Clinical prediction rule for bacteremia with pyelonephritis and hospitalization judgment: chi-square automatic interaction detector (CHAID) decision tree analysis model
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 Ho...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2022-01-01
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| Series: | Journal of International Medical Research |
| Online Access: | https://doi.org/10.1177/03000605211065658 |
| _version_ | 1798035344723017728 |
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| author | Sayato Fukui Akihiro Inui Mizue Saita Daiki Kobayashi Toshio Naito |
| author_facet | Sayato Fukui Akihiro Inui Mizue Saita Daiki Kobayashi Toshio Naito |
| author_sort | Sayato Fukui |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T20:56:55Z |
| format | Article |
| id | doaj.art-d975968afc3e4094909b7796abf694b7 |
| institution | Directory Open Access Journal |
| issn | 1473-2300 |
| language | English |
| last_indexed | 2024-04-11T20:56:55Z |
| publishDate | 2022-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Journal of International Medical Research |
| spelling | doaj.art-d975968afc3e4094909b7796abf694b72022-12-22T04:03:39ZengSAGE PublishingJournal of International Medical Research1473-23002022-01-015010.1177/03000605211065658Clinical prediction rule for bacteremia with pyelonephritis and hospitalization judgment: chi-square automatic interaction detector (CHAID) decision tree analysis modelSayato FukuiAkihiro InuiMizue SaitaDaiki KobayashiToshio NaitoObjective 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.https://doi.org/10.1177/03000605211065658 |
| spellingShingle | Sayato Fukui Akihiro Inui Mizue Saita Daiki Kobayashi Toshio Naito Clinical prediction rule for bacteremia with pyelonephritis and hospitalization judgment: chi-square automatic interaction detector (CHAID) decision tree analysis model Journal of International Medical Research |
| title | Clinical prediction rule for bacteremia with pyelonephritis and hospitalization judgment: chi-square automatic interaction detector (CHAID) decision tree analysis model |
| title_full | Clinical prediction rule for bacteremia with pyelonephritis and hospitalization judgment: chi-square automatic interaction detector (CHAID) decision tree analysis model |
| title_fullStr | Clinical prediction rule for bacteremia with pyelonephritis and hospitalization judgment: chi-square automatic interaction detector (CHAID) decision tree analysis model |
| title_full_unstemmed | Clinical prediction rule for bacteremia with pyelonephritis and hospitalization judgment: chi-square automatic interaction detector (CHAID) decision tree analysis model |
| title_short | Clinical prediction rule for bacteremia with pyelonephritis and hospitalization judgment: chi-square automatic interaction detector (CHAID) decision tree analysis model |
| title_sort | clinical prediction rule for bacteremia with pyelonephritis and hospitalization judgment chi square automatic interaction detector chaid decision tree analysis model |
| url | https://doi.org/10.1177/03000605211065658 |
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