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

Area of Science:

• Oncology
• Pulmonology
• Biomarker Discovery

Background:

• Early diagnosis of malignant pleural effusion (MPE) is crucial but current prediction models lack clinical simplicity.
• Existing methods hinder widespread adoption in demanding clinical settings.

Purpose of the Study:

• To develop a straightforward and effective clinical prediction scoring system.
• To differentiate malignant pleural effusion (MPE) from benign pleural effusion (BPE) using a novel scoring system.

Main Methods:

• Retrospective analysis of patients with MPE or benign pleural effusion (BPE).
• Development of a prediction model using serum neuron-specific enolase, serum cytokeratin 19 fragment (CYFRA21-1), pleural carcinoembryonic antigen (CEA), and pleural CEA to serum CEA ratio.
• Modification of the model into a scoring system with assigned points for each biomarker.

Main Results:

• The scoring system incorporates serum neuron-specific enolase (6 points), CYFRA21-1 (8 points), pleural CEA (10 points), and pleural CEA/serum CEA ratio (9 points).
• Scores >12 indicate high MPE risk; scores ≤12 indicate low MPE risk.
• The system demonstrated high predictive value and clinical benefits in differentiating MPE from BPE.

Conclusions:

• A simple clinical prediction scoring system for MPE has been developed.
• The scoring system shows significant clinical benefits and aids in distinguishing MPE from BPE.
• This tool can assist clinicians in the early and accurate separation of malignant from benign pleural effusions.