Background: Haematological malignancy is an important cause of pleural effusion. Pleural effusions secondary to haematological malignancy are usually lymphocyte predominant. However, several other conditions such as carcinoma, tuberculosis, and chronic heart failure also cause lymphocytic effusions. Lymphocyte subset (LS) analysis may be a useful test to identify haematological malignancy in patients with lymphocytic effusions. However, research into their utility in pleural effusion diagnostic algorithms has not yet been published. Objectives: We aimed to determine the clinical utility of pleural fluid LS analysis and whether it can be applied to a diagnostic algorithm to identify effusions secondary to haematological malignancy. The secondary aim was to evaluate the diagnostic value of pleural fluid differential cell count. Methods: Consecutive consenting patients presenting to our pleural service between 2008 and 2013 underwent thoracentesis and differential cell count analysis. We proposed an algorithm which selected patients with lymphocytic effusions (>50%) to have further fluid sent for LS analysis. Two independent consultants agreed on the cause of the original effusion after a 12-month follow-up period. Results: A total of 60 patients had samples sent for LS analysis. LS analysis had an 80% sensitivity (8/10) and a 100% specificity for the diagnosis of haematological malignancy. The positive and negative predictive values were 100 and 96.1%, respectively. Overall 344 differential cell counts were analysed; 16% of pleural effusions with a malignant aetiology were neutrophilic or eosinophilic at presentation. A higher neutrophil and eosinophil count was associated with benign diagnoses, whereas a higher lymphocyte count was associated with malignant diagnoses. Conclusions: LS analysis may identify haematological malignancy in a specific cohort of patients with undiagnosed pleural effusions. A pleural fluid differential cell count provides useful additional information to streamline patient pathway decisions.

Background: Haematological malignancy is an important cause of pleural effusion. Pleural effusions secondary to haematological malignancy are usually lymphocyte predominant. However, several other conditions such as carcinoma, tuberculosis, and chronic heart failure also cause lymphocytic effusions. Lymphocyte subset (LS) analysis may be a useful test to identify haematological malignancy in patients with lymphocytic effusions. However, research into their utility in pleural effusion diagnostic algorithms has not yet been published. Objectives: We aimed to determine the clinical utility of pleural fluid LS analysis and whether it can be applied to a diagnostic algorithm to identify effusions secondary to haematological malignancy. The secondary aim was to evaluate the diagnostic value of pleural fluid differential cell count. Methods: Consecutive consenting patients presenting to our pleural service between 2008 and 2013 underwent thoracentesis and differential cell count analysis. We proposed an algorithm which selected patients with lymphocytic effusions (>50%) to have further fluid sent for LS analysis. Two independent consultants agreed on the cause of the original effusion after a 12-month follow-up period. Results: A total of 60 patients had samples sent for LS analysis. LS analysis had an 80% sensitivity (8/10) and a 100% specificity for the diagnosis of haematological malignancy. The positive and negative predictive values were 100 and 96.1%, respectively. Overall 344 differential cell counts were analysed; 16% of pleural effusions with a malignant aetiology were neutrophilic or eosinophilic at presentation. A higher neutrophil and eosinophil count was associated with benign diagnoses, whereas a higher lymphocyte count was associated with malignant diagnoses. Conclusions: LS analysis may identify haematological malignancy in a specific cohort of patients with undiagnosed pleural effusions. A pleural fluid differential cell count provides useful additional information to streamline patient pathway decisions.