This study aimed to quantify how patient risk factors relate to COVID-19 severity across categories 1–5 in a prospective, hospital-based cohort. We hypothesised that greater severity would be associated with higher odds of intensive care unit (ICU) admission and in-hospital mortality. Secondary aims were to assess associations with age, viral variants, symptom clusters, lymphocyte count, fasting blood glucose and cytokine profiles.

Prospective cohort study.

A secondary-care/tertiary-care hospital and linked community settings in Cheras, Kuala Lumpur, Malaysia.

This study was nested within the COVGEN project, a prospective COVID-19 cohort conducted at Hospital Canselor Tuanku Muhriz UKM (HCTM), Cheras Health Clinic and the Bandar Tun Razak COVID-19 Assessment Centre in Cheras, Kuala Lumpur, Malaysia, from 1 August 2021 to 31 October 2022. 2532 participants were enrolled at baseline. Eligible participants were Malaysian citizens aged 12–18 years (paediatric/adolescent) or ≥18 years who had reverse transcription-polymerase chain reaction–confirmed COVID-19 at recruitment and resided in Kuala Lumpur or Selangor. Patients who had a clinically unstable condition and those who declined participation (personally or via a next-of-kin or legal representative) were excluded. This analysis included 559 patients hospitalised at HCTM; after excluding five with incomplete questionnaires, 554 remained for analysis (413 admitted to general wards and 141 to ICUs). Categories 3–5 comprised hospitalised patients, whereas categories 1–2 included hospitalised individuals and a subset recruited from community settings.

Primary outcomes included disease severity (categories 4–5 vs 1–3), ICU admission and in-hospital mortality. Secondary outcomes included associations with age strata, viral variant (delta vs omicron), symptom clusters, lymphocyte count, fasting blood glucose and cytokines: interferon gamma-inducible protein 10, interferon gamma, interleukins 8, 10, 2, 6 and 7 and tumour necrosis factor alpha.

141 of 554 (25.5%) patients required ICU care. Compared with milder categories, category 5 was associated with markedly higher odds of ICU admission (OR 204.50; 95% CI 37.54 to 1114.18; p55 versus

An increasing clinical severity category was strongly associated with ICU admission and mortality. Age, delta infection, specific symptom clusters, lymphopenia, hyperglycaemia and pro-inflammatory cytokines identified higher-risk patients, supporting risk-stratified management and prioritisation for enhanced monitoring.

Drug poisoning, caused predominantly by fentanyl in the unregulated drug supply, is the leading cause of death among persons 10–59 years in British Columbia (BC), Canada. In March 2020, in response to the emergence of the COVID-19 pandemic, the province of BC released the Risk Mitigation Guidance (RMG) as a clinical tool for physicians and nurse practitioners, allowing prescribers to provide selective withdrawal management medications, such as hydromorphone, dextroamphetamine, diazepam and others, as a legal and regulated supply of pharmaceutical alternatives to individuals who were at-risk of COVID-19 and overdose. In July 2021, the government of BC released the prescribed safer supply (PSS) policy, extending the scope beyond the COVID-19 pandemic and initial medications offered under the RMG. Recent studies have shown clear benefits among people with a diagnosed opioid use disorder who were prescribed PSS, in reducing mortality, as well as improving retention on opioid agonist treatment for those who were coprescribed PSS medications. The objective of the analysis detailed in this protocol is to use a cross-model comparison approach, comparing two independently developed models which are currently used in public health institutions in BC, to estimate the impact of this policy on opioid overdose-related mortality, while also considering the potential negative impacts of PSS medication diversion to those who are opioid naïve. This project will add to the limited evidence-base on the population-level impact of pharmaceutical alternatives interventions to date.

We have identified two appropriate mathematical models to evaluate the impact of PSS on the number of opioid overdose-related deaths within BC from the inception of the programme (March 2020) until December 2022. We will use recently established guidelines on conducting a cross-model comparison to identify structural and parameter differences between the models and perform adaptation steps to generate the counterfactual scenarios. These will include creating additional health states for the population representing individuals receiving PSS, and parameterising the overdose risk, mortality and retention in the new compartments from a comprehensive population-level data set. Harmonisation will be conducted to ensure that both models evaluate the same scenarios with the same data. Further sensitivity analyses will be conducted to consider alternative counterfactual scenarios and changes to the population following the implementation of the intervention.

This study is exempt from research ethics board review, as outlined in the Tri-Council Policy Statement, because it relies on data that is available in the public domain and there is no possibility of identifying individual persons. Results of the model validation analysis will be distributed through peer-reviewed journals and knowledge translation materials posted on the websites of the BC Centre for Disease Control and Centre for Advancing Health Outcomes.

https://osf.io/kju2p/overview.

Diagnosing pulmonary tuberculosis (PTB) in children is challenging owing to paucibacillary disease, non-specific symptoms and signs and challenges in microbiological confirmation. Chest X-ray (CXR) interpretation is fundamental for diagnosis and classifying disease as severe or non-severe. In adults with PTB, there is substantial evidence showing the usefulness of artificial intelligence (AI) in CXR interpretation, but very limited data exist in children.

A prospective two-stage study of children with presumed PTB in three sites (one in South Africa and two in Pakistan) will be conducted. In stage I, eligible children will be enrolled and comprehensively investigated for PTB. A CXR radiological reference standard (RRS) will be established by an expert panel of blinded radiologists. CXRs will be classified into those with findings consistent with PTB or not based on RRS. Cases will be classified as confirmed, unconfirmed or unlikely PTB according to National Institutes of Health definitions. Data from 300 confirmed and unconfirmed PTB cases and 250 unlikely PTB cases will be collected. An AI-CXR algorithm (qXR) will be used to process CXRs. The primary endpoint will be sensitivity and specificity of AI to detect confirmed and unconfirmed PTB cases (composite reference standard); a secondary endpoint will be evaluated for confirmed PTB cases (microbiological reference standard). In stage II, a multi-reader multi-case study using a cross-over design will be conducted with 16 readers and 350 CXRs to assess the usefulness of AI-assisted CXR interpretation for readers (clinicians and radiologists). The primary endpoint will be the difference in the area under the receiver operating characteristic curve of readers with and without AI assistance in correctly classifying CXRs as per RRS.

The study has been approved by a local institutional ethics committee at each site. Results will be published in academic journals and presented at conferences. Data will be made available as an open-source database.

PACTR202502517486411