Development and validation of a novel scoring system developed from a nomogram to identify malignant pleural effusion

Abstract

Background: This study aimed to establish and validate a novel scoring system based on a nomogram for the differential diagnosis of malignant pleural effusion (MPE) and benign pleural effusion (BPE).

Methods: Patients with PE and confirmed aetiology who underwent diagnostic thoracentesis were included in this study. One retrospective set (N = 1261) was used to develop and internally validate the predictive model. The clinical, radiological and laboratory features were collected and subjected to logistic regression analyses. The primary predictive model was displayed as a nomogram and then modified into a novel scoring system, which was externally validated in an independent set (N = 172).

Findings: The novel scoring system was composed of fever (3 points), erythrocyte sedimentation rate (4 points), effusion adenosine deaminase (7 points), serum carcinoembryonic antigen (CEA) (4 points), effusion CEA (10 points) and effusion/serum CEA (8 points). With a cutoff value of 15 points, the area under the curve, specificity and sensitivity for identifying MPE were 0.913, 89.10%, and 82.63%, respectively, in the training set, 0.922, 93.48%, 81.51%, respectively, in the internal validation set and 0.912, 87.61%, 81.36%, respectively, in the external validation set. Moreover, this scoring system was exclusively applied to distinguish lung cancer with PE from tuberculous pleurisy and showed a favourable diagnostic performance in the training and validation sets.

Interpretation: This novel scoring system was developed from a retrospective study and externally validated in an independent set based on six easily accessible clinical variables, and it exhibited good diagnostic performance for identifying MPE.

Funding: NFSC grants (no. 81572942, no. 81800094).

Keywords: Clinical features; Diagnostic value; Malignant pleural effusion; Nomogram; Scoring system.

Fig. 1

Flowchart of participant selection: (a) Renmin Hospital of Wuhan University set; and (b) Wuhan Union Hospital set.

Fig. 2

Forest plot of the significant parameters in the multivariate regression analysis.

Fig. 3

Calibration and clinical use of a diagnostic nomogram for the discrimination of MPE and BPE. (a) Diagnostic nomogram for identifying MPE from BPE. (b) Calibration curve of the diagnostic nomogram. (c) DCA of the diagnostic nomogram.

Fig. 4

Discrimination and calibration of the scoring system for discrimination of MPE and BPE. ROC curves of the scoring system in the training set (a), internal validation set (b) and external validation set (c). Calibration curves of the scoring system in the training set (d), internal validation set (e) and external validation set (f).

Fig. 5

Diagnostic ability and calibration of the scoring system for discrimination of lung cancer with PE and TPE. ROC curves of the scoring system in the training set (a), internal validation set (b) and external validation set (c). Calibration curves of the scoring system in the training set (d), internal validation set (e) and external validation set (f).