Evidence map›Paper›PMID 41488918›Full record

ArticleFrontiers in physiology2025

Prognostic value of the relative neutrophil-monocyte-to-lymphocyte-albumin ratio in chronic lower respiratory diseases: a multicenter retrospective analysis.

Xu Chen, Yi Zhang, Xueyuan Wang, Liping Ye, Kaijia Shi, Xinghan Tian

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Article in Frontiers in physiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Xu Chen *Department of Intensive Care Unit, Yantai Yuhuangding Hospital, Yantai, China.
Yi Zhang *Department of Intensive Care Unit, Yantai Yuhuangding Hospital, Yantai, China.
Xueyuan WangDepartment of Intensive Care Unit, Yantai Yuhuangding Hospital, Yantai, China.
Liping YeDepartment of Intensive Care Unit, Yantai Yuhuangding Hospital, Yantai, China.
Kaijia ShiLishui Central Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, China.
Xinghan TianDepartment of Intensive Care Unit, Yantai Yuhuangding Hospital, Yantai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Chronic lower respiratory diseases (CLRDs) remain major causes of global mortality. Because conventional inflammatory markers have limited prognostic utility, we developed and validated the relative neutrophil-monocyte-lymphocyte-albumin ratio (NMLAR), defined as (Neutrophil% × Monocyte% × 100)/(Lymphocyte% × Albumin [g/dL]), as a novel biomarker to predict CLRD-specific mortality. Methods: Immune infiltration of CLRDs was analyzed based on GEO datasets. We then analyzed 9,236 adults with CLRD from NHANES 1999-2014, excluding individuals with missing core variables. Machine learning algorithms (Boruta, SVM-RFE, XGBoost) were applied to identify key predictors. Cox proportional hazards models and restricted cubic spline (RCS) functions were used to evaluate the association between NMLAR and mortality outcomes, and stratified analyses were conducted across clinically relevant subgroups. Model performance was assessed by Harrell's C-index, calibration plots, and decision-curve analysis (DCA). Findings were externally validated in NHANES 2015-2018 (n = 2,107), the MIMIC-IV v3.1 ICU cohort (n = 2,120), and a real-world Zhejiang Provincial ICU cohort (n = 161). Results: Immune profiling showed increased neutrophils/monocytes and reduced lymphocytes in CLRD and acute states. Higher baseline NMLAR was consistently associated with increased risks of both all-cause and CLRD-specific mortality and demonstrated superior predictive performance compared with conventional inflammatory markers. In NHANES, fully adjusted models indicated an approximately linear dose-response, with each 1-unit increment in NMLAR corresponding to a ∼7% higher risk of all-cause mortality and an ∼8% higher risk of CLRD-specific mortality. In the MIMIC cohort, NMLAR remained independently associated with 14-365-day mortality even after adjustment for critical care-specific covariates (SOFA score, CRRT, invasive mechanical ventilation, vasopressor use), with a threshold effect identified at 12.10. In the Zhejiang ICU cohort, NMLAR independently predicted 30-day mortality (HR per unit increase ≈1.09), with a threshold at 13.32. Notably, models derived from NHANES demonstrated moderate discriminatory ability, satisfactory calibration, and clinical net benefit when externally validated in both ICU cohorts, underscoring the robustness and generalizability of NMLAR as a prognostic biomarker across diverse clinical settings. Conclusion: NMLAR is a simple, robust, and clinically applicable biomarker for mortality risk in CLRD, demonstrating consistent prognostic value across population-based, critical care, and real-world cohorts.

Indexed as

chronic lower respiratory diseasesinflammationmachine learningNHANESNMLARprognostic biomarker

Identifiers

PMID41488918
PMCPMC12756091

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.