12 research outputs found

    Derivation and validation of a severity scoring tool for COVID-19 illness in low-resource setting

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    Background The COVID-19 pandemic has profoundly impacted some of the most vulnerable populations in lowresource settings (LRS) across the globe. These settings tend to have underdeveloped healthcare systems that are exceptionally vulnerable to the strain of an outbreak such as SARS-CoV-2. LRS-based clinicians are in need of effective and contextually appropriate triage and assessment tools that have been purpose-designed to aid in evaluating the severity of potential COVID-19 patients. In the context of the COVID-19 crisis, a low-input severity scoring tool could be a cornerstone of ensuring timely access to appropriate care and justified use of critically limited resources. Aim and objectives The aim of this research was to develop and validate a tool to assist frontline providers in rapidly predicting severe COVID-19 disease in LRS. To achieve this aim, the following objectives were defined: identify existing methods of risk stratification of suspected COVID-19 patients worldwide; establish predictors of severe COVID-19 illness measurable in LRS; derive a risk stratification tool to assist facility-based healthcare providers in LRS in evaluating in-hospital mortality risk; and validate tool SST in the African setting using real-world data. Methods To achieve the aim of this dissertation, quantitative and review methodologies were employed across four studies. First, a scoping review was conducted to identify all studies describing screening, triage, and severity scoring of suspected COVID-19 patients worldwide. These tools were then compared to usability and feasibility standards for LRS emergency units, to determine viable tool options for such settings. Following this, a systematic review and meta-analysis were undertaken to evaluate existing literature for associations between COVID-19 illness severity, and historical characteristics, clinical presentations, and investigations measurable in LRS. Three online databases were searched to identify all studies assessing potential associations between clinical characteristics and investigations, and COVID-19 illness severity. Data for all variables that were statistically analysed in relation to COVID19 disease severity were extracted and a meta-analysis was conducted to generate pooled odds ratios for individual variables' predictive abilities. In the third study, machine learning was used on data from a retrospective cohort of Sudanese COVID-19 patients to derive the AFEM COVID-19 Mortality Score (AFEM-CMS), a contextually appropriate mortality index for COVID-19. Following this, a fourth study was conducted with a more recent Sudanese dataset to validate the tool. Results The scoping review identified COVID-19 risk stratification 23 tools with potential feasibility for use in LRS. Of these, none had been validated in LRS. The systematic review then identified 79 eligible articles, including data from 27713 individual patients with laboratory-confirmed COVID-19. A total of 202 features were studied in relation to COVID-19 severity across these articles, of which 81 were deemed feasible for assessment in LRS. Meta-analysis of two demographic features, 21 comorbidities, and 21 presenting signs and symptoms with appropriate data available identified 19 significant predictors of severe COVID-19, including: past medical history of stroke (pOR: 3.08 (95% CI [1.95, 4.88])), shortness of breath (pOR: 2·78 (95% CI [2·24-3·46])), chronic kidney disease (pOR: 2.55 (95% CI [1.52-4.29])), and presence of any comorbidity (pOR: 2.41 (95% CI [2.01-2.89])). These significant predictors of severe COVID-19 were then considered for inclusion in the AFEM-CMS. Data from 467 COVID-19 patientsin Sudan were used to derive two versions of the tool. Both include age, sex, number of comorbidities, Glasgow Coma Scale, respiratory rate, and systolic blood pressure; in settings with pulse oximetry, oxygen saturation is included and, in settings without access, heart rate is included. The AFEM-CMS showed good discrimination: The model including pulse oximetry had a C-statistic of 0.775 (95% CI: 0.737-0.813) and the model excluding it had a C-statistic of 0.719 (95% CI: 0.678- 0.760). The tool was then validated against a second set of data from Sudan and found to once again have reasonable discriminatory power in identifying those at greatest risk of death from COVID-19: The model including pulse oximetry had a C-statistic of 0.732 (95% CI: 0.687-0.777) and the model excluding pulse oximetry had a C-statistic of 0.696 (0.645-0.747). Conclusions and relevance This dissertation establishes what is, to our knowledge, the first COVID-19 mortality prediction tool intentionally designed for frontline providers in LRS and validated in such a setting. The derivation and validation of the AFEM-CMS highlight the feasibility and potential impact of real-time development of clinical tools to improve patient care, even in times of surge in LRS. This study is just one of hundreds of efforts across all resource levels suggesting that rapid use of machine learning methodologies holds promise in improving responses to pandemics and other emergencies. It is our hope that, in future health crises, LRS-based clinicians and researchers can refer to these techniques to inform contextually and situationally appropriate clinical tools and reduce morbidity and mortality

    The Convergence of Human and Artificial Intelligence on Clinical Care - Part I

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    This edited book contains twelve studies, large and pilots, in five main categories: (i) adaptive imputation to increase the density of clinical data for improving downstream modeling; (ii) machine-learning-empowered diagnosis models; (iii) machine learning models for outcome prediction; (iv) innovative use of AI to improve our understanding of the public view; and (v) understanding of the attitude of providers in trusting insights from AI for complex cases. This collection is an excellent example of how technology can add value in healthcare settings and hints at some of the pressing challenges in the field. Artificial intelligence is gradually becoming a go-to technology in clinical care; therefore, it is important to work collaboratively and to shift from performance-driven outcomes to risk-sensitive model optimization, improved transparency, and better patient representation, to ensure more equitable healthcare for all

    Pulmonary fibrosis: one manifestation, various diseases

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    This research topic collection entitled “Pulmonary Fibrosis: one manifestation, various diseases”, involving authors from different countries, confirms that this disease is a hot topic (Confalonieri P et al.,2022, Orlandi M et al., 2022). There are over 200 different types of pulmonary fibrosis (PF), the most common is the idiopathic pulmonary fbrosis (IPF), called idiopathic because it has no known cause. Another rare form is familial PF, for which several studies reported correlation with few genes. An important group of PF are due to other diseases, for example, autoimmune diseases such as rheumatoid arthritis, systemic sclerosis or Sjogren’s syndrome (Ruaro et al., 2022, Trombetta AC et al., 2017, Bernero Eet al., 2013). PF could correlate to viral infections (e.g. COVID-19), gastroesophageal reflux disease (GERD) (Baratella E et al, 2021, Ruaro et al., 2018), and the exposure to various materials (including naturally occurring such as bird or animal droppings, and occupational such as asbestos or silica). Furthermore, smoking, radiation treatments, and certain drugs can increase risk of developing PF. In the first article (Saketkoo et al.) of the collection, the authors evaluate the use of International Classification of Functioning, Disability, and Health (ICF) approved by World Health Organization (WHO) in patients affected by interstitial lung diseases (ILD)

    Annual SHOT Report 2020

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    SHOT is affiliated to the Royal College of Pathologists. This report is produced by SHOT working with MHRAKey SHOT messages • Ensuring transfusion teams are well resourced: Clinical and laboratory teams can function optimally only if adequately staffed and well resourced. Healthcare leaders and management must ensure that staff have access to the correct information technology (IT) equipment and financial resources for safe and effective functioning • Addressing knowledge gaps, cognitive biases, and holistic training: Transfusion training with a thorough and relevant knowledge base in transfusion to all clinical and laboratory staff along with training in patient safety principles, understanding human factors and quality improvement approaches are essential. It is important that staff understand how cognitive biases contribute to poor decision making so that they can be mitigated appropriately • Patient safety culture: Fostering a strong and effective safety culture that is ‘just and learning’ is vital to ensure reduction in transfusion incidents and errors, thus directly improving patient safety • Standard operating procedures (SOP): SOP need to be simple, clear, easy to follow and explain the rationale for each step. This will then ensure staff are engaged and more likely to be compliant and follow the SOP • Learning from near misses: Reporting and investigating near misses helps identify and control risks before actual harm results, thus providing valuable opportunities to improve transfusion safety • Learning from the pandemic: The learning from the pandemic experiences should be captured in every organisation, by everyone in healthcare and used to improve patient safet

    P14.01 An example of too much too soon? A review of caesarean sections performed in the first stage of labour in Kenya

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    Objective: Caesarean Section (CS) has potential short and long-term complications and is associated with excess maternal death. Decisions to perform (CS) are frequently made by inexperienced and unsupported non-specialist doctors, sometimes resulting in inappropriate decision-making and surgery. Our study assesses decision-making for CS in the first stage of labour in Kenya. Method: A panel of one UK and six Kenyan expert obstetricians reviewed clinical data extracted from 87 case-notes, that were randomly selected from a series obtained from seven referral hospitals in five Kenyan counties over six months in 2020. Following a preliminary review of the data and email discussion, an online panel was convened to discuss outstanding cases where consensus was yet to be reached. Agreement was reached by the panel in all but 5 cases. Results: In 41.3% cases, CS was considered appropriate, including 8% where CS was performed too late. The decision to delivery interval exceeded 2 h in 58.6% cases, including 16 cases of non-reassuring fetal status. In 10.3% it was considered that due to delay, further reassessment should have occurred. In 9.1% the CS was done too soon. There was insufficient information available to make a full assessment in 21.8% of cases. In 11.5% the CS was inappropriate. Conclusion: This review demonstrates that unnecessary caesarean sections are being performed, while some with appropriate indications are subject to delays. There is need for improved support for decision-making, coupled with improved record-keeping, improved quality of fetal monitoring during labour and more timely surgery when necessary

    P14.02 An electronic behaviour diary: Monitoring the effects of advanced obstetric surgical skills training

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    Objective: Training should lead to improvements in the quality of clinical care delivery. It is essential to follow up participants after a training intervention to monitor changes in behaviour associated with adoption of lessons learned into clinical practice. We introduced an electronic diary to facilitate monitoring whilst minimising effort for participants. Method: An electronic diary was created using a freely available on-line platform. Following a training intervention on advanced obstetric surgical skills, obstetric residents from Kenya were invited to pilot completing the diary after their labour ward shifts. Entries were anonymised. Participants were asked to enumerate the times they utilised specific skills, or to state why they had been unable to do so, using tick box options. Reflections on skills used were entered using free comments. Results: All participants reported changed behaviours, for example, improved surgical knot-tying, safer needle handling, separate closure of uterine incision angles and techniques for delivery of the impacted fetal head. 6 reported conducting vaginal breech birth and 6 performed vacuum-assisted birth. All reported improvements in use of the safe surgical checklist, obtaining consent and respectful maternity care. 7 had participated in newborn resuscitation. Reflections suggested participants experienced improved levels of confidence and satisfaction when implementing new skills. Conclusion: This pilot study has demonstrated the feasibility of monitoring clinical behaviour change following training using an electronic platform. Monitoring the effect of training is essential to prove that training results in improvements to clinical practice. We plan to roll out this intervention following future training interventions

    P04.41 Exploring reasons for and outcomes of second stage caesarean section and assisted vaginal birth in selected hospitals in Kenya

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    Objective: Obstetric vacuum devices for assisted vaginal birth (AVB) can avoid the need for unnecessary second-stage caesarean sections (SSCS), associated with increased morbidity and mortality. Despite emergency obstetric training since 2019, AVB was rarely performed. This study sought to better understand missed opportunities and reasons for non-performance of AVB in Kenya. Method: A mixed-methods design incorporated a review of randomly selected SSCS and AVB case notes, and key informant interviews with healthcare providers, from 8 purposively selected, high-volume hospitals in Kenya. The reviews were carried out by four experienced obstetricians (3 Kenyan, 1 British). The interviews were semi-structured and conducted online and analysed using a thematic approach. Results: Six AVB and 66 SSCS cases were reviewed. Nine percent of SSCS could have been AVB, and 58% reviewers were unable to determine appropriateness due to poor record keeping. Perinatal mortality was 9%, and 11% of infants and 9% of mothers experienced complications following SSCS. Twenty interviews, with obstetricians, midwives and medical officers, explored themes of previous experience, confidence, and adequacy of training relating to AVB. Reasons for non-performance included lack of equipment and staff. Conclusion: Increases in appropriate use of AVB could save the lives of infants and mothers and reduce ongoing morbidity. In order to achieve this, the varied reasons for non-performance of AVB need to be systematically addressed at local, regional and national levels
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