A Molassiotis1, Z Stamataki, E Kontopantelis. 1. School of Nursing, Midwifery & Social Work, University of Manchester, Manchester, UK, alex.molasiotis@polyu.edu.hk.
Abstract
BACKGROUND: A number of risk factors have been implicated in the development of chemotherapy-induced nausea/vomiting (CINV). Our aim was to develop a risk prediction model and identify patients at high risk for developing CINV before their chemotherapy treatment. PATIENTS AND METHODS: A multisite, observational, prospective longitudinal design was used. Participants were 336 chemotherapy-naïve cancer patients providing 791 assessments. They completed measures to assess potential risk factors for CINV, including socio-demographic and clinical/treatment-related characteristics, symptom distress, expectations for CINV and state-trait anxiety. CINV was measured with the MASCC Antiemesis Tool. Participants were divided randomly to a training set (=286) and a test set (=50). Random-effects models were run to ascertain the contribution of risk factors in the development of CINV using the training sample. Specificity and sensitivity of the model were assessed in both sets of samples. RESULTS: Younger age, history of nausea/vomiting, trait anxiety and fatigue were linked with higher levels of CINV, and use of moderately and low emetogenic chemotherapy were linked with lower CINV. The model's specificity were 55.4 and 50.0 % and sensitivity were 80.3 and 79.0 % in the training and test sample, respectively. A dynamic web-based tool is freely available for use by clinicians. CONCLUSION: This model of risk prediction for CINV can be an aid to clinical decision-making and assist clinicians to rationalise antiemetic use with their patients.
BACKGROUND: A number of risk factors have been implicated in the development of chemotherapy-induced nausea/vomiting (CINV). Our aim was to develop a risk prediction model and identify patients at high risk for developing CINV before their chemotherapy treatment. PATIENTS AND METHODS: A multisite, observational, prospective longitudinal design was used. Participants were 336 chemotherapy-naïve cancer patients providing 791 assessments. They completed measures to assess potential risk factors for CINV, including socio-demographic and clinical/treatment-related characteristics, symptom distress, expectations for CINV and state-trait anxiety. CINV was measured with the MASCC Antiemesis Tool. Participants were divided randomly to a training set (=286) and a test set (=50). Random-effects models were run to ascertain the contribution of risk factors in the development of CINV using the training sample. Specificity and sensitivity of the model were assessed in both sets of samples. RESULTS: Younger age, history of nausea/vomiting, trait anxiety and fatigue were linked with higher levels of CINV, and use of moderately and low emetogenic chemotherapy were linked with lower CINV. The model's specificity were 55.4 and 50.0 % and sensitivity were 80.3 and 79.0 % in the training and test sample, respectively. A dynamic web-based tool is freely available for use by clinicians. CONCLUSION: This model of risk prediction for CINV can be an aid to clinical decision-making and assist clinicians to rationalise antiemetic use with their patients.
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