Evidence map›Paper›PMID 42401858›Full record

ArticleBMC cancer2026

LAR/NMLR stratify mortality risk before and after checkpoint inhibitor pneumonitis in NSCLC: multi-state and time-dependent analyses.

Qingwei Zhang, Yijiao Xu, Jianying Liu, Yuting Wang

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Article in BMC cancer, 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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5 · Who and what money

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4 authors.

Qingwei Zhang *Department of Respiratory, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China.
Yijiao Xu *Department of Respiratory, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China.
Jianying LiuDepartment of Gastroenterology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China. liu.jianying@zsxmhospital.com.
Yuting WangIntensive Care Unit, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China. wang.yuting@zsxmhospital.com.ORCID http://orcid.org/0000-0002-9735-4408

Funding

Natural Science Foundation of Fujian Province no. 2025J011499Xiamen Healthcare Guidance Project no. 3502Z20244ZD1115
6 · The paper itself

Abstract

backgroundCheckpoint inhibitor pneumonitis (CIP) is a clinically important immune-related adverse event in non-small cell lung cancer (NSCLC), but its time-varying occurrence complicates prognostic assessment.

objectiveThis study evaluated whether baseline lactate dehydrogenase-to-albumin ratio (LAR) and neutrophil-monocyte-to-lymphocyte ratio (NMLR) were associated with CIP onset and mortality risk before and after CIP.

methodsThis retrospective cohort included adults with advanced or recurrent NSCLC treated with immune checkpoint inhibitors (ICIs) at Zhongshan Hospital (Xiamen), Fudan University, between 2021 and 2024. CIP incidence was estimated using cumulative incidence functions with death without prior CIP as a competing event. Transition-specific associations were evaluated using an illness-death multi-state model: CIP-free at-risk state after ICI initiation (state 0), post-CIP state (state 1), and death as the absorbing state (state 2). Overall survival (OS) was assessed using a time-dependent Cox model with CIP modeled as a time-varying exposure. Restricted cubic splines (RCS), calibration analyses, penalized sensitivity analyses, and benchmark comparisons with the lung immune prognostic index (LIPI) were performed.

resultsAmong 202 patients, 24 developed CIP and 119 died during follow-up. In parsimonious transition-specific Cox models, cause-specific hazard ratios (csHRs) showed that zLAR and zNMLR were not associated with CIP onset, but were associated with death without prior CIP (0→2: zLAR csHR 1.22, 95% CI 1.00-1.50; zNMLR csHR 1.28, 95% CI 1.09-1.51) and death after CIP (1→2: zLAR csHR 2.47, 95% CI 0.93-6.57; zNMLR csHR 3.14, 95% CI 1.32-7.45). In the time-dependent Cox model, CIP was not significantly associated with mortality, whereas zLAR (HR 1.25, 95% CI 1.01-1.55) and zNMLR (HR 1.25, 95% CI 1.01-1.54) remained prognostic. Adding zLAR and zNMLR to the clinical base model increased the C-index from 0.623 to 0.682. The dual-outcome risk map showed partial separation between predicted CIP and death risks.

conclusionBaseline LAR and NMLR were associated with mortality risk rather than the occurrence of CIP in ICI-treated NSCLC patients. These simple blood-based indices may help identify patients who require closer survival-oriented monitoring, while CIP risk should be assessed together with clinical pulmonary factors.

Indexed as

Carcinoma, Non-Small-Cell LungImmune Checkpoint InhibitorsL-Lactate DehydrogenaseLung NeoplasmsPneumoniaSerum AlbuminAgedFemaleHumansLymphocytesMaleMiddle AgedMonocytesNeutrophilsPrognosisRetrospective StudiesImmune Checkpoint InhibitorsL-Lactate DehydrogenaseSerum AlbuminCheckpoint Inhibitor Pneumonitis (CIP)Immune Checkpoint Inhibitors (ICIs)Lactate dehydrogenase-to-Albumin Ratio (LAR)Multi-state modelNeutrophil–Monocyte-to-Lymphocyte Ratio (NMLR)Non-Small Cell Lung Cancer (NSCLC)

Identifiers

PMID42401858
PMCPMC13631982

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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.