Breast and Lung Effusion Survival Score 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 (MPEs) 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 through 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 was 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 treated from 2010 through 2013. Using LENT (pleural fluid lactate dehydrogenase, Eastern Cooperative Oncology Group performance score, neutrophil-to-lymphocyte ratio and tumor type) variables, we generated both discrete (LENT-D) and continuous (LENT-C) models, assessing discrete vscontinuous predictors' performances. The development and validation cohort included 562 and 727 patients, respectively. The Mantel-Cox test demonstrated interactions between cancer type and neutrophil to lymphocyte ratio (P< .0001), pleural fluid lactate dehydrogenase (P= .029), and bilateral effusion (P= .002). DSMs for lung, breast, and hematologic malignancies showed 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.