Antihypertensive treatment is effective at reducing the risk of cardiovascular disease, but is associated with adverse events, particularly in older patients with frailty. As a result, deprescribing antihypertensive medications is recommended in some clinical guidelines despite limited evidence from a few small randomised controlled trials. The aim of the OPTIMISE2 trial is to examine the safety, efficacy and cost-effectiveness of deprescribing antihypertensive treatment in older adults with controlled systolic blood pressure, who are at higher risk of adverse events. The OPTIMISE2 trial aims to enrol 3014 participants into the trial and actively follow them up for 1 year. Participants are aged 75 years and above and taking two or more blood pressure lowering drugs, with controlled blood pressure readings (systolic blood pressure < 140 mmHg if aged 75–79 years or < 150 mmHg if aged 80+ years), frail and/or at a higher risk of serious drug-related side effects of hypotension, syncope and falls. The trial randomises (1:1) participants to either step-down medication reduction (withdrawal of one antihypertensive medication at a time, at 4-week intervals with regular monitoring of blood pressure) or usual care (no medication reduction mandated). The choice of medications to withdraw is at the discretion of participating general practitioners or prescribers. The primary outcome is emergency hospitalisation or death within 1 year of randomisation. The primary objective is to determine whether antihypertensive deprescribing is non-inferior to usual care, with up to a 5

BACKGROUND:Lung cancer screening is effective for people at higher risk of the disease, but there is no international consensus on eligibility criteria. Some programmes use risk factors; others use multivariable risk scores, which might target an older, more comorbid population and thus limit life years gained. In this study, we compare frailty, comorbidities and overall survival between different eligible populations. METHODS:Participants aged 55-74 years undergoing lung cancer risk assessment in the Yorkshire Lung Screening Trial were analysed, comparing those who met the US Preventive Services Task Force 2021 lung cancer screening criteria (USPSTF2021) criteria against established risk-based criteria currently used in screening protocols (Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial risk model (PLCOm2012) ≥1.51%, used internationally, and the Liverpool Lung Project risk model (version 2) (LLPv2) ≥2.5%, used in the UK), examining the number of individuals with frailty and comorbidities selected by each approach. In addition, risk score thresholds were set to select equivalent numbers of people screened compared with USPSTF2021. Data recorded in primary care prior to randomisation were retrospectively extracted to allow calculation of the electronic Frailty Index (eFI) and an overall comorbidity count. Frailty, comorbidity counts and 3-year overall survival were compared between these various populations. RESULTS:Of 11 994 individuals aged 55-74 undergoing risk assessment, 3502 were eligible by USPSTF2021, 3139 by PLCOm2012 ≥1.51% and 3957 by LLPv2 ≥2.5%. The proportion of individuals with moderate/severe frailty was lower for the USPSTF2021 population (10.6%) compared with PLCOm2012 ≥1.51% (13.1%, adjusted p=0.0777) and LLPv2 ≥2.5% (13.4%, adjusted p=0.0272). The USPSTF2021 identified significantly fewer individuals with multiple comorbidities (30.8%) than the PLCOm2012 (36.1%, adjusted p=0.0033) and the LLPv2 (37.3%, adjusted p=0.0001).When compared in equivalent populations, both PLCOm2012 with a threshold of 1.32%, and LLPv2 with a threshold of 2.92%, had a higher proportion of people both with moderate/severe frailty (12.6%, adjusted p=0.221 and 14.0%, adjusted p=0.0067 respectively) and multiple comorbidities (35.1%, adjusted p=0.0211 and 38.5%, adjusted p<0.0001 respectively) than USPSTF2021.There were no apparent differences in 3-year overall survival between the eligible populations overlapping 95% CIs across risk groups. CONCLUSION:These data suggest that currently used risk models identify populations with a small increase in moderate/severe frailty and multimorbidity compared to the USPSTF2021 criteria, but there is no evidence to suggest that this results in differences in 3-year overall survival.

Background:Half of older people in hospital have frailty and are at increased risk of re-admission or death following discharge. Although short-term rehabilitation can reduce early re-admissions, benefits are attenuated over time. It is unknown whether extended rehabilitation for older people with frailty can improve outcomes. Trial design:Pragmatic, multicentre, individually randomised controlled parallel-group superiority trial with economic evaluation and embedded process evaluation. Methods:Participants: Eligible participants were 65 years or older with mild/moderate/severe frailty (score of 5-7 on Clinical Frailty Scale) admitted to hospital with acute illness or injury, then discharged home directly or from intermediate care (post-acute care) rehabilitation services. People with significant cognitive impairment and care home residents were among those ineligible. Recruitment took place from December 2017 to August 2021, with follow-up till August 2022. Interventions: Participants were randomly assigned (1.28 : 1) to the Home-based Older People's Exercise programme - a 24-week home-based manualised, progressive exercise intervention delivered by National Health Service therapists as extended rehabilitation, or usual care (control). Randomisation occurred after the participant had been discharged from hospital or intermediate care. Participants were not masked to allocation. Main outcome measures: The primary outcome was physical health-related quality of life, measured using the physical component score of the modified Short Form 36-item health questionnaire at 12 months. Secondary outcomes at 6 and 12 months included physical and mental health-related quality of life, functional independence, death, hospitalisations and care home admissions. Researchers involved in data collection were masked to allocation. Data sources: Primary and secondary outcomes were obtained via self-report questionnaire at 6 and 12 months. Hospitalisations and deaths were collected from routine healthcare data. Results:We randomised 740 participants (410 Home-based Older People's Exercise, 330 control) across 15 sites. Four hundred and seventy-nine (64.7%) participants completed 12-month follow-up. One hundred and eighty-eight Home-based Older People's Exercise participants (45.9%) completed 24 weeks of intervention delivery. Over half of participants completed more than 75% of prescribed exercises. Intention-to-treat analyses (258 Home-based Older People's Exercise participants, 208 control participants for primary outcome) showed no evidence that Home-based Older People's Exercise was superior to control for 12-month physical component score (adjusted mean difference -0.22, 95% confidence interval -1.47 to 1.03; p = 0.73). There was some evidence of a higher rate of all-cause hospitalisations in the control arm (incidence rate ratio 1.12, 95% confidence interval 1.00 to 1.25; p = 0.05), but no evidence of differences in other outcomes. The process evaluation found the intervention was largely delivered as intended and proved acceptable to most participants. The economic analysis showed incremental costs of Home-based Older People's Exercise plus usual care of GB£1401 (mean per participant), compared with usual care alone. There was a 0.024 quality-adjusted life-year improvement in Home-based Older People's Exercise compared to control. The incremental cost-effectiveness ratio was £58,375. Limitations:This trial was delivered during especially challenging circumstances that included the COVID-19 pandemic. We examined outcomes taking account of this but detected no difference in primary or secondary outcomes, providing reassurance that COVID-19 was unlikely to have influenced trial results. Conclusions:Based on our findings, we do not recommend routine commissioning of extended rehabilitation for older people with frailty after discharge home from hospital or intermediate care, following an acute admission with illness or injury. Future work:Future work should consider how existing core intermediate care and community rehabilitation services should be best organised and delivered to ensure that older people with frailty feel ready for discharge from rehabilitation, and are enabled to maintain their independence. Funding:This synopsis presents independent research funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme as award number 15/43/07.

Introduction With many homes failing to meet minimum housing standards, concerns have been raised about the effects on peoples’ physical and mental health. Cold and damp homes are a particular hazard. The effects of growing austerity and poverty have increased the impact on individuals and the wider society. Given that those affected by poor housing tend to be the most socially deprived, any harms and health inequalities are accentuated. Recognising the health, social and economic consequences of poor housing, governments have implemented policies and provided funding to improve the standards of homes. Retrofitting homes with warmth-related improvements is thought to improve peoples’ health and wellbeing, promote social cohesion and benefit economic growth. It is less certain if the costs of retrofitting homes are a good use of scarce funds.Methods Using recognised guidance, this commentary critically appraises an economic evaluation of the costs and benefits of improving social housing, assessing the implications for future practice.Results Despite uncertainties that remain in terms of the cost-effectiveness of retrofitting homes, benefits to people’s health and wellbeing, as well as cost-savings to households and the NHS are evident.Discussion Such initiatives should be continued, accompanied by high quality research into their costs and benefits.

[This corrects the article DOI: 10.1371/journal.pmed.1004223.].

BACKGROUND:Depression affects almost 60% of stroke survivors, impacting on recovery and quality-of-life. Depression may be treated with medication or talk-based therapies. One randomised controlled trial showed a talk-based therapy called motivational interviewing-based intervention (MIBI) delivered early after stroke reduced depressive symptoms at 3 and 12 months post-stroke compared with receiving usual care alone. However, it was unclear if the benefit was due to the specific MIBI components or simply the additional attention received. This trial aims to determine the effect of MIBI plus usual care on reducing depressive symptoms post-stroke, relative to usual care (UC), and to an attention control (AC) (social attention without therapeutic content, i.e. general conversation). METHODS:Patients admitted following acute stroke meeting the study eligibility criteria (not currently receiving talk-based therapy and not currently having severe depression) will be recruited across 18 UK hospitals within 28 days of stroke. A total of 1287 participants will be randomised on a 1:1:1 ratio into three groups: MIBI + UC; AC + UC; UC. Participants in MIBI + UC and AC + UC will additionally have remote (telephone/online) sessions with MIBI therapists or AC providers respectively, for four 45-min weekly sessions, beginning within 6 weeks of randomisation. Participant self-report measures of depression (primary outcome, Patient Health Questionnaire (PHQ-9) at 3 months) and quality-of-life will be collected at baseline, 6 weeks and 3 months post-randomisation. The proposed mechanism of effect, via participants' self-efficacy and confidence, and the impact of MIBI dose and/or therapeutic alliance on outcome will also be explored. If benefit of MIBI + UC over UC is demonstrated, mixed effects regression will be fitted to outcome data from all three arms, and appropriate parametrisation with MIBI + UC as the reference group. A mixed-methods process evaluation comprising quantitative assessment of intervention adherence and fidelity and semi-structured interviews with a purposive sample of study participants (n ~ 18) and MIBI/AC staff (n ~ 15) will explore participation, acceptability, and considerations for implementation. An economic evaluation will explore cost-effectiveness. DISCUSSION:The results will inform whether any observed improvements in mood are a natural change over time, due to attention, or a therapeutic change attributable to MIBI. If MIBI is shown to effectively reduce depressive symptoms, the process evaluation will inform implementation of the intervention into clinical care. TRIAL REGISTRATION:ISRCTN, ISRCTN17065351. Registered 01/02/2022, https://www.isrctn.com/ISRCTN17065351 .

OBJECTIVE:To evaluate whether home-based extended rehabilitation for older people with frailty after hospitalisation with an acute illness or injury can improve physical health-related quality of life. TRIAL DESIGN:Multi-centre, individually randomised controlled parallel group superiority trial. SETTING:Recruitment from 15 NHS Trusts in England, with home-based intervention delivery. PARTICIPANTS:Eligible participants were 65 years or older with mild/moderate/severe frailty (5-7 on Clinical Frailty Scale) admitted to hospital with acute illness/injury, then discharged home. INTERVENTIONS:Participants were randomly assigned (1.28:1) to the Home-based Older People's Exercise (HOPE) programme-a 24-week home-based manualised, progressive exercise intervention as extended rehabilitation, or usual care (control). MAIN OUTCOME MEASURES:Primary outcome was physical health-related quality of life, measured using the physical component summary (PCS) of the modified Short Form 36-item health questionnaire (SF36) at 12 months. Secondary outcomes at six and 12 months included functional independence, death, hospitalisations and care home admissions. RESULTS:We randomised 740 participants (410 HOPE, 330 control). Intention-to-treat analyses showed no evidence that HOPE was superior to control for 12-month PCS score (adjusted mean difference -0.22, 95% CI -1.47 to 1.03; P = .73). There was some evidence of a higher rate of all-cause hospitalisations in the control arm (incidence rate ratio 1.12, 95% CI 1.00 to 1.25; P = .05). The intervention was not cost-effective. CONCLUSIONS:We do not recommend routine commissioning of extended rehabilitation for older people with frailty after discharge home from hospital or intermediate care, following an acute admission with a medical illness or injury. TRIAL REGISTRATION:ISRCTN-13927531 (19/04/2017).

Background Globally, 5.5 million people die from stroke each year and 13% of all stroke deaths occur in India. The programme had two main projects: IMPROVIng StrokE care in India and IMPROVIng Stroke care in India – Advancing The INSTRuCT Operations and Network, delivered through six workstreams. IMPROVIng StrokE care in India aimed to explore the feasibility and acceptability of implementing three evidence-based care bundles into practice. IMPROVIng Stroke care in India – Advancing The INSTRuCT Operations and Network aimed to explore the feasibility of implementing care bundle 1 in four additional hospital sites; the provision of post-discharge care in seven hospitals; and the feasibility of establishing a multicentre ethics approval process within the Indian Stroke Clinical Trial network. IMPROVIng StrokE methods A multicentre, feasibility study and nested process evaluation. Three care bundles were implemented sequentially at three hospitals. Care bundle 1: a Global Evaluation of Swallowing and a hydration ‘Osmolarity App’. Care bundle 2: a Standardised Neurological OBservation Schedule for Stroke. Care bundle 3: post-discharge patient/carer education in the form of animations. Process and outcomes were evaluated in each of four cohorts admitted between July 2019 and November 2021. IMPROVIng Stroke care in India – Advancing The INSTRuCT Operations and Network methods Semistructured interviews (all work packages) and focus groups (work package 3) with purposive samples of health professionals, patients and carers (work packages 1 and 2), ethics committee members and principal investigators (work package 3). IMPROVIng StrokE findings Of 707 patients screened, 515 were eligible and 379 (73.6%) were recruited to the study; 118 (31.1%) female, mean age 59 years (12.8 standard deviation). Overall median National Institute Health Stroke Scale was 9 (interquartile range 5–16); 285 (75.5%) of participants had a modified Rankin Scale ≥ 3. Global Evaluation of Swallowing swallow evaluations increased to 51 (56.0%) and calculated osmolarity was recorded in 67 (75.3%) in care bundle 1, maintained in care bundles 2 and 3. Standardised Neurological OBservation Schedule for Stroke was recorded at similar levels and maintained to care bundle 3. Four animations were provided in hospital to all relevant carers and patients in care bundle 3. The process evaluation found that care bundle implementation resulted in improved decision-making, new and extended roles and responsibilities. Barriers to implementation were patient/carer literacy, physical and workforce resources and organisational culture. IMPROVIng Stroke care in India – Advancing The INSTRuCT Operations and Network findings Work package 1: 16 clinical staff and 12 patient/carers reported variation in practice arising from a lack of specialists and resources. Work package 2: 66 clinical staff and 102 patient/carers identified unmet needs related to swallowing problems and the psychological impact of stroke. Work package 3 (multiple stakeholders): 12 interview and 2 focus group participants ( n = 18) identified some streamlining of ethical approvals following the coronavirus disease discovered in 2019 pandemic, but sustainable standardised procedures were needed for multicentre studies. Conclusion Evidence-based, context-specific interventions to improve the basic elements of stroke care can be successfully implemented and sustained as an acceptable model of care. Future work Future work is planned to explore the needs of patients and their carers to identify and develop low-cost post-discharge rehabilitation services. Limitations The impact of coronavirus disease on hospital systems and processes of care is likely to have influenced the results of the study, including fewer patients recruited than planned, the admission of more severe stroke patients, delays in patients being admitted to Stroke Units and the rotation of trained staff to other hospital departments. Funding This synopsis presents independent research funded by the National Institute for Health and Care Research (NIHR) Global Health Research programme as award number 16/137/16.

Digital literacy and internet access are increasingly recognised as “super social determinants of health” because of their influence on wider social determinants of health. Older people are more likely to experience digital exclusion, with research suggesting several factors influencing digital engagement. To explore the barriers and facilitators older adults reported regarding their digital engagement. A cross-sectional population-based questionnaire was administered to adults aged 65+ to explore digital engagement and identify characteristics associated with digital exclusion. Optional free-text responses were analysed using the capability, opportunity, motivation and behaviour (COM-B) model and Theoretical Domains Framework (TDF) to examine barriers and facilitators influencing digital engagement. Comments were coded using deductive framework analysis across the TDF domains. Coding was undertaken by multiple researchers to ensure consistency, with discrepancies resolved through discussion. Domain-specific comments were narratively summarised to describe recurring patterns, similarities and differences in participants’ experiences of digital engagement. 1,114 survey participants provided free-text responses. Analysis identified barriers and facilitators across all TDF domains. The most prominent were Environmental context and resources, Beliefs about capabilities, Social influence, and Cognitive and interpersonal skills. Environmental context comments highlighted cost, poor connectivity, limited access to digital resources, and preferences for offline services as influencing digital engagement. Social support facilitated digital engagement but also highlighted concerns around dependency on others. Beliefs about capabilities focused on confidence in using technology and varied considerably. Perceived capability was influenced by familiarity, usability, and the ability to troubleshoot problems independently. Cognitive and interpersonal skills affected participants’ ability to learn and engage with digital technologies independently. This study highlights the complexity of factors influencing older adults’ digital engagement. Framed around the COM-B model, findings indicated that opportunity and motivation were discussed more frequently than capability. While access to technology remains important, digital inclusion also depends on confidence, perceptions of technology, social influences, and equitable access. Findings suggest that interventions should extend beyond improving access alone and adopt a multi-level approach to facilitating digital engagement amongst older people involving service designers, policymakers, health and social care professionals, and community organisations.

BACKGROUND/OBJECTIVES:Simple frailty assessments, such as the clinical frailty scale (CFS), are prognostic for worse outcomes in older adults with cancer and could support treatment decision-making. This interview study aims to explore clinicians' experiences of using simple frailty assessments in oncology, including the impacts on patient care and barriers and facilitators to successful implementation. METHODS:Semi-structured individual interviews were conducted with clinicians at three UK sites that had implemented CFS screening in lung cancer clinics as part of a national pilot, to explore how frailty assessments are applied and are impacting care. Purposive sampling targeted a range of professionals involved in assessing frailty and making treatment decisions. Recordings were transcribed verbatim and analysed thematically. RESULTS:Ten clinicians participated, and four main themes were identified. 'Assessing fitness and frailty' explores the central role of performance status (PS), as well as its limitations, and what frailty assessments add. 'Scoring and interpreting CFS' describes the ease and relative yield of CFS use, particularly for patients with 'borderline' PS scores (e.g., PS 1-2 or 2-3), and the importance of contextual interpretation. 'Role of frailty and impacts of assessment' highlights how frailty assessments can enhance patient-centered care and support, and clinical and shared decision-making, with potential for streamlined care and system-level benefits. 'Barriers and facilitators to implementation' are described, including time, culture, guidance, and training, with recommendations provided. CONCLUSIONS:Assessing frailty has wide-ranging potential benefits for patients, oncology teams, and the wider system, but barriers must be overcome. Specific recommendations are provided to support the routine implementation of frailty assessments, which is a key step towards the benefits of frailty-informed care being realised at scale.

Deep learning architectures are increasingly proposed for patient trajectory modeling in electronic health records (EHRs), yet their advantage over simpler, more interpretable models is rarely subjected to rigorous empirical scrutiny in real-world clinical settings. We present a comprehensive patient timeline pipeline applied to elderly patients in CPRD Aurum, incorporating 260 clinical conditions classified via a three-tier automated framework including specialised detection logic for 17 complex conditions. Using this infrastructure, we benchmark Temporal Graph Convolutional Neural Networks (TG-CNN) against Logistic Regression with LASSO regularisation and Random Forests for predicting 12-month all-cause emergency hospitalisation risk, motivated by (but not filtered to) the elevated risk of adverse drug reactions. Under cross-validation, TG-CNN achieves a marginally higher mean AUC-ROC than LASSO (0.712 vs. 0.705), whereas on the held-out test set LASSO achieves the highest discrimination of three models (AUC-ROC 0.733, versus 0.710 for Random Forest and 0.702 for TG-CNN). We show, that discrimination alone is an incomplete criterion for clinical deployment: after Platt calibration, LASSO is the only model with an acceptable calibration slope (0.817), while Random Forest (0.759) and, TG-CNN (0.391) remain substantially miscalibrated. We argue that LASSO, not the highest-discriminating model, is the model best suited to direct clinical deployment. We present lessons for the machine learning and healthcare community regarding data infrastructure, model selection, and value of calibration and interpretability in high-stakes decision support.

BACKGROUND:Structured medication reviews (SMRs) are an essential component of medication optimization, especially for patients with multimorbidity and polypharmacy. However, the process remains challenging due to the complexities of patient data, time constraints, and the need for coordination among health care professionals (HCPs). This study explores HCPs' perspectives on the integration of artificial intelligence (AI)-assisted tools to enhance the SMR process, with a focus on the potential benefits of and barriers to adoption. OBJECTIVE:This study aims to identify the key user requirements for AI-assisted tools to improve the efficiency and effectiveness of SMRs, specifically for patients with multimorbidity, complex polypharmacy, and frailty. METHODS:A qualitative study was conducted involving focus groups and semistructured interviews with HCPs and patients in the United Kingdom. Participants included physicians, pharmacists, clinical pharmacologists, psychiatrists from primary and secondary care, a policy maker, and patients with multimorbidity. Data were analyzed using a hybrid inductive and deductive thematic analysis approach to identify themes related to AI-assisted tool functionality, workflow integration, user-interface visualization, and usability in the SMR process. RESULTS:Four major themes emerged from the analysis: innovative AI potential, optimizing electronic patient record visualization, functionality of the AI tool for SMRs, and facilitators of and barriers to AI tool implementation. HCPs identified the potential of AI to support patient identification and prioritizing those at risk of medication-related harm. AI-assisted tools were viewed as essential in detecting prescribing gaps, drug interactions, and patient risk trajectories over time. Participants emphasized the importance of presenting patient data in an intuitive format, with a patient interface for shared decision-making. Suggestions included color-coding blood results, highlighting critical medication reviews, and providing timelines of patient medical histories. HCPs stressed the need for AI tools to integrate seamlessly with existing electronic patient record systems and provide actionable insights without overwhelming users with excessive notifications or "pop-up" alerts. Factors influencing the uptake of AI-assisted tools included the need for user-friendly design, evidence of tool effectiveness (though some were skeptical about the predictive accuracy of AI models), and addressing concerns around digital exclusion. CONCLUSIONS:The findings highlight the potential for AI-assisted tools to streamline and optimize the SMR process, particularly for patients with multimorbidity and complex polypharmacy. However, successful implementation depends on addressing concerns related to workflow integration, user acceptance, and evidence of effectiveness. User-centered design is crucial to ensure that AI-assisted tools support HCPs in delivering high-quality, patient-centered care while minimizing cognitive overload and alert fatigue.

Despite advances in stroke care, 12

Background:Our aim was to develop and evaluate the electronic frailty index+, a prognostic tool, including four integrated prognostic-decision models, to stratify older people into subgroups for targeting key interventions. Methods:Prognostic model development, internal validation and external validation using large data sets and longitudinal cohort study data, with decision curve and health economic analysis. Population:Patients aged 65+ years. Key outcomes:The 12-month outcomes for prognostic models: new home care package care home admission emergency department attendance/hospitalisation with fall/fracture all-cause mortality. Statistical methods:We developed and internally validated models for our key outcomes in one large data set. We used internal-external cross-validation for the home care model and full external validation for the remaining three models in a second large data set. We used CARE75+ to investigate additional predictive value of clinical measures practical for primary care. Decision curve analysis:We translated the prognostic models into a framework to support clinical decision-making. Health economic evaluation:We integrated the falls prediction models with effect size estimates from network meta-analysis to examine potential cost savings. Results:We used data from 660,417 patients in SAIL, 88,947 in Connected Bradford and 252 CARE75+ participants. Model performance was promising in internal-external cross-validation, with average calibration slope 1.00 (95% confidence interval 0.99 to 1.01), average calibration-in-the-large -0.01 (95% confidence interval -0.02 to 0.01), average observed/expected ratio 0.99 (95% confidence interval 0.98 to 1.01) and average C-statistic 0.81 (95% confidence interval 0.81 to 0.81). Emergency department attendance/hospitalisation with fall/fracture:Model performance was promising on internal and external validation, although with some evidence for overprediction of falls risk, with calibration slope 1.25 (95% confidence interval 1.24 to 1.27), calibration-in-the-large -0.931 (95% confidence interval -0.938 to -0.920), observed/expected ratio 0.43 (95% confidence interval 0.42 to 0.44), C-statistic 0.83 (0.82 to 0.83). Care home admission:Model performance was promising on internal validation, but it showed some miscalibration on external validation, with calibration slope 0.75 (95% CI 0.74 to 0.76), calibration-in-the-large -1.60 (-1.62 to -1.58) and observed/expected ratio 0.25 (95% CI 0.24 to 0.25), C-statistic of 0.86 (95% CI 0.86 to 0.86). All-cause mortality:The model showed excellent performance across the full range of predicted risks on external validation, with average calibration slope 1.00 (0.98 to 1.01), average calibration-in-the-large -0.23 (-0.27 to -0.19), average observed/expected ratio 0.77 (0.75 to 0.79) and average C-statistic 0.83 (0.82 to 0.83). Economic modelling:Modelling indicated that provision of multifactorial assessment and treatment for people with an annual falls risk of ≥ 40% has the largest cost reduction per targeted person (£1025). Discussion:All four prediction models have promising predictive performance, although some had evidence of overprediction of risk (miscalibration). Decision curve analysis indicates potential clinical utility, and economic modelling provides novel information for policy-makers and commissioners. Future work:Future research should include model impact studies to evaluate use of the models in routine care. Limitations:We were unable to complete external validation of the home care prediction model. Study registration:This study is registered as ClinicalTrials.gov ID NCT04113174. Funding:This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 127905) and is published in full in Health Technology Assessment; Vol. 30, No. 61. See the NIHR Funding and Awards website for further award information.

Background:HomeHealth is a home-based, voluntary sector service supporting older people with mild frailty to maintain independence through behaviour change. Support workers discuss the person's priorities and enable setting/achieving goals around mobility, nutrition, socialising and/or psychological well-being. Aims:We tested clinical and cost-effectiveness of HomeHealth for maintaining independence in older people with mild frailty in a randomised controlled trial. Methods:Design: Single-blind, parallel randomised controlled trial open between 18 January 2021 and 4 July 2023, with mixed-methods process evaluation. Setting: Community-dwelling older people aged 65+ years with mild frailty from 27 general practices and community settings in London, Yorkshire and Hertfordshire. Randomisation: Participants were randomised 1 : 1 to receive HomeHealth or treatment as usual. Outcomes: Primary outcome was independence in activities of daily living (modified Barthel Index), analysed using linear mixed models. Secondary outcomes included frailty phenotype score, extended activities of daily living, well-being, psychological distress, loneliness, cognition, falls and mortality. Health economic outcomes included quality of life, capability and service use, including hospital admissions. Cost-effectiveness acceptability curves and cost-effectiveness planes were used to represent the probability of cost-effectiveness compared to treatment as usual. Process evaluation: We conducted semistructured interviews with participants receiving the intervention, HomeHealth workers and other stakeholders supporting service delivery. Interviews were thematically analysed. Fidelity of audio-recorded appointments was assessed by two independent raters. We evaluated potential mechanisms of impact using data from appointments attended, types of goals set and progress towards goals. Findings:We recruited 388 participants, mean age 81.4 years (standard deviation 6.5), 64% female and 94% White British/European. HomeHealth did not improve Barthel Index scores at 12 months (0.250, 95% confidence interval -0.932 to 1.432). At 6 months, we found small significant reductions in psychological distress (-1.237, 95% confidence interval -2.127 to -0.348), and frailty phenotype score (-0.252, 95% confidence interval -0.487 to -0.017). At 12 months, we found significant improvements in well-being (1.449, 95% confidence interval 0.124 to 2.775), reduced unplanned admissions (incidence rate ratio 0.65, 95% confidence interval 0.54 to 0.92) with lower associated costs (-£586/participant, 95% confidence interval -351 to -821). There were no differences in other outcomes. HomeHealth dominates treatment as usual with a negative point estimate for incremental costs (-796, 95% confidence interval -2016 to 424), positive point estimate for incremental quality-adjusted life-years (0.009, -0.021 to 0.039) and high probability of cost-effectiveness. Process evaluation: Sixty-four semistructured interviews were completed, including 49 participants and 15 HomeHealth workers/stakeholders. The service was acceptable and safe, with good fidelity of delivery. Participants made progress on personalised goals, most working on enhancing mobility. They found the service empowering, and received emotional/practical support. Engagement was more challenging when participants identified no need for change, had significant memory impairment or new/declining illness. Flexibility around varying symptoms and incorporating behaviour change into existing routines promoted engagement. Conclusion:HomeHealth did not improve independent functioning for older people with mild frailty. There were small significant improvements in frailty status, psychological distress and well-being and a 35% reduction in unplanned admissions, with high probability of cost-effectiveness. Limitations:We used a pragmatic design with intervention delivery in real-world settings during/after the COVID-19 pandemic, potentially with more variability in delivery. Our findings might not apply to other geographical settings/healthcare systems. Funding:This synopsis presents independent research funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme as award number NIHR128334.

Most fall risk tools are developed for older adults; evidence in working-age populations receiving mental health services is limited. To externally validate the existing eFalls prediction model in working-age adults (18–65 years) within a UK integrated teaching National Health Service (NHS) foundation trust and assess the impact of simple recalibration. We conducted a retrospective validation using routinely collected electronic health records from a large integrated teaching NHS foundation trust in the north of England. The published eFalls coefficients were applied to derive 12-month fall/fracture risk. Performance was evaluated using discrimination (C-statistic), calibration-in-the-large (CITL), calibration slope, observed-to-expected (O/E) ratio, calibration plots, and decision curve analysis (DCA). A logistic recalibration (intercept and slope) using the original linear predictor was then fitted on the full cohort and applied uniformly to subgroups (sex; mental health, learning disability). Among 32,410 adults (fall rate 2.07%), the model showed good discrimination (C-statistic = 0.777). Before recalibration, calibration was suboptimal (CITL = 1.36; O/E = 1.60; slope = 1.22). Recalibration restored alignment (CITL $$\approx$$ 0; O/E $$\approx$$ 1; slope $$\approx$$ 1) without changing discrimination. Subgroup analyses revealed degraded performance in learning disability groups (e.g., AUC 0.696–0.739; marked underprediction), whereas sex and mental-health-only groups were closer to overall performance. DCA indicated positive net benefit across clinically relevant thresholds (10–25%). The eFalls model showed reasonable performance in working-age adults receiving mental health or learning disability services following simple recalibration. However, discrimination was lower among individuals with learning disabilities, suggesting that recalibration alone may be insufficient and that further model refinement and validation in this subgroup are warranted.