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J App Pharm Sci. 2016; 6(5): 022-028

Development of a Predictor Model for Quality of Life in Cancer Patients with Adverse Drug Reactions due to Cancer Chemotherapy

Smita Khandelwal, Laxminarayana Kurady Bairy, M. S.Vidyasagar, Asha Kamath, James Gonsalves, Bharti Chogtu.


Cancer is one of the leading causes of morbidity and mortality worldwide. There are various detrimental symptoms experienced by a cancer patient due to the disease and the undergoing treatment which adversely affect the Quality of Life (QOL) in these patients. Therefore, QOL and its evaluation have turn out to be progressively vital in the health care system. Hence, the aim of our study was to develop a predictor model to predict the QOL in cancer patients receiving chemotherapy. The study was carried out in the Department of Radiotherapy and Oncology, Kasturba hospital, Manipal, a tertiary care hospital. Predictor model was developed to predict the Quality of Life Scores (QOLS) using multivariate regression analysis. A total of 387 patients participated in the study. Mean age of patients was 50.85 ± 11.82 years (95% CI, 49.66-52.03). In our study, 16.54% had poor global health status/QOL, 72.35% had average and 11.11% had a high global health status/QOL. A significant difference was found in the QOLS based on age group, site of cancer, drugs used in treatment of cancer, age as a predisposing factor and organ system affected due to ADRs (respiratory system, sensory system, skin and appendages). In the predictor model, the Coefficient of determination R-square (r2) was found to be 0.3267 indicating that 32.67% of the variation in the ‘quality of life score’ is explained by the independent variables included in the model. The F (45, 341) = 3.67, p < 0.001 indicating the overall significance of the regression model. Thus, the study showed that there are various predictors that can assess the QOL in cancer patients which can further serve as a guide to implement timely interventions to improve patients QOL.

Key words: Quality of life, Chemotherapy, Tertiary care hospital, Multivariate regression, Predictor model,

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