Suzanne H. Richards

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: Design: 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: Prognostic modelling: 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. New home care package: 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.

Electronic Palliative Care Co-ordination Systems are central to end-of-life care policy despite critical gaps in the evidence base underpinning their use. It is unclear how Electronic Palliative Care Co-ordination Systems are being implemented across England and there is a lack of research to understand their use in routine care. To understand how Electronic Palliative Care Co-ordination Systems are currently being used in routine care and to guide the development of interventions to support their optimal implementation and maximise patient benefit. A mixed-method approach was adopted across the five-stage programme of research. Packages of work included: (1) a national online survey of end-of-life care leads at clinical commissioning groups in England; (2) an online survey of community and hospital-based health and care professionals in West Yorkshire and London; (3) semistructured interviews with a sample of survey respondents; (4) focus groups and interviews with patients with progressive chronic illnesses and carers and (5) regional and national Theory of Change workshops. Two-thirds of surveyed regions have Electronic Palliative Care Co-ordination Systems in place. Where present, there is limited use of Electronic Palliative Care Co-ordination Systems to document patients' wishes and preferences. Low uptake may be explained by challenges experienced by health professionals. This includes limited interoperability across electronic health record systems in which Electronic Palliative Care Co-ordination Systems are typically nested, affecting information sharing across settings. Uncertainty was common across health and care professionals relating to what Electronic Palliative Care Co-ordination Systems are, who they are for and how organisations should monitor them. Mid-range programme theory was generated to guide the development and evaluation of Electronic Palliative Care Co-ordination Systems alongside recommendations for decision-makers and commissioners for palliative and end-of-life care in England. The project was conducted across England with in-depth health and care professional engagement in two regions of England. This may limit the applicability of findings across England, although the project included large regions with multiple Electronic Palliative Care Coordination Systems operating at different levels of maturity. Electronic Palliative Care Co-ordination Systems are not working as intended for palliative and end-of-life care delivery in England. The application of the recommendations generated through this project can ensure consideration of the wider ecosystem in which Electronic Palliative Care Co-ordination Systems are being implemented, guiding system development that promotes health and care professional engagement and facilitates patient and carer access to Electronic Palliative Care Co-ordination Systems records. Future research is required to develop approaches that seek to improve health professional engagement with digital approaches to advance care planning documentation and sharing. Furthermore, there is a need to develop and evaluate patient and carer access to digital advance care planning records. Lastly, the mid-range programme theory generated through this project requires continued refinement and use to evaluate Electronic Palliative Care Co-ordination Systems' implementation. This synopsis presents independent research funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme as award number NIHR129171.

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.

Objectives: We aimed to develop a new complex intervention, the ‘COuld it Be RA’ (COBRA) tool, to support the implementation of a clinical prediction model to identify people likely to be anti-cyclic citrullinated peptide (CCP) positive and at risk of rheumatoid arthritis in primary care. Methods: The COBRA tool was developed using the UK Medical Research Council and National Institute for Health and Care Research complex intervention research framework. This study involved three sequential phases with primary care clinicians: a qualitative descriptive study, clinician consultation engagement workshops and a think-aloud interview study. Ethical approval was obtained for all three phases. Results: Sixteen primary care clinicians participated in semistructured interviews to identify barriers and facilitators. An initial list of nine candidate components for the intervention, including design considerations, was developed. During phase 2 workshops with eight participants, four components were prioritised as ‘Must have’ or ‘Should have’: the clinical decision support system (CDSS); guidance on using the CDSS/associated actions; evidence for the CDSS; patient education resources. A COBRA tool prototype incorporating these components was developed. Twelve participants tested the prototype during think-aloud interviews. Key perceived benefits of the COBRA tool included supporting clinicians’ decision-making and reducing unnecessary anti-CCP testing. Over 40 changes were made to the COBRA tool. Conclusion: Our research included the views of clinicians and PPI representatives and was underpinned by a complex intervention research framework. This was critical to understanding barriers and facilitators to implementing the clinical prediction model in primary care and developing the COBRA tool.

Background: The COVID-19 pandemic prompted widespread use of remote (telephone and online) communication in general practice in England, which exacerbated long-term pressures from staffing shortages. The public perceived problems with access. Aim: To explore patient and staff perspectives on changing processes in general practice. Design & setting: A mixed-methods study (patient survey and staff focus groups) in a sample of 22 general practices in England, varied by size, region, deprivation, and demography, was conducted in 2022. Method: An online survey was delivered by short message service (SMS) text to adult patients at 21 practices. Data from answers to an open-ended question about patients’ experiences were analysed using summative content and thematic analysis. Virtual focus groups conducted with four categories of staff (GPs, nurses, receptionists and administrators, and practice managers) covered teamworking, roles, patient interactions, adapting to change, and workload. Data were transcribed and analysed using framework and thematic methods. Themes common to patients and staff were identified. Results: Overall median survey response was 10.9% (interquartile range 9.7%–14.6%); 14 401 patient responders provided 10 348 comments, 51.2% were positive. Patient and staff perspectives overlapped in two areas. The first, ‘contact and communication’, encapsulated differing views around access. The second, ‘non-clinical roles and patient care’, concerned the allocation of appointments and the roles of receptionists. Patients reported barriers to getting timely appointments with their chosen professional while staff were seeking ways to manage the volume of communications. Use of non-clinical staff to triage appointment requests was unpopular with patients and receptionists felt clinically unqualified. Conclusion: Effective methods are needed to improve patient communication with practices and access. Receptionists require recognition and training for their pivotal role.

The development of interventions in healthcare often lacks a robust theoretical basis, which may contribute to suboptimal engagement and effectiveness. This paper provides insights into and practical guidance on the development of complex interventions in healthcare, using the example of a digital self-management tool for children with Perthes’ disease, called the Non-Surgical Treatment of Perthes’ (NON-STOP) app. We applied the Medical Research Council framework, used psychological theory, and integrated stakeholder engagement to develop the intervention. The lessons learned and considerations for the developments of other complex interventions provide practical actions for clinicians and researchers in orthopaedics.Cite this article: Bone Jt Open 2025;6(7):822–827.

Introduction: Numerous digital approaches are being explored internationally to support the elicitation, documentation and sharing of advance care planning information. In England, Electronic Palliative Care Coordination Systems have been developed for this purpose, often as a template stored as part of an electronic clinical record system. Despite Electronic Palliative Care Coordination Systems being central to the end-of-life care policy, there has been a lack of exploration of the perspectives of healthcare professionals who use Electronic Palliative Care Coordination Systems and are critical to their implementation. Objective: This study addresses this gap aiming to examine community and hospital-based healthcare professionals’ perceptions of Electronic Palliative Care Coordination Systems on advance care planning and the delivery of palliative care. Design and methods: A cross-sectional online survey. Setting and participants: The target sample comprised health professionals from the main professional groups supporting patients with chronic progressive illnesses in West Yorkshire and London. The survey included items adapted from the Normalisation MeAsure Development questionnaire implementation measure. Survey responses were analysed using descriptive statistics and latent class analysis. Free-text responses relating to alternative approaches to advance care planning documentation were analysed using a directed content analysis approach. Results: Five hundred and sixty-nine health professionals responded to the survey from West Yorkshire (n = 189; 33.2%) and London (n = 380; 66.8%). The largest proportion of respondents came from general practice teams (n = 254; 44.6%). There were prominent differences in responses, with respondents in London more likely to report being familiar with Electronic Palliative Care Coordination Systems. However, West Yorkshire respondents rated Electronic Palliative Care Coordination Systems more highly in terms of being a legitimate part of their role. Across professional groups, respondents from both hospice and care home teams were more likely to view Electronic Palliative Care Coordination Systems as being worthwhile. Commonly reported barriers to the use of Electronic Palliative Care Coordination Systems included not having access to electronic devices, lack of training and lack of knowledge relating to advance care plans. Limitations: There was a dominance of responses from participants based on primary care practices, which may reflect general practitioners being largely responsible for initiating an Electronic Palliative Care Coordination Systems record in one region of the survey (West Yorkshire). This survey is reliant on self-reported responses to items and may have also included respondents more engaged with or interested in Electronic Palliative Care Coordination Systems, representing a skewed positive perspective of the systems and how they are being used in practice. Discussion: Variation exists in the way in which Electronic Palliative Care Coordination Systems are perceived and used across both geographical regions surveyed and the professional groups involved in palliative care delivery. There continue to be challenges experienced by those using Electronic Palliative Care Coordination Systems, including not having access to electronic devices, lack of training and lack of knowledge relating to advance care plans. Future work: Future research to identify the preferences of health professionals and patients as potential users of Electronic Palliative Care Coordination Systems could guide the development of ergonomic systems that account for the multiple challenges (i.e. physical, cognitive and organisational aspects) involved in their implementation. Funding: This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme as award number NIHR129171.

Background: It is increasingly recognized that frailty should be assessed and considered in treatment decision making in patients with cancer. This review and meta-analysis synthesizes existing evidence evaluating the association between baseline frailty and systemic anticancer treatment outcomes in adults with cancer. Methods: Five databases were systematically searched from database inception to January 2023 to identify prognostic factor studies (cohort or case-control design) reporting the associations between validated frailty assessments (pretreatment) and follow-up outcomes in adults with solid-organ malignancy undergoing systemic anticancer treatment. Risk of bias was assessed via Quality of Prognosis Studies in Systematic Reviews tool. Where appropriate, associations between frailty and outcomes (survival, toxicity, treatment tolerance, functional decline/quality of life, and hospitalization) were synthesized in meta-analysis and presented as forest plots. Results: A total of 58 studies met inclusion criteria. They were undertaken in a range of tumor sites and mainly in older patients and advanced and/or palliative disease settings. Most had low or moderate risk of bias. Nine frailty assessment tools were evaluated. Four outcomes were synthesized in meta-analysis, which demonstrated the prognostic value of 2 tools: Geriatric-8 (survival, treatment tolerance, hospitalization) and Vulnerable Elders Survey-13 (survival, toxicity, treatment tolerance). Overall pooled estimates indicate that frailty conveys an increased risk of mortality (hazard ratio [HR] = 1.68, 95% confidence interval [CI] = 1.41 to 2.00), toxicity (odds ratio [OR] 1.83, 95% CI = 1.24 to 2.68), treatment intolerance (OR = 1.68, 95% CI = 1.32 to 2.12), and hospitalization (OR = 1.94, 95% CI = 1.32 to 2.83). Conclusion: Simple, brief frailty assessments including Geriatric-8 and Vulnerable Elders Survey-13 are prognostic for a range of important outcomes in patients undergoing systemic anticancer treatment. Risk estimates should be used to support shared decision making.