Gunatilake, Samal, Lodge, David, Neville, Daniel M., Jones, Thomas L., Fogg, Carole, Bassett, Paul, Begum, Selina, Kerley, Sumita, Marshall, Laura, Glaysher, Sharon, Elliott, Scott, Stores, Rebecca, Bishop, Lesley J. and Chauhan, Anoop J. (2021) Predicting survival in Malignant Pleural Mesothelioma using routine clinical and laboratory characteristics. BMJ Open Respiratory Research, 8 (1). (doi:10.1136/bmjresp-2019-000506).
Abstract
Introduction
The prognosis of Malignant Pleural Mesothelioma (MPM) is poor, with a median survival of 8-12 months. The ability to predict prognosis in MPM would help clinicians to make informed decisions regarding treatment, and identify appropriate research opportunities for patients. The aims of this study were to examine associations between clinical and pathological information gathered during routine care, and prognosis of MPM patients, and to develop a six-month mortality risk prediction model.
Methods
A retrospective cohort study of patients diagnosed with MPM at Queen Alexandra Hospital, Portsmouth, UK between December 2009 and September 2013. Multivariate analysis was performed on routinely-available histological, clinical and laboratory data to assess the association between different factors and 6-month survival, with significant associations used to create a model to predict the risk of death within 6 months of diagnosis with MPM.
Results
100 patients were included in the analysis. Variables significantly associated with patient survival in multivariate analysis were age (HR 1.31, 95% CI 1.09 to 1.56) smoking status (current smoker HR 3.42, 95% CI 1.11 to 4.20), chest pain (HR 2.14, 95% CI 1.23 to 3.72), weight loss (HR 2.13, 95% CI 1.18 to 3.72), platelet count (HR 1.05, 95% CI 1.00 to 1.10), Urea (HR 2.73, 95% CI 1.31 to 5.69) and Adjusted Calcium (HR 1.47, 95% CI 1.10 to 1.94). The resulting risk model had a c-statistic value of 0.76. A Hosmer-Lemeshow test confirmed good calibration of the model against the original dataset.
Conclusion
Risk of death at 6 months in patients with a confirmed diagnosis of MPM can be predicted using variables readily available in clinical practice. The risk prediction model we have developed may be used to influence treatment decisions in MPM patients. Further validation of the model requires evaluation of its performance on a separate dataset.
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