BLESS models: Improving Survival Prediction in Patients with Malignant Pleural Effusion and Metastasis.

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Evidence-based guidelines recommend management strategies for malignant pleural effusions (MPE) based on life expectancy. Existent risk-prediction rules do not provide precise individualized survival estimates.Can a newly developed continuous risk-prediction survival model for patients with MPE and known metastatic disease provide precise survival estimates?Single-center retrospective cohort study of patients with proven malignancy, pleural effusion, and known metastatic disease undergoing thoracentesis from 2014-2017. The outcome was time from thoracentesis to death. Risk factors were identified using Cox-proportional hazards models. Effect-measure modification (EMM) was tested using the Mantel-Cox test and addressed by using disease-specific models (DSMs) or interaction terms. Three DSMs and a combined model using interactions were generated. Discrimination was evaluated using Harrell's C-statistic. Calibration was assessed by observed-minus-predicted probability graphs at specific time-points. Models were validated using patients from 2010-2013. Using LENT variables, we generated both discrete (LENT-D) and continuous (LENT-C) models, assessing discrete vs. continuous predictors' performance.The development and validation cohort included 562 and 727 patients respectively. Mantel-Cox test demonstrated interactions between cancer type and neutrophil-lymphocyte-ratio (p <0.0001), pleural fluid LDH (p = 0.029), and bilateral effusion (p = 0.002). DSMs for lung, breast and hematologic malignancies had C-statistics of 0.72, 0.72, and 0.62 respectively; the combined model´s C-statistics was 0.67. LENT-D (C-statistic = 0.60) and LENT-C (C-statistic = 0.65) models underperformed.EMM is present between cancer type and other predictors, thus DSMs outperformed the models that failed to account for this. Discrete risk-prediction models lacked enough precision to be useful for individual level predictions.


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Authors: Sofia Molina, Gabriela Martinez-Zayas, Paula V Sainz, Cheuk H Leung, Liang Li, Horiana B Grosu, Roberto Adachi, David E Ost

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