Twins with alveolar capillary dysplasia with misalignment of pulmonary veins: Strategies for diagnosis and management

We present a case of dichorionic-diamniotic twin females who developed hypoxemic respiratory failure. They were ultimately diagnosed by lung biopsy with alveolar capillary dysplasia with misalignment of pulmonary veins. This case highlights a practical approach to reaching a diagnosis in infants with suspected developmental lung disease.

Case report: Pneumatosis in a neonate with thrombocytopenia absent radius syndrome

BACKGROUND

Thrombocytopenia absent radius (TAR) syndrome is a rare disease with an estimated prevalence of one in 200,000 live births. TAR is associated with cardiac and renal anomalies as well as gastrointestinal problems such as CMPA. Typically neonates with CMPA present with mild intolerance, with few reports in the literature of more severe intolerance resulting in pneumatosis. We present a case of a male infant with thrombocytopenia absent radius (TAR) syndrome who developed gastric and colonic pneumatosis intestinalis.

CASE DESCRIPTION

An eight-day-old male infant born at 36 weeks gestation with a diagnosis of TAR, presented with bright red blood in his stool. At this time he was on full formula feeds. Given continued bright red blood within his stool, an abdominal radiograph was obtained which was consistent with colonic and gastric pneumatosis. A complete blood count (CBC) was notable for worsening thrombocytopenia, anemia and eosinophilia. Once enteral feeds were held there was rapid resolution of the radiographic findings and resolution of his bloody stool. He was ultimately diagnosed with a cow's milk protein allergy (CMPA).

CONCLUSION

Though there are reports of CMPA in patients with TAR, the severity of this patient's presentation with both colonic and gastric pneumatosis is unique. Without the knowledge of the association of CMPA with TAR, this case could have been misdiagnosed and led to reintroduction of cow's milk containing formula, resulting in further complications. This case highlights the importance of a timely diagnosis and severity of CMPA in this population.

114 THE EFFECTIVENESS OF A 26-WEEK CLASS AND HOME-BASED OTAGO EXERCISE PROGRAMME ON BALANCE CONFIDENCE AND PHYSICAL PERFORMANCE IN OLDER ADULTS

Background

Falls have a substantial burden on the Irish economy and on the individual. The Otago Exercise Programme (OEP) is a cost-effective falls prevention programme that can prevent 35% of falls. This study aims to evaluate the effectiveness of a 26-week modified OEP using a combined delivery approach of physiotherapists and Exercise Therapists (ET) on balance confidence and physical performance in older adults in rural Ireland.

Methods

Study-design: A prospective pre-post single arm intervention study. Older adults (≥60 years) were recruited from HSE waiting lists for the OEP which consisted of a group-class (1x/week) and a home OEP (3x/week). Participants were assessed at baseline, 6, 16 and 26 weeks. Outcome measures (OMs) were compared pre and post intervention using Paired T-Tests and Wilcoxon Signed Ranks tests. A repeated-measures ANOVA compared OMs across the time points.

Results

Improvements were detected in the CONFbal scale (diff = 2.12, 95% CI=1.353-2.803, p<0.01), 30-second sit-to-stand (diff = 3.49, CI= 2.361-4.201, p<0.01), timed-up and go (diff = 2.38, CI=8.08-12.0, p<0.01), functional reach (diff = 9.158, CI=6.758-11.294, p<0.01), 180° turn (diff = 1.04, CI=3-5, p<0.01), and the four-test balance scale (diff = 0.61, CI=1.0-3.0, p<0.01). Significant changes are noted for each measure (p<0.05) with small-moderate positive effects (ηp2=0.32-0.512). The most significant changes in the 180° turn are seen between week-0 (4.92 ±1.784) and week-6 (4.44 ±1.258) followed by a plateau in results.

Conclusion

Confidence and physical performance improved significantly pre and post intervention, suggesting the physiotherapist and ET combined delivery of the programme is effective. Static and dynamic balance improvements may plateau after 6-weeks of the OEP suggesting the participants may not have been adequately challenged. Lower limb strength and mobility continued to improve throughout suggesting the modified OEP is an effective intervention for older adults in Ireland.

64 HIGH-DOSE STEROIDS IN HAEMATOLOGICAL MALIGNANCY: A RED FLAG FOR BONE HEALTH

Background

Bone-protective treatment should always be considered when commencing glucocorticoid therapy in individuals at high risk of fracture, as outlined in recent guidelines (National Osteoporosis Guideline Group, July 2018). Despite this, many patients receive high dose glucocorticoids (>7.5mg/kg/day or equivalent for >3 months) during treatment of haematological malignancy without formal assessment of bone health.

Methods

Twenty-five patients with Non-Hodgkin’s Lymphoma (NHL) on high dose steroid chemotherapy protocols were retrospectively assessed. The inclusion criterion was defined as any patient commenced on a cyclophosphamide, doxorubicin, vincristine and prednisolone (CHOP) containing protocol from 1/8/2020 to 1/8/21. Data was collected from electronic and written patient records. Information regarding patient age, diagnosis, steroid preparation and dose, presence of risk factors for osteoporosis and calcium and vitamin D prescriptions were collected. Radiological imaging was reviewed to assess for the occurrence of fragility fractures. Each patient had a FRAX® score calculated to assess fracture risk.

Results

The mean age of patients studied was 67 years and equally distributed between males and females. All patients had NHL. 32% (n=25) of patients were identified to be at high risk for fracture (FRAX® score >20% ten-year probability of major osteoporotic fracture). 16% had already established fragility fractures prior to commencing steroid treatment. 28% of patients were females over the age of 70 years. Only 12% of patients received vitamin D and calcium supplementation. No patient commenced bone protection therapy.

Conclusion

Omission of bone health assessment prior to high dose glucocorticoid treatment is prevalent in haematological malignancy and places patients at risk of significant morbidity. Patients are not routinely considered for protective measures including calcium and vitamin D supplementation and bone protection therapy. A simple risk-assessment tool and education to staff and patients prior to glucocorticoid therapy could significantly improve practice in this area. It is now planned to introduce routine risk assessment for this cohort of patients, with re-audit following implementation.

Automated diagnosis and prognosis of COVID-19 pneumonia from initial ER chest X-rays using deep learning

Background

Airspace disease as seen on chest X-rays is an important point in triage for patients initially presenting to the emergency department with suspected COVID-19 infection. The purpose of this study is to evaluate a previously trained interpretable deep learning algorithm for the diagnosis and prognosis of COVID-19 pneumonia from chest X-rays obtained in the ED.

Methods

This retrospective study included 2456 (50% RT-PCR positive for COVID-19) adult patients who received both a chest X-ray and SARS-CoV-2 RT-PCR test from January 2020 to March of 2021 in the emergency department at a single U.S. institution. A total of 2000 patients were included as an additional training cohort and 456 patients in the randomized internal holdout testing cohort for a previously trained Siemens AI-Radiology Companion deep learning convolutional neural network algorithm. Three cardiothoracic fellowship-trained radiologists systematically evaluated each chest X-ray and generated an airspace disease area-based severity score which was compared against the same score produced by artificial intelligence. The interobserver agreement, diagnostic accuracy, and predictive capability for inpatient outcomes were assessed. Principal statistical tests used in this study include both univariate and multivariate logistic regression.

Results

Overall ICC was 0.820 (95% CI 0.790–0.840). The diagnostic AUC for SARS-CoV-2 RT-PCR positivity was 0.890 (95% CI 0.861–0.920) for the neural network and 0.936 (95% CI 0.918–0.960) for radiologists. Airspace opacities score by AI alone predicted ICU admission (AUC = 0.870) and mortality (0.829) in all patients. Addition of age and BMI into a multivariate log model improved mortality prediction (AUC = 0.906).

Conclusion

The deep learning algorithm provides an accurate and interpretable assessment of the disease burden in COVID-19 pneumonia on chest radiographs. The reported severity scores correlate with expert assessment and accurately predicts important clinical outcomes. The algorithm contributes additional prognostic information not currently incorporated into patient management.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07617-7.

Deep learning model to quantify left atrium volume on routine non-contrast chest CT and predict adverse outcomes

BACKGROUND

Low-dose computed tomography (LDCT) are performed routinely for lung cancer screening. However, a large amount of nonpulmonary data from these scans remains unassessed. We aimed to validate a deep learning model to automatically segment and measure left atrial (LA) volumes from routine NCCT and evaluate prediction of cardiovascular outcomes.

METHODS

We retrospectively evaluated 273 patients (median age 69 years, 55.5% male) who underwent LDCT for lung cancer screening. LA volumes were quantified by three expert cardiothoracic radiologists and a prototype AI algorithm. LA volumes were then indexed to the body surface area (BSA). Expert and AI LA volume index (LAVi) were compared and used to predict cardiovascular outcomes within five years. Logistic regression with appropriate univariate statistics were used for modelling outcomes.

RESULTS

There was excellent correlation between AI and expert results with an LAV intraclass correlation of 0.950 (0.936-0.960). Bland-Altman plot demonstrated the AI underestimated LAVi by a mean 5.86 ​mL/m². AI-LAVi was associated with new-onset atrial fibrillation (AUC 0.86; OR 1.12, 95% CI 1.08-1.18, p ​< ​0.001), HF hospitalization (AUC 0.90; OR 1.07, 95% CI 1.04-1.13, p ​< ​0.001), and MACCE (AUC 0.68; OR 1.04, 95% CI 1.01-1.07, p ​= ​0.01).

CONCLUSION

This novel deep learning algorithm for automated measurement of LA volume on lung cancer screening scans had excellent agreement with manual quantification. AI-LAVi is significantly associated with increased risk of new-onset atrial fibrillation, HF hospitalization, and major adverse cardiac and cerebrovascular events within 5 years.

Adaptive Manufacturing for Healthcare During the COVID-19 Emergency and Beyond

During the COVID-19 pandemic, global health services have faced unprecedented demands. Many key workers in health and social care have experienced crippling shortages of personal protective equipment, and clinical engineers in hospitals have been severely stretched due to insufficient supplies of medical devices and equipment. Many engineers who normally work in other sectors have been redeployed to address the crisis, and they have rapidly improvised solutions to some of the challenges that emerged, using a combination of low-tech and cutting-edge methods. Much publicity has been given to efforts to design new ventilator systems and the production of 3D-printed face shields, but many other devices and systems have been developed or explored. This paper presents a description of efforts to reverse engineer or redesign critical parts, specifically a manifold for an anaesthesia station, a leak port, plasticware for COVID-19 testing, and a syringe pump lock box. The insights obtained from these projects were used to develop a product lifecycle management system based on Aras Innovator, which could with further work be deployed to facilitate future rapid response manufacturing of bespoke hardware for healthcare. The lessons learned could inform plans to exploit distributed manufacturing to secure back-up supply chains for future emergency situations. If applied generally, the concept of distributed manufacturing could give rise to "21st century cottage industries" or "nanofactories," where high-tech goods are produced locally in small batches.

P2498Exercise capacity and all-cause mortality in remote Indigenous and non-Indigenous populations

Background

Exercise capacity is a powerful predictor of all-cause mortality. However, its association with Indigenous populations in Central Australia, who face disproportionate health burdens, is unclear. Given the isolation from tertiary centers, exercise testing could provide useful local risk-stratification.

Purpose

To characterize the association of exercise capacity with all-cause mortality in Indigenous and non-Indigenous individuals in remote Central Australia.

Methods

Demographic, medication, and all-cause mortality data were prospectively collected from patients undergoing exercise stress tests from 2007–2017.

Results

A total of 3,414 patients (34% Indigenous) were included. At 4.8±2.9 years of follow-up, 86 (2.5%) deaths had occurred. Each 1-MET increase in exercise capacity conferred a 14% lower risk for mortality among Indigenous individuals (HR 0.86, 95% CI 0.79–0.94) and 20% lower risk for mortality among non-Indigenous individuals (HR 0.80, 95% CI 0.73–0.89) after adjusting for age, comorbidities, and medications. Mortality risk reduction for each 1-MET increase in exercise capacity was similar (p=0.32) for Indigenous and non-Indigenous individuals.

Conclusions

Exercise capacity is a significant predictor of all-cause mortality in Indigenous and non-Indigenous individuals. These findings have important clinical implications towards exercise capacity for risk-stratification and preventative importance of physical activity.

Utility of state-level influenza disease burden and severity estimates to investigate an apparent increase in reported severe cases of influenza A(H1N1) pdm09 - Arizona, 2015-2016

The Arizona Department of Health Services identified unusually high levels of influenza activity and severe complications during the 2015-2016 influenza season leading to concerns about potential increased disease severity compared with prior seasons. We estimated state-level burden and severity to compare across three seasons using multiple data sources for community-level illness, hospitalisation and death. Severity ratios were calculated as the number of hospitalisations or deaths per community case. Community influenza-like illness rates, hospitalisation rates and mortality rates in 2015-2016 were higher than the previous two seasons. However, ratios of severe disease to community illness were similar. Arizona experienced overall increased disease burden in 2015-2016, but not increased severity compared with prior seasons. Timely estimates of state-specific burden and severity are potentially feasible and may provide important information during seemingly unusual influenza seasons or pandemic situations.

Quality Improvement of Clinical Handover in a Liaison Psychiatry Department: A Three-Phase Audit

Aims To audit and improve quality of clinical handover in a Liaison Psychiatry Department. Methods An 11-item Handover Checklist was used to carry out a baseline audit and immediate and delayed post-intervention re-audits. The intervention consisted of checklist posters, an electronic database and staff training. Results There was an immediate and sustained improvement in most areas, including baseline demographics and components of SBAR communication (Situation, Background, Assessment, Recommendation). The improvement between baseline and delayed re-audit was statistically significant (P=0.007). Conclusion Poor handover practices contribute to negative outcomes in healthcare but there remains a dearth of guidelines in most specialties.This audit is important as it describes an intervention which improved clinical handover at a time of significant clinical risk. This practice could be applied effectively to other services and is relevant to all specialties. Six to twelve-monthly re-audit and longitudinal assessment of clinical outcomes are recommended to maintain the practices implemented in this audit.

A non-linear data mining parameter selection algorithm for continuous variables

In this article, we propose a new data mining algorithm, by which one can both capture the non-linearity in data and also find the best subset model. To produce an enhanced subset of the original variables, a preferred selection method should have the potential of adding a supplementary level of regression analysis that would capture complex relationships in the data via mathematical transformation of the predictors and exploration of synergistic effects of combined variables. The method that we present here has the potential to produce an optimal subset of variables, rendering the overall process of model selection more efficient. This algorithm introduces interpretable parameters by transforming the original inputs and also a faithful fit to the data. The core objective of this paper is to introduce a new estimation technique for the classical least square regression framework. This new automatic variable transformation and model selection method could offer an optimal and stable model that minimizes the mean square error and variability, while combining all possible subset selection methodology with the inclusion variable transformations and interactions. Moreover, this method controls multicollinearity, leading to an optimal set of explanatory variables.

The basal translation rate of authentic HIV-1 RNA is regulated by 5'UTR nt-pairings at junction of R and U5

The paradigm protein synthesis rate is regulated by structural complexity of the 5′untranslated region (UTR) derives from bacterial and other riboswitches. In-solution, HIV-1 5′UTR forms two interchangeable long-range nucleotide (nt) -pairings, one sequesters the gag start codon promoting dimerization while the other sequesters the dimer initiation signal preventing dimerization. While the effect of these nt-pairings on dimerization and packaging has been documented their effect on authentic HIV translation in cellulo has remained elusive until now. HIV^NL4-3 5′UTR substitutions were designed to individually stabilize the dimer-prone or monomer-prone conformations, validated in-solution, and introduced to molecular clones. The effect of 5′UTR conformation on ribosome loading to HIV unspliced RNA and rate of Gag polypeptide synthesis was quantified in cellulo. Monomer- and dimer-prone 5′UTRs displayed equivalent, basal rate of translation. Gain-of-function substitution U103, in conjunction with previously defined nt-pairings that reorient AUG to flexible nt-pairing, significantly activated the translation rate, indicating the basal translation rate is under positive selection. The observed translation up-mutation focuses attention to nt-pairings at the junction of R and U5, a poorly characterized structure upstream of the characterized HIV riboswitch and demonstrates the basal translation rate of authentic HIV RNA is regulated independently of monomer:dimer equilibrium of the 5′UTR.

Diagnostic criteria for inclusion body myositis

Inclusion body myositis (IBM) was first identified as a specific disorder about 40 years ago and is now recognized to be the most frequently presenting primary myopathy in middle age and beyond. Initial characterization was based on the observation of specific pathological features distinguishing it from polymyositis. It was soon appreciated that there were also distinguishing clinical features. The earliest diagnostic criteria were heavily biased towards pathological features, but over time revised criteria have given increasing importance to certain clinical features. Until the specific cause of IBM is determined, and the basic pathogenetic mechanisms are better understood, there can be no diagnostic gold-standard against which to compare the sensitivity and specificity of any proposed diagnostic criteria, but such criteria are essential to ensure that patients entering clinical, epidemiological, genetic, pathological or therapeutic studies represent a homogeneous population. It is likely that any currently accepted diagnostic criteria will, once a gold-standard is eventually established, be shown to have 'missed' patients with atypical features, but that has to be accepted to make certain that current studies are not contaminated by patients who do not have IBM. In other words, in everyday clinical practice there will be the occasional patient who an experienced myologist strongly suspects has IBM, but does not meet current criteria - the criteria lack sensitivity. But if the criteria are so broad as to include all such atypical cases, they would be likely to include patients who do not in fact have IBM - they would lack specificity. The sensitivity and specificity of existing criteria have been reviewed recently, in so far as it is possible to do so, and found to have high specificity but variable sensitivity.

Northern Territory perspectives on heart failure with comorbidities – understanding trial validity and exploring collaborative opportunities to broaden the evidence base

Congestive Heart Failure (CHF) is an ambulatory care sensitive condition, associated with significant morbidity and mortality, rarely with cure. Outpatient based pharmacological management represents the main and most important aspect of care, and is usually lifelong. This narrative styled opinion review looks at the pharmacological agents recommended in the guidelines in context of the Northern Territory (NT) of Australia. We explore the concept of validity, a term used to describe the basis of standardising a particular trial or study and the population to which it is applicable. We aim to highlight the problems of the current guidelines based approach. We also present alternatives that could utilise the core principles from major trials, while incorporating regional considerations, which could benefit clients living in the NT and remote Australia.