Frailsafe: from conception to national breakthrough collaborative

The number of people aged over 60 years worldwide is projected to rise from 605 million in 2000 to almost 2 billion by 2050, while those over 80 years will quadruple to 395 million. Two-thirds of UK acute hospital admissions are over 65, the highest consultation rate in general practice is in those aged 85-89 and the average age of elective surgical patients is increasing. Adjusting medical systems to meet the demographic imperative has been recognised by the World Health Organisation to be the next global healthcare priority and is a key feature of discussions on policy, health services structures, workforce reconfiguration and frontline care delivery

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電気通信大学201

An undue emphasis on rural older adults in the Chief Medical Officer's annual report 2023?

The Chief Medical Officer's annual report 2023 presents an incomplete and skewed picture of the geography of older people in England. We show that there are higher absolute numbers of older people in urban areas in England and Wales, in contrast to key messages from the CMO report which suggest greater need in rural areas based on relative metrics. The absolute size of the urban-rural difference in the population of older people is projected to grow by 2043. Older adults in urban areas are much more likely to live in deprived areas than older adults in rural areas. The absolute number and prevalence of older adults in poorer health is also higher in urban areas, leading to greater healthcare needs. Policy-makers need to consider both absolute and relative demographic trends as well as making use of direct measures of health when planning how healthcare services for older adults are distributed geographically in England

Reduced Amygdala and Ventral Striatal Activity to Happy Faces in PTSD Is Associated with Emotional Numbing

There has been a growing recognition of the importance of reward processing in PTSD, yet little is known of the underlying neural networks. This study tested the predictions that (1) individuals with PTSD would display reduced responses to happy facial expressions in ventral striatal reward networks, and (2) that this reduction would be associated with emotional numbing symptoms. 23 treatment-seeking patients with Posttraumatic Stress Disorder were recruited from the treatment clinic at the Centre for Traumatic Stress Studies, Westmead Hospital, and 20 trauma-exposed controls were recruited from a community sample. We examined functional magnetic resonance imaging responses during the presentation of happy and neutral facial expressions in a passive viewing task. PTSD participants rated happy facial expression as less intense than trauma-exposed controls. Relative to controls, PTSD participants revealed lower activation to happy (-neutral) faces in ventral striatum and and a trend for reduced activation in left amygdala. A significant negative correlation was found between emotional numbing symptoms in PTSD and right ventral striatal regions after controlling for depression, anxiety and PTSD severity. This study provides initial evidence that individuals with PTSD have lower reactivity to happy facial expressions, and that lower activation in ventral striatal-limbic reward networks may be associated with symptoms of emotional numbing

Patient and carer experiences of clinical uncertainty and deterioration, in the face of limited reversibility:A comparative observational study of the AMBER care bundle

BACKGROUND: Clinical uncertainty is emotionally challenging for patients and carers and creates additional pressures for those clinicians in acute hospitals. The AMBER care bundle was designed to improve care for patients identified as clinically unstable, deteriorating, with limited reversibility and at risk of dying in the next 1-2 months.AIM: To examine the experience of care supported by the AMBER care bundle compared to standard care in the context of clinical uncertainty, deterioration and limited reversibility.DESIGN: A comparative observational mixed-methods study using semi-structured qualitative interviews and a followback survey.SETTING/PARTICIPANTS: Three large London acute tertiary National Health Service hospitals. Nineteen interviews with 23 patients and carers (10 supported by AMBER care bundle and 9 standard care). Surveys completed by next of kin of 95 deceased patients (59 AMBER care bundle and 36 standard care).RESULTS: The AMBER care bundle was associated with increased frequency of discussions about prognosis between clinicians and patients (χ(2) = 4.09, p = 0.04), higher awareness of their prognosis by patients (χ(2) = 4.29, p = 0.04) and lower clarity in the information received about their condition (χ(2) = 6.26, p = 0.04). Although the consistency and quality of communication were not different between the two groups, those supported by the AMBER care bundle described more unresolved concerns about caring for someone at home.CONCLUSION: Awareness of prognosis appears to be higher among patients supported by the AMBER care bundle, but in this small study this was not translated into higher quality communication, and information was judged less easy to understand. Adequately powered comparative evaluation is urgently needed.</p

Data consistency in the English Hospital Episodes Statistics database

BACKGROUND: To gain maximum insight from large administrative healthcare datasets it is important to understand their data quality. Although a gold standard against which to assess criterion validity rarely exists for such datasets, internal consistency can be evaluated. We aimed to identify inconsistencies in the recording of mandatory International Statistical Classification of Diseases and Related Health Problems, tenth revision (ICD-10) codes within the Hospital Episodes Statistics dataset in England. METHODS: Three exemplar medical conditions where recording is mandatory once diagnosed were chosen: autism, type II diabetes mellitus and Parkinson's disease dementia. We identified the first occurrence of the condition ICD-10 code for a patient during the period April 2013 to March 2021 and in subsequent hospital spells. We designed and trained random forest classifiers to identify variables strongly associated with recording inconsistencies. RESULTS: For autism, diabetes and Parkinson's disease dementia respectively, 43.7%, 8.6% and 31.2% of subsequent spells had inconsistencies. Coding inconsistencies were highly correlated with non-coding of an underlying condition, a change in hospital trust and greater time between the spell with the first coded diagnosis and the subsequent spell. For patients with diabetes or Parkinson's disease dementia, the code recording for spells without an overnight stay were found to have a higher rate of inconsistencies. CONCLUSIONS: Data inconsistencies are relatively common for the three conditions considered. Where these mandatory diagnoses are not recorded in administrative datasets, and where clinical decisions are made based on such data, there is potential for this to impact patient care

Variability in COVID-19 in-hospital mortality rates between national health service trusts and regions in England: A national observational study for the Getting It Right First Time Programme

Background A key first step in optimising COVID-19 patient outcomes during future case-surges is to learn from the experience within individual hospitals during the early stages of the pandemic. The aim of this study was to investigate the extent of variation in COVID-19 outcomes between National Health Service (NHS) hospital trusts and regions in England using data from March–July 2020. Methods This was a retrospective observational study using the Hospital Episode Statistics administrative dataset. Patients aged ≥ 18 years who had a diagnosis of COVID-19 during a hospital stay in England that was completed between March 1st and July 31st, 2020 were included. In-hospital mortality was the primary outcome of interest. In secondary analysis, critical care admission, length of stay and mortality within 30 days of discharge were also investigated. Multilevel logistic regression was used to adjust for covariates. Findings There were 86,356 patients with a confirmed diagnosis of COVID-19 included in the study, of whom 22,944 (26.6%) died in hospital with COVID-19 as the primary cause of death. After adjusting for covariates, the extent of the variation in-hospital mortality rates between hospital trusts and regions was relatively modest. Trusts with the largest baseline number of beds and a greater proportion of patients admitted to critical care had the lowest in-hospital mortality rates. Interpretation There is little evidence of clustering of deaths within hospital trusts. There may be opportunities to learn from the experience of individual trusts to help prepare hospitals for future case-surges

Accuracy of Risk Estimates from the iPrevent Breast Cancer Risk Assessment and Management Tool.

BACKGROUND: iPrevent is an online breast cancer (BC) risk management decision support tool. It uses an internal switching algorithm, based on a woman's risk factor data, to estimate her absolute BC risk using either the International Breast Cancer Intervention Study (IBIS) version 7.02, or Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm version 3 models, and then provides tailored risk management information. This study assessed the accuracy of the 10-year risk estimates using prospective data. METHODS: iPrevent-assigned 10-year invasive BC risk was calculated for 15 732 women aged 20-70 years and without BC at recruitment to the Prospective Family Study Cohort. Calibration, the ratio of the expected (E) number of BCs to the observed (O) number and discriminatory accuracy were assessed. RESULTS: During the 10 years of follow-up, 619 women (3.9%) developed BC compared with 702 expected (E/O = 1.13; 95% confidence interval [CI] =1.05 to 1.23). For women younger than 50 years, 50 years and older, and BRCA1/2-mutation carriers and noncarriers, E/O was 1.04 (95% CI = 0.93 to 1.16), 1.24 (95% CI = 1.11 to 1.39), 1.13 (95% CI = 0.96 to 1.34), and 1.13 (95% CI = 1.04 to 1.24), respectively. The C-statistic was 0.70 (95% CI = 0.68 to 0.73) overall and 0.74 (95% CI = 0.71 to 0.77), 0.63 (95% CI = 0.59 to 0.66), 0.59 (95% CI = 0.53 to 0.64), and 0.65 (95% CI = 0.63 to 0.68), respectively, for the subgroups above. Applying the newer IBIS version 8.0b in the iPrevent switching algorithm improved calibration overall (E/O = 1.06, 95% CI = 0.98 to 1.15) and in all subgroups, without changing discriminatory accuracy. CONCLUSIONS: For 10-year BC risk, iPrevent had good discriminatory accuracy overall and was well calibrated for women aged younger than 50 years. Calibration may be improved in the future by incorporating IBIS version 8.0b

iPrevent®: a tailored, web-based, decision support tool for breast cancer risk assessment and management

We aimed to develop a user-centered, web-based, decision support tool for breast cancer risk assessment and personalized risk management. Using a novel model choice algorithm, iPrevent&reg; selects one of two validated breast cancer risk estimation models (IBIS or BOADICEA), based on risk factor data entered by the user. Resulting risk estimates are presented in simple language and graphic formats for easy comprehension. iPrevent&reg; then presents risk-adapted, evidence-based, guideline-endorsed management options. Development was an iterative process with regular feedback from multidisciplinary experts and consumers. To verify iPrevent&reg;, risk factor data for 127 cases derived from the Australian Breast Cancer Family Study were entered into iPrevent&reg;, IBIS (v7.02), and BOADICEA (v3.0). Consistency of the model chosen by iPrevent&reg; (i.e., IBIS or BOADICEA) with the programmed iPrevent&reg; model choice algorithm was assessed. Estimated breast cancer risks from iPrevent&reg; were compared with those attained directly from the chosen risk assessment model (IBIS or BOADICEA). Risk management interventions displayed by iPrevent&reg; were assessed for appropriateness. Risk estimation model choice was 100% consistent with the programmed iPrevent&reg;logic. Discrepant 10-year and residual lifetime risk estimates of &gt;1% were found for 1 and 4 cases, respectively, none was clinically significant (maximal variation 1.4%). Risk management interventions suggested by iPrevent&reg; were 100% appropriate. iPrevent&reg; successfully integrates the IBIS and BOADICEA risk assessment models into a decision support tool that provides evidence-based, risk-adapted risk management advice. This may help to facilitate precision breast cancer prevention discussions between women and their healthcare providers