Clinical prediction rule for bacteremia with pyelonephritis and hospitalization judgment: chi-square automatic interaction detector (CHAID) decision tree analysis model. Issue 1 (January 2022)

• Record Type: Journal Article
• Authors: Fukui, Sayato; Inui, Akihiro; Saita, Mizue; Kobayashi, Daiki; Naito, Toshio
• Is Part Of: Journal of international medical research. Volume 50:Issue 1(2022)
• Publication Date: 2022-01
• Subjects: Pyelonephritis -- bacteremia -- risk factor -- chi-square automatic interaction detector analysis -- predictive rule -- hospitalization; Medicine -- Periodicals; Pharmacology -- Periodicals; 610.5
• DOI: 10.1177/03000605211065658
• Languages: English
• ISSNs: 0300-0605

Abstract

Objective: This study was performed to identify predictive factors for bacteremia among patients with pyelonephritis using a chi-square automatic interaction detector (CHAID) decision tree analysis model. Methods: This retrospective cross-sectional survey was performed at Juntendo University Nerima Hospital, Tokyo, Japan and included all patients with pyelonephritis from whom blood cultures were taken. At the time of blood culture sample collection, clinical information was extracted from the patients' medical charts, including vital signs, symptoms, laboratory data, and culture results. Factors potentially predictive of bacteremia among patients with pyelonephritis were analyzed using Student's t -test or the chi-square test and the CHAID decision tree analysis model. Results: In total, 198 patients (60 (30.3%) men, 138 (69.7%) women; mean age, 74.69 ± 15.27 years) were included in this study, of whom 92 (46.4%) had positive blood culture results. The CHAID decision tree analysis revealed that patients with a white blood cell count of >21, 000/μL had a very high risk (89.5%) of developing bacteremia. Patients with a white blood cell count of ≤21, 000/μL plus chills plus an aspartate aminotransferase concentration of >19 IU/L constituted the high-risk group (69.0%). Conclusion: The present results are extremely useful for predicting the results of bacteremia among patients with pyelonephritis.