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1.
PLoS Comput Biol ; 18(1): e1009778, 2022 01.
Article in English | MEDLINE | ID: covidwho-1634452

ABSTRACT

The clinical outcome of SARS-CoV-2 infection varies widely between individuals. Machine learning models can support decision making in healthcare by assessing fatality risk in patients that do not yet show severe signs of COVID-19. Most predictive models rely on static demographic features and clinical values obtained upon hospitalization. However, time-dependent biomarkers associated with COVID-19 severity, such as antibody titers, can substantially contribute to the development of more accurate outcome models. Here we show that models trained on immune biomarkers, longitudinally monitored throughout hospitalization, predicted mortality and were more accurate than models based on demographic and clinical data upon hospital admission. Our best-performing predictive models were based on the temporal analysis of anti-SARS-CoV-2 Spike IgG titers, white blood cell (WBC), neutrophil and lymphocyte counts. These biomarkers, together with C-reactive protein and blood urea nitrogen levels, were found to correlate with severity of disease and mortality in a time-dependent manner. Shapley additive explanations of our model revealed the higher predictive value of day post-symptom onset (PSO) as hospitalization progresses and showed how immune biomarkers contribute to predict mortality. In sum, we demonstrate that the kinetics of immune biomarkers can inform clinical models to serve as a powerful monitoring tool for predicting fatality risk in hospitalized COVID-19 patients, underscoring the importance of contextualizing clinical parameters according to their time post-symptom onset.


Subject(s)
Antibodies, Viral/blood , COVID-19 , SARS-CoV-2/immunology , Adult , Aged , Aged, 80 and over , Biomarkers/blood , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19/immunology , COVID-19/therapy , Computational Biology , Diagnosis, Computer-Assisted , Female , Humans , Male , Middle Aged , Prognosis , Spike Glycoprotein, Coronavirus/immunology , Young Adult
2.
JCI Insight ; 6(4)2021 02 22.
Article in English | MEDLINE | ID: covidwho-1039949

ABSTRACT

Convalescent plasma with severe acute respiratory disease coronavirus 2 (SARS-CoV-2) antibodies (CCP) may hold promise as a treatment for coronavirus disease 2019 (COVID-19). We compared the mortality and clinical outcome of patients with COVID-19 who received 200 mL of CCP with a spike protein IgG titer ≥ 1:2430 (median 1:47,385) within 72 hours of admission with propensity score-matched controls cared for at a medical center in the Bronx, between April 13 and May 4, 2020. Matching criteria for controls were age, sex, body mass index, race, ethnicity, comorbidities, week of admission, oxygen requirement, D-dimer, lymphocyte counts, corticosteroid use, and anticoagulation use. There was no difference in mortality or oxygenation between CCP recipients and controls at day 28. When stratified by age, compared with matched controls, CCP recipients less than 65 years had 4-fold lower risk of mortality and 4-fold lower risk of deterioration in oxygenation or mortality at day 28. For CCP recipients, pretransfusion spike protein IgG, IgM, and IgA titers were associated with mortality at day 28 in univariate analyses. No adverse effects of CCP were observed. Our results suggest CCP may be beneficial for hospitalized patients less than 65 years, but data from controlled trials are needed to validate this finding and establish the effect of aging on CCP efficacy.


Subject(s)
Antibodies, Neutralizing/administration & dosage , Antibodies, Viral/administration & dosage , COVID-19/therapy , SARS-CoV-2/immunology , Adult , Age Factors , Aged , Aged, 80 and over , Antibodies, Neutralizing/blood , Antibodies, Neutralizing/immunology , Antibodies, Viral/blood , Antibodies, Viral/immunology , COVID-19/immunology , COVID-19/mortality , COVID-19/virology , Female , Hospital Mortality , Humans , Immunization, Passive/methods , Male , Middle Aged , New York City/epidemiology , Propensity Score , Retrospective Studies , Spike Glycoprotein, Coronavirus/immunology , Treatment Outcome , COVID-19 Serotherapy
3.
J Interv Card Electrophysiol ; 59(2): 337-345, 2020 Nov.
Article in English | MEDLINE | ID: covidwho-640049

ABSTRACT

PURPOSE: Hydroxychloroquine, chloroquine, and azithromycin have been used for treatment of COVID-19, but may cause QT prolongation. Minority populations are disproportionately impacted by COVID-19. This study evaluates the risk of QT prolongation and subsequent outcomes after administration of these medications in largely underrepresented minority COVID-19 patients. METHODS: We conducted an observational study on hospitalized COVID-19 patients in the Montefiore Health System (Bronx, NY). We examined electrocardiograms (ECG) pre/post-medication initiation to evaluate QTc, HR, QRS duration, and presence of other arrhythmias. RESULTS: One hundred five patients (mean age 67 years; 44.8% F) were analyzed. The median time from the first dose of any treatment to post-medication ECG was 2 days (IQR: 1-3). QTc in men increased from baseline (440 vs 455 ms, p < 0.001), as well as in women (438 vs 463 ms, p < 0.001). The proportion of patients with QT prolongation increased significantly (14.3% vs 34.3%, p < 0.001) even when adjusted for electrolyte abnormalities. The number of patients whose QTc > 500 ms was significantly increased after treatment (16.2% vs. 4.8%, p < 0.01). Patients with either QTc > 500 ms or an increase of 60 ms had a higher frequency of death (47.6% vs. 22.6%, p = 0.02) with an odds ratio of 3.1 (95% CI: 1.1-8.7). Adjusting for race/ethnicity yielded no significant associations. CONCLUSIONS: Hydroxychloroquine, chloroquine, and/or azithromycin were associated with QTc prolongation but did not result in fatal arrhythmias. Our findings suggest that any harm is unlikely to outweigh potential benefits of treatment. Careful risk-benefit analyses for individual patients should guide the use of these medications. Randomized control trials are necessary to evaluate their efficacies.


Subject(s)
Antimalarials/adverse effects , Azithromycin/adverse effects , Coronavirus Infections/drug therapy , Electrocardiography/methods , Long QT Syndrome/chemically induced , Pneumonia, Viral/drug therapy , Age Distribution , Aged , Aged, 80 and over , Antimalarials/administration & dosage , Azithromycin/administration & dosage , COVID-19 , Chloroquine/administration & dosage , Chloroquine/adverse effects , Coronavirus Infections/diagnosis , Coronavirus Infections/epidemiology , Female , Follow-Up Studies , Hospitalization/statistics & numerical data , Humans , Hydroxychloroquine/administration & dosage , Hydroxychloroquine/adverse effects , Incidence , Long QT Syndrome/diagnostic imaging , Long QT Syndrome/drug therapy , Long QT Syndrome/epidemiology , Male , Middle Aged , Pandemics/prevention & control , Pandemics/statistics & numerical data , Pneumonia, Viral/diagnosis , Pneumonia, Viral/epidemiology , Risk Assessment , Sex Distribution , Urban Population
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