Mental health prior to and during the COVID-19 pandemic in individuals with bipolar disorder: Insights from prospective longitudinal data.

OBJECTIVES: Many studies have examined the impact of COVID-19 on the mental health of the public, but few have focused on individuals with existing severe mental illness with longitudinal data before and during the pandemic. AIMS: To investigate the impact of the COVID-19 pandemic on the mental health of people with bipolar disorder (BD). METHODS: In an ongoing study of people with BD who used an online mood monitoring tool, True Colours, 356 participants provided weekly data on their mental health. Symptoms of depression, mania, insomnia, and suicidal thoughts were compared in 2019 and 2020. From May 2020, participants also provided weekly data on the effect of the COVID-19 pandemic on anxiety, coping strategies, access to care, and medications. RESULTS: On average, symptoms of depression, mania, insomnia, and suicidal thoughts did not significantly differ in 2020 compared to 2019, but there was evidence of heterogeneity. There were high rates of anxiety about the pandemic and its impact on coping strategies, which increased to over 70% of responders in January 2021. A significant proportion of participants reported difficulty accessing routine care (27%) and medications (21%). CONCLUSIONS: Although mood symptoms did not significantly increase during the pandemic overall, we observed heterogeneity among our BD sample and other impacted areas. Individuals' unique histories and psychosocial circumstances are key and should be explored in future qualitative studies. The significant impacts of the pandemic may take time to manifest, particularly among those who are socioeconomically disadvantaged, highlighting the need for further long-term prospective studies.

Using item response theory (IRT) to improve the efficiency of the Simple Clinical Colitis Activity Index (SCCAI) for patients with ulcerative colitis.

BACKGROUND: The SCCAI was designed to facilitate assessment of disease activity in ulcerative colitis (UC). We aimed to interrogate the metric properties of individual items of the SCCAI using item response theory (IRT) analysis, to simplify and improve its performance. METHODS: The original 9-item SCCAI was collected through TrueColours, a real-time software platform which allows remote entry and monitoring of patients with UC. Data were securely uploaded onto Dementias Platform UK Data Portal, where they were analysed in Stata 16.1 SE. A 2-parameter (2-PL) logistic IRT model was estimated to evaluate each item of the SCCAI for its informativeness (discrimination). A revised scale was generated and re-assessed following systematic removal of items. RESULTS: SCCAI data for 516 UC patients (41 years, SD = 15) treated in Oxford were examined. After initial item deletion (Erythema nodosum, Pyoderma gangrenosum), a 7-item scale was estimated. Discrimination values (information) ranged from 0.41 to 2.52 indicating selected item inefficiency with three items < 1.70 which is a suggested discriminatory value for optimal efficiency. Systematic item deletions found that a 4-item scale (bowel frequency day; bowel frequency nocturnal; urgency to defaecation; rectal bleeding) was more informative and discriminatory of trait severity (discrimination values of 1.50 to 2.78). The 4-item scale possesses higher scalability and unidimensionality, suggesting that the responses to items are either direct endorsement (patient selection by symptom) or non-endorsement of the trait (disease activity). CONCLUSION: Reduction of the SCCAI from the original 9-item scale to a 4-item scale provides optimum trait information that will minimise response burden. This new 4-item scale needs validation against other measures of disease activity such as faecal calprotectin, endoscopy and histopathology.

Acute aortic dissection during pregnancy: Trials and tribulations.

BACKGROUND: Type A acute aortic dissection (TAAD) during pregnancy is a life-threatening event for both the mother and the unborn baby. Pregnancy has been recognized as an independent risk factor for TAAD, postulated to be due to physiological changes that cause hyperdynamic circulation. This review seeks to outline the current controversies around this unique group. METHODS: A comprehensive literature search was carried out across large databases to assimilate relevant papers regarding acute aortic dissection in pregnant women. RESULTS: The presentation can be atypical in many cases and further concern from clinicians of fetal radiation exposure can result in missed or delayed diagnoses. Investigation via the quickest form of imaging, whether computed tomography, magnetic resonance imaging, or transesophageal echocardiography, should be carried out promptly due to the high risk of mortality. Surgical management of TAAD in pregnancy revolves primarily around the decision to deliver the fetus concomitantly or to perform the aortic repair with the fetus in utero. CONCLUSIONS: Management of this group includes rapid and dynamic assessment without delay. From conception to postpartum, there are multiple stages in which to manage these women. Challenges in carrying out management in the form of operative techniques and cardiopulmonary bypass place the fetus at risk and must be approached with caution, particularly as there is little evidence-base for many of these decisions. Further research into reducing maternal and fetal mortality is necessary.

The True Colours Remote Symptom Monitoring System: A Decade of Evolution.

The True Colours remote mood monitoring system was developed over a decade ago by researchers, psychiatrists, and software engineers at the University of Oxford to allow patients to report on a range of symptoms via text messages, Web interfaces, or mobile phone apps. The system has evolved to encompass a wide range of measures, including psychiatric symptoms, quality of life, and medication. Patients are prompted to provide data according to an agreed personal schedule: weekly, daily, or at specific times during the day. The system has been applied across a number of different populations, for the reporting of mood, anxiety, substance use, eating and personality disorders, psychosis, self-harm, and inflammatory bowel disease, and it has shown good compliance. Over the past decade, there have been over 36,000 registered True Colours patients and participants in the United Kingdom, with more than 20 deployments of the system supporting clinical service and research delivery. The system has been adopted for routine clinical care in mental health services, supporting more than 3000 adult patients in secondary care, and 27,263 adolescent patients are currently registered within Oxfordshire and Buckinghamshire. The system has also proven to be an invaluable scientific resource as a platform for research into mood instability and as an electronic outcome measure in randomized controlled trials. This paper aimed to report on the existing applications of the system, setting out lessons learned, and to discuss the implications for tailored symptom monitoring, as well as the barriers to implementation at a larger scale.

Clinical decision support systems for maternity care: a systematic review and meta-analysis.

Background: The use of Clinical Decision Support Systems (CDSS) is increasing throughout healthcare and may be able to improve safety and outcomes in maternity care, but maternity care has key differences to other disciplines that complicate the use of CDSS. We aimed to identify evaluated CDSS and synthesise evidence of their impact on maternity care. Methods: We conducted a systematic review for articles published before 24th May 2024 that described i) CDSS that ii) investigated the impact of their use iii) in maternity settings. Medline, CINAHL, CENTRAL and HMIC were searched for articles relating to evaluations of CDSS in maternity settings, with forward- and backward-citation tracing conducted for included articles. Risk of bias was assessed using the Mixed Methods Assessment Tool, and CDSS were described according to the clinical problem, purpose, design, and technical environment. Quantitative results from articles reporting appropriate data were meta-analysed to estimate odds of a CDSS achieving its desired outcome using a multi-level random effects model, first by individual CDSS and then across all CDSS. PROSPERO ID: CRD42022348157. Findings: We screened 12,039 papers and included 87 articles describing 47 unique CDSS. 24 articles (28%) described randomised controlled trials, 30 (34%) described non-randomised interventional studies, 10 (11%) described mixed methods studies, 10 (11%) described qualitative studies, 7 (8%) described quantitative descriptive studies, and 7 (8%) described economic evaluations. 49 (56%) were in High-Income Countries and 38 (44%) in Low- and Middle-Income countries, with no CDSS trialled in both income categories. Meta-analysis of 35 included studies found an odds ratio for improved outcomes of 1.69 (95% confidence interval 1.24-2.30). There was substantial variation in effects, aims, CDSS types, context, study designs, and outcomes. Interpretation: Most CDSS evaluations showed improvements in outcomes, but there was heterogeneity in all aspects of design and evaluation of systems. CDSS are increasingly important in delivering healthcare, and Electronic Health Records and mHealth will increase their availability, but traditional epidemiological methods may be limited in guiding design and demonstrating effectiveness due to rapid CDSS development lifecycles and the complex systems in which they are embedded. Development methods that are attentive to context, such as Human Centred Design, will help to meet this need. Funding: None.

Which computable biomedical knowledge objects will be regulated? Results of a UK workshop discussing the regulation of knowledge libraries and software as a medical device.

Introduction: To understand when knowledge objects in a computable biomedical knowledge library are likely to be subject to regulation as a medical device in the United Kingdom. Methods: A briefing paper was circulated to a multi-disciplinary group of 25 including regulators, lawyers and others with insights into device regulation. A 1-day workshop was convened to discuss questions relating to our aim. A discussion paper was drafted by lead authors and circulated to other authors for their comments and contributions. Results: This article reports on those deliberations and describes how UK device regulators are likely to treat the different kinds of knowledge objects that may be stored in computable biomedical knowledge libraries. While our focus is the likely approach of UK regulators, our analogies and analysis will also be relevant to the approaches taken by regulators elsewhere. We include a table examining the implications for each of the four knowledge levels described by Boxwala in 2011 and propose an additional level. Conclusions: If a knowledge object is described as directly executable for a medical purpose to provide decision support, it will generally be in scope of UK regulation as "software as a medical device." However, if the knowledge object consists of an algorithm, a ruleset, pseudocode or some other representation that is not directly executable and whose developers make no claim that it can be used for a medical purpose, it is not likely to be subject to regulation. We expect similar reasoning to be applied by regulators in other countries.

User perspectives on long-term remote active electronic self-monitoring of mood symptoms in bipolar spectrum disorders.

BACKGROUND: User feedback is crucial in the development of electronic self-monitoring tools for bipolar spectrum disorders (BSD). Previous studies have examined user experiences in small samples self-monitoring over relatively short time periods. We aimed to explore the experiences of a large sample of individuals with BSD engaged in long-term remote active electronic self-monitoring. METHODS: An online survey, containing closed and open questions, was sent to participants with BSD enrolled on the Bipolar Disorder Research Network (BDRN) True Colours mood-monitoring system. Questions related to experiences of using True Colours, including viewing mood graphs, and sharing data with healthcare professionals (HCPs) and/or family/friends. RESULTS: Response rate was 62.7 % (n = 362). 88.4 % reported finding using True Colours helpful. Commonly reported benefits were having a visual record of mood changes, patterns/triggers and identifying early warning signs. Limitations included questions not being comprehensive or revealing anything new. One third had shared their graphs, with 89.9 % finding it helpful to share with HCPs and 78.7 % helpful to share with family/friends. Perceived benefits included aiding communication and limitations included lack of interest/understanding from others. LIMITATIONS: Responder bias may be present. Findings may not be generalisable to all research cohorts. CONCLUSIONS: The majority of participants valued long-term self-monitoring. Personalisation and ease of use were important. A potential challenge is continued use when mood is long-term stable, highlighting the need for measures to be sensitive to small changes. Sharing self-monitoring data with HCPs may enhance communication of the lived experience of those with BSD. Future research should examine HCPs' perspectives.

Modelling clinical narrative as computable knowledge: The NICE computable implementation guidance project.

Introduction: Translating narrative clinical guidelines to computable knowledge is a long-standing challenge that has seen a diverse range of approaches. The UK National Institute for Health and Care Excellence (NICE) Content Advisory Board (CAB) aims ultimately to (1) guide clinical decision support and other software developers to increase traceability, fidelity and consistency in supporting clinical use of NICE recommendations, (2) guide local practice audit and intervention to reduce unwarranted variation, (3) provide feedback to NICE on how future recommendations should be developed. Objectives: The first phase of work was to explore a range of technical approaches to transition NICE toward the production of natively digital content. Methods: Following an initial 'collaborathon' in November 2022, the NICE Computable Implementation Guidance project (NCIG) was established. We held a series of workstream calls approximately fortnightly, focusing on (1) user stories and trigger events, (2) information model and definitions, (3) horizon-scanning and output format. A second collaborathon was held in March 2023 to consolidate progress across the workstreams and agree residual actions to complete. Results: While we initially focussed on technical implementation standards, we decided that an intermediate logical model was a more achievable first step in the journey from narrative to fully computable representation. NCIG adopted the WHO Digital Adaptation Kit (DAK) as a technology-agnostic method to model user scenarios, personae, processes and workflow, core data elements and decision-support logic. Further work will address indicators, such as prescribing compliance, and implementation in document templates for primary care patient record systems. Conclusions: The project has shown that the WHO DAK, with some modification, is a promising approach to build technology-neutral logical specifications of NICE recommendations. Implementation of concurrent computable modelling by multidisciplinary teams during guideline development poses methodological and cultural questions that are complex but tractable given suitable will and leadership.

Patient-reported Outcomes: the ICHOM Standard Set for Inflammatory Bowel Disease in Real-life Practice Helps Quantify Deficits in Current Care.

BACKGROUND: Patient-reported outcome measures [PROMs] are key to documenting outcomes that matter most to patients and are increasingly important to commissioners of health care seeking value. We report the first series of the ICHOM Standard Set for Inflammatory Bowel Disease [IBD]. METHODS: Patients treated for ulcerative colitis [UC] or Crohn's disease [CD] in our centre were offered enrolment into the web-based TrueColours-IBD programme. Through this programme, e-mail prompts linking to validated questionnaires were sent for symptoms, quality of life, and ICHOM IBD outcomes. RESULTS: The first 1299 consecutive patients enrolled [779 UC, 520 CD] were studied with median 270 days of follow-up (interquartile range [IQR] 116, 504). 671 [52%] were female, mean age 42 years (standard deviation [sd] 16), mean body mass index [BMI] 26 [sd 5.3]. At registration, 483 [37%] were using advanced therapies. Median adherence to fortnightly quality of life reporting and quarterly outcomes was 100% [IQR 48, 100%] and 100% [IQR 75, 100%], respectively. In the previous 12 months, prednisolone use was reported by 229 [29%] patients with UC vs 81 [16%] with CD, p <0.001; 202 [16%] for <3 months; and 108 [8%] for >3 months. An IBD-related intervention was reported by 174 [13%] patients, and 80 [6%] reported an unplanned hospital admission. There were high rates of fatigue [50%] and mood disturbance [23%]. CONCLUSIONS: Outcomes reported by patients illustrate the scale of the therapeutic deficit in current care. Proof of principle is demonstrated that PROM data can be collected continuously with little burden on health care professionals. This may become a metric for quality improvement programmes or to compare outcomes.

OpenClinical.net: Artificial intelligence and knowledge engineering at the point of care.

OBJECTIVE: OpenClinical.net is a way of disseminating clinical guidelines to improve quality of care whose distinctive feature is to combine the benefits of clinical guidelines and other human-readable material with the power of artificial intelligence to give patient-specific recommendations. A key objective is to empower healthcare professionals to author, share, critique, trial and revise these 'executable' models of best practice. DESIGN: OpenClinical.net Alpha (www.openclinical.net) is an operational publishing platform that uses a class of artificial intelligence techniques called knowledge engineering to capture human expertise in decision-making, care planning and other cognitive skills in an intuitive but formal language called PROforma.3 PROforma models can be executed by a computer to yield patient-specific recommendations, explain the reasons and provide supporting evidence on demand. RESULTS: PROforma has been validated in a wide range of applications in diverse clinical settings and specialties, with trials published in high impact peer-reviewed journals. Trials have included patient workup and risk assessment; decision support (eg, diagnosis, test and treatment selection, prescribing); adaptive care pathways and care planning. The OpenClinical software platform presently supports authoring, testing, sharing and maintenance. OpenClinical's open-access, open-source repository Repertoire currently carries approximately 50+ diverse examples (https://openclinical.net/index.php?id=69). CONCLUSION: OpenClinical.net is a showcase for a PROforma-based approach to improving care quality, safety, efficiency and better patient experience in many kinds of routine clinical practice. This human-centred approach to artificial intelligence will help to ensure that it is developed and used responsibly and in ways that are consistent with professional priorities and public expectations.

From guidelines to careflows: modelling and supporting complex clinical processes.

Research on computer interpretable clinical guidelines has largely focused on individual points of care rather than processes of care. Whether we consider simple aids like clinical alerts and reminders or more sophisticated data interpretation and decision-making, guideline developers tend to focus on specific tasks rather than processes like care plans and pathways which are extended in time. In contrast, research on business process modelling has demonstrated notations and tools which deal directly with process modelling, but has not been concerned with problems like data interpretation and decision making. In this chapter we describe these two traditions, and compare some of their strengths and weaknesses. We also briefly discuss the distinct theoretical frameworks which have grown up around them, notably Petri nets for workflow modelling and mathematical logics for guidelines. We conclude that these offer complementary views of clinical processes and that a key research challenge is find a way of unifying them.

Development of a Clinical Decision Support System for Living Kidney Donor Assessment Based on National Guidelines.

BACKGROUND: Live donor nephrectomy is an operation that places the donor at risk of complications without the possibility of medical benefit. Rigorous donor selection and assessment is therefore essential to ensure minimization of risk and for this reason robust national guidelines exist. Previous studies have demonstrated poor adherence to donor guidelines. METHODS: We developed a clinical decision support system (CDSS), based on national living donor guidelines, to facilitate the identification of contraindications, additional investigations, special considerations, and the decision as to nephrectomy side in potential living donors. The CDSS was then tested with patient data from 45 potential kidney donors. RESULTS: The CDSS comprises 17 core tasks completed by either patient or nurse, and 17 optional tasks that are triggered by certain patient demographics or conditions. Decision rules were able to identify contraindications, additional investigations, special considerations, and predicted operation side in our patient cohort. Seventeen of 45 patients went on to donate a kidney, of whom 7 had major contraindications defined in the national guidelines, many of which were not identified by the clinical team. Only 43% of additional investigations recommended by national guidelines were completed, with the most frequently missed investigations being oral glucose tolerance testing and routine cancer screening. CONCLUSIONS: We have demonstrated the feasibility of turning a complex set of national guidelines into an easy-to-use machine-readable CDSS. Comparison with real-world decisions suggests that use of this CDSS may improve compliance with guidelines and informed consent tailored to individual patient risks.

Making the most of the waiting room: Electronic patient engagement, a mixed methods study.

OBJECTIVE: The purpose of this study was to explore whether patients with musculoskeletal conditions would agree to use digital technologies to learn about research registries and make a decision about signing up whilst in the clinic waiting room. METHODS: Patients were recruited from four hospital clinics across Oxfordshire. We used an explanatory mixed methods design with two sequential phases comprising an exploratory, cross-sectional questionnaire (n = 84), followed by focus group interviews (n = 8) to provide context for the findings from the questionnaire. Multivariate ordinal logistic regression models were used to explore relationships between patient preferences and characteristics. Thematic analysis was used to understand the reasons for patient preferences regarding digital technologies and research registries. RESULTS: As participants' age increased, they were more likely to report a preference for face-to-face recruitment methods compared to those using digital technologies. Findings from the focus groups indicated this was primarily due to a fear of technology and physical limitations associated with a patient's condition. Patients also reported a preference for making a decision about signing up at a later date, which was attributed to patients feeling distracted whilst in the waiting room due to anxieties related to their upcoming appointment. CONCLUSIONS: Many patients with musculoskeletal conditions in the UK may be interested in learning about opportunities to participate in research whilst using digital technologies within the waiting room. The results suggest the need for choice regarding the presentation and format of information and whether it can be accessed at a later date at home.