Introduction Accurate prognostication is difficult in malignant pleural mesothelioma (MPM). We developed a set of robust computational models to quantify the prognostic value of routinely available clinical data, which form the basis of published MPM prognostic models.
Methods Data regarding 269 patients with MPM were allocated to balanced training (n=169) and validation sets (n=100). Prognostic signatures (minimal length best performing multivariate trained models) were generated by least absolute shrinkage and selection operator regression for overall survival (OS), OS <6 months and OS <12 months. OS prediction was quantified using Somers DXY statistic, which varies from 0 to 1, with increasing concordance between observed and predicted outcomes. 6-month survival and 12-month survival were described by area under the curve (AUC) scores.
Results Median OS was 270 (IQR 140–450) days. The primary OS model assigned high weights to four predictors: age, performance status, white cell count and serum albumin, and after cross-validation performed significantly better than would be expected by chance (mean DXY0.332 (±0.019)). However, validation set DXY was only 0.221 (0.0935–0.346), equating to a 22% improvement in survival prediction than would be expected by chance. The 6-month and 12-month OS signatures included the same four predictors, in addition to epithelioid histology plus platelets and epithelioid histology plus C-reactive protein (mean AUC 0.758 (±0.022) and 0.737 (±0.012), respectively). The <6-month OS model demonstrated 74% sensitivity and 68% specificity. The <12-month OS model demonstrated 63% sensitivity and 79% specificity. Model content and performance were generally comparable with previous studies.
Conclusions The prognostic value of the basic clinical information contained in these, and previously published models, is fundamentally of limited value in accurately predicting MPM prognosis. The methods described are suitable for expansion using emerging predictors, including tumour genomics and volumetric staging.
- Prediction models
- pleural disease
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Contributors KGB conceived the study. All authors made substantial contributions to the design of the work, and the acquisition, analysis or interpretation of data. KGB, ACK, DLH and MB drafted the work. All other authors revised the work critically for important intellectual content. All authors approved the final version published and agree to be accountable for all aspects of the work, including ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Funding KGB is partly funded by a National Health Service Research Scotland Senior Fellowship and acknowledges recent relevant grant funding from the Chief Scientist’s Office (ETM/285) and the British Lung Foundation (MPG16-7).
Competing interests DLH and MB are employees of Fios Genomics, a contract research organisation contracted to provide bioinformatics services to support this work.
Ethics approval This project was reviewed and approved by the Ethics Manager and Caldicott Guardian of NHS Greater Glasgow and Clyde.
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement Applications for use of the study data for subsequent studies will be considered, subject to appropriate regulatory and ethics approval.
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