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Background COVID-19 is a new multi-organ disease causing considerable worldwide morbidity and mortality. While many recognized pathophysiological mechanisms are involved, their exact causal relationships remain opaque. Better understanding is needed for predicting their progression, targeting therapeutic approaches, and improving patient outcomes. While many mathematical causal models describe COVID-19 epidemiology, none have described its pathophysiology. Methods In early 2020, we began developing such causal models. The SARS-CoV-2 virus’s rapid and extensive spread made this particularly difficult: no large patient datasets were publicly available; the medical literature was flooded with sometimes conflicting pre-review reports; and clinicians in many countries had little time for academic consultations. We used Bayesian network (BN) models, which provide powerful calculation tools and directed acyclic graphs (DAGs) as comprehensible causal maps. Hence, they can incorporate both expert opinion and numerical data, and produce explainable, updatable results. To obtain the DAGs, we used extensive expert elicitation (exploiting Australia’s exceptionally low COVID-19 burden) in structured online sessions. Groups of clinical and other specialists were enlisted to filter, interpret and discuss the literature and develop a current consensus. We encouraged inclusion of theoretically salient latent (unobservable) variables, likely mechanisms by extrapolation from other diseases, and documented supporting literature while noting controversies. Our method was iterative and incremental: systematically refining and validating the group output using one-on-one follow-up meetings with original and new experts. 35 experts contributed 126 hours face-to-face, and could review our products. Results We present two key models, for the initial infection of the respiratory tract and the possible progression to complications, as causal DAGs and BNs with corresponding verbal descriptions, dictionaries and sources. These are the first published causal models of COVID-19 pathophysiology. Conclusions Our method demonstrates an improved procedure for developing BNs via expert elicitation, which other teams can implement to model emergent complex phenomena. Our results have three anticipated applications: (i) freely disseminating updatable expert knowledge; (ii) guiding design and analysis of observational and clinical studies; (iii) developing and validating automated tools for causal reasoning and decision support. We are developing such tools for the initial diagnosis, resource management, and prognosis of COVID-19, parameterized using the ISARIC and LEOSS databases.

Pneumonia remains a leading cause of hospitalization and death among young children worldwide, and the diagnostic challenge of differentiating bacterial from non-bacterial pneumonia is the main driver of antibiotic use for treating pneumonia in children. Causal Bayesian networks (BNs) serve as powerful tools for this problem as they provide clear maps of probabilistic relationships between variables and produce results in an explainable way by incorporating both domain expert knowledge and numerical data. We used domain expert knowledge and data in combination and iteratively, to construct, parameterise and validate a causal BN to predict causative pathogens for childhood pneumonia. Expert knowledge elicitation occurred through a series of group workshops, surveys and one-on-one meetings involving 6-8 experts from diverse domain areas. The model performance was evaluated based on both quantitative metrics and qualitative expert validation. Sensitivity analyses were conducted to investigate how the target output is influenced by varying key assumptions of a particularly high degree of uncertainty around data or domain expert knowledge. Designed to apply to a cohort of children with X-ray confirmed pneumonia who presented to a tertiary paediatric hospital in Australia, the resulting BN offers explainable and quantitative predictions on a range of variables of interest, including the diagnosis of bacterial pneumonia, detection of respiratory pathogens in the nasopharynx, and the clinical phenotype of a pneumonia episode. Satisfactory numeric performance has been achieved including an area under the receiver operating characteristic curve of 0.8 in predicting clinically-confirmed bacterial pneumonia with sensitivity 88% and specificity 66% given certain input scenarios (i.e., information that is available and entered into the model) and trade-off preferences (i.e., relative weightings of the consequences of false positive versus false negative predictions). We specifically highlight that a desirable model output threshold for practical use is very dependent upon different input scenarios and trade-off preferences. Three commonly encountered scenarios were presented to demonstrate the potential usefulness of the BN outputs in various clinical pictures. To our knowledge, this is the first causal model developed to help determine the causative pathogen for paediatric pneumonia. We have shown how the method works and how it would help decision making on the use of antibiotics, providing insight into how computational model predictions may be translated to actionable decisions in practice. We discussed key next steps including external validation, adaptation and implementation. Our model framework and the methodological approach can be adapted beyond our context to broad respiratory infections and geographical and healthcare settings.

Background Diagnosing urinary tract infections (UTIs) in children in the emergency department (ED) is challenging due to the variable clinical presentations and difficulties in obtaining a urine sample free from contamination. Clinicians need to weigh a range of observations to make timely diagnostic and management decisions, a difficult task to achieve without support due to the complex interactions among relevant factors. Directed acyclic graphs (DAG) and causal Bayesian networks (BN) offer a way to explicitly outline the underlying disease, contamination and diagnostic processes, and to further make quantitative inference on the event of interest thus serving as a tool for decision support. Methods We prospectively collected data on children present to ED with suspected UTIs. Through knowledge elicitation workshops and one-on-one meetings, a DAG was co-developed with clinical domain experts (the Expert DAG) to describe the causal relationships among variables relevant to paediatric UTIs. The Expert DAG was combined with prospective data and further domain knowledge to inform the development of an application-oriented BN (the Applied BN), designed to support the diagnosis of UTI. We assessed the performance of the Applied BN using quantitative and qualitative methods. Results We summarised patient background, clinical and laboratory characteristics of 431 episodes of suspected UTIs enrolled from May 2019 to November 2020. The Expert DAG was presented with a narrative description, elucidating how infection, specimen contamination and management pathways causally interact to form the complex picture of paediatric UTIs. Parameterised using prospective data and expert-elicited parameters, the Applied BN achieved an excellent and stable performance in predicting Escherichia coli culture results, with a mean area under the receiver operating characteristic curve of 0.86 and a mean log loss of 0.48 based on 10-fold cross-validation. The BN predictions were reviewed via a validation workshop, and we illustrate how they can be presented for decision support using three hypothetical clinical scenarios. Conclusion Causal BNs created from both expert knowledge and data can integrate case-specific information to provide individual decision support during the diagnosis of paediatric UTIs in ED. The model aids the interpretation of culture results and the diagnosis of UTIs, promising the prospect of improved patient care and judicious use of antibiotics.

BackgroundFollowing a relative absence in winter 2020, a large resurgence of respiratory syncytial virus (RSV) detections occurred during the 2020/2021 summer in Western Australia. This seasonal shift was linked to SARS-CoV-2 public health measures. We examine the epidemiology and RSV testing of respiratory-coded admissions, and compare clinical phenotype of RSV-positive admissions between 2019 and 2020.MethodAt a single tertiary paediatric centre, International Classification of Diseases, 10th edition Australian Modification-coded respiratory admissions longer than 12 hours were combined with laboratory data from 1 January 2019 to 31 December 2020. Data were grouped into bronchiolitis, other acute lower respiratory infection (OALRI) and wheeze, to assess RSV testing practices. For RSV-positive admissions, demographics and clinical features were compared between 2019 and 2020.ResultsRSV-positive admissions peaked in early summer 2020, following an absent winter season. Testing was higher in 2020: bronchiolitis, 94.8% vs 89.2% (p=0.01); OALRI, 88.6% vs 82.6% (p=0.02); and wheeze, 62.8% vs 25.5% (p<0.001). The 2020 peak month, December, contributed almost 75% of RSV-positive admissions, 2.5 times the 2019 peak. The median age in 2020 was twice that observed in 2019 (16.4 vs 8.1 months, p<0.001). The proportion of RSV-positive OALRI admissions was greater in 2020 (32.6% vs 24.9%, p=0.01). There were no clinically meaningful differences in length of stay or disease severity.InterpretationThe 2020 RSV season was in summer, with a larger than expected peak. There was an increase in RSV-positive non-bronchiolitis admissions, consistent with infection in older RSV-naïve children. This resurgence raises concern for regions experiencing longer and more stringent SARS-CoV-2 public health measures.

Correspondence to Dr David Anthony Foley, Microbiology, PathWest Laboratory Medicine Western Australia, Perth, WA 6009, Australia; drdavidanthonyfoley@gmail.com An interseasonal resurgence of respiratory syncytial virus (RSV) was observed in Western Australia at the end of 2020. NPI, non-pharmaceutical intervention; OALRI, other acute lower respiratory tract infection; RSV, respiratory syncytial virus; WRS, wheeze responsive to salbutamol. Table 1 Comparison of RSV presentations in Western Australia in 2019 and 2020/21 seasons by clinical phenotype and rates per age group in Western Australia Metropolitan region Season 2019 2020/21 Duration (weeks) 28 14 Total admissions 398 563 Clinical phenotype N (% total) N (% total) P value Bronchiolitis 236 (59.3) 242 (43) <0.001 OALRI 94 (23.6) 167 (29.7) 0.04 Wheeze responsive to salbutamol 31 (7.8) 93 (16.5) <0.001 Other 37 (9.3) 61 (10.8) 0.4 Median age in months (IQR) 7.5 (2.2–22) 14.7 (4.4–24.8) <0.001 WA Metropolitan region N (% total) 332 (83.4) 519 (92.2) <0.001 Under 12 months N 194 210 Rate per 1000 7.8 8.4 0.46 95% CI 6.7–9 7.3–9.7 Between 12 and 24 months N 70 167 Rate per 1000 2.7 6.7 <0.001 95% CI 2.1 to 3.5 5.7 to 7.8 Between 24 and 48 months N 43 104 Rate per 1000 0.8 2.1 <0.001 95% CI 0.6 to 1.1 1.7 to 2.5 N, number; OALRI, other acute lower respiratory tract infection; RSV, respiratory syncytial virus; URTI, upper respiratory tract infection; WA, Western Australia.

ObjectiveDespite the many benefits of paediatric Outpatient Parenteral Antimicrobial Therapy (OPAT) programmes, there are risks associated with delivering inpatient-level care outside of hospital. There is a paucity of evidence defining how best to mitigate these risks. We examined the impact of introducing a dedicated medical team to OPAT, to define the role of increased medical oversight in improving patient outcomes in this cohort.DesignA prospective 24-month pre–post observational cohort study.SettingThe Hospital in the Home (HiTH) programme at Princess Margaret Hospital (PMH) for Children, Western Australia.PatientsAll OPAT admissions to HiTH, excluding haematology/oncology patients.InterventionsPMH introduced a dedicated OPAT medical support team in July 2015 to improve adherence to best-practice guidelines for patient monitoring and review.Main outcome measuresDuration of OPAT, adherence to monitoring guidelines, drug-related and line-related adverse events and readmission to hospital.ResultsThere were a total of 502 OPAT episodes over 24 months, with 407 episodes included in analyses. Following the introduction of the OPAT medical team, adherence to monitoring guidelines improved (OR 4.90, 95% CI 2.48 to 9.66); significantly fewer patients required readmission to hospital (OR 0.45, 95% CI 0.24 to 0.86) and there was a significant reduction in the proportion of patients receiving prolonged (≥7 days) OPAT (OR 0.67, 95% CI 0.45 to 0.99).ConclusionThe introduction of a formal medical team to HiTH demonstrated a positive clinical impact on OPAT patients’ outcomes. These findings support the ongoing utility of medical governance in a nurse-led HiTH service.