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      Development and validation of a Hospital Frailty Risk Score focusing on older people in acute care settings using electronic hospital records: an observational study


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          Older people are increasing users of health care globally. We aimed to establish whether older people with characteristics of frailty and who are at risk of adverse health-care outcomes could be identified using routinely collected data.


          A three-step approach was used to develop and validate a Hospital Frailty Risk Score from International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnostic codes. First, we carried out a cluster analysis to identify a group of older people (≥75 years) admitted to hospital who had high resource use and diagnoses associated with frailty. Second, we created a Hospital Frailty Risk Score based on ICD-10 codes that characterised this group. Third, in separate cohorts, we tested how well the score predicted adverse outcomes and whether it identified similar groups as other frailty tools.


          In the development cohort (n=22 139), older people with frailty diagnoses formed a distinct group and had higher non-elective hospital use (33·6 bed-days over 2 years compared with 23·0 bed-days for the group with the next highest number of bed-days). In the national validation cohort (n=1 013 590), compared with the 429 762 (42·4%) patients with the lowest risk scores, the 202 718 (20·0%) patients with the highest Hospital Frailty Risk Scores had increased odds of 30-day mortality (odds ratio 1·71, 95% CI 1·68–1·75), long hospital stay (6·03, 5·92–6·10), and 30-day readmission (1·48, 1·46–1·50). The c statistics (ie, model discrimination) between individuals for these three outcomes were 0·60, 0·68, and 0·56, respectively. The Hospital Frailty Risk Score showed fair overlap with dichotomised Fried and Rockwood scales (kappa scores 0·22, 95% CI 0·15–0·30 and 0·30, 0·22–0·38, respectively) and moderate agreement with the Rockwood Frailty Index (Pearson's correlation coefficient 0·41, 95% CI 0·38–0·47).


          The Hospital Frailty Risk Score provides hospitals and health systems with a low-cost, systematic way to screen for frailty and identify a group of patients who are at greater risk of adverse outcomes and for whom a frailty-attuned approach might be useful.


          National Institute for Health Research.

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          Measuring diagnoses: ICD code accuracy.

          To examine potential sources of errors at each step of the described inpatient International Classification of Diseases (ICD) coding process. The use of disease codes from the ICD has expanded from classifying morbidity and mortality information for statistical purposes to diverse sets of applications in research, health care policy, and health care finance. By describing a brief history of ICD coding, detailing the process for assigning codes, identifying where errors can be introduced into the process, and reviewing methods for examining code accuracy, we help code users more systematically evaluate code accuracy for their particular applications. We summarize the inpatient ICD diagnostic coding process from patient admission to diagnostic code assignment. We examine potential sources of errors at each step and offer code users a tool for systematically evaluating code accuracy. Main error sources along the "patient trajectory" include amount and quality of information at admission, communication among patients and providers, the clinician's knowledge and experience with the illness, and the clinician's attention to detail. Main error sources along the "paper trail" include variance in the electronic and written records, coder training and experience, facility quality-control efforts, and unintentional and intentional coder errors, such as misspecification, unbundling, and upcoding. By clearly specifying the code assignment process and heightening their awareness of potential error sources, code users can better evaluate the applicability and limitations of codes for their particular situations. ICD codes can then be used in the most appropriate ways.
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            Frailty in relation to the accumulation of deficits.

            This review article summarizes how frailty can be considered in relation to deficit accumulation. Recalling that frailty is an age-associated, nonspecific vulnerability, we consider symptoms, signs, diseases, and disabilities as deficits, which are combined in a frailty index. An individual's frailty index score reflects the proportion of potential deficits present in that person, and indicates the likelihood that frailty is present. Although based on a simple count, the frailty index shows several interesting properties, including a characteristic rate of accumulation, a submaximal limit, and characteristic changes with age in its distribution. The frailty index, as a state variable, is able to quantitatively summarize vulnerability. Future studies include the application of network analyses and stochastic analytical techniques to the evaluation of the frailty index and the description of other state variables in relation to frailty.
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              Detection of older people at increased risk of adverse health outcomes after an emergency visit: the ISAR screening tool.

              To develop a self-report screening tool to identify older people in the emergency department (ED) of a hospital at increased risk of adverse health outcomes, including: death, admission to a nursing home or long-term hospitalization, or a clinically significant decrease in functional status. Prospective (6-month) follow-up study of a cohort of ED patients aged 65 and older. The EDs of four acute-care hospitals in Montreal, Quebec, Canada. Community-dwelling patients aged 65 and older who came to the EDs during the weekday shift over a 3-month recruitment period. Patients were excluded if they could not be interviewed either because of their medical condition or because of cognitive impairment and no other informant was available. Measures ascertained at the ED visit included: 27 self-report screening questions on social, physical, and mental risk factors; medical history; use of hospital services, medications, and alcohol; and the Older American Resources and Services (OARS) activities of daily living (ADL) scale. At follow-up, the OARS scale was readministered by telephone, and other adverse health outcomes were ascertained. Among 1673 patients who completed the follow-up measures, 488 (29.2%) had an adverse health outcome. Scale development and selection methods included logistic regression, receiver operating characteristic curves, and expert judgment. The proposed screening tool (ISAR) comprises six self-report questions on functional dependence (premorbid and acute change), recent hospitalization, impaired memory and vision, and polymedication. The tool performed well in the total cohort aged 65 and older, and in sub-groups defined by disposition (admitted or released from ED), language of questionnaire administration (French or English), information source (patient or other), and other characteristics. The ISAR is a short self-report questionnaire that can quickly identify older patients in the ED at increased risk of several adverse health outcomes and those with current disability.

                Author and article information

                Lancet (London, England)
                05 May 2018
                05 May 2018
                : 391
                : 10132
                : 1775-1782
                [a ]Department of Geriatric Medicine, Lyon Teaching Hospital, Lyon, France
                [b ]The Nuffield Trust, London, UK
                [c ]Department of Business Intelligence, Manchester University NHS Foundation Trust, Manchester, UK
                [d ]Department of Public Health, Cardiff University, Cardiff, UK
                [e ]Department of Health Policy, London School of Economics, London, UK
                [f ]Institute for Ageing, Newcastle University, Newcastle, UK
                [g ]Academic Geriatric Medicine, University of Southampton, Southampton, UK
                [h ]Data Analytics Team, The Health Foundation, London, UK
                [i ]Department of Health Sciences, College of Life Sciences, University of Leicester, Leicester, UK
                Author notes
                [* ]Correspondence to: Prof Simon Conroy, Department of Health Sciences, College of Life Sciences, University of Leicester, Centre for Medicine, Leicester, LE1 7RH, UK spc3@ 123456le.ac.uk

                Joint first authors

                © 2018 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).




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