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|Title:||Computational prediction of state anxiety in Asian patients with cancer susceptible to chemotherapy-induced nausea and vomiting||Authors:||Yap, K.Y.-L.
Chemotherapy-induced nausea and vomiting
Clinical CINV end points
Principal component analysis
|Issue Date:||Apr-2012||Citation:||Yap, K.Y.-L., Low, X.H., Chui, W.K., Chan, A. (2012-04). Computational prediction of state anxiety in Asian patients with cancer susceptible to chemotherapy-induced nausea and vomiting. Journal of Clinical Psychopharmacology 32 (2) : 207-217. ScholarBank@NUS Repository. https://doi.org/10.1097/JCP.0b013e31824888a1||Abstract:||State anxiety, a risk factor for chemotherapy-induced nausea and vomiting (CINV), is a subjective symptom and difficult to quantify. Clinicians need appropriate anxiety measures to assess patients' risks of CINV. This study aimed to determine the anxiety characteristics that can predict CINV based on computational analysis of an objective assessment tool. A single-center, prospective, observational study was carried out between January 2007 and July 2010. Patients with breast, head and neck, and gastrointestinal cancers were recruited and treated with a variety of chemotherapy protocols and appropriate antiemetics. Chemotherapy-induced nausea and vomiting characteristics and antiemetic use were recorded using a standardized diary, whereas patients' anxiety characteristics were evaluated using the Beck Anxiety Inventory. Principal component (PC) analysis was performed to analyze the anxiety characteristics. A subset known as principal variables, which had the highest PC weightings, was identified for patients with and without complete response, complete protection, and complete control. Chemotherapy-induced nausea and vomiting events and anxiety characteristics of 710 patients were collated; 51%, 30%, and 20% were on anthracycline-, oxaliplatin-, and cisplatin-based therapies, respectively. Most patients suffered from delayed CINV, with decreasing proportions achieving complete response (58%), complete protection (42%), and complete control (27%). Seven symptoms (fear of dying, fear of the worst, unable to relax, hot/cold sweats, nervousness, faintness, numbness) were identified as potential CINV predictors. This study demonstrates the usefulness of PC analysis, an unsupervised machine learning technique, to identify 7 anxiety characteristics that are useful as clinical CINV predictors. Clinicians should be aware of these characteristics when assessing CINV in patients on emetogenic chemotherapies. Copyright © 2012 by Lippincott Williams & Wilkins.||Source Title:||Journal of Clinical Psychopharmacology||URI:||http://scholarbank.nus.edu.sg/handle/10635/105766||ISSN:||02710749||DOI:||10.1097/JCP.0b013e31824888a1|
|Appears in Collections:||Staff Publications|
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