Please use this identifier to cite or link to this item: https://doi.org/10.5487/TR.2012.28.2.081
Title: Exploring chemotherapy-induced toxicities through multivariate projection of risk factors: Prediction of nausea and vomiting
Authors: Yap, K.Y.
Low, X.H. 
Chan, A. 
Keywords: Chemotherapy-induced nausea and vomiting
Multivariate projection
Principal component analysis
Principal variables
Risk factors
Issue Date: Jun-2012
Source: Yap, K.Y.,Low, X.H.,Chan, A. (2012-06). Exploring chemotherapy-induced toxicities through multivariate projection of risk factors: Prediction of nausea and vomiting. Toxicological Research 28 (2) : 81-91. ScholarBank@NUS Repository. https://doi.org/10.5487/TR.2012.28.2.081
Abstract: Many risk factors exist for chemotherapy-induced nausea and vomiting (CINV). This study utilized a multivariate projection technique to identify which risk factors were predictive of CINV in clinical practice. A single-centre, prospective, observational study was conducted from January 2007~July 2010 in Singapore. Patients were on highly (HECs) and moderately emetogenic chemotherapies with/without radiotherapy. Patient demographics and CINV risk factors were documented. Daily recording of CINV events was done using a standardized diary. Principal component (PC) analysis was performed to identify which risk factors could differentiate patients with and without CINV. A total of 710 patients were recruited. Majority were females (67%) and Chinese (84%). Five risk factors were potential CINV predictors: histories of alcohol drinking, chemotherapy-induced nausea, chemotherapy-induced vomiting, fatigue and gender. Period (ex-/current drinkers) and frequency of drinking (social/chronic drinkers) differentiated the CINV endpoints in patients on HECs and anthracycline-based, and XELOX regimens, respectively. Fatigue interference and severity were predictive of CINV in anthracycline-based populations, while the former was predictive in HEC and XELOX populations. PC analysis is a potential technique in analyzing clinical population data, and can provide clinicians with an insight as to what predictors to look out for in the clinical assessment of CINV. We hope that our results will increase the awareness among clinician-scientists regarding the usefulness of this technique in the analysis of clinical data, so that appropriate preventive measures can be taken to improve patients' quality of life.
Source Title: Toxicological Research
URI: http://scholarbank.nus.edu.sg/handle/10635/105946
ISSN: 19768257
DOI: 10.5487/TR.2012.28.2.081
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