IDIBELL researchers identify three clinical profiles of patients with COVID-19 with prognostic implications

  • A study published in The Lancet Infectious Diseases, in which Bellvitge University Hospital (HUB) and the Bellvitge Biomedical Research Institute (IDIBELL) have participated, has identified three phenotypes (clinical profiles or forms of patient presentation) of COVID-19, each with a different mortality.

The identification of these profiles can facilitate the clinical management of patients, as it will make it possible to more accurately distinguish low-risk individuals from those who need closer monitoring during hospitalization. It can also lead to research into the pathophysiological mechanisms behind each phenotype, which will allow better selection of participants in drug trials according to the mechanism and clinical use of each drug.

This multicenter work led by researchers from the Virgen Macarena University Hospital in Seville has among its first authors Dr. Jordi Carratalá, head of the Infectious Diseases Service of the HUB, coordinator of the Infectious Diseases and Transplants Program of IDIBELL and professor of Medicine at the University of Barcelona.

The study analyzed data from two cohorts of 4,035 and 2,226 patients treated in 127 Spanish hospitals during the first wave of the pandemic. More than 70 variables from these patients were studied, such as age, sex, symptoms, underlying diseases, or laboratory and radiodiagnostic data, among others.

The results determined that patients with phenotype A – who have a mortality of less than 5% – are younger people and mostly women, with mild viral symptoms and normal inflammatory parameters, among other characteristics. Patients with phenotype B – who have a mortality of between 15 and 20% – are people with obesity, low blood lymphocyte levels and moderately high inflammation parameters. Finally, patients with phenotype C – who have a mortality of between 40 and 60% – are older people, with more comorbidities and higher inflammatory parameters.

The large number of variables initially needed to identify the phenotypes made the authors devise a simpler probabilistic model, with only 16 variables, which already allows us to predict which phenotype can be assigned to each patient. This model has been developed as a mobile phone application so it can be used in the clinical practice.


Mortality prediction model

On the other hand, an article published by the same group of researchers in the journal Thorax sets out the development and validation of a model for predicting mortality in patients with COVID-19 entering hospital emergencies. The work has been coordinated from the Gregorio Marañón University General Hospital and has involved researchers from the Infectious Diseases Service of the HUB and IDIBELL, led by Dr. Carratalá.

For the development and validation of this model, cohort data from 4,035 and 2,126 patients from 127 hospitals in Spain were used. The conclusion has been that a simple prediction score, based on readily available clinical and laboratory data, provides a useful tool for predicting with a high degree of accuracy the probability of mortality at 30 days. Its simplicity can allow physicians to quickly stratify patient risk.

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