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

Research output: Contribution to journalArticle

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Abstract

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 affects the Quality of Life (QOL) in these patients. Therefore, QOL and its evaluation have turned 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 the 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 the 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.

Original languageEnglish
Pages (from-to)22-28
Number of pages7
JournalJournal of Applied Pharmaceutical Science
Volume6
Issue number5
DOIs
Publication statusPublished - 01-05-2016

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Drug-Related Side Effects and Adverse Reactions
Quality of Life
Drug Therapy
Neoplasms
Health Status
Tertiary Healthcare
Tertiary Care Centers
Causality
Respiratory System
Radiotherapy
Multivariate Analysis
Age Groups
Regression Analysis
Morbidity
Delivery of Health Care
Skin
Mortality
Therapeutics

All Science Journal Classification (ASJC) codes

  • Medicine (miscellaneous)
  • Pharmacology, Toxicology and Pharmaceutics(all)
  • Pharmacology (medical)

Cite this

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title = "Development of a predictor model for quality of life in cancer patients with adverse drug reactions due to cancer chemotherapy",
abstract = "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 affects the Quality of Life (QOL) in these patients. Therefore, QOL and its evaluation have turned 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 the 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 the 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.",
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Development of a predictor model for quality of life in cancer patients with adverse drug reactions due to cancer chemotherapy. / Khandelwal, Smita; Bairy, Laxminarayana Kurady; Vidyasagar, M. S.; Kamath, Asha; Gonsalves, James; Chogtu, Bharti.

In: Journal of Applied Pharmaceutical Science, Vol. 6, No. 5, 01.05.2016, p. 22-28.

Research output: Contribution to journalArticle

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AB - 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 affects the Quality of Life (QOL) in these patients. Therefore, QOL and its evaluation have turned 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 the 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 the 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.

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