scholarly journals Clinical prediction rule for bacteremia with pyelonephritis and hospitalization judgment: chi-square automatic interaction detector (CHAID) decision tree analysis model

2022 ◽  
Vol 50 (1) ◽  
pp. 030006052110656
Author(s):  
Sayato Fukui ◽  
Akihiro Inui ◽  
Mizue Saita ◽  
Daiki Kobayashi ◽  
Toshio Naito
Keyword(s):  
High Risk ◽  
Blood Culture ◽  
Decision Tree ◽  
Blood Cell ◽  
Decision Tree Analysis ◽  
Analysis Model ◽  
Blood Cell Count ◽  
Chi Square ◽  
Tree Analysis ◽  
Automatic Interaction Detector

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.

scholarly journals Clinical Prediction Rule for Bacteremia Among Patients with Pyelonephritis and Hospitalization Judgment: Chi-Squared Automatic Interaction Detector (CHAID) Decision Tree Analysis Model

2021 ◽  
Author(s):  
Sayato Fukui ◽  
Akihiro Inui ◽  
Mizue Saita ◽  
Daiki Kobayashi ◽  
Toshio Naito
Keyword(s):  
High Risk ◽  
Blood Culture ◽  
Decision Tree ◽  
Blood Cell ◽  
White Blood Cell ◽  
Decision Tree Analysis ◽  
Analysis Model ◽  
Tree Analysis ◽  
Chi Squared ◽  
Automatic Interaction Detector

Abstract Background: Positive risk factors for bacteremia among patients with pyelonephritis have not been defined using a Chi-Squared Automatic Interaction Detector (CHAID) Decision Tree Analysis Model. Purpose: We sought to identify predictive factors for bacteremia among patients with pyelonephritis and therefore which patients need hospitalization.Methods: This retrospective cross-sectional survey was performed at the Juntendo University Nerima Hospital, Tokyo, Japan and comprised all patients with pyelonephritis from whom blood cultures were taken from January 1, 2010 to July 31, 2020. At the time of blood culture sample collection, clinical information was obtained from medical charts, along with vital signs, quick Sequential Organ Failure Assessment (qSOFA), subjective symptoms, objective physical findings, laboratory findings, and results of blood and urine cultures. Factors potentially predictive of bacteremia among patients with pyelonephritis were analyzed using the Student’s t-test or chi-squared test and the CHAID decision tree analysis model.Results: A total of 198 patients (male:female, 60 (30.3%):138 (69.7%), ages (mean±SD) 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 White blood cell >21,000/μL had a quite-high-risk (89.5%) of developing bacteremia. Patients with White blood cell ≤21,000/μL plus Chill plus Aspartate aminotransferase >19 IU/L represented a high-risk group (69.0%). Conversely, patients with White blood cell ≤21,000/μL plus non-Chill plus Albumin >3.60 g/dL were at a low risk (16.3%) of developing bacteremia.Conclusion: Our results emphasize the importance of hospitalization among high-risk and quite-high-risk groups of pyelonephritis patients.

Predicting peri‐implant disease: Chi‐square automatic interaction detection (CHAID) decision tree analysis of risk indicators

2019 ◽  
Vol 90 (8) ◽  
pp. 834-846 ◽  
Author(s):  
Momen A. Atieh ◽  
Ju Keat Pang ◽  
Kylie Lian ◽  
Stephanie Wong ◽  
Andrew Tawse‐Smith ◽  
...  
Keyword(s):  
Decision Tree ◽  
Risk Indicators ◽  
Decision Tree Analysis ◽  
Chi Square ◽  
Tree Analysis ◽  
Interaction Detection

scholarly journals Predicting risk of early discontinuation of exclusive breastfeeding at a Brazilian referral hospital for high-risk neonates and infants: a decision-tree analysis

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Maíra Domingues Bernardes Silva ◽  
Raquel de Vasconcellos Carvalhaes de Oliveira ◽  
Davi da Silveira Barroso Alves ◽  
Enirtes Caetano Prates Melo
Keyword(s):  
High Risk ◽  
Decision Tree ◽  
Hospital Stay ◽  
Decision Trees ◽  
Hospital Discharge ◽  
Exclusive Breastfeeding ◽  
World Health ◽  
Feeding Practice ◽  
Decision Tree Analysis ◽  
Tree Analysis

Abstract Background Determinants at several levels may affect breastfeeding practices. Besides the known historical, socio-economic, cultural, and individual factors, other components also pose major challenges to breastfeeding. Predicting existing patterns and identifying modifiable components are important for achieving optimal results as early as possible, especially in the most vulnerable population. The goal of this study was building a tree-based analysis to determine the variables that can predict the pattern of breastfeeding at hospital discharge and at 3 and 6 months of age in a referral center for high-risk infants. Methods This prospective, longitudinal study included 1003 infants and was conducted at a high-risk public hospital in the following three phases: hospital admission, first visit after discharge, and monthly telephone interview until the sixth month of the infant’s life. Independent variables were sorted into four groups: factors related to the newborn infant, mother, health service, and breastfeeding. The outcome was breastfeeding as per the categories established by the World Health Organization (WHO). For this study, we performed an exploratory analysis at hospital discharge and at 3 and at 6 months of age in two stages, as follows: (i) determining the frequencies of baseline characteristics stratified by breastfeeding indicators in the three mentioned periods and (ii) decision-tree analysis. Results The prevalence of exclusive breastfeeding (EBF) was 65.2% at hospital discharge, 51% at 3 months, and 20.6% at 6 months. At hospital discharge and the sixth month, the length of hospital stay was the most important predictor of feeding practices, also relevant at the third month. Besides the mother’s and child’s characteristics (multiple births, maternal age, and parity), the social context, work, feeding practice during hospitalization, and hospital practices and policies on breastfeeding influenced the breastfeeding rates. Conclusions The combination algorithm of decision trees (a machine learning technique) provides a better understanding of the risk predictors of breastfeeding cessation in a setting with a large variability in expositions. Decision trees may provide a basis for recommendations aimed at this high-risk population, within the Brazilian context, in light of the hospital stay at a neonatal unit and period of continuous feeding practice.

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