Deep learning model to predict the need for mechanical ventilation using chest X-ray images in hospitalised patients with COVID-19.

OBJECTIVES: There exists a wide gap in the availability of mechanical ventilator devices and their acute need in the context of the COVID-19 pandemic. An initial triaging method that accurately identifies the need for mechanical ventilation in hospitalised patients with COVID-19 is needed. We aimed to investigate if a potentially deteriorating clinical course in hospitalised patients with COVID-19 can be detected using all X-ray images taken during hospitalisation. METHODS: We exploited the well-established DenseNet121 deep learning architecture for this purpose on 663 X-ray images acquired from 528 hospitalised patients with COVID-19. Two Pulmonary and Critical Care experts blindly and independently evaluated the same X-ray images for the purpose of validation. RESULTS: We found that our deep learning model predicted the need for mechanical ventilation with a high accuracy, sensitivity and specificity (90.06%, 86.34% and 84.38%, respectively). This prediction was done approximately 3 days ahead of the actual intubation event. Our model also outperformed two Pulmonary and Critical Care experts who evaluated the same X-ray images and provided an incremental accuracy of 7.24%-13.25%. CONCLUSIONS: Our deep learning model accurately predicted the need for mechanical ventilation early during hospitalisation of patients with COVID-19. Until effective preventive or treatment measures become widely available for patients with COVID-19, prognostic stratification as provided by our model is likely to be highly valuable.

The Cervical Dysplasia Worksheet: A Longitudinal Map of Cervical Dysplasia Cytology and Histology Tests and Procedures.

OBJECTIVE: Our objectives were to describe the Cervical Dysplasia Worksheet (CDW), provide evidence of its feasibility to view patient cervical dysplasia results over time, and evaluate patient attitudes toward the tool in the setting of abnormal result follow-up. MATERIALS AND METHODS: The CDW augments the ASCCP guidelines for managing abnormal cervical cancer screenings by visually depicting cervical cytological and histological history along a color gradient showing severity. We evaluated tool feasibility by graphing a retrospectively reviewed convenience sample of patient data. A cross-sectional evaluation of the tool was then performed to assess patient attitudes in the setting of either dysplasia or colposcopy clinic. Patients had their data graphed on the CDW and explained to them before their clinical encounter. They then gave general comments about the tool and filled out a short evaluation survey. RESULTS: The large majority of retrospective patient data (N = 167) fit well within the CDW with roughly 20% requiring space for additional comments. Among the 30 patients who participated in our evaluation, almost all agreed (n = 29, 96.7%) that the tool helped them understand their history and results and that they would use the tool in the future. CONCLUSIONS: The CDW is a novel tool to display a patient's cervical dysplasia history to visualize treatment and future care while enhancing patient-provider communication. Patient evaluation of the tool was largely positive, and suggestions will be taken into consideration for future modification. Further evaluation of the CDW among healthcare providers is needed to analyze its efficacy in the clinical setting.

Discovery of the Tyrobetaine Natural Products and Their Biosynthetic Gene Cluster via Metabologenomics.

Natural products (NPs) are a rich source of medicines, but traditional discovery methods are often unsuccessful due to high rates of rediscovery. Genetic approaches for NP discovery are promising, but progress has been slow due to the difficulty of identifying unique biosynthetic gene clusters (BGCs) and poor gene expression. We previously developed the metabologenomics method, which combines genomic and metabolomic data to discover new NPs and their BGCs. Here, we utilize metabologenomics in combination with molecular networking to discover a novel class of NPs, the tyrobetaines: nonribosomal peptides with an unusual trimethylammonium tyrosine residue. The BGC for this unusual class of compounds was identified using metabologenomics and computational structure prediction data. Heterologous expression confirmed the BGC and suggests an unusual mechanism for trimethylammonium formation. Overall, the discovery of the tyrobetaines shows the great potential of metabologenomics combined with molecular networking and computational structure prediction for identifying interesting biosynthetic reactions and novel NPs.