional supplements and prophylactic antibiotics. Consensus could not be reached on the inclusion of acetylcholinesterase inhibitors. Full consensus was reached on the exclusion of anticoagulants and antidepressants from the list. Conclusion STOPPFrail comprises 27 criteria relating to medications that are potentially inappropriate in frail older patients with limited life expectancy. STOPPFrail may assist physicians in deprescribing medications in these patients.

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STOPPFrail (Screening Tool of Older Persons Prescriptions in Frail Adults with Limited Life Expectancy): Consensus Validation

https://cyberleninka.org/article/n/865223.pdf

Advance care planning: A systematic review of randomised controlled trials conducted with older adults

Objectives Population ageing has been associated with an increase in comorbid chronic disease, functional dependence, disability and associated higher health care costs. Frailty Syndromes have been proposed as a way to define this group within older persons. We explore whether frailty syndromes are a reliable methodology to quantify clinically significant frailty within hospital settings, and measure trends and geospatial variation using English secondary care data set Hospital Episode Statistics (HES). Setting National English Secondary Care Administrative Data HES. Participants All 50 540 141 patient spells for patients over 65 years admitted to acute provider hospitals in England (January 2005—March 2013) within HES. Primary and secondary outcome measures We explore the prevalence of Frailty Syndromes as coded by International Statistical Classification of Diseases, Injuries and Causes of Death (ICD-10) over time, and their geographic distribution across England. We examine national trends for admission spells, inpatient mortality and 30-day readmission. Results A rising trend of admission spells was noted from January 2005 to March 2013(daily average admissions for month rising from over 2000 to over 4000). The overall prevalence of coded frailty is increasing (64 559 spells in January 2005 to 150 085 spells by Jan 2013). The majority of patients had a single frailty syndrome coded (10.2% vs total burden of 13.9%). Cognitive impairment and falls (including significant fracture) are the most common frailty syndromes coded within HES. Geographic variation in frailty burden was in keeping with known distribution of prevalence of the English elderly population and location of National Health Service (NHS) acute provider sites. Overtime, in-hospital mortality has decreased (>65 years) whereas readmission rates have increased (esp.>85 years). Conclusions This study provides a novel methodology to reliably quantify clinically significant frailty. Applications include evaluation of health service improvement over time, risk stratification and optimisation of services.

https://bmjopen.bmj.com/content/bmjopen/5/10/e008456.full.pdf

Quantifying the prevalence of frailty in English hospitals

Objectives Population 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. Setting English Secondary Care emergency admissions to National Health Service (NHS) acute providers. Participants There 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 measures Outcomes 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). Results Frailty 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. Conclusions Frailty 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.

https://bmjopen.bmj.com/content/bmjopen/5/10/e008457.full.pdf

Jty Soong

Papers

Quantifying the prevalence of frailty in English hospitals

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