Prediction modeling using routine clinical parameters to stratify survival in malignant pleural mesothelioma patients complicated with malignant pleural effusion

Background Malignant pleural effusion (MPE) is common in malignant pleural mesothelioma (MPM). The survival of patients with MPM and MPE is heterogeneous. The LENT and BRIMS scores using routine clinical parameters were developed to predict the survival of patients with unselected MPE and MPM, respectively. This study aimed to stratify the survival of selected MPM patients with MPE. Methods Data were collected from subjects diagnosed with MPM and MPE. The LENT and BRIMS scores were applied using a combination of clinical variables to stratify subjects and compare survival characteristics. Results In total, 101 patients with MPM complicated by MPE were included in the study. The median follow‐up time was 71 months (interquartile range: 24–121 months). Overall median survival was 24 (interquartile range: 12–52 months). Based on the LENT score, the low‐, moderate‐, and high‐risk groups accounted for 65.3% (66 cases), 34.7% (35 cases), and 0%, respectively. The cumulative survival rates of the two groups were statistically significant (p = 0.031). The area under the curve (AUC) of the LENT score was 0.662. Based on the BRIMS score, the first, second, third, and fourth risk groups accounted for 1.0% (1 case), 42.9% (35 cases), 28.7% (29 cases), and 19.4% (36 cases), respectively. Survival was significantly higher in patients in the risk groups 1 and 2 than in patients in the risk groups 3 and 4 (p = 0.037). The AUC of the BRIMS score was 0.605. Conclusions Using routinely available clinical variables, both LENT and BRIMS scores could stratify selected MPM and MPE patients into risk groups with statistically different survival.
The individual survival of patients with MPM combined with MPE varied greatly in this study. Both LENT and BRIMS scores could help stratify the survival risk of patients with MPM complicated by MPE with fair‐to‐good performance. More effective predictive models should be established in adequately designed prospective studies.