Evidence map›Paper›PMID 38698751›Full record

ArticleCombinatorial chemistry & high throughput screening2025

Effect of Novel Inflammatory Biomarkers on Adverse Outcomes in Patients with Interstitial Lung Disease and Pneumonia: A Multicenter Retrospective Cohort Study.

Wenmei Bai, Yide Wang, Fengsen Li

Abstract readMulticenter Study
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Article in Combinatorial chemistry & high throughput screening, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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5citing papers in PubMed
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3 · Its place in the literature

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5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Wenmei BaiThe Fourth Clinical College of Xinjiang Medical University, Urumqi, China.
Yide WangThe Fourth Clinical College of Xinjiang Medical University, Urumqi, China.
Fengsen LiThe Fourth Clinical College of Xinjiang Medical University, Urumqi, China.

Funding

Autonomous Region Health Youth Medical Science and Technology Talent Project wjwy202229Natural Science Foundation of Xinjiang Uygur Autonomous Region 2023D01C140
6 · The paper itself

Abstract

backgroundInterstitial lung disease frequently coincides with pneumonia in clinical settings, and both conditions are closely associated with immunoinflammation. The Systemic Immune Inflammatory Index (SII) is a recently identified marker, and its connection to the prognosis of individuals suffering from interstitial lung disease and concurrent pneumonia remains unclear. The objective of this study was to scrutinize the correlation between varying SII levels and unfavorable outcomes in patients grappling with interstitial lung disease complicated by pneumonia.

methodsThis study encompassed a retrospective multicenter cohort of 324 patients diagnosed with interstitial lung disease and pneumonia, all receiving glucocorticoid treatment during their hospitalization. We initially conducted ROC analysis to determine the optimal SII threshold. Subsequently, we examined disparities in clinical symptoms, physical signs, clinical test data, and other clinical attributes among patients with differing SII levels. Later, we employed the Kaplan-Meier survival curve method to assess the association between distinct SII levels and the 30-day and 90-day mortality rates in patients dealing with interstitial lung disease complicated by pneumonia. Finally, a Cox regression model was employed to identify factors influencing adverse prognosis in these patients. RESULTS AND DISCUSSION: The findings demonstrated that the optimal SII threshold for predicting 30-day mortality was 1416.97, with an AUC of 0.633 (95% CI: 0.559-0.708) and a P value of <0.001. For 90-day mortality, the optimal SII threshold was 994.59, yielding an AUC of 0.628 (95% CI: 0.56-0.697) and a P value of <0.001. Noteworthy statistical distinctions emerged in dyspnea, cyanosis, and oxygenation index among patients with varying SII levels. Additionally, invasive mechanical ventilation, non-invasive ventilation, and extended infection duration independently constituted 30-day and 90-day mortality risk factors. Elevated heart rate and higher SII levels emerged as independent risk factors for 90-day mortality.

conclusionTo some extent, SII levels exhibit correlations with the clinical manifestations in patients grappling with interstitial lung disease complicated by pneumonia. Notably, a high SII level is an independent predictor for an unfavorable prognosis in these patients. Nevertheless, these findings warrant further validation through prospective cohort studies.

Indexed as

InflammationLung Diseases, InterstitialPneumoniaAgedBiomarkersCohort StudiesFemaleHumansMaleMiddle AgedPrognosisRetrospective StudiesBiomarkerscox regression model analysisInterstitial lung diseasekaplan-meier survival analysis.pneumoniaprognosissystemic immune inflammatory index

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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.