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Methodological and Clinical Gaps in Biomarker-Guided Weaning for Sepsis-Induced ARDS [Letter]
Received 10 April 2026
Accepted for publication 16 April 2026
Published 25 April 2026 Volume 2026:19 612924
DOI https://doi.org/10.2147/JIR.S612924
Checked for plagiarism Yes
Editor who approved publication: Dr Xin Du
Cheng-Wei Lu,1,2 Kuo-Chen Chang1
1Department of Anesthesiology, Far Eastern Memorial Hospital, New Taipei, Taiwan; 2Department of Mechanical Engineering, Yuan Ze University, Taoyuan, Taiwan
Correspondence: Cheng-Wei Lu, Department of Anesthesiology, Far Eastern Memorial Hospital, 21, Section 2, Nan-Ya South Road, Banqiao Dist, New Taipei, Taiwan, Tel +886-2-89667000, ext. 2383, Fax +886-2-23680782, Email [email protected]
View the original paper by Dr Muhoza and colleagues
Dear editor
We read with interest the narrative review by Muhoza et al1 on circulating and respiratory biomarkers in sepsis-induced ARDS While the paper covers considerable ground, several issues that bear on its clinical applicability were not addressed.
First, the review draws heavily on COVID-19 ARDS cohorts to support thresholds for sRAGE, suPAR, and IL-6, without acknowledging that SARS-CoV-2–induced lung injury is immunobiologically distinct from classical bacterial sepsis-induced ARDS. The two conditions differ in endothelial injury patterns, type I interferon signalling, and complement activation,2 and cutoffs derived from one context may not translate reliably to the other. A clearer separation of evidence by etiology would substantially strengthen the review’s conclusions.
Second, the manuscript proposes specific diagnostic thresholds — such as suPAR ≥14.01 ng/mL — without discussing how renal function affects biomarker clearance. AKI complicates up to 40–50% of sepsis-induced ARDS cases,3 and suPAR, sRAGE, and presepsin are all partially renally cleared. Elevated plasma concentrations in oliguric patients may therefore reflect impaired clearance rather than inflammatory burden,4 a distinction with direct implications for bedside decision-making.
Third, the review endorses IL-6 as a key prognostic marker while simultaneously recommending IL-6 receptor antagonists and corticosteroids as standard therapy — an inconsistency that is not reconciled. Active treatment with tocilizumab or dexamethasone suppresses the very markers proposed for monitoring, and declining levels may reflect pharmacological suppression rather than genuine clinical improvement.5 Rather than relying on absolute concentration thresholds, future studies should examine the rate of biomarker change over 24–48 hours, which has been shown to carry independent prognostic information even in treated patients.6
Fourth, the evidence presented is almost entirely cross-sectional. Serial measurements of IL-6, Ang-2, and syndecan-1 have been shown to outperform single time-point values in predicting weaning outcomes and mortality,7,8 yet no guidance is offered on when during a spontaneous breathing trial biomarkers should be measured or how they might be integrated with clinical indices such as rapid shallow breathing index into a composite prediction model.9,10 Beyond study design, practical barriers receive little attention: without rapid point-of-care assays,11 many of these markers will remain research tools rather than clinical instruments.
Finally, the review attributes geographic variation in ARDS prevalence to healthcare-system factors but overlooks two sources of biological heterogeneity that are equally important. Host genetic variation — including SNPs in the AGER gene encoding sRAGE and in the IL6 promoter — can drive baseline biomarker differences unrelated to disease severity,12 potentially leading to misclassification in precision-medicine frameworks. Separately, the exclusive focus on humoral markers neglects the mechanical dimension of weaning failure: sepsis-induced diaphragmatic dysfunction contributes substantially to extubation failure and cannot be captured by circulating proteins alone. Incorporating diaphragm ultrasound-derived indices or neural respiratory drive measurements alongside biomarker panels13 would provide a more complete picture of a patient’s readiness for liberation from mechanical ventilation.
We thank the authors for a thorough review of a clinically important topic and hope these remarks are of use.
Data Sharing Statement
Data sharing is not applicable to this article as no data were created or analyzed in this communication.
Author Contributions
Cheng-Wei Lu– Conceptualization, Supervision, Validation, Writing – original draft, Writing – Review & Editing; Kuo-Chen Chang – Conceptualization, Writing – Review & Editing. All authors agreed to the journal where this communication was submitted, agreed to the final version submitted for publication and agreed to be accountable for the contents of this communication.
Funding
No funding was received.
Disclosure
The authors have no other conflicts of interest to declare in this communication.
References
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10. Stivi T, Padawer D, Dirini N, Nachshon A, Batzofin BM, Ledot S. Using artificial intelligence to predict mechanical ventilation weaning success in patients with respiratory failure, including those with acute respiratory distress syndrome. J Clin Med. 2024;13(5):1505. doi:10.3390/jcm13051505
11. Plebani M. Point-of-care testing in the era of value-based laboratory medicine. Clin Chem Lab Med. 2026. doi:10.1515/cclm-2026-0309
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13. Dres M, Dubé BP, Mayaux J, et al. Coexistence and impact of limb muscle and diaphragm weakness at time of liberation from mechanical ventilation in medical intensive care unit patients. Am J Respir Crit Care Med. 2017;195(1):57–66. doi:10.1164/rccm.201602-0367OC
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