Cost-sensitive ordinal classification methods to predict SARS-CoV-2 pneumonia severity.

Objective: To study the suitability of costsensitive ordinal artificial intelligence-machine learning (AIML) strategies in the prognosis of SARS-CoV-2 pneumonia severity.

Materials & Methods: Observational, retrospective, longitudinal, cohort study in 4 hospitals in Spain. Information regarding demographic and clinical status was supplemented by socioeconomic data and air pollution exposures.

Extracting relevant predictive variables for COVID-19 severity prognosis: An exhaustive comparison of feature selection techniques.

With the COVID-19 pandemic having caused unprecedented numbers of infections and deaths, large research efforts have been undertaken to increase our understanding of the disease and the factors which determine diverse clinical evolutions. Here we focused on a fully data-driven exploration regarding which factors (clinical or otherwise) were most informative for SARS-CoV-2 pneumonia severity prediction via machine learning (ML). In particular, feature selection techniques (FS), designed to reduce the dimensionality of data, allowed us to characterize which of our variables were the most useful for ML prognosis.

An NMR-Based Model to Investigate the Metabolic Phenoreversion of COVID-19 Patients throughout a Longitudinal Study.

After SARS-CoV-2 infection, the molecular phenoreversion of the immunological response and its associated metabolic dysregulation are required for a full recovery of the patient. This process is patient-dependent due to the manifold possibilities induced by virus severity, its phylogenic evolution and the vaccination status of the population. We have here investigated the natural history of COVID-19 disease at the molecular level, characterizing the metabolic and immunological phenoreversion over time in large cohorts of hospitalized severe patients (n = 886) and non-hospitalized recovered patients that self-reported having passed the disease (n = 513).

Bacteraemic pneumococcal pneumonia and SARS-CoV-2 pneumonia: differences and similarities.

Objective: To analyse differences in clinical presentation and outcome between bacteraemic pneumococcal community-acquired pneumonia (B-PCAP) and sSvere Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) pneumonia.

Methods: This observational multi-centre study was conducted on patients hospitalized with B-PCAP between 2000 and 2020 and SARS-CoV-2 pneumonia in 2020. Thirty-day survival, predictors of mortality, and intensive care unit (ICU) admission were compared.

Prospective comparison of severity scores for predicting clinically relevant outcomes for patients hospitalized with community-acquired pneumonia.

Background: The comparative accuracy and discriminatory power of three validated rules for predicting clinically relevant outcomes other than mortality in patients hospitalized with community-acquired pneumonia (CAP) are unknown.

Methods: We prospectively compared the newly developed severe community-acquired pneumonia (SCAP) score, pneumonia severity index (PSI), and the British Thoracic Society confusion, urea > 7 mmol/L, respiratory rate > or = 30 breaths/min, BP < 90 mm Hg systolic or < 60 mm Hg diastolic, age > or = 65 years (CURB-65) rule in an internal validation cohort of 1,189 consecutive adult inpatients with CAP from one hospital and an external validation cohort of 671 consecutive adult inpatients from three other hospitals. Major adverse outcomes were admission to ICU, need for mechanical ventilation, progression to severe sepsis, or treatment failure.