Predicting risk of chemotherapy-induced severe neutropenia: A pooled analysis in individual patients data with advanced lung cancer
| Autor: | J. Crawford, Chen Shen, Melisa L. Wong, Thomas E. Stinchcombe, Xiaofei Wang, Herbert Pang, James Chung-Man Ho, Yingzhou Liu, Xiaowen Cao, Apar Kishor Ganti |
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| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Pulmonary and Respiratory Medicine Oncology Male Cancer Research medicine.medical_specialty Lung Neoplasms Neutropenia Pleural effusion medicine.medical_treatment Logistic regression Article 03 medical and health sciences 0302 clinical medicine Clinical Trials Phase II as Topic Internal medicine Carcinoma Non-Small-Cell Lung Antineoplastic Combined Chemotherapy Protocols medicine Humans Stage (cooking) Lung cancer Aged Randomized Controlled Trials as Topic Models Statistical Performance status business.industry Incidence medicine.disease Prognosis Small Cell Lung Carcinoma Gemcitabine United States Radiation therapy 030104 developmental biology Clinical Trials Phase III as Topic ROC Curve 030220 oncology & carcinogenesis Female business medicine.drug Follow-Up Studies |
| Zdroj: | Lung Cancer |
| ISSN: | 1872-8332 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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