scholarly journals External validation of the electronic Frailty Index using the population of Wales within the Secure Anonymised Information Linkage Databank

2019 ◽  
Vol 48 (6) ◽  
pp. 922-926 ◽  
Author(s):  
Joe Hollinghurst ◽  
Richard Fry ◽  
Ashley Akbari ◽  
Andy Clegg ◽  
Ronan A Lyons ◽  
...  
Keyword(s):  
Cox Regression ◽  
Care Home ◽  
Frailty Index ◽  
External Validation ◽  
Care Services ◽  
Primary Care Data ◽  
Hazard Ratios ◽  
Health And Social Care ◽  
National Implementation ◽  
Transition Times

Abstract Background frailty has major implications for health and social care services internationally. The development, validation and national implementation of the electronic Frailty Index (eFI) using routine primary care data has enabled change in the care of older people living with frailty in England. Aims to externally validate the eFI in Wales and assess new frailty-related outcomes. Study design and setting retrospective cohort study using the Secure Anonymised Information Linkage (SAIL) Databank, comprising 469,000 people aged 65–95, registered with a SAIL contributing general practice on 1 January 2010. Methods four categories (fit; mild; moderate and severe) of frailty were constructed using recognised cut points from the eFI. We calculated adjusted hazard ratios (HRs) from Cox regression models for validation of existing outcomes: 1-, 3- and 5-year mortality, hospitalisation, and care home admission for validation. We also analysed, as novel outcomes, 1-year mortality following hospitalisation and frailty transition times. Results HR trends for the validation outcomes in SAIL followed the original results from ResearchOne and THIN databases. Relative to the fit category, adjusted HRs in SAIL (95% CI) for 1-year mortality following hospitalisation were 1.05 (95% CI 1.03-1.08) for mild frailty, 1.24 (95% CI 1.21-1.28) for moderate frailty and 1.51 (95% CI 1.45-1.57) for severe frailty. The median time (lower and upper quartile) between frailty categories was 2,165 days (lower and upper quartiles: 1,510 and 2,831) from fit to mild, 1,155 days (lower and upper quartiles: 756 and 1,610) from mild to moderate and 898 days (lower and upper quartiles: 584 and 1,275) from moderate to severe. Conclusions further validation of the eFI showed robust predictive validity and utility for new outcomes.

scholarly journals SOCIAL AND BIOLOGICAL PREDICTORS OF HOSPITAL ADMISSIONS FOR A FALL IN THE ENGLISH LONGITUDINAL STUDY OF AGEING (ELSA)

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S60-S60
Author(s):  
Jessica G Abell ◽  
Camille Lassale ◽  
Andrew Steptoe ◽  
G David Batty ◽  
Paola Zaninotto
Keyword(s):  
Longitudinal Study ◽  
Older People ◽  
Hospital Admissions ◽  
Cox Regression ◽  
Premature Mortality ◽  
Care Services ◽  
Health And Social Care ◽  
Icd 10 ◽  
First Occurrence ◽  
English Longitudinal Study

Abstract Falls are the most frequent type of accidents among older people, with one in three people aged over 65 falling each year. Falls serious enough to result in hospital admission are especially problematic, since they can lead to an increased likelihood of future disability, loss of independence, and premature mortality. Understanding the factors that may determine the risk of experiencing a fall, which requires admission to hospital, is therefore an important priority. This paper seeks to examine this issue using Hospital Episode Statistics (HES) data – administrative data from English hospitals in the National Health Service (NHS). These data have recently been linked with the English Longitudinal Study of Ageing (ELSA). We examine the association between a range of predictors (demographic, social environment, physical and mental functioning) drawn from wave 4 of ELSA with the first occurrence of hospitalisation due to an accidental fall, identified using ICD-10 codes. Analysis using Cox regression suggest a range of factors are negatively associated with admission to hospital with diagnosis of a fall, such as living alone (HR=1.42; 95% CI: 1.19, 1.68), urinary incontinence (HR=1.33; 95% CI: 1.09, 1.61) and depressive symptoms (HR=1.50; 95% CI: 1.23, 1.82). High walking speed (HR=0.30; 95% CI: 0.23, 0.39) and good hand-grip strength (HR=0.97; 95% CI: 0.96, 0.98) were found to be protective. The prevention of serious falls amongst older people will require determinants to be identified and managed effectively by health and social care services.

scholarly journals Functional Aging Index Complements Frailty in Prediction of Entry Into Care and Mortality

2019 ◽  
Vol 74 (12) ◽  
pp. 1980-1986 ◽  
Author(s):  
Deborah Finkel ◽  
Ola Sternäng ◽  
Juulia Jylhävä ◽  
Ge Bai ◽  
Nancy L Pedersen
Keyword(s):  
Predictive Value ◽  
Cox Regression ◽  
Frailty Index ◽  
Rate Of Change ◽  
Chronological Age ◽  
Biological Aging ◽  
Growth Curve Models ◽  
Aging Index ◽  
Hazard Ratios

Abstract Background The aim of this study was to develop a functional aging index (FAI) that taps four body systems: sensory (vision and hearing), pulmonary, strength (grip strength), and movement (gait speed) and to test the predictive value of FAI for entry into care and mortality. Method Growth curve models and Cox regression models were applied to data from 1,695 individuals from three Swedish longitudinal studies of aging. Participants were aged 45–93 at intake and data from up to eight follow-up waves were available. Results The rate of change in FAI was twice as fast after age 75 as before, women demonstrated higher mean FAI, but no sex differences in rates of change with chronological age were identified. FAI predicted entry into care and mortality, even when chronological age and a frailty index were included in the models. Hazard ratios indicated that FAI was a more important predictor of entry into care for men than women, whereas it was a stronger predictor of mortality for men than women. Conclusions Measures of biological aging and functional aging differ in their predictive value for entry into care and mortality for men and women, suggesting that both are necessary for a complete picture of the aging process across genders.

scholarly journals The Simplified Comorbidity Index (SCI) - a new tool for prediction of non-relapse mortality in allogeneic HCT

2021 ◽  
Author(s):  
Roni Shouval ◽  
Joshua Alexander Fein ◽  
Christina Cho ◽  
Scott Avecilla ◽  
Josel D Ruiz ◽  
...  
Keyword(s):  
Cell Transplantation ◽  
Hematopoietic Cell Transplantation ◽  
Cox Regression ◽  
External Validation ◽  
Risk Groups ◽  
Hematopoietic Cell ◽  
Hazard Ratios ◽  
Small Set ◽  
Comorbidity Index ◽  
The Impact

Individual comorbidities have distinct contributions to non-relapse mortality (NRM) following allogeneic hematopoietic cell transplantation (allo-HCT). We studied the impact of comorbidities both individually and in combination in a single-center cohort of 573 adult patients who underwent CD34-selected allo-HCT following myeloablative conditioning. Pulmonary disease, moderate to severe hepatic comorbidity, cardiac disease of any type, and renal dysfunction were associated with increased NRM in multivariable Cox regression models. A Simplified Comorbidity Index (SCI) composed of the four comorbidities predictive of NRM, as well as age > 60 years, stratified patients into five groups with a stepwise increase in NRM. NRM rates ranged from 11.4% to 49.9% by stratum, with adjusted hazard ratios of 1.84, 2.59, 3.57, and 5.38. The SCI was also applicable in an external cohort of 230 patients who underwent allo-HCT with unmanipulated grafts following intermediate-intensity conditioning. The area under the ROC curve (AUC) of the SCI for 1-year NRM was 70.3 and 72.0 over the development and external-validation cohorts, respectively; corresponding AUCs of the Hematopoietic Cell Transplantation-specific Comorbidity Index (HCT-CI) were 61.7 and 65.7. In summary, a small set of comorbidities, aggregated into the Simplified Comorbidity Index, are highly predictive of NRM. The new index stratifies patients into distinct risk groups, was validated in an external cohort, and provides higher discrimination than the HCT-CI.

Private sector participation in health and social care services in Scotland: assessing the risk

2007 ◽  
Vol 73 (2) ◽  
pp. 275-292 ◽  
Author(s):  
Darinka Asenova ◽  
William Stein ◽  
Claire McCann ◽  
Alasdair Marshall
Keyword(s):  
Private Sector ◽  
Social Care ◽  
Care Home ◽  
Private Finance Initiative ◽  
Study Approach ◽  
Care Services ◽  
Health And Social Care ◽  
The Uk ◽  
High Level ◽  
Private Financing

The UK Government faces increased pressure to provide health and social care services more cheaply yet at a high level of quality. Increased private sector involvement in the funding and delivery of services is seen as a major part of the solution. When assessing the relative merits of approaches to private versus public sector provision, risk may be an important differentiator. This article explores some key points of comparison on risk issues and builds a framework for the assessment of risk-related issues. A twin case study approach is adopted: a care home for older people and a Private Finance Initiative (PFI) hospital. The analysis suggests that in the case of both private financing and of private delivery of health and social care services, the increased involvement of the private sector necessitates rigorous risk assessment and management.

scholarly journals Study protocol for investigating the impact of community home modification services on hospital utilisation for fall injuries: a controlled longitudinal study using data linkage

BMJ Open ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. e026290 ◽  
Author(s):  
Joe Hollinghurst ◽  
Ashley Akbari ◽  
Richard Fry ◽  
Alan Watkins ◽  
Damon Berridge ◽  
...  
Keyword(s):  
Length Of Stay ◽  
Linked Data ◽  
Social Care ◽  
Hospital Admissions ◽  
Care Home ◽  
Frailty Index ◽  
Healthy Ageing ◽  
National Database ◽  
Health And Social Care ◽  
The Impact

IntroductionThis study will evaluate the effectiveness of home adaptations, both in preventing hospital admissions due to falls for older people, and improving timely discharge. Results will provide evidence for services at the interface between health and social care, informing policies seeking to promote healthy ageing through prudent healthcare and fall prevention.Methods and analysisAll individuals living in Wales, UK, aged 60 years and over, will be included in the study using anonymised linked data from the Secure Anonymised Information Linkage Databank. We will use a national database of home modifications implemented by the charity organisation Care & Repair Cymru (C&R) from 2009 to 2017 to define an intervention cohort. We will use the electronic Frailty Index to assign individual levels of frailty (fit, mild, moderate or severe) and use these to create a comparator group (non-C&R) of people who have not received a C&R intervention. Coprimary outcomes will be quarterly numbers of emergency hospital admissions attributed to falls at home, and the associated length of stay. Secondary outcomes include the time in moving to a care home following a fall, and the indicative financial costs of care for individuals who had a fall. We will use appropriate multilevel generalised linear models to analyse the number of hospital admissions related to falls. We will use Cox proportional hazard models to compare the length of stay for fall-related hospital admissions and the time in moving to a care home between the C&R and non-C&R cohorts. We will assess the impact per frailty group, correct for population migration and adjust for confounding variables. Indicative costs will be calculated using financial codes for individual-level hospital stays. Results will provide evidence for services at the interface between health and social care, informing policies seeking to promote healthy ageing through prudent healthcare and prevention.Ethics and disseminationInformation governance requirements for the use of record-linked data have been approved and only anonymised data will be used in our analysis. Our results will be submitted for publication in peer-reviewed journals. We will also work with lay members and the knowledge transfer team at Swansea University to create communication and dissemination materials on key findings.

scholarly journals A comparison of two national frailty scoring systems

2020 ◽  
Author(s):  
Joe Hollinghurst ◽  
Gemma Housley ◽  
Alan Watkins ◽  
Andrew Clegg ◽  
Thomas Gilbert ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Care Home ◽  
Frailty Index ◽  
Scoring Systems ◽  
Weighted Kappa ◽  
Pearson’S Correlation ◽  
Pearson’S Correlation Coefficient ◽  
Hazard Ratios ◽  
Pearson's Correlation ◽  
Pearson's Correlation Coefficient

Abstract Background The electronic Frailty Index (eFI) has been developed in primary care settings. The Hospital Frailty Risk Score (HFRS) was derived using secondary care data. Objective Compare the two different tools for identifying frailty in older people admitted to hospital. Design and Setting Retrospective cohort study using the Secure Anonymised Information Linkage Databank, comprising 126,600 people aged 65+ who were admitted as an emergency to hospital in Wales from January 2013 up until December 2017. Methods Pearson’s correlation coefficient and weighted kappa were used to assess the correlation between the tools. Cox and logistic regression were used to estimate hazard ratios (HRs) and odds ratios (ORs). The Concordance statistic and area under the receiver operating curves (AUROC) were estimated to determine discrimination. Results Pearson’s correlation coefficient was 0.26 and the weighted kappa was 0.23. Comparing the highest to the least frail categories in the two scores the HRs for 90-day mortality, 90-day emergency readmission and care home admissions within 1-year using the HFRS were 1.41, 1.69 and 4.15 for the eFI 1.16, 1.63 and 1.47. Similarly, the ORs for inpatient death, length of stay greater than 10 days and readmission within 30-days were 1.44, 2.07 and 1.52 for the HFRS, and 1.21, 1.21 and 1.44 for the eFI. AUROC was determined as having no clinically relevant difference between the tools. Conclusions The eFI and HFRS have a low correlation between their scores. The HRs and ORs were higher for the increasing frailty categories for both the HFRS and eFI.

scholarly journals Construction and Validation of a Frailty Index in Primary Care in Italy: The Health-Search Frailty Index

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 533-534
Author(s):  
Davide Vetrano ◽  
Alberto Zucchelli ◽  
Graziano Onder ◽  
Roberto Bernabei ◽  
Laura Fratiglioni ◽  
...  
Keyword(s):  
Primary Care ◽  
Frailty Index ◽  
External Validation ◽  
Geographical Area ◽  
Population Level ◽  
Survival Models ◽  
National Study ◽  
Personalized Care ◽  
Primary Care Data ◽  
Data Driven Approach

Abstract Recognizing frailty in primary care is important to implement personalized care pathways and for prognostication. The aim of this study was to build and validate a frailty index based on routinely collected primary care data in Italy. We used clinical data from 308,280 Italian primary care patients 60+ with at least 5 years of follow-up, part of the Health Search Database. A heuristic algorithm was used to select the deficits to be included in a highly performant frailty index. The fitness of the index was assessed through the c-statistics derived by survival models. Results were externally validated using the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K). After testing 3.4 million of deficits combinations, 25 deficits were selected to be included in the Health Search Frailty Index (HS-FI). After adjusting by sex, age and geographical area, the HS-FI was associated with 5-year mortality (HR per 0.1 increase 1.99; 95%CI 1.95-2.02) and hospitalization rate (HR per 0.1 increase 1.25; 95%CI 1.23-1.27). In the external validation cohort, HS-FI independently predicted mortality, hospitalization, incident disability, incident dementia, and incident falls. This is the first frailty index built following a data-driven approach, using national representative primary care data. The implementation of such tool – derived by routinely collected data – in primary care software will ease the prompt, comparable and reliable recognition of frailty at the population level.

scholarly journals A radiomics-based model for predicting prognosis of locally advanced gastric cancer in the preoperative setting

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jaeseung Shin ◽  
Joon Seok Lim ◽  
Yong-Min Huh ◽  
Jie-Hyun Kim ◽  
Woo Jin Hyung ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Advanced Gastric Cancer ◽  
Cox Regression ◽  
Characteristic Curve ◽  
External Validation ◽  
Locally Advanced ◽  
Clinical Model ◽  
Locally Advanced Gastric Cancer ◽  
Contrast Enhanced Ct ◽  
Preoperative Ct

AbstractThis study aims to evaluate the performance of a radiomic signature-based model for predicting recurrence-free survival (RFS) of locally advanced gastric cancer (LAGC) using preoperative contrast-enhanced CT. This retrospective study included a training cohort (349 patients) and an external validation cohort (61 patients) who underwent curative resection for LAGC in 2010 without neoadjuvant therapies. Available preoperative clinical factors, including conventional CT staging and endoscopic data, and 438 radiomic features from the preoperative CT were obtained. To predict RFS, a radiomic model was developed using penalized Cox regression with the least absolute shrinkage and selection operator with ten-fold cross-validation. Internal and external validations were performed using a bootstrapping method. With the final 410 patients (58.2 ± 13.0 years-old; 268 female), the radiomic model consisted of seven selected features. In both of the internal and the external validation, the integrated area under the receiver operating characteristic curve values of both the radiomic model (0.714, P < 0.001 [internal validation]; 0.652, P = 0.010 [external validation]) and the merged model (0.719, P < 0.001; 0.651, P = 0.014) were significantly higher than those of the clinical model (0.616; 0.594). The radiomics-based model on preoperative CT images may improve RFS prediction and high-risk stratification in the preoperative setting of LAGC.

scholarly journals Genetic co-expression networks contribute to creating predictive model and exploring novel biomarkers for the prognosis of breast cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chi-Ming Chu ◽  
Huan-Ming Hsu ◽  
Chi-Wen Chang ◽  
Yuan-Kuei Li ◽  
Yu-Jia Chang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prediction Models ◽  
Reference Model ◽  
External Validation ◽  
Risk Scores ◽  
Consistent Finding ◽  
Hazard Ratios ◽  
Genetic Predictors ◽  
Novel Biomarkers ◽  
Cox Proportional Hazard Regression

AbstractGenetic co-expression network (GCN) analysis augments the understanding of breast cancer (BC). We aimed to propose GCN-based modeling for BC relapse-free survival (RFS) prediction and to discover novel biomarkers. We used GCN and Cox proportional hazard regression to create various prediction models using mRNA microarray of 920 tumors and conduct external validation using independent data of 1056 tumors. GCNs of 34 identified candidate genes were plotted in various sizes. Compared to the reference model, the genetic predictors selected from bigger GCNs composed better prediction models. The prediction accuracy and AUC of 3 ~ 15-year RFS are 71.0–81.4% and 74.6–78% respectively (rfm, ACC 63.2–65.5%, AUC 61.9–74.9%). The hazard ratios of risk scores of developing relapse ranged from 1.89 ~ 3.32 (p < 10–8) over all models under the control of the node status. External validation showed the consistent finding. We found top 12 co-expressed genes are relative new or novel biomarkers that have not been explored in BC prognosis or other cancers until this decade. GCN-based modeling creates better prediction models and facilitates novel genes exploration on BC prognosis.

Fracture Risk in Patients with Myasthenia Gravis: A Population-Based Cohort Study

2021 ◽  
pp. 1-8
Author(s):  
Charles Kassardjian ◽  
Jessica Widdifield ◽  
J. Michael Paterson ◽  
Alexander Kopp ◽  
Chenthila Nagamuthu ◽  
...  
Keyword(s):  
Myasthenia Gravis ◽  
Lower Risk ◽  
Cox Regression ◽  
Large Population ◽  
Population Based ◽  
Osteoporosis Prevention ◽  
Inception Cohort ◽  
Hazard Ratios ◽  
Multiple Covariates ◽  
Region Of Residence

Background: Prednisone is a common treatment for myasthenia gravis (MG), and osteoporosis is a known potential risk of chronic prednisone therapy. Objective: Our aim was to evaluate the risk of serious fractures in a population-based cohort of MG patients. Methods: An inception cohort of patients with MG was identified from administrative health data in Ontario, Canada between April 1, 2002 and December 31, 2015. For each MG patient, we matched 4 general population comparators based on age, sex, and region of residence. Fractures were identified through emergency department and hospitalization data. Crude overall rates and sex-specific rates of fractures were calculated for the MG and comparator groups, as well as rates of specific fractures. Adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox regression. Results: Among 3,823 incident MG patients (followed for a mean of 5 years), 188 (4.9%) experienced a fracture compared with 741 (4.8%) fractures amongst 15,292 matched comparators. Crude fracture rates were not different between the MG cohort and matched comparators (8.71 vs. 7.98 per 1000 patient years), overall and in men and women separately. After controlling for multiple covariates, MG patients had a significantly lower risk of fracture than comparators (HR 0.74, 95% CI 0.63–0.88). Conclusions: In this large, population-based cohort of incident MG patients, MG patients were at lower risk of a major fracture than comparators. The reasons for this finding are unclear but may highlight the importance osteoporosis prevention.

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