构建和比较基于血清的多种预后模型,以预测慢性阻塞性肺病急性加重的预后。

IF 4.2 2区 医学 Q2 IMMUNOLOGY
Journal of Inflammation Research Pub Date : 2024-11-07 eCollection Date: 2024-01-01 DOI:10.2147/JIR.S461961
Na Wang, Guangdong Wang, Mengcong Li, Tingting Liu, Wenwen Ji, Tinghua Hu, Zhihong Shi
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引用次数: 0

摘要

目的:慢性阻塞性肺疾病(AECOPD)的急性加重与严重的预后不良有关。淋巴细胞与单核细胞比值(LMR)、中性粒细胞与淋巴细胞比值(NLR)、嗜酸性粒细胞与淋巴细胞比值(ELR)、嗜碱性粒细胞与淋巴细胞比值(BLR)、血小板与淋巴细胞比值(PLR)以及血红蛋白、白蛋白、淋巴细胞和血小板(HALP)是反映炎症、免疫状态和营养状况的重要指标。本研究评估了这些指标在 AECOPD 中的预测价值,并根据这些指标建立了评估 AECOPD 预后的预测模型:我们回顾性地收集了 2609 名 AECOPD 患者的数据。评估的结果包括呼吸衰竭(RF)、重症监护室(ICU)住院、机械通气(MV)和 30 天再入院。我们评估了 LMR、NLR、PLR、BLR、ELR 和 HALP 预测 AECOPD 患者预后的能力。此外,基于这些指标,我们利用 LASSO 回归和多变量分析建立了预测 AECOPD 患者预后的模型。我们使用AUCs评估了这些指标的预测价值和模型的性能:LMR对RF的AUC为0.612,对ICU住院时间的AUC为0.715,对MV的AUC为0.714,对30天再入院的AUC为0.624。其他指标,包括 NLR、PLR、BLR、EMR 和 HALP,在预测 AECOPD 的这些结果方面显示出 0.621 至 0.699 的 AUC。使用 LASSO 回归和多变量分析建立的模型显示,RF 的 AUC 为 0.717,ICU 住院时间的 AUC 为 0.773,MV 为 0.780,30 天再入院的 AUC 为 0.682。将 LMR、NLR、PLR、BLR、ELR 和 HALP 单独纳入模型可进一步提高预测性能,尤其是 LMR(RF 的 AUC 为 0.753,ICU 住院的 AUC 为 0.797,MV 的 AUC 为 0.802,30 天再入院的 AUC 为 0.697)。697)、NLR(RF 的 AUC 值为 0.753,ICU 住院率为 0.796,MV 为 0.802,30 天再入院率为 0.698)和 HALP(RF 的 AUC 值为 0.752,ICU 住院率为 0.790,MV 为 0.797,30 天再入院率为 0.697):结论:LMR、NLR、PLR、BLR、ELR 和 HALP 指标在预测 AECOPD 患者的预后方面表现良好。将这些指标整合到预后模型中可显著提高其预测效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Construction and Comparison of Multiple Serum-Based Prognostic Models for Predicting the Prognosis of Acute Exacerbations of Chronic Obstructive Pulmonary Disease.

Purpose: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with significant poor prognosis. Lymphocyte-to-Monocyte Ratio (LMR), Neutrophil-to-Lymphocyte Ratio (NLR), Eosinophil-to-Lymphocyte Ratio (ELR), Basophil-to-Lymphocyte Ratio (BLR), Platelet-to-Lymphocyte Ratio (PLR), and Hemoglobin, Albumin, Lymphocyte, and Platelet (HALP) are vital indicators for inflammation, immune status, and nutritional condition. This study evaluated the predictive value of these indicators in AECOPD and developed predictive models to assess the prognosis of AECOPD based on these indicators.

Patients and methods: We retrospectively collected data from 2609 AECOPD patients. The outcomes assessed included occurrence of respiratory failure (RF), intensive care unit (ICU) stay, mechanical ventilation (MV), and 30-day readmission. We evaluated the predictive ability of LMR, NLR, PLR, BLR, ELR, and HALP for predicting the prognosis of AECOPD patients. Furthermore, based on these indicators, we utilized LASSO regression and multivariable analysis to develop models for predicting the prognosis of AECOPD patients. The predictive value of these indicators and the performance of the models were assessed using AUCs.

Results: LMR exhibited AUCs of 0.612 for RF, 0.715 for ICU stay, 0.714 for MV, and 0.624 for 30-day readmission. Other indicators, including NLR, PLR, BLR, EMR, and HALP, showed AUCs ranging from 0.621 to 0.699 for predicting these outcomes in AECOPD. The models developed using LASSO regression and multivariable analysis yielded AUCs of 0.717 for RF, 0.773 for ICU stay, 0.780 for MV, and 0.682 for 30-day readmission. Incorporating LMR, NLR, PLR, BLR, ELR, and HALP into the models individually further enhanced predictive performance, particularly with LMR (AUCs of 0.753 for RF, 0.797 for ICU stay, 0.802 for MV, and 0.697 for 30-day readmission), NLR (AUCs of 0.753 for RF, 0.796 for ICU stay, 0.802 for MV, and 0.698 for 30-day readmission), and HALP (AUCs of 0.752 for RF, 0.790 for ICU stay, 0.797 for MV, and 0.697 for 30-day readmission).

Conclusion: Indicators of LMR, NLR, PLR, BLR, ELR, and HALP showed good performance in predicting outcomes for AECOPD patients. The integration of these indicators into prognostic models significantly enhances their predictive efficacy.

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来源期刊
Journal of Inflammation Research
Journal of Inflammation Research Immunology and Microbiology-Immunology
CiteScore
6.10
自引率
2.20%
发文量
658
审稿时长
16 weeks
期刊介绍: An international, peer-reviewed, open access, online journal that welcomes laboratory and clinical findings on the molecular basis, cell biology and pharmacology of inflammation.
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