Identifying Determinants Of Quality Of Life In Patients Undergoing Systemic Therapy For Solid Tumors.
JOURNAL OF CLINICAL ONCOLOGY(2017)
摘要
6596 Background: Knowing which factors compromise quality of life (QoL) in patients undergoing cancer treatments can help oncologists provide more effective care. To identify these factors, we conducted a single-centered cross-sectional study examining the relationships between patient-reported QoL, adverse events (AE), and treatment characteristics. Methods: Consecutive patients attending an outpatient chemotherapy unit completed two questionnaires (EORTC QLQ-C30 and National Cancer Institute PRO-CTCAE) per visit to identify factors contributing to the lowest global QoL score [QLQ-C30 QL2, range 0 (worst)–100 (best)] over a 6-week period. QL2 was correlated to each PRO-CTCAE item and treatment characteristic (tumor type, drug class, number of cycles, and treatment intent) using multiple regression, adjusted for age, sex, and use of concurrent radiotherapy. To determine whether QoL can be reliably modeled by machine learning, ten algorithms were compared for performance in classifying patients into dichotomized QL2 subgroups. Results: One hundred and fifteen of 130 patients (157/244 visits) completed up to 6 sets of questionnaires (median QL2: 67, IQR: 50–83). No difference was found between QL2 and treatment characteristics (at α Bonferroni =5×10 -4 ). However, QL2 was significantly associated with AE in gastrointestinal, respiratory, attention, pain, sleep/wake, and mood categories. Using AE as covariates, support vector machine with radial basis kernel was the best at classifying patients into QoL groups (mean bootstrapped area under ROC curve 0.812, 95% CI 0.700–0.925). Conclusions: Patient-reported QoL is associated with multiple AE, but not with characteristics of systemic therapy. Machine learning analysis suggests that a combined AE analysis may reliably characterize a patient’s QoL. [Table: see text]
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关键词
solid tumors,systemic therapy,patients,life
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