Predicting the need for mechanical ventilation and mortality in hospitalized COVID-19 patients who received heparin.

Although several studies have utilized AI (artificial intelligence)-based solutions to enhance the decision making for mechanical ventilation, as well as, for mortality in COVID-19, the extraction of explainable predictors regarding heparin's effect in intensive care and mortality has been left unresolved. In the present study, we developed an explainable AI (XAI) workflow to shed light into predictors for admission in the intensive care unit (ICU), as well as, for mortality across those hospitalized COVID-19 patients who received heparin. AI empowered classifiers, such as, the hybrid Extreme gradient boosting (HXGBoost) with customized loss functions were trained on time-series curated clinical data to develop robust AI models. Shapley additive explanation analysis (SHAP) was conducted to determine the positive or negative impact of the predictors in the model's output. The HXGBoost predicted the risk for intensive care and mortality with 0.84 and 0.85 accuracy, respectively. SHAP analysis indicated that the low percentage of lymphocytes at day 7 along with increased FiO2 at days 1 and 5, low SatO2 at days 3 and 7 increase the probability for mortality and highlight the positive effect of heparin administration at the early days of hospitalization for reducing mortality.

ICU admission and mortality classifiers for COVID-19 patients based on subgroups of dynamically associated profiles across multiple timepoints.

The coronavirus disease 2019 (COVID-19) which is caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is consistently causing profound wounds in the global healthcare system due to its increased transmissibility. Currently, there is an urgent unmet need to identify the underlying dynamic associations among COVID-19 patients and distinguish patient subgroups with common clinical profiles towards the development of robust classifiers for ICU admission and mortality. To address this need, we propose a four step pipeline which: (i) enhances the quality of multiple timeseries clinical data through an automated data curation workflow, (ii) deploys Dynamic Bayesian Networks (DBNs) for the detection of features with increased connectivity based on dynamic association analysis across multiple points, (iii) utilizes Self Organizing Maps (SOMs) and trajectory analysis for the early identification of COVID-19 patients with common clinical profiles, and (iv) trains robust multiple additive regression trees (MART) for ICU admission and mortality classification based on the extracted homogeneous clusters, to identify risk factors and biomarkers for disease progression. The contribution of the extracted clusters and the dynamically associated clinical data improved the classification performance for ICU admission to sensitivity 0.83 and specificity 0.83, and for mortality to sensitivity 0.74 and specificity 0.76. Additional information was included to enhance the performance of the classifiers yielding an increase by 4% in sensitivity and specificity for mortality. According to the risk factor analysis, the number of lymphocytes, SatO2, PO2/FiO2, and O2 supply type were highlighted as risk factors for ICU admission and the percentage of neutrophils and lymphocytes, PO2/FiO2, LDH, and ALP for mortality, among others. To our knowledge, this is the first study that combines dynamic modeling with clustering analysis to identify homogeneous groups of COVID-19 patients towards the development of robust classifiers for ICU admission and mortality.

A 68-year-old man with haemoptysis and extensive ipsilateral lung infiltrates.

Can you diagnose this 68-year-old male with 24-h history of haemoptysis, 5-year history of productive cough and ipsilateral lung infiltrates? https://bit.ly/3tyhANB.

Identification and treatment of T2-low asthma in the era of biologics.

Currently, and based on the development of relevant biologic therapies, T2-high is the most well-defined endotype of asthma. Although much progress has been made in elucidating T2-high inflammation pathways, no specific clinically applicable biomarkers for T2-low asthma have been identified. The therapeutic approach of T2-low asthma is a problem urgently needing resolution, firstly because these patients have poor response to steroids, and secondly because they are not candidates for the newer targeted biologic agents. Thus, there is an unmet need for the identification of biomarkers that can help the diagnosis and endotyping of T2-low asthma. Ongoing investigation is focusing on neutrophilic airway inflammation mediators as therapeutic targets, including interleukin (IL)-8, IL-17, IL-1, IL-6, IL-23 and tumour necrosis factor-α; molecules that target restoration of corticosteroid sensitivity, mainly mitogen-activated protein kinase inhibitors, tyrosine kinase inhibitors and phosphatidylinositol 3-kinase inhibitors; phosphodiesterase (PDE)3 inhibitors that act as bronchodilators and PDE4 inhibitors that have an anti-inflammatory effect; and airway smooth muscle mass attenuation therapies, mainly for patients with paucigranulocytic inflammation. This article aims to review the evidence for noneosinophilic inflammation being a target for therapy in asthma; discuss current and potential future therapeutic approaches, such as novel molecules and biologic agents; and assess clinical trials of licensed drugs in the treatment of T2-low asthma.