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. 2024 Jul;13(7):1130-1143.
doi: 10.1002/psp4.13145. Epub 2024 Jun 5.

Joint modeling of monocyte HLA-DR expression trajectories predicts 28-day mortality in severe SARS-CoV-2 patients

Collaborators, Affiliations

Joint modeling of monocyte HLA-DR expression trajectories predicts 28-day mortality in severe SARS-CoV-2 patients

Gaelle Baudemont et al. CPT Pharmacometrics Syst Pharmacol. 2024 Jul.

Abstract

The recent SarsCov2 pandemic has disrupted healthcare system notably impacting intensive care units (ICU). In severe cases, the immune system is dysregulated, associating signs of hyperinflammation and immunosuppression. In the present work, we investigated, using a joint modeling approach, whether the trajectories of cellular immunological parameters were associated with survival of COVID-19 ICU patients. This study is based on the REA-IMMUNO-COVID cohort including 538 COVID-19 patients admitted to ICU between March 2020 and May 2022. Measurements of monocyte HLA-DR expression (mHLA-DR), counts of neutrophils, of total lymphocytes, and of CD4+ and CD8+ subsets were performed five times during the first month after ICU admission. Univariate joint models combining survival at day 28 (D28), hospital discharge and longitudinal analysis of those biomarkers' kinetics with mixed-effects models were performed prior to the building of a multivariate joint model. We showed that a higher mHLA-DR value was associated with a lower risk of death. Predicted mHLA-DR nadir cutoff value that maximized the Youden index was 5414 Ab/C and led to an AUC = 0.70 confidence interval (95%CI) = [0.65; 0.75] regarding association with D28 mortality while dynamic predictions using mHLA-DR kinetics until D7, D12 and D20 showed AUCs of 0.82 [0.77; 0.87], 0.81 [0.75; 0.87] and 0.84 [0.75; 0.93]. Therefore, the final joint model provided adequate discrimination performances at D28 after collection of biomarker samples until D7, which improved as more samples were collected. After severe COVID-19, decreased mHLA-DR expression is associated with a greater risk of death at D28 independently of usual clinical confounders.

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Conflict of interest statement

The authors declare no competing financial interests in relation to the work.

Figures

FIGURE 1
FIGURE 1
Scheme of the data used in this analysis, the modeling steps and evaluation procedures.
FIGURE 2
FIGURE 2
Kaplan–Meier of the overall survival until Day 28 (D28). The gray area around the survival curve represents the associated 95% confidence interval (95% CI). The numbers of patients at risk and the cumulative numbers of censored patients along time are displayed under the plot.
FIGURE 3
FIGURE 3
Spaghetti plots of mHLA‐DR, CD4+ lymphocytes, CD8+ lymphocytes, Neutrophils and lymphocytes according to vital status at Day 28 (D28). The blue curve represents the smoothed conditional mean at each time.
FIGURE 4
FIGURE 4
Individual fits of mHLA‐DR kinetics and survival rate for six patients. Solid vertical lines indicate the D28 vital status: green if the patient is still alive and red in case of death with the discharge time represented by dashed green vertical lines. The blue dots are observed levels of mHLA‐DR, the solid and dashed blue curves are the 2.5th, 50th, and 97.5th percentiles of model predicted mHLA‐DR levels and the solid and dashed black curves are the estimated 2.5th, 50th, and 97.5th percentiles of individual model predicted survival rates. Patients were selected to represent profiles with data up until D7, D12, and D20 each and for both survival status.

References

    1. Serafim RB, Póvoa P, Souza‐Dantas V, Kalil AC, Salluh JIF. Clinical course and outcomes of critically ill patients with COVID‐19 infection: a systematic review. Clin Microbiol Infect. 2021;27(1):47‐54. - PMC - PubMed
    1. Aziz S, Arabi YM, Alhazzani W, et al. Managing ICU surge during the COVID‐19 crisis: rapid guidelines. Intensive Care Med. 2020;46(7):1303‐1325. - PMC - PubMed
    1. Osuchowski MF, Winkler MS, Skirecki T, et al. The COVID‐19 puzzle: deciphering pathophysiology and phenotypes of a new disease entity. Lancet. Respir Med. 2021;9(6):622‐642. - PMC - PubMed
    1. Zhudenkov K, Gavrilov S, Sofronova A, et al. A workflow for the joint modeling of longitudinal and event data in the development of therapeutics: tools, statistical methods, and diagnostics. CPT Pharmacometrics Syst Pharmacol. 2022;11(4):425‐437. - PMC - PubMed
    1. Latouche A, Porcher R, Chevret S. A note on including time‐dependent covariate in regression model for competing risks data. Biom J. 2005;47(6):807‐814. - PubMed