A simple and efficient clinical prediction scoring system to identify malignant pleural effusion.

Background: Early diagnosis of malignant pleural effusion (MPE) is of great significance. Current prediction models are not simple enough to be widely used in heavy clinical work.

Objectives: We aimed to develop a simple and efficient clinical prediction scoring system to distinguish MPE from benign pleural effusion (BPE).

Design: This retrospective study involved patients with MPE or BPE who were admitted in West China Hospital from December 2010 to September 2016.

Methods: Patients were divided into training, testing, and validation set. Prediction model was developed from training set and modified to a scoring system. The diagnostic efficacy and clinical benefits of the scoring system were estimated in all three sets.

Results: Finally, 598 cases of MPE and 1094 cases of BPE were included. Serum neuron-specific enolase, serum cytokeratin 19 fragment (CYFRA21-1), pleural carcinoembryonic antigen (CEA), and ratio of pleural CEA to serum CEA were selected to establish the prediction models in training set, which were modified to the scoring system with scores of 6, 8, 10, and 9 points, respectively. Patients with scores >12 points have high MPE risk while ⩽12 points have low MPE risk. The scoring system has a high predictive value and good clinical benefits to differentiate MPE from BPE or lung-specific MPE from BPE.

Conclusion: This study developed a simple clinical prediction scoring system and was proven to have good clinical benefits, and it may help clinicians to separate MPE from BPE.