Development of a proteomic signature associated with severe disease for patients with COVID-19 using data from 5 multicenter, randomized, controlled, and prospective studies.

Significant progress has been made in preventing severe COVID-19 disease through the development of vaccines. However, we still lack a validated baseline predictive biologic signature for the development of more severe disease in both outpatients and inpatients infected with SARS-CoV-2. The objective of this study was to develop and externally validate, via 5 international outpatient and inpatient trials and/or prospective cohort studies, a novel baseline proteomic signature, which predicts the development of moderate or severe (vs mild) disease in patients with COVID-19 from a proteomic analysis of 7000 + proteins. The secondary objective was exploratory, to identify (1) individual baseline protein levels and/or (2) protein level changes within the first 2 weeks of acute infection that are associated with the development of moderate/severe (vs mild) disease. For model development, samples collected from 2 randomized controlled trials were used. Plasma was isolated and the SomaLogic SomaScan platform was used to characterize protein levels for 7301 proteins of interest for all studies. We dichotomized 113 patients as having mild or moderate/severe COVID-19 disease. An elastic net approach was used to develop a predictive proteomic signature. For validation, we applied our signature to data from three independent prospective biomarker studies. We found 4110 proteins measured at baseline that significantly differed between patients with mild COVID-19 and those with moderate/severe COVID-19 after adjusting for multiple hypothesis testing. Baseline protein expression was associated with predicted disease severity with an error rate of 4.7% (AUC = 0.964). We also found that five proteins (Afamin, I-309, NKG2A, PRS57, LIPK) and patient age serve as a signature that separates patients with mild COVID-19 and patients with moderate/severe COVID-19 with an error rate of 1.77% (AUC = 0.9804). This panel was validated using data from 3 external studies with AUCs of 0.764 (Harvard University), 0.696 (University of Colorado), and 0.893 (Karolinska Institutet). In this study we developed and externally validated a baseline COVID-19 proteomic signature associated with disease severity for potential use in both outpatients and inpatients with COVID-19.

Feasibility of advanced composite endpoint analysis: Post-hoc insights from the vitamin intervention for stroke prevention randomized control trial.

BACKGROUND AND AIMS: Stroke is a major cause of mortality and disability, highlighting the importance of prevention. Clinical trials play an important role in evaluating interventions that can maximize stroke prevention. Traditional composite endpoints (TCE) used in clinical trials have limitations, as they pool together events of varying clinical importance. Weighted composite endpoints (WCE) have emerged as a solution to address these limitations and provide more accurate assessments of outcomes. In this study, we investigate the use of WCE in a previously reported negative clinical trial for stroke prevention. METHODS AND RESULTS: We analyzed data from the Vitamin Intervention for Stroke Prevention (VISP) trial, which compared high dose and low dose multivitamin therapy. We utilized weighted methods to analyze time-to-event outcomes with censoring. The primary outcomes of interest were time to nonfatal stroke, nonfatal coronary events, and death. We calculated modified Kaplan-Meier (KM) curves for each intervention group. We also performed a modified log-rank test to assess significant differences based on the weighted KM curves. The analysis included 3668 VISP trial participants, and most remained event-free throughout the study period. The TCE KM curve showed no significant difference in outcomes between high dose and low dose groups. Similarly, the WCE KM curves, with different weights assigned to each outcome, did not reveal significant differences in outcomes between the studied groups. CONCLUSION: This post-hoc analysis confirms the negative trial results of VISP and demonstrates the feasibility of using WCE in assessing nutrition-based interventions for stroke prevention.

BivRec: an R package for the nonparametric and semiparametric analysis of bivariate alternating recurrent events.

BACKGROUND: Bivariate alternating recurrent event data can arise in longitudinal studies where patients with chronic diseases go through two states that occur repeatedly, e.g., care periods and break periods. However, there was no statistical software that provided tools for the analysis of such data. To meet this software need, we developed BivRec, a package for R that contains a set of tools for exploratory, nonparametric and semiparametric regression analysis of bivariate alternating recurrent events. RESULTS: The BivRec package provides functions for nonparametric estimations for the joint distribution of bivariate gap times (bivrecNP) and semiparametric regression methods for evaluating covariate effects on the two types of gap times under the accelerated failure time model framework (bivrecReg). The package also provides exploratory data analysis tools such as a visualization of the gap times by groups. We utilize a subset of the South Verona Psychiatric Case Register (PCR) data to illustrate the use of the BivRec package for the reviewed methods. CONCLUSIONS: We demonstrate BivRec's capability for data visualization, nonparametric and regression based analysis, as well as data simulation. The package has default methods with satisfactory performance despite the complexity of calculations and fills a gap in software for statistical analysis of bivariate alternating recurrent events. BivRec is accessible under the GPL-3 General Public License through CRAN, facilitating its installation.

Two-sample survival probability curves: A graphical approach for the analysis of time to event data in clinical trials.

With the aim to improve the communication of trial results, we introduce a novel graphical approach that complements the analysis of time to event outcomes in two-arm randomized trials. We define the so-called two-sample survival probability curve and propose a nonparametric estimator of the curve based on a random walk using Kaplan-Meier survival estimates for the two arms. We then use the estimated curve to visualize treatment effect as well as potential effect modification of factors of interest. We also propose to estimate two-sample survival probability curves within the framework of the Cox model to graphically assess model fit. The proposed two-sample survival probability plot puts trials in a standardized [0,1] × [0,1] space, allowing for a simple visualization of the main effect, effect modification, and the adequacy of a model fit.

Metabolite profiles associated with disease progression in influenza infection.

BACKGROUND: We performed metabolomic profiling to identify metabolites that correlate with disease progression and death. METHODS: We performed a study of adults hospitalized with Influenza A(H1N1)pdm09. Cases (n = 32) were defined by a composite outcome of death or transfer to the intensive care unit during the 60-day follow-up period. Controls (n = 64) were survivors who did not require transfer to the ICU. Four hundred and eight metabolites from eight families were measured on plasma sample at enrollment using a mass spectrometry based Biocrates platform. Conditional logistic regression was used to summarize the association of the individual metabolites and families with the composite outcome and its major two components. RESULTS: The ten metabolites with the strongest association with disease progression belonged to five different metabolite families with sphingolipids being the most common. The acylcarnitines, glycerides, sphingolipids and biogenic metabolite families had the largest odds ratios based on the composite endpoint. The tryptophan odds ratio for the composite is largely associated with death (OR 17.33: 95% CI, 1.60-187.76). CONCLUSIONS: Individuals that develop disease progression when infected with Influenza H1N1 have a metabolite signature that differs from survivors. Low levels of tryptophan had a strong association with death. REGISTRY: ClinicalTrials.gov Identifier: NCT01056185.