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1.
Sci Rep ; 11(1): 21136, 2021 10 27.
Article in English | MEDLINE | ID: covidwho-1493228

ABSTRACT

The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters to help to identify patients with COVID-19 who are at higher risk of death. The training cohort included all patients admitted to Fondazione Policlinico Gemelli with COVID-19 from March 5, 2020, to November 5, 2020. Afterward, the model was tested on all patients admitted to the same hospital with COVID-19 from November 6, 2020, to February 5, 2021. The primary outcome was in-hospital case-fatality risk. The out-of-sample performance of the model was estimated from the training set in terms of Area under the Receiving Operator Curve (AUROC) and classification matrix statistics by averaging the results of fivefold cross validation repeated 3-times and comparing the results with those obtained on the test set. An explanation analysis of the model, based on the SHapley Additive exPlanations (SHAP), is also presented. To assess the subsequent time evolution, the change in paO2/FiO2 (P/F) at 48 h after the baseline measurement was plotted against its baseline value. Among the 921 patients included in the training cohort, 120 died (13%). Variables selected for the model were age, platelet count, SpO2, blood urea nitrogen (BUN), hemoglobin, C-reactive protein, neutrophil count, and sodium. The results of the fivefold cross-validation repeated 3-times gave AUROC of 0.87, and statistics of the classification matrix to the Youden index as follows: sensitivity 0.840, specificity 0.774, negative predictive value 0.971. Then, the model was tested on a new population (n = 1463) in which the case-fatality rate was 22.6%. The test model showed AUROC 0.818, sensitivity 0.813, specificity 0.650, negative predictive value 0.922. Considering the first quartile of the predicted risk score (low-risk score group), the case-fatality rate was 1.6%, 17.8% in the second and third quartile (high-risk score group) and 53.5% in the fourth quartile (very high-risk score group). The three risk score groups showed good discrimination for the P/F value at admission, and a positive correlation was found for the low-risk class to P/F at 48 h after admission (adjusted R-squared = 0.48). We developed a predictive model of death for people with SARS-CoV-2 infection by including only easy-to-obtain variables (abnormal blood count, BUN, C-reactive protein, sodium and lower SpO2). It demonstrated good accuracy and high power of discrimination. The simplicity of the model makes the risk prediction applicable for patients in the Emergency Department, or during hospitalization. Although it is reasonable to assume that the model is also applicable in not-hospitalized persons, only appropriate studies can assess the accuracy of the model also for persons at home.


Subject(s)
COVID-19/mortality , Machine Learning , Pandemics , SARS-CoV-2 , Aged , Aged, 80 and over , Blood Cell Count , Blood Chemical Analysis , COVID-19/blood , Cohort Studies , Female , Hospital Mortality , Humans , Male , Middle Aged , Models, Statistical , Multivariate Analysis , Oxygen/blood , Pandemics/statistics & numerical data , ROC Curve , Risk Factors , Rome/epidemiology
2.
Liver Int ; 41(11): 2560-2577, 2021 11.
Article in English | MEDLINE | ID: covidwho-1434779

ABSTRACT

Metabolic diseases are associated with a higher risk of a severer coronavirus disease 2019 (COVID-19) course, since fatty liver is commonly associated with metabolic disorders, fatty liver itself is considered as a major contributor to low-grade inflammation in obesity and diabetes. Recently a comprehensive term, metabolic (dysfunction) associated fatty liver disease (MAFLD), has been proposed. The hepatic inflammatory status observed in MAFLD patients is amplified in presence of severe acute respiratory syndrome coronavirus 2 infection. Intestinal dysbiosis is a powerful activator of inflammatory mediator production of liver macrophages. The intestinal microbiome plays a key role in MAFLD progression, which results in non-alcoholic steatohepatitis and liver fibrosis. Therefore, patients with metabolic disorders and COVID-19 can have a worse outcome of COVID-19. This literature review attempts to disentangle the mechanistic link of MAFLD from COVID-19 complexity and to improve knowledge on its pathophysiology.


Subject(s)
COVID-19 , Metabolic Diseases , Non-alcoholic Fatty Liver Disease , Humans , Immunity , SARS-CoV-2
3.
Preprint | SSRN | ID: ppcovidwho-1447

ABSTRACT

Background: SARS-CoV-2 belongs to a subgrouof coronaviruses rampant in bats for centuries. It has caused the COVID-19 pandemic. Most patients recover, but a m

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