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.

Background Epidural analgesia is routinely used for postoperative pain control following abdominal surgeries, yet data regarding the safety and efficacy of epidural analgesia is controversial. Methods Pain-related and clinical perioperative data were extracted and correlated with baseline clinicopathologic data and method of analgesia (epidural versus intravenous patient-controlled analgesia) in patients who underwent hepatectomy from 2012 to 2014. Chronic pain was defined by specific narcotic requirements preoperatively. Results Eighty-seven patients underwent hepatectomy with 60% having epidurals placed for postoperative pain control. Epidural patients underwent more major hepatectomies and open resections. Comparison of pain scores between both groups demonstrated no significant difference (all p>.05). A significantly lower proportion of TEA patients required additional IV pain medications than those with IVPCA (p<0.001). There was no major effect of epidural analgesia on time to ambulation or complications (all p>0.05). After adjusting for perioperative factors, and surgical extent and approach, no significant differences in fluids administered or length of stay were detected. Conclusions Overall postoperative outcomes were not significantly different based on method of analgesia after adjusting for type and extent of hepatic resection. Though patients with epidurals underwent more extensive operations they required less additional IV pain medications than IVPCA patients.

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.

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