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Endothelium Activation Markers in Severe Hospitalized COVID-19 Patients: Role in Mortality Risk Prediction.

ABSTRACT: Introduction Endothelial damage and hypercoagulability are major players behind the hemostatic derangement of SARS-CoV-2 infection. Aim In this prospective study we assessed endothelial and inflammatory biomarkers in a cohort of COVID-19 patients, aiming to identify predictive factors of in-hospital mortality. Methods COVID-19 patients hospitalized in intensive care (ICU) and non-ICU units at 2 Bergamo (Italy) hospitals from March 23 to May 30, 2020, were enrolled. Markers of endothelium activation including von-Willebrand factor (vWF), soluble thrombomodulin (sTM), and fibrinolytic proteins (t-PA and PAI-1) were measured. Additionally, D-dimer, Fibrinogen, FVIII, nucleosomes, C reactive protein (CRP) and procalcitonin were assessed. Results Sixty-three (45 ICU, and 18 non-ICU) patients, with a median age of 62 years were analyzed. Increased plasma levels of D-dimer, FVIII, fibrinogen, nucleosomes, CRP, and procalcitonin were observed in the whole cohort. Extremely elevated vWF levels characterized all patients (highest values in ICU-subjects). After a median time of 30 days, death occurred in 13 (21%) patients. By multivariable analysis, vWF-activity, neutrophil-count and PaO2/FiO2 were significantly associated with death. Using these variables, a linear score with 3-risk groups was generated that provided a cumulative incidence of death of 0% in the low-, 32% in the intermediate-, and 78% in the high-risk group. Conclusions COVID-19-induced hemostatic abnormalities are exacerbated by the severity of the disease and strongly correlate with the inflammatory status, underlying the link between coagulation, endothelial activation, and inflammation. Our study provides evidence for a role of vWF, together with neutrophils and PaO2/FiO2, as a significant predictor of in-hospital mortality by SARSCoV-2 infection.

SUBMITTER: Marchetti M

PROVIDER: S-EPMC8255108 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundPrevious studies have identified COVID-19 risk factors, such as age and chronic health conditions, linked to severe outcomes and mortality. However, accurately predicting severe illness in COVID-19 patients remains challenging, lacking precise methods.ObjectiveThis study aimed to leverage clinical real-world data and multiple machine-learning algorithms to formulate innovative predictive models for assessing the risk of severe outcomes or mortality in hospitalized patients with COVID-19.MethodsData were obtained from the Taipei Medical University Clinical Research Database (TMUCRD) including electronic health records from three Taiwanese hospitals in Taiwan. This study included patients admitted to the hospitals who received an initial diagnosis of COVID-19 between January 1, 2021, and May 31, 2022. The primary outcome was defined as the composite of severe infection, including ventilator use, intubation, ICU admission, and mortality. Secondary outcomes consisted of individual indicators. The dataset encompassed demographic data, health status, COVID-19 specifics, comorbidities, medications, and laboratory results. Two modes (full mode and simplified mode) are used; the former includes all features, and the latter only includes the 30 most important features selected based on the algorithm used by the best model in full mode. Seven machine learning was employed algorithms the performance of the models was evaluated using metrics such as the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity.ResultsThe study encompassed 22,192 eligible in-patients diagnosed with COVID-19. In the full mode, the model using the light gradient boosting machine algorithm achieved the highest AUROC value (0.939), with an accuracy of 85.5%, a sensitivity of 0.897, and a specificity of 0.853. Age, vaccination status, neutrophil count, sodium levels, and platelet count were significant features. In the simplified mode, the extreme gradient boosting algorithm yielded an AUROC of 0.935, an accuracy of 89.9%, a sensitivity of 0.843, and a specificity of 0.902.ConclusionThis study illustrates the feasibility of constructing precise predictive models for severe outcomes or mortality in COVID-19 patients by leveraging significant predictors and advanced machine learning. These findings can aid healthcare practitioners in proactively predicting and monitoring severe outcomes or mortality among hospitalized COVID-19 patients, improving treatment and resource allocation.

Project description:A predictive model to automatically identify the earliest determinants of both hospital discharge and mortality in hospitalized COVID-19 patients could be of great assistance to caregivers if the predictive information is generated and made available in the immediate hours following admission. To identify the most important predictors of hospital discharge and mortality from measurements at admission for hospitalized COVID-19 patients. Observational cohort study. Electronic records from hospitalized patients. Patients admitted between March 3 rd and August 24 th with COVID-19 in Johns Hopkins Health System hospitals. 216 phenotypic variables collected within 48 hours of admission. We used age-stratified (<60 and >=60 years) random survival forests with competing risks to identify the most important predictors of death and discharge. Fine-Gray competing risk regression (FGR) models were then constructed based on the most important RSF-derived covariates. Of 2212 patients, 1913 were discharged (age 57±19, time-to-discharge 9±11 days) while 279 died (age 75±14, time to death 14±15 days). Patients >= 60 years were nearly 10 times as likely to die within 60 days of admission as those <60. As the pandemic evolved, the rate of hospital discharge increased in both older and younger patients. Incident death and hospital discharge were accurately predicted by measures of respiratory distress, inflammation, infection, renal function, red cell turn over and cardiac stress. FGR models for each of hospital discharge and mortality as outcomes based on these variables performed well in the older (AUC 0.80-0.85 at 60-days) and younger populations (AUC >0.90 at 60-days). We identified markers collected within 2 days of admission that predict hospital discharge and mortality in COVID-19 patients and provide prediction models that may be used to guide patient care. Our proposed model suggests that hospital discharge and mortality can be forecasted with high accuracy based on 8-10 variables at this stage of the COVID-19 pandemic. Our findings also point to several specific pathways that could be the focus of future investigations directed at reducing mortality and expediting hospital discharge among COVID-19 patients. Probability of hospital discharge increased over the course of the pandemic. Question: Can we predict the likelihood of hospital discharge as well as mortality from data obtained in the first 48 hours from admission in hospitalized COVID-19 patients?Findings: Models based on extensive phenotyping mined directly from electronic medical records followed by variable selection, accounted for the competing events of hospital death versus discharge, predicted both death and discharge with area under the receiver operating characteristic curves of >0.80.Meaning: Hospital discharge and mortality can be forecasted with high accuracy based on just 8-10 variables, and the probability of hospital discharge increased over the course of the pandemic.

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Endothelium Activation Markers in Severe Hospitalized COVID-19 Patients: Role in Mortality Risk Prediction.

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