109451
论文已发表
注册即可获取德孚的最新动态
IF 收录期刊
- 3.4 Breast Cancer (Dove Med Press)
- 3.2 Clin Epidemiol
- 2.6 Cancer Manag Res
- 2.9 Infect Drug Resist
- 3.7 Clin Interv Aging
- 5.1 Drug Des Dev Ther
- 3.1 Int J Chronic Obstr
- 6.6 Int J Nanomed
- 2.6 Int J Women's Health
- 2.9 Neuropsych Dis Treat
- 2.8 OncoTargets Ther
- 2.0 Patient Prefer Adher
- 2.2 Ther Clin Risk Manag
- 2.5 J Pain Res
- 3.0 Diabet Metab Synd Ob
- 3.2 Psychol Res Behav Ma
- 3.4 Nat Sci Sleep
- 1.8 Pharmgenomics Pers Med
- 2.0 Risk Manag Healthc Policy
- 4.1 J Inflamm Res
- 2.0 Int J Gen Med
- 3.4 J Hepatocell Carcinoma
- 3.0 J Asthma Allergy
- 2.2 Clin Cosmet Investig Dermatol
- 2.4 J Multidiscip Healthc
已发表论文
基于泛免疫炎症值构建预测慢性阻塞性肺疾病急性加重患者不良临床结局的诺模图模型
Authors Weng S, Huang Y, Wu X, Chen Q
Received 27 June 2025
Accepted for publication 10 October 2025
Published 17 October 2025 Volume 2025:18 Pages 14353—14364
DOI https://doi.org/10.2147/JIR.S546857
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Cynthia Koziol-White
Shunhua Weng,1,* Yanjin Huang,2,* Xuejuan Wu,3 Qiong Chen4
1Department of Intensive Care Medicine, Xiamen Haicang Hospital, Xiamen, Fujian, People’s Republic of China; 2Department of Nursing, Xiamen Haicang Hospital, Xiamen, Fujian, People’s Republic of China; 3Department of Respiratory Medicine, Xiamen Haicang Hospital, Xiamen Haicang Hospital, Xiamen, Fujian, People’s Republic of China; 4Department of Rehabilitation Medicine, Xiamen Haicang Hospital, Xiamen, Fujian, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Qiong Chen, Department of Rehabilitation Medicine, Xiamen Haicang Hospital, No. 89 Haiyu Road, Haicang District, Xiamen, Fujian, 361026, People’s Republic of China, Tel +86 177 50612461, Email chenqiongfff@163.com
Purpose: This study evaluated the predictive value of the pan-immune-inflammation value (PIV) and developed a nomogram that integrated the PIV to predict adverse clinical outcomes in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD).
Patients and Methods: In a retrospective, single-center study, 522 patients with AECOPD were randomized 7:3 into the training and validation cohorts. Univariate and multivariate logistic regression were used to determine independent predictors of adverse clinical outcomes. After the optimal cutoff value for the PIV was determined, the training cohort was divided into the high-PIV and low-PIV groups. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were then performed to construct and validate the nomogram.
Results: The regression results identified age, serum albumin (ALB) level, partial pressure of carbon dioxide (PaCO2) level, and the PIV as independent predictors of adverse clinical outcomes, and they were included in the nomogram model. The AUCs of the nomogram model that included these four variables in the training and validation cohorts were 0.720 (95% confidence interval [CI]: 0.658– 0.783) and 0.733 (95% CI: 0.626– 0.840), respectively. The calibration curves of the two cohorts showed good prediction accuracy (Hosmer-Lemeshow test: both P > 0.05), and the DCA proved that the prediction model has some clinical value.
Conclusion: Age, ALB level, PaCO2 level, and the PIV are independent predictors of adverse clinical outcomes in patients with AECOPD and may help healthcare providers identify patients at high risk of adverse outcomes during early admission. Although promising, the nomogram model has only moderate predictive performance. Further studies are required to identify additional significant factors to develop a higher-performing prediction model with which to make more accurate decisions in clinical practice.
Keywords: pan-immune-inflammation value, nomogram, acute exacerbation of chronic obstructive pulmonary disease
1Department of Intensive Care Medicine, Xiamen Haicang Hospital, Xiamen, Fujian, People’s Republic of China; 2Department of Nursing, Xiamen Haicang Hospital, Xiamen, Fujian, People’s Republic of China; 3Department of Respiratory Medicine, Xiamen Haicang Hospital, Xiamen Haicang Hospital, Xiamen, Fujian, People’s Republic of China; 4Department of Rehabilitation Medicine, Xiamen Haicang Hospital, Xiamen, Fujian, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Qiong Chen, Department of Rehabilitation Medicine, Xiamen Haicang Hospital, No. 89 Haiyu Road, Haicang District, Xiamen, Fujian, 361026, People’s Republic of China, Tel +86 177 50612461, Email chenqiongfff@163.com
Purpose: This study evaluated the predictive value of the pan-immune-inflammation value (PIV) and developed a nomogram that integrated the PIV to predict adverse clinical outcomes in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD).
Patients and Methods: In a retrospective, single-center study, 522 patients with AECOPD were randomized 7:3 into the training and validation cohorts. Univariate and multivariate logistic regression were used to determine independent predictors of adverse clinical outcomes. After the optimal cutoff value for the PIV was determined, the training cohort was divided into the high-PIV and low-PIV groups. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were then performed to construct and validate the nomogram.
Results: The regression results identified age, serum albumin (ALB) level, partial pressure of carbon dioxide (PaCO2) level, and the PIV as independent predictors of adverse clinical outcomes, and they were included in the nomogram model. The AUCs of the nomogram model that included these four variables in the training and validation cohorts were 0.720 (95% confidence interval [CI]: 0.658– 0.783) and 0.733 (95% CI: 0.626– 0.840), respectively. The calibration curves of the two cohorts showed good prediction accuracy (Hosmer-Lemeshow test: both P > 0.05), and the DCA proved that the prediction model has some clinical value.
Conclusion: Age, ALB level, PaCO2 level, and the PIV are independent predictors of adverse clinical outcomes in patients with AECOPD and may help healthcare providers identify patients at high risk of adverse outcomes during early admission. Although promising, the nomogram model has only moderate predictive performance. Further studies are required to identify additional significant factors to develop a higher-performing prediction model with which to make more accurate decisions in clinical practice.
Keywords: pan-immune-inflammation value, nomogram, acute exacerbation of chronic obstructive pulmonary disease