Evidence map›Paper›PMID 42228737›Full record

ArticlePloS one2026

The prognostic value of the early neutrophil-to-lymphocyte ratio for 28-day mortality in sepsis patients: A machine learning-based investigation of the MIMIC database.

Jiyang Liao, Qianwen Xiang, Xingwang Chen, Long Wu, Houwang Chen, Zhijun Yao, Huachu Wu, Jianbo Lai

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Article in PloS one, 2026. 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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8 authors.

Jiyang LiaoDepartment of Intensive Care Unit, Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Shenzhen, Guangdong Province, China.
Qianwen XiangDepartment of Cardiovascular Medicine, Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Shenzhen, Guangdong Province, China.
Xingwang ChenDepartment of Intensive Care Unit, Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Shenzhen, Guangdong Province, China.
Long WuDepartment of Intensive Care Unit, Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Shenzhen, Guangdong Province, China.
Houwang ChenDepartment of Intensive Care Unit, Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Shenzhen, Guangdong Province, China.
Zhijun YaoDepartment of Intensive Care Unit, Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Shenzhen, Guangdong Province, China.
Huachu WuDepartment of Intensive Care Unit, Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Shenzhen, Guangdong Province, China.
Jianbo LaiDepartment of Intensive Care Unit, Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Shenzhen, Guangdong Province, China.ORCID https://orcid.org/0000-0002-2790-7468

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe neutrophil-to-lymphocyte ratio (NLR) has shown inconsistent prognostic value in individuals with sepsis. This study aimed to clarify its ability to predict 28-day mortality via a machine learning-based analysis of a large ICU database.

methodsThis retrospective analysis employed data from the MIMIC-IV database (v3.1). The Boruta algorithm combined with XGBoost was used for two-stage feature selection. Patients were stratified by NLR quartiles into three groups (low: <4.34, intermediate: 4.34-14.70, and high: >14.70). This study defined 28-day mortality as the primary outcome. Associations between the NLR and mortality were evaluated by using multivariable logistic regression (progressively adjusted for demographic, clinical, and machine learning-derived features), along with restricted cubic splines. Sensitivity analyses included quantifying NLR feature importance via machine learning and performing subgroup analyses across clinical strata.

resultsThis cohort study included 4,376 patients with a 28-day mortality rate of 18.4%. Compared with the SOFA and SAPS II scores, the prediction performance of XGBoost was superior (ROC-AUC 0.875; 95% CI 0.854-0.896; PR-AUC 0.603). Although the NLR ranked 14th in SHAP-based feature importance, multivariable analysis confirmed its independent association with elevated mortality risk: 28-day (OR 1.16; 95% CI 1.06-1.27; p < 0.001), in-hospital (OR 1.13; 95% CI 1.03-1.24; p < 0.001), and ICU (OR 1.14; 95% CI 1.03-1.25; p = 0.008). Stratified analyses indicated consistent mortality associations for the NLR, with enhanced predictive value being observed in patients aged >45 years and those with SOFA scores ≤4 or SAPS II scores >29. Specifically, patients ≥65 years of age demonstrated a 17% increase in 28-day mortality risk (p = 0.019), and patients with a SOFA score ≤4 exhibited a greater than 20% elevated risk across all of the endpoints (p < 0.001), whereas no significant association was observed in the SOFA ≥9 subgroup (p = 0.369).

conclusionsThe NLR effectively identifies inflammation-driven mortality risk for early sepsis patients but fails to predict outcomes for patients with terminal organ failure. This biphasic predictive pattern highlights the unique value of the NLR in moderate sepsis risk stratification but cautions against its use in cases of advanced disease. Its value lies in dynamic monitoring rather than static risk assessment.

Indexed as

LymphocytesMachine LearningNeutrophilsSepsisAgedBoosting Machine Learning AlgorithmsDatabases, FactualFemaleHumansIntensive Care UnitsMaleMiddle AgedPrognosisRetrospective Studies

Identifiers

PMID42228737
PMCPMC13229304

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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.