Data-Driven Clinical Pharmacy Research: Utilizing Machine Learning and Medical Big Data.

To conduct clinical pharmacy research, we often face the limitations of conventional statistical methods and single-center observational study. To overcome these issues, we have conducted data-driven research using machine learning methods and medical big data. Decision tree analysis, one of the typical machine learning methods, has a flowchart-like structure that allows users to easily and quantitatively evaluate the occurrence percentage of events due to the combination of multiple factors by answering related questions with Yes or No. Using this feature, we first developed a risk prediction model for acute kidney injury caused by vancomycin, a condition we frequently encounter in clinical practice. Additionally, by replacing the prediction target from a binary variable (i.e., presence or absence of adverse drug reactions) to a continuous variable (i.e., drug dosage), we built a model to estimate the initial dose of vancomycin required to reach the optimal blood level recommended by guidelines. We found its accuracy to be better than that of conventional dose-setting algorithms. Moreover, employing Japanese medical big data such as the claims database helped us overcome the major limitations of conventional clinical pharmacy research such as institutional bias caused by single-center studies. We demonstrated that the combined use of machine learning and medical big data could generate high-quality evidence leveraging the strengths of each approach. Data-driven clinical pharmacy research using machine learning and medical big data has enabled researchers to surpass the limitations of conventional research and produce clinically valuable findings.

Left atrial function in patients with rheumatic mitral stenosis: addressing prognostic insights beyond atrial fibrillation prediction.

Aims: Rheumatic mitral stenosis (MS) frequently leads to impaired left atrial (LA) function because of pressure overload, highlighting the underlying atrial pathology. Two-dimensional speckle tracking echocardiography (2D-STE) offers early detection of LA dysfunction, potentially improving risk assessment in patients with MS. This study aims to evaluate the predictive value of LA function assessed by 2D-STE for clinical outcomes in patients with MS. Methods and results: Between 2011 and 2021, patients with MS underwent LA function assessment using 2D-STE, with focus on the reservoir phase (LASr). Atrial fibrillation (AF) development constituted the primary outcome, with death or valve replacement as the secondary outcome. Conditional inference trees were employed for analysis, validated through sample splitting. The study included 493 patients with MS (mean valve area 1.1 ± 0.4 cm2, 84% female). At baseline, 166 patients (34%) had AF, with 62 patients (19%) developing AF during follow-up. LASr emerged as the primary predictor for new-onset AF, with a threshold of 17.9%. Over a mean 3.8-year follow-up, 125 patients (25%) underwent mitral valve replacement, and 32 patients (6.5%) died. A decision tree analysis identified key predictors such as age, LASr, severity of tricuspid regurgitation (TR), net atrioventricular compliance (C n), and early percutaneous mitral valvuloplasty, especially in patients aged ≤49 years, where LASr, with a threshold of 12.8%, significantly predicted adverse outcomes. Conclusion: LASr emerged as a significant predictor of cardiovascular events in this MS cohort, validated through a decision tree analysis. Patients were stratified into low- or high-risk categories for adverse outcomes, taking into account LASr, age, TR severity, and C n.

Factors Predicting Treatment Adherence in Outpatients with Cancer-Related Edema: Decision Tree Analysis.

Purpose: This study aimed to determine the combination of factors associated with continuity of care in outpatients with cancer-related edema six months after the initial visit. Patients and Methods: A total of 101 outpatients were divided into two groups: continuation (n=65) and non-continuation (n=36) groups. Details regarding age, body mass index, sex, affected extremities (upper or lower), site of edema (unilateral or bilateral), International Society of Lymphology (ISL) classification, presence of distant metastasis, and overall score on the lymphedema quality of life questionnaire (LYMQOL) were obtained before initial lymphedema care. In this study, we performed a decision tree analysis using a classification and regression tree (CART) to detect the combination of factors associated with the continuity of edema care for cancer-related edema. Results: Significant differences were observed in the site of edema (unilateral or bilateral) and distant metastasis between the two groups. In the decision tree using CART analysis, the factors selected to influence the possibility of continuation were the side of edema as the first layer, and body mass index of 23.0 and distant metastasis (with/without) as the second layer. Outpatients with unilateral edema and a body mass index higher than 23.0 were most likely to be able to continue care. In contrast, outpatients with bilateral edema and distant metastasis had greater difficulty in continuing care. Conclusion: In this study, factors that were suggested to influence the continuity of cancer-related edema care were the side with edema, body mass index higher than 23.0, and distant metastasis. This information may be helpful for developing care strategies and improving patient adherence.

Chi-Squared Automatic Interaction Detection Decision Tree Analysis of Social Determinants for Low Birth Weight in Virginia.

This study provides additional context to the literature regarding the social inequities that impact birth outcomes in Virginia using a decision tree analysis. Chi-squared automatic interaction detection data analysis (CHAID) was performed using data from the Virginia birth registry for the years 2015-2019. Birth weight was the outcome variable, while sociodemographic factors and maternity care deserts were the explanatory variables. The prevalence of low birth weight in Virginia was of 8.1%. The CHAID decision tree model demonstrated multilevel interaction among risk factors with three levels, with a total of 34 nodes. All the variables reached significance in the model, with race/ethnicity being the first major predictor variable, each category of race and ethnicity having different significant predictors, followed by prenatal care and maternal education in the next levels. These findings signify modifiable risk factors for low birth weight, in prioritizing efforts such as programs and policies. CHAID decision tree analysis provides an effective approach to detect target populations for further intervention as pathways derived from this decision tree shed light on the different predictors of high-risk population in each of the race/ethnicity demographic categories in Virginia.

Comparison between the sodium chloride content and labeled salt value of industrially prepared Japanese single dishes.

Salt intake reduction is a global concern. In particular, Japanese consume higher amounts of salt than those of other ethnicities. The sodium content is mentioned on the label of industrially prepared dishes with an intention of reducing salt intake. This study aimed to evaluate the difference between the actual sodium content and labeled salt value of industrially prepared Japanese single dishes. Samples labeled "estimated" were collected and classified as Japanese, Western, and Chinese cuisines. The sodium content ranged from 180 to 1011 mg/100 g. The sodium content was higher than their reported values in other countries. Specifically, Chinese dishes contained high amounts of sodium, although the chloride content was similar across cuisine styles. Further, the molar ratio (i.e., sodium/chloride) had no significant effect on the difference between the actual content and labeled value. The measured salt contents were 20% higher than the labeled values. The results of decision tree analysis indicated that if the labeled salt value of stir-fried foods is determined by calculation, the actual sodium content is much higher than the labeled salt value. These findings are crucial for customers, dietitian, and researchers as they refer to the labeled salt value to determine the sodium content of industrially prepared foods.

Mediating Factors Associated With Physical Activity in Older Adults at Increased Dementia Risk.

While regular physical-activity (PA) is beneficial, multimorbid individuals at increased dementia risk may exhibit reduced PA levels. Thus, a more comprehensive understanding of mediating factors responsible for inactivity in this population is needed. This study investigated the impact of a multimodal intervention on PA changes at 24-month follow-up and associated mediating factors among community-dwelling patients aged 60-77, with increased dementia risk determined by the CAIDE Dementia Risk Score. Of 1030 participants recruited, 819 completed the assessment. Thus, a generalized estimating equations model initially assessed differences in PA over 24 months, followed by a tree analysis identifying mediating factors influencing PA changes post-intervention. While no significant effect on regular PA was found during the follow-up (P = .674), subgroup analysis revealed improved self-efficacy (P = .000) associated with increased engagement in PA. Incorporating self-efficacy elements into future strategies is crucial for promoting PA among individuals with multimorbidity and at increased dementia risk.

Diagnostic Assessment of Endoscopic Ultrasonography-Fine Needle Aspiration Cytology in the Pancreas: A Comparison between Liquid-Based Preparation and Conventional Smear.

Background and Objectives: This study aimed to elucidate the cytologic characteristics and diagnostic usefulness of endoscopic ultrasonography-fine needle aspiration cytology (EUS-FNAC) by comparing it with liquid-based preparation (LBP) and conventional smear (CS) in pancreas. Methods: The diagnostic categories (I through VII) were classified according to the World Health Organization Reporting System for Pancreaticobiliary Cytopathology. Ten cytologic features, including nuclear and additional features, were evaluated in 53 cases subjected to EUS-FNAC. Nuclear features comprised irregular nuclear contours, nuclear enlargement, hypochromatic nuclei with parachromatin clearing, and nucleoli. Additional cellular features included isolated atypical cells, mucinous cytoplasm, drunken honeycomb architecture, mitosis, necrotic background, and cellularity. A decision tree analysis was conducted to assess diagnostic efficacy. Results: The diagnostic concordance rate between LBP and CS was 49.1% (26 out of 53 cases). No significant differences in nuclear features were observed between categories III (atypical), VI (suspicious for malignancy), and VII (malignant). The decision tree analysis of LBP indicated that cases with moderate or high cellularity and mitosis could be considered diagnostic for those exhibiting nuclear atypia. Furthermore, in CS, mitosis, isolated atypical cells, and necrotic background exerted a more significant impact on the diagnosis of EUS-FNAC. Conclusions: Significant parameters for interpreting EUS-FNAC may differ between LBP and CS. While nuclear atypia did not influence the diagnosis of categories III, VI, and VII, other cytopathologic features, such as cellularity, mitosis, and necrotic background, may present challenges in diagnosing EUS-FNAC.

Taekwondo win-loss determining factors using data mining-based decision tree analysis: focusing on game analysis for evidence-based coaching.

BACKGROUND: Purpose In this study, the purpose of this study is to identify the determinants of winning and losing in taekwondo by applying decision tree analysis, one of the data mining techniques, based on the 2022 World taekwondo championships women's competition. METHODS: 272 women's games in the taekwondo championships in Guadalajara held by the WT in 2022 were used. For data processing, an independent sample t-test was performed for differences in game content variables according to the win/lose group, and a decision tree analysis was performed to confirm game content variables affecting the win/lose group. To check the predictive power of the model, classification accuracy, standard error, and misclassification estimates were calculated. All statistical significance levels were set at 0.05. RESULTS: First, it was found that there was no statistically significant difference only in body attack (attempt) and number of kicking variables according to the winning and losing groups(p > .05), and there were differences in all other game content variables(p < .05). Second, as a result of conducting a decision tree analysis to confirm the determinants of winning and losing in taekwondo sparring, winning situation, tie situation, and number of kicks were identified as important variables. CONCLUSION: The World taekwondo championships are analyzed in the currently changed taekwondo competition rules to identify important factors, and at the same time, based on this, data-based coaching is expected to improve performance.

Physical performance strongly predicts all-cause mortality risk in a real-world population of older diabetic patients: machine learning approach for mortality risk stratification.

Background: Prognostic risk stratification in older adults with type 2 diabetes (T2D) is important for guiding decisions concerning advance care planning. Materials and methods: A retrospective longitudinal study was conducted in a real-world sample of older diabetic patients afferent to the outpatient facilities of the Diabetology Unit of the IRCCS INRCA Hospital of Ancona (Italy). A total of 1,001 T2D patients aged more than 70 years were consecutively evaluated by a multidimensional geriatric assessment, including physical performance evaluated using the Short Physical Performance Battery (SPPB). The mortality was assessed during a 5-year follow-up. We used the automatic machine-learning (AutoML) JADBio platform to identify parsimonious mathematical models for risk stratification. Results: Of 977 subjects included in the T2D cohort, the mean age was 76.5 (SD: 4.5) years and 454 (46.5%) were men. The mean follow-up time was 53.3 (SD:15.8) months, and 209 (21.4%) patients died by the end of the follow-up. The JADBio AutoML final model included age, sex, SPPB, chronic kidney disease, myocardial ischemia, peripheral artery disease, neuropathy, and myocardial infarction. The bootstrap-corrected concordance index (c-index) for the final model was 0.726 (95% CI: 0.687-0.763) with SPPB ranked as the most important predictor. Based on the penalized Cox regression model, the risk of death per unit of time for a subject with an SPPB score lower than five points was 3.35 times that for a subject with a score higher than eight points (P-value <0.001). Conclusion: Assessment of physical performance needs to be implemented in clinical practice for risk stratification of T2D older patients.

Investigation of Factors Related to the Week 1 Cumulated Ambulation Score in Patients With Proximal Femoral Fractures Post-surgery Using Decision Tree Analysis.

This study aimed to identify factors associated with the Cumulated Ambulation Score (CAS) in the first week post-surgery (Week 1 CAS) in patients with proximal femoral fractures. Proximal femoral fractures are prevalent in the elderly, with rising incidence rates and significant social and functional implications. The ability to walk postoperatively is a critical determinant of patient prognosis. The study included 53 patients out of 79 who underwent surgery for proximal femoral fractures at the orthopedics department of Oita Oka Hospital from January 2021 to December 2021. Exclusion criteria were pre-existing walking difficulties, inability to be evaluated in the first postoperative week, non-weight bearing post-surgery, and complications during hospitalization. The physical therapy program followed Oita Oka Hospital's clinical path, starting ambulation with a walker within the first week post-surgery. Data collected included patient background, surgical techniques, pre-injury ambulatory status, and pre-admission residential environment. Physical function assessments one week postoperatively included range of motion (ROM), manual muscle testing (MMT), pain evaluation (NRS), and CAS. Statistical analyses involved the Shapiro-Wilk test, independent t-test, Mann-Whitney U test, chi-square test, and decision tree analysis using classification and regression trees (CART). Patients were categorized into 'favorable' and 'poor' groups based on Week 1 CAS. Significant differences in dementia presence and pre-admission living environment were noted between groups. Knee extension MMT at Week 1 postoperatively showed a significant difference. The decision tree analysis identified knee extension MMT as the primary variable, with a threshold of 3.5. In patients with MMT below 3.5, dementia presence was a secondary factor, with 81% in the poor CAS group. In patients with MMT above 3.5, knee extension strength continued to be significant. The model's accuracy was 64%, with precision at 71%, recall at 63%, and an F1-score of 0.67. The study highlights knee extension MMT and dementia presence as significant factors influencing Week 1 CAS in patients with proximal femoral fractures. The poor CAS group had a higher proportion of dementia and weaker knee extension MMT. Dementia hinders rehabilitation effectiveness, impacting recovery in knee extension strength and CAS. The decision tree analysis provided an intuitive understanding of variable interrelationships, emphasizing knee extension strength as the primary factor, followed by dementia in cases with lower MMT scores. This study elucidated factors related to Week 1 CAS in postoperative patients with proximal femoral fractures. Knee extension MMT emerged as the initial factor, followed by the presence of dementia, influencing Week 1 CAS. These findings are crucial for rehabilitation planning and long-term prognostic predictions in this patient population. However, the study's limitations include potential selection bias and a small sample size, necessitating further research for enhanced predictive accuracy.

Cardiorespiratory fitness and muscular fitness correlates in youth: A hierarchy of behavioral, contextual, and health-related outcomes.

OBJECTIVES: Despite the health importance of identifying correlates of physical fitness in youth, no investigation to date has explored the influence of behavioral, health-related, and contextual correlates simultaneously. We investigated the hierarchical relationship of multiple modifiable correlates favoring or diminishing cardiorespiratory and muscular fitness in youth. DESIGN: Cross-sectional investigation. METHODS: In a sample of 5174 children and adolescents, 31 correlates were hierarchized according to their impact on cardiorespiratory and muscular fitness assessed using the FITESCOLA® fitness battery. A Chi-squared Automatic Interaction Detection approach was employed and measures of correlation and association were used to investigate the relationship between physical fitness and correlates. RESULTS: In children, body mass index was the most relevant factor to discriminate between high and low cardiorespiratory and muscular fitness of the upper, middle, and lower body. While body mass index was more important than any other correlate to differentiate levels of upper and lower body muscular fitness during adolescence, specific characteristics of sports participation emerged as key factors to discriminate between high and low cardiorespiratory fitness and middle body muscular fitness. Other correlates, including the self-report of active recess time, active commuting to school, favorable neighborhood conditions, and limited time on screens and cellphones, were demonstrative of favorable physical fitness levels. CONCLUSIONS: Both body composition and sports-related characteristics emerged as the two most relevant factors of physical fitness in youth. Additional health benefits may be obtained from building supportive environments for sports and healthy exercise habits within the household and at different school education levels.

Risk Groups by the Needs for Help: Asian American Breast Cancer Survivors.

To supply proper and sufficient information and coaching through cancer education, it is important to decide who are risk groups among a target population. A decision tree analysis could help decide the characteristics of the risk groups. This study aimed to identify the combined characteristics of Asian American breast cancer survivors that were closely linked to high needs for help during their breast cancer survivorship process. The data on the needs for help among 185 Asian American breast cancer survivors from a parent clinical trial were included in this analysis. The instruments included the Supportive Care Needs Survey-Short Form 34 (SCNS-SF34) and multiple scales to measure the factors influencing the women's needs for help. The data were analyzed using latent profile analyses and decision tree analyses. The characteristics of the group with the highest needs for help were different depending on the types of needs. For instance, the group with the highest patient care/support needs for help score had high global symptom distress scores (cut point = 2.25) and high physical symptom distress (cut point = 1.57) and did not have regular access to health care (mean = 95.00; node 10). The findings suggest several risk groups to target in future interventions for cancer education to reduce the needs for help among this specific population. Multiple factors that could influence the needs for help among Asian American breast cancer survivors need to be considered in future intervention development for cancer education.

Decision tree analysis for age estimation in living individuals: integrating cervical and dental radiographic evaluations within a South African population.

Age estimation in living individuals around the age of 18 years is medico-legally important in undocumented migrant cases and in countries like South Africa where many individuals are devoid of identification documents. Establishing whether an individual is younger than 18 years largely influences the legal procedure that should be followed in dealing with an undocumented individual. The aim of this study was to combine dental third molar and anterior inferior apophysis ossification data for purposes of age estimation, by applying a decision tree analysis. A sample comprising of 871 black South African individuals (n = 446 males, 425 = females) with ages ranging between 15 and 24 years was analyzed using panoramic and cephalometric radiographs. Variables related to the left upper and lower third molars and cervical vertebral ring apophysis ossification of C2, C3, and C4 vertebrae analyzed in previous studies were combined in a multifactorial approach. The data were analyzed using a pruned decision tree function for classification. Male and female groups were handled separately as a statistically significant difference was found between the sexes in the original studies. A test sample of 30 individuals was used to determine if this approach could be used with confidence in estimating age of living individuals. The outcomes obtained from the test sample indicated a close correlation between the actual ages (in years and months) and the predicted ages (in years only), demonstrating an average age difference of 0.47 years between the corresponding values. This method showed that the application of decision tree analysis using the combination of third molar and cervical vertebral development is usable and potentially valuable in this application.

Flowchart for predicting achieving the target area under the concentration-time curve of vancomycin in critically ill Japanese patients: A multicenter retrospective study.

INTRODUCTION: In therapeutic drug monitoring (TDM) of vancomycin (VCM), the area under the concentration-time curve (AUC) is related to the clinical efficacy and toxicity. Therefore, herein, we examined the factors associated with achieving the target AUC at follow-up and developed a decision flowchart for achieving the target AUC in critically ill patients. METHODS: This multicenter retrospective observational study was conducted at eight hospitals. We retrospectively analyzed data from patients who had received VCM in the intensive care unit from January 2020 to December 2022. Decision-tree (DT) analysis was performed using factors with p < 0.1 in univariate analysis as the independent variables. Case data were split up to two times, and four subgroups were included. The primary endpoint was achieving the target AUC at the follow-up TDM (AUCfollow-up) and target AUCfollow-up achievement was defined as an AUC of 400-600 µg‧h/mL. The initial AUC values were calculated with the 2-point concentrations (peak and trough) using the Bayesian estimation software Practical AUC-guided TDM (PAT). RESULTS: Among 70 patients (median age [interquartile range], 66 [56, 79] years; 50 % women), the AUCfollow-up was achieved in 70 % (49/70). Three factors were selected for the decision flow chart: predicted AUCfollow-up of 400-600 µg‧h/mL, dosing at 12-h intervals, and CCr of 130 mL/min/1.73 m2 or higher; the accuracy was adequate (92 %, R2 0.52). CONCLUSION: We successfully identified the factors associated with achieving the target AUC of VCM at follow-up TDM and developed a simple-to-use DT model. However, the validity of the findings needs to be evaluated.

Development of a risk prediction model for surgical site infection after lower third molar surgery.

BACKGROUND: There is little evidence regarding risk prediction for surgical site infection (SSI) after lower third molar (L3M) surgery. METHODS: We conducted a nested case-control study to develop a multivariable logistic model for predicting the risk of SSI after L3M surgery. Data were obtained from Hokkaido University Hospital from April 2013 to March 2020. Multiple imputation was applied for the missing values. We conducted decision tree (DT) analysis to evaluate the combinations of factors affecting SSI risk. RESULTS: We identified 648 patients. The final model retained the available distal space (Pell & Gregory II [p = 0.05], Pell & Gregory III [p < 0.01]), depth (Pell & Gregory B [p < 0.01], Pell & Gregory C [p < 0.01]), surgeon's experience (3-10 years [p = 0.25], <3 years [p < 0.01]), and simultaneous extraction of both L3M [p < 0.01]; the concordance-statistic was 0.72. The DT analysis demonstrated that patients with Pell and Gregory B or C and simultaneous extraction of both L3M had the highest risk of SSI. CONCLUSIONS: We developed a model for predicting SSI after L3M surgery with adequate predictive metrics in a single center. This model will make the SSI risk prediction more accessible.

Priority analysis of factors affecting family planning practices among Ethiopians using a decision tree analysis.

This study aimed to identify the priorities of the factors affecting family planning practices in Ethiopia, with the ultimate aim of providing evidence to the Ethiopian government and international organizations for establishing family planning policies or family planning-related project plans. Multi-stage, clustered, stratified random sampling was performed on a total of 35,479 men aged above 15 years and women of childbearing age (15-49 years) in two metropolitan cities and five regions of Ethiopia. The survey was conducted by face-to-face interview using Tablet PC, and 25,972 responses, excluding censored data were included in the final analysis. The collected data were first analyzed using the Pearson chi-square test, independent sample t-test, and F-test with the Tukey HSD method as a post-hoc. Second, a decision-making tree analysis was conducted to identify priority factors affecting the decision to implement family planning The primary factor affecting the family planning practice was 'spouse or sexual partner's support for family planning (P <.001)'. 'Region (p <.001)' was the second priority factor for both the group receiving 'support for family planning by spouse or partner' and the group not receiving it. The third priority factors appeared differed by region in both groups. It is necessary to establish family planning policy and program plans targeting both men and women, and participation of religious and community leaders is considered important in the establishment process. In addition, it is considered necessary to enhance the professionalism by dividing the education and evaluation process for HEW to provide high-quality counseling, education, and services on family planning at the community level.

Factors associated with fall risk of community-dwelling older people: A decision tree analysis.

Objective: To examine the predictive attributes for accidental falls in community-dwelling older people in Hong Kong using decision tree analysis. Methods: We recruited 1151 participants with an average age of 74.8 years by convenience sampling from a primary healthcare setting to carry out the cross-sectional study over 6 months. The whole dataset was divided into two sets, namely training set and test set, which respectively occupied 70% and 30% of the whole dataset. The training dataset was used first; decision tree analysis was used to identify possible stratifying variables that could help to generate separate decision models. Results: The number of fallers was 230 with 20% 1-year prevalence. There were significant differences in gender, use of walking aids, presence of chronic diseases, and co-morbidities including osteoporosis, depression, and previous upper limb fractures, and performance in the Timed Up and Go test and the Functional Reach test among the baselines between the faller and non-faller groups. Three decision tree models for the dependent dichotomous variables (fallers, indoor fallers, and outdoor fallers) were generated, with overall accuracy rates of the models of 77.40%, 89.44% and 85.76%, respectively. Timed Up and Go, Functional Reach, body mass index, high blood pressure, osteoporosis, and number of drugs taken were identified as stratifying variables in the decision tree models for fall screening. Conclusion: The use of decision tree analysis for clinical algorithms for accidental falls in community-dwelling older people creates patterns for decision-making in fall screening, which also paves the way for utility-based decision-making using supervised machine learning in fall risk detection.

Machine Learning-Based Prediction of Digoxin Toxicity in Heart Failure: A Multicenter Retrospective Study.

Digoxin toxicity (plasma digoxin concentration ≥0.9 ng/mL) is associated with worsening heart failure (HF). Decision tree (DT) analysis, a machine learning method, has a flowchart-like model where users can easily predict the risk of adverse drug reactions. The present study aimed to construct a flowchart using DT analysis that can be used by medical staff to predict digoxin toxicity. We conducted a multicenter retrospective study involving 333 adult patients with HF who received oral digoxin treatment. In this study, we employed a chi-squared automatic interaction detection algorithm to construct DT models. The dependent variable was set as the plasma digoxin concentration (≥ 0.9 ng/mL) in the trough during the steady state, and factors with p < 0.2 in the univariate analysis were set as the explanatory variables. Multivariate logistic regression analysis was conducted to validate the DT model. The accuracy and misclassification rates of the model were evaluated. In the DT analysis, patients with creatinine clearance <32 mL/min, daily digoxin dose ≥1.6 µg/kg, and left ventricular ejection fraction ≥50% showed a high incidence of digoxin toxicity (91.8%; 45/49). Multivariate logistic regression analysis revealed that creatinine clearance <32 mL/min and daily digoxin dose ≥1.6 µg/kg were independent risk factors. The accuracy and misclassification rates of the DT model were 88.2 and 46.2 ± 2.7%, respectively. Although the flowchart created in this study needs further validation, it is straightforward and potentially useful for medical staff in determining the initial dose of digoxin in patients with HF.

[Influencing Factors of Cadmium Content in Wheat Grain: A Meta-analysis and Decision Tree Analysis].

Rapid urbanization, industrialization, and agricultural intensification have resulted in serious soil problems, such as soil acidification and cadmium pollution, affecting food security and human health. Wheat is the second largest food crop in China and has a strong accumulation capacity for cadmium. Understanding the influencing factors of cadmium content in wheat grain is crucial to realize the safe production of wheat. However, a comprehensive and quantitative analysis of how soil physicochemical properties and cultivars affect wheat cadmium accumulation is lacking. The Meta-analysis and decision tree analysis of 56 related studies published in the past 10 years showed that the proportion of cadmium content in soil and wheat grain exceeding the national standard was 52.6% and 64.1%, respectively. Among soil physical and chemical properties, pH, organic matter, available phosphorus, and total soil cadmium content were the important factors affecting the cadmium content in wheat grains. When soil pH ≤ 5.5 and 5.5

Predictive power of the DSM-5 criteria for internet use disorder: A CHAID decision-tree analysis.

Introduction: Although the majority of internet users enjoy the internet as a recreational activity, some individuals report problematic internet use behaviors causing negative psychosocial consequences. Therefore, it is important to have precise and valid diagnostic criteria to ensure suitable treatment for those affected and avoid over-pathologization. Methods: The aim of the present study was to determine which of the nine DSM-5 criteria of internet gaming disorder (IGD) are crucial in distinguish pathological from non-pathological internet use based on the questionnaire-based response behavior of the participants by applying the Chi-squared automatic interaction detection (CHAID) decision tree analysis. Under consideration of the nine DSM-5 criteria for IGD and according to the short-form scale to assess Internet Gaming Disorder (IGDS-SF9) the DSM-5 criteria were formulated as questions and applied to the broader concept of Internet Use Disorder (IUD). The nine questions were answered on a 5-point Likert scale from "never" to "very often." In accordance with the IGDS-SF9 participants were assigned to IUD-5plus if at least 5 of the 9 criteria were answered with "very often." The study was conducted in Germany (N = 37,008; mean age: 32 years, SD = 13.18, 73.8% male). Results: Although "loss of control," "continued overuse" and "mood regulation" were the most endorsed criteria, the analysis indicated that the criterion "jeopardizing" was found as the best predictor for IUD-5plus, followed by "loss of interest" and "continued overuse." Overall 64.9% of all participants who were in the IUD-5plus, could been identified by the fulfillment of the three criteria mentioned above. Discussion: The results found support for adjustment of the DSM-5 criteria of IGD in accordance to ICD-11. If the predictive power of the three criteria can be replicated in future representative studies, such a decision tree can be used as guidance for diagnostics to capture the particularly relevant criteria.