Diarrhoea and preadmission antibiotic exposure in COVID-19: a retrospective cohort study of 1153 hospitalised patients.

OBJECTIVE: The aims of this study were to describe community antibiotic prescribing patterns in individuals hospitalised with COVID-19, and to determine the association between experiencing diarrhoea, stratified by preadmission exposure to antibiotics, and mortality risk in this cohort. DESIGN/METHODS: Retrospective study of the index presentations of 1153 adult patients with COVID-19, admitted between 1 March 2020 and 29 June 2020 in a South London NHS Trust. Data on patients' medical history (presence of diarrhoea, antibiotic use in the previous 14 days, comorbidities); demographics (age, ethnicity, and body mass index); and blood test results were extracted. Time to event modelling was used to determine the risk of mortality for patients with diarrhoea and/or exposure to antibiotics. RESULTS: 19.2% of the cohort reported diarrhoea on presentation; these patients tended to be younger, and were less likely to have recent exposure to antibiotics (unadjusted OR 0.64, 95% CI 0.42 to 0.97). 19.1% of the cohort had a course of antibiotics in the 2 weeks preceding admission; this was associated with dementia (unadjusted OR 2.92, 95% CI 1.14 to 7.49). After adjusting for confounders, neither diarrhoea nor recent antibiotic exposure was associated with increased mortality risk. However, the absence of diarrhoea in the presence of recent antibiotic exposure was associated with a 30% increased risk of mortality. CONCLUSION: Community antibiotic use in patients with COVID-19, prior to hospitalisation, is relatively common, and absence of diarrhoea in antibiotic-exposed patients may be associated with increased risk of mortality. However, it is unclear whether this represents a causal physiological relationship or residual confounding.

Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol.

INTRODUCTION: Standards for Reporting of Diagnostic Accuracy Study (STARD) was developed to improve the completeness and transparency of reporting in studies investigating diagnostic test accuracy. However, its current form, STARD 2015 does not address the issues and challenges raised by artificial intelligence (AI)-centred interventions. As such, we propose an AI-specific version of the STARD checklist (STARD-AI), which focuses on the reporting of AI diagnostic test accuracy studies. This paper describes the methods that will be used to develop STARD-AI. METHODS AND ANALYSIS: The development of the STARD-AI checklist can be distilled into six stages. (1) A project organisation phase has been undertaken, during which a Project Team and a Steering Committee were established; (2) An item generation process has been completed following a literature review, a patient and public involvement and engagement exercise and an online scoping survey of international experts; (3) A three-round modified Delphi consensus methodology is underway, which will culminate in a teleconference consensus meeting of experts; (4) Thereafter, the Project Team will draft the initial STARD-AI checklist and the accompanying documents; (5) A piloting phase among expert users will be undertaken to identify items which are either unclear or missing. This process, consisting of surveys and semistructured interviews, will contribute towards the explanation and elaboration document and (6) On finalisation of the manuscripts, the group's efforts turn towards an organised dissemination and implementation strategy to maximise end-user adoption. ETHICS AND DISSEMINATION: Ethical approval has been granted by the Joint Research Compliance Office at Imperial College London (reference number: 19IC5679). A dissemination strategy will be aimed towards five groups of stakeholders: (1) academia, (2) policy, (3) guidelines and regulation, (4) industry and (5) public and non-specific stakeholders. We anticipate that dissemination will take place in Q3 of 2021.

Hospital bed capacity and usage across secondary healthcare providers in England during the first wave of the COVID-19 pandemic: a descriptive analysis.

OBJECTIVE: In this study, we describe the pattern of bed occupancy across England during the peak of the first wave of the COVID-19 pandemic. DESIGN: Descriptive survey. SETTING: All non-specialist secondary care providers in England from 27 March27to 5 June 2020. PARTICIPANTS: Acute (non-specialist) trusts with a type 1 (ie, 24 hours/day, consultant-led) accident and emergency department (n=125), Nightingale (field) hospitals (n=7) and independent sector secondary care providers (n=195). MAIN OUTCOME MEASURES: Two thresholds for 'safe occupancy' were used: 85% as per the Royal College of Emergency Medicine and 92% as per NHS Improvement. RESULTS: At peak availability, there were 2711 additional beds compatible with mechanical ventilation across England, reflecting a 53% increase in capacity, and occupancy never exceeded 62%. A consequence of the repurposing of beds meant that at the trough there were 8.7% (8508) fewer general and acute beds across England, but occupancy never exceeded 72%. The closest to full occupancy of general and acute bed (surge) capacity that any trust in England reached was 99.8% . For beds compatible with mechanical ventilation there were 326 trust-days (3.7%) spent above 85% of surge capacity and 154 trust-days (1.8%) spent above 92%. 23 trusts spent a cumulative 81 days at 100% saturation of their surge ventilator bed capacity (median number of days per trust=1, range: 1-17). However, only three sustainability and transformation partnerships (aggregates of geographically co-located trusts) reached 100% saturation of their mechanical ventilation beds. CONCLUSIONS: Throughout the first wave of the pandemic, an adequate supply of all bed types existed at a national level. However, due to an unequal distribution of bed utilisation, many trusts spent a significant period operating above 'safe-occupancy' thresholds despite substantial capacity in geographically co-located trusts, a key operational issue to address in preparing for future waves.

The Trail Making test: a study of its ability to predict falls in the acute neurological in-patient population.

OBJECTIVE: To determine whether tests of cognitive function and patient-reported outcome measures of motor function can be used to create a machine learning-based predictive tool for falls. DESIGN: Prospective cohort study. SETTING: Tertiary neurological and neurosurgical center. SUBJECTS: In all, 337 in-patients receiving neurosurgical, neurological, or neurorehabilitation-based care. MAIN MEASURES: Binary (Y/N) for falling during the in-patient episode, the Trail Making Test (a measure of attention and executive function) and the Walk-12 (a patient-reported measure of physical function). RESULTS: The principal outcome was a fall during the in-patient stay ( n = 54). The Trail test was identified as the best predictor of falls. Moreover, addition of other variables, did not improve the prediction (Wilcoxon signed-rank P < 0.001). Classical linear statistical modeling methods were then compared with more recent machine learning based strategies, for example, random forests, neural networks, support vector machines. The random forest was the best modeling strategy when utilizing just the Trail Making Test data (Wilcoxon signed-rank P < 0.001) with 68% (± 7.7) sensitivity, and 90% (± 2.3) specificity. CONCLUSION: This study identifies a simple yet powerful machine learning (Random Forest) based predictive model for an in-patient neurological population, utilizing a single neuropsychological test of cognitive function, the Trail Making test.

Systematic Review of Health-Related Work Outcome Measures and Quality Criteria-Based Evaluations of Their Psychometric Properties.

OBJECTIVE: To examine the state of psychometric validation in the health-related work outcome literature. DATA SOURCES: We searched PubMed, PubMed Central, CINAHL, Embase (plus Embase Classic), and PsycINFO from inception to January 2016 using the following search terms: stroke, multiple sclerosis, epilepsy, spinal cord injury, brain injury, musculoskeletal disease, work, absenteeism, presenteeism, occupation, employment, job, outcome measure, assessment, work capacity evaluation, scale, and questionnaire. STUDY SELECTION: From the 22,676 retrieved abstracts, 597 outcome measures were identified. Inclusion was based on content analysis. There were 95 health-related work outcome measures retained; of these, 2 were treated as outliers and therefore are discussed separately. All 6 authors individually organized the 93 remaining scales based on their content. DATA EXTRACTION: A follow-up search using the same sources, and time period, with the name of the outcome measures and the terms psychometric, reliability, validity, and responsiveness, identified 263 unique classical test theory psychometric property datasets for the 93 tools. An assessment criterion for psychometric properties was applied to each article, and where consensus was not achieved, the rating delivered by most of the assessors was reported. DATA SYNTHESIS: Of the articles reported, 18 reporting psychometric data were not accessible and therefore could not be assessed. There were 39 that scored <20% of the maximum achievable score, 106 scored between 20% and 40%, 82 scored between 40% and 60%, 15 scored between 60% and 80%, and only 1 scored >80%. The 3 outcome measures associated with the highest scoring datasets were the Sheehan Disability Scale, the Fear Avoidance Beliefs Questionnaire, and the assessment of the Subjective Handicap of Epilepsy. Finally, only 2 psychometric validation datasets reported the complete set of baseline psychometric properties. CONCLUSIONS: This systematic review highlights the current limitations of the health-related work outcome measure literature, including the limited number of robust tools available.