Use of Platelet Parameters in the Differential Diagnosis of Lung Adenocarcinoma-Associated Malignant Pleural Effusion and Tuberculous Pleural Effusion

Author:

Ai Ling1ORCID,Li Jingyuan2ORCID,Ye Ting2ORCID,Wang Wenjun1ORCID,Li Yuying1ORCID

Affiliation:

1. Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, China

2. Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, China

Abstract

Background. Both malignant pleural effusion (MPE) and tuberculous pleural effusion (TPE) are common etiologies of pleural effusion; the present study was conducted to establish the diagnostic value of platelet parameters in the differential diagnosis of MPE and TPE. Methods. This retrospective study enrolled patients with lung adenocarcinoma-associated MPE and TPE. Platelet parameter data, including platelet count (PLT), mean platelet volume (MPV), plateletcrit (PCT), platelet distribution width (PDW), and platelet-larger cell ratio (P-LCR), were collected. Principal component analysis and multiple logistic regression modelling were carried out to assess the diagnostic value of these platelet parameters. Results. The MPE group and the TPE group enrolled 270 and 433 patients, respectively. Demographic characteristics of patients were more female and higher age in the MPE group. MPV, PDW, and P-LCR were significantly higher in MPE patients, while PLT and PCT were significantly higher in TPE patients. Principal component analysis generated two principal components (PCs) based on above platelet parameters. After adjusting for confounding factors including gender and age, multiple logistic regression showed positive association between PC1 and MPE. Conclusion. Platelet parameters were potential biomarkers in distinguishing lung adenocarcinoma-associated MPE from TPE. A patient with lower PLT and PCT and higher MPV, PDW, and P-LCR was more likely to be diagnosed as the former. Principal component analysis and multiple logistic regression performed well in improving multicollinearity, adjusting confounding factors, and identifying important risk factors for MPE.

Funder

Affiliated Hospital of Southwest Medical University

Publisher

Hindawi Limited

Subject

Biochemistry (medical),Clinical Biochemistry,Genetics,Molecular Biology,General Medicine

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