Lifespan of COVID-19 living guideline recommendations: a survival analysis (preprint)

Background NICE has maintained a portfolio of COVID-19 living guidelines since March 2020. Recommendations within these living guidelines are subject to continuous surveillance and updates in response to triggers. However, the lifespan of individual living guideline recommendations and features that may impact on whether a recommendation becomes out of date sooner, is unknown. Objectives This study aimed to describe the length of time NICE COVID-19 living guideline recommendations have remained valid. Methods All guidelines within the NICE COVID-19 portfolio were included to determine the lifespan of living guideline recommendations. Data were collected on all recommendations that had been developed, undergone surveillance or updated between 1 March 2020 and 31 August 2022. Information on initial publication date, decision to update, and update publication date was extracted. Updates were labelled as major changes in evidence synthesis or minor changes without a substantial change in evidence base. Any recommendation that had not been updated or withdrawn was censored. Survival analysis (Kaplan-Meier Curve) was carried out to determine the lifespan of recommendations. Results Overall, 26 COVID-19 living guidelines and 1182 recommendations were included in the analysis. Living recommendations had median survival time of 739 days (IQR: 332, 781). Based on recommendation type, intervention recommendations had a shorter survival time (354 days, IQR 312, 775) compared to diagnosis (368 days, IQR: 328, 795), patient experience (733 days, IQR: 345, 795) and service delivery (739 days, IQR: 643, 781). Within intervention type, pharmacological recommendations had shortest survival time versus non-pharmacological recommendations [335 days (IQR: 161, 775) vs 775 days (IQR: 354, 775)]. Updates were published an average of 29.12 days following a surveillance decision. Conclusion Within living guidelines, some recommendations need to be updated sooner than others. This study outlines the value of a flexible responsive approach to surveillance within the living mode according to pace of change and expectation of update triggers.

Primary Care Post-COVID syndrome Diagnosis and Referral Coding (preprint)

Introduction - Guidelines for diagnosing and managing Post-COVID syndrome have been rapidly developed. Consistency of the application of these guidelines in primary care is unknown. Electronic health records provide an opportunity to review the use of codes relating to Post-COVID syndrome. This paper explores the use of primary care records as a surrogate uptake measure for NICEs rapid guideline managing the long-term effects of COVID-19 by measuring the use of Post-COVID syndrome diagnosis and referral codes in the pathway. Method - With the approval of NHS England we used routine clinical data from the OpenSafely-EMIS/-TPP platforms. Counts of Post-COVID syndrome diagnosis and referral codes were generated from a cohort of all adults, establishing numbers of diagnoses and referrals following diagnosis. The relationship between Post-COVID syndrome diagnosis and referral codes was explored with reference to NICEs rapid guideline. Results - Of over 45 million patients, 69,220 (0.15%) had a Post-COVID syndrome diagnostic code, and 67,741 (0.15%) had a referral code. 78% of referral codes did not have an associated diagnosis code. 79% of diagnosis codes had no subsequent referral code. Only 18,633 (0.04%) had both. There were higher rates of both diagnosis and referral in those who were more deprived, female and some ethnic groups. Discussion - This study demonstrates variation in diagnosis and referral coding rates for Post-COVID syndrome across different patient groups. The results, with limited crossover of referral and diagnostic codes, suggest only one type of code is usually recorded. Recording one code limits the use of routine data for monitoring Post-COVID syndrome diagnosis and management, but suggests several areas for improvement in coding. Post-COVID syndrome coding, particularly diagnosis coding, needs to improve before administrators and researchers can use it to evaluate care pathways.

Living with Long COVID: Implementing a living approach to the NICE guideline on managing the long-term effects of COVID-19 (preprint)

Objectives The aim of this paper is to describe the development and implementation of a flexible living approach to maintaining the currency of NICE's long-term effects of COVID-19 (LTE) guideline and monitoring the uncertain evidence base of this condition. Study Design and Setting The NICE COVID-19 team reviewed its practical experiences of establishing a living approach to developing and maintaining the LTE guideline, including initial development, maintenance and eventual transition to a lower intensity model. The methods and processes were described narratively over the first 2 years of the guideline's lifespan. This was combined with quantitative data on emerging and cumulative evidence over the period to chart the evidence landscape. Results Following publication, the initial timepoint-based update process evolved into a flexible living approach with remote topic expert engagement. Experts engaged with the new process with a 64% response rate to the online surveys. Emerging evidence increased rapidly following publication [11,405 studies assessed in 2021 and 13,181 in 2022] and was captured by continuous surveillance. There were no urgent triggers for updating from the studies identified in 2022 via the living approach, saving considerable resources over the timepoint based approach which would commit resources to planning and convening expert panel meetings. A total of 184 studies with a potential future impact were summarised to capture the cumulative evidence base. Experts highlighted ongoing research and implementation issues which have further informed surveillance of the guideline. After a sustained period without triggers for updating, the living approach was restricted to the highest priority areas with surveillance of ongoing studies. Conclusion This paper illustrates a flexible living approach taken to a novel condition with an evolving evidence landscape. Currency of some living guidelines can be maintained without the need for frequent updating.

Managing the evidence infodemic: Automation approaches used for developing NICE COVID-19 living guidelines (preprint)

Background and Objectives The National Institute for Health and Care Excellence (NICE) produces evidence-based guidance and advice for health, public health and social care practitioners in England and Wales. Between March 2020 and March 2022, NICE produced 24 COVID-19 guidelines to support healthcare workers during the COVID-19 pandemic. This article outlines three automation strategies NICE utilised to facilitate faster processing of evidence on COVID-19 and describes the value of those approaches when there is an increasing volume of evidence and demand on resources. Study Design and Setting Text classification using machine learning, and regular expression-based pattern matching were used to automate screening of literature search results. Relevant clinical trials were tracked by automated monitoring of clinical trial databases and Pubmed. Results The strategies discussed here brought considerable efficiencies in the processing time without impacting on quality compared to equivalent manual efforts. Additionally, the paper illustrates how to incorporate automation into established processes of the evidence management pipeline. Conclusions We have demonstrated through testing and use in live guideline development and surveillance that these are effective and low risk approaches at managing high volumes of evidence. Highlights - To illustrate how NICE utilised automation to handle the Covid-19 ‘infodemic’-managing the ‘infodemic’ of evidence surveillance is a shared global issue. - To outline automation strategies to facilitate faster processing of evidence, especially when there is an increasing volume of evidence and demand on resources. - How automation can be included in established processes without disrupting business as usual operations. - Automation can take many forms, and depending on risk appetite, can be supplemented with manual checking. - Automation can be adopted easily with the right tools and techniques.