A retrospective analysis to identify the factors affecting infection in patients undergoing chemotherapy

• Published in: European journal of oncology nursing : the official journal of European Oncology Nursing Society Vol. 19; no. 6; pp. 597 - 603
• Main Authors: Park, Ji Hyun, Kim, Hyeon-Young, Lee, Hanna, Yun, Eun Kyoung
• Format: Journal Article
• Language: English
• Published: Scotland Elsevier Ltd 01.12.2015
• Subjects: Adult; Age Distribution; Aged; Aged, 80 and over; Antineoplastic Combined Chemotherapy Protocols - administration & dosage; Antineoplastic Combined Chemotherapy Protocols - adverse effects; Chemotherapy; Cohort Studies; Decision Support Techniques; Decision tree; Decision Trees; Female; Hematology, Oncology and Palliative Medicine; Hospitals, University; Humans; Immunocompromised Host; Incidence; Infection; Infection - epidemiology; Infection - immunology; Infection - microbiology; Logistic Models; Logistic regression; Male; Middle Aged; Neoplasms - drug therapy; Neoplasms - epidemiology; Neoplasms - immunology; Neoplasms - pathology; Nursing; Prognosis; Republic of Korea; Retrospective Studies; Risk Factors; Sex Distribution; Republic of Korea; South Korea; Infection; Logistic regression; Chemotherapy; Decision tree
• ISSN: 1462-3889; 1532-2122
• DOI: 10.1016/j.ejon.2015.03.006

Abstract Purpose This study compares the performance of the logistic regression and decision tree analysis methods for assessing the risk factors for infection in cancer patients undergoing chemotherapy. Method The subjects were 732 cancer patients who were receiving chemotherapy at K university hospital in Seoul, Korea. The data were collected between March 2011 and February 2013 and were processed for descriptive analysis, logistic regression and decision tree analysis using the IBM SPSS Statistics 19 and Modeler 15.1 programs. Results The most common risk factors for infection in cancer patients receiving chemotherapy were identified as alkylating agents, vinca alkaloid and underlying diabetes mellitus. The logistic regression explained 66.7% of the variation in the data in terms of sensitivity and 88.9% in terms of specificity. The decision tree analysis accounted for 55.0% of the variation in the data in terms of sensitivity and 89.0% in terms of specificity. As for the overall classification accuracy, the logistic regression explained 88.0% and the decision tree analysis explained 87.2%. Conclusions The logistic regression analysis showed a higher degree of sensitivity and classification accuracy. Therefore, logistic regression analysis is concluded to be the more effective and useful method for establishing an infection prediction model for patients undergoing chemotherapy.