Xiaowen Cao1, Apar Kishor Ganti2, Thomas Stinchcombe3, Melisa L Wong4, James C Ho5, Chen Shen1, Yingzhou Liu1, Jeffery Crawford3, Herbert Pang5, Xiaofei Wang1. 1. Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA. 2. Department of Internal Medicine, Veterans Affairs Nebraska Western Iowa Health Care System and University of Nebraska Medical Center, Omaha, NE, USA. Electronic address: aganti@unmc.edu. 3. Department of Medicine, Duke University School of Medicine, Durham, NC, USA. 4. Department of Medicine, University of California, San Francisco, San Francisco, CA, USA. 5. Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Abstract
OBJECTIVES: Neutropenia is associated with the risk of life-threatening infections, chemotherapy dose reductions and delays that may compromise outcomes. This analysis was conducted to develop a prediction model for chemotherapy-induced severe neutropenia in lung cancer. MATERIALS AND METHODS: Individual patient data from existing cooperative group phase II/III trials of stages III/IV non-small cell lung cancer or extensive small-cell lung cancer were included. The data were split into training and testing sets. In order to enhance the prediction accuracy and the reliability of the prediction model, lasso method was used for both variable selection and regularization on the training set. The selected variables was fit to a logistic model to obtain regression coefficients. The performance of the final prediction model was evaluated by the area under the ROC curve in both training and testing sets. RESULTS: The dataset was randomly separated into training [7606 (67 %) patients] and testing [3746 (33 %) patients] sets. The final model included: age (>65 years), gender (male), weight (kg), BMI, insurance status (yes/unknown), stage (IIIB/IV/ESSCLC), number of metastatic sites (1, 2 or ≥3), individual drugs (gemcitabine, taxanes), number of chemotherapy agents (2 or ≥3), planned use of growth factors, associated radiation therapy, previous therapy (chemotherapy, radiation, surgery), duration of planned treatment, pleural effusion (yes/unknown), performance status (1, ≥2) and presence of symptoms (yes/unknown). CONCLUSIONS: We have developed a relatively simple model with routinely available pre-treatment variables, to predict for neutropenia. This model should be independently validated prospectively. Published by Elsevier B.V.
OBJECTIVES:Neutropenia is associated with the risk of life-threatening infections, chemotherapy dose reductions and delays that may compromise outcomes. This analysis was conducted to develop a prediction model for chemotherapy-induced severe neutropenia in lung cancer. MATERIALS AND METHODS: Individual patient data from existing cooperative group phase II/III trials of stages III/IV non-small cell lung cancer or extensive small-cell lung cancer were included. The data were split into training and testing sets. In order to enhance the prediction accuracy and the reliability of the prediction model, lasso method was used for both variable selection and regularization on the training set. The selected variables was fit to a logistic model to obtain regression coefficients. The performance of the final prediction model was evaluated by the area under the ROC curve in both training and testing sets. RESULTS: The dataset was randomly separated into training [7606 (67 %) patients] and testing [3746 (33 %) patients] sets. The final model included: age (>65 years), gender (male), weight (kg), BMI, insurance status (yes/unknown), stage (IIIB/IV/ESSCLC), number of metastatic sites (1, 2 or ≥3), individual drugs (gemcitabine, taxanes), number of chemotherapy agents (2 or ≥3), planned use of growth factors, associated radiation therapy, previous therapy (chemotherapy, radiation, surgery), duration of planned treatment, pleural effusion (yes/unknown), performance status (1, ≥2) and presence of symptoms (yes/unknown). CONCLUSIONS: We have developed a relatively simple model with routinely available pre-treatment variables, to predict for neutropenia. This model should be independently validated prospectively. Published by Elsevier B.V.
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