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.

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.

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.

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.

A decision tree analysis to predict clinical outcome of minimally invasive lumbar decompression surgery for lumbar spinal stenosis with and without coexisting spondylolisthesis and scoliosis

BACKGROUND CONTEXT

Implementing machine learning techniques, such as decision trees, known as prediction models that use logical construction diagrams, are rarely used to predict clinical outcomes.

PURPOSE

To develop a clinical prediction rule to predict clinical outcomes in patients who undergo minimally invasive lumbar decompression surgery for lumbar spinal stenosis with and without coexisting spondylolisthesis and scoliosis using a decision tree model.

STUDY DESIGN/SETTING

A retrospective analysis of prospectively collected data.

PATIENT SAMPLE

This study included 331 patients who underwent minimally invasive surgery for lumbar spinal stenosis and were followed up for ≥2 years at 1 institution.

OUTCOME MEASURES

Self-report measures: The Japanese Orthopedic Association (JOA) scores and low back pain (LBP)/leg pain/leg numbness visual analog scale (VAS) scores. Physiologic measures: Standing sagittal spinopelvic alignment, computed tomography, and magnetic resonance imaging results.

METHODS

Low achievement in clinical outcomes were defined as the postoperative JOA score at the 2-year follow-up <25 points. Univariate and multiple logistic regression analysis and chi-square automatic interaction detection (CHAID) were used for analysis.

RESULTS

The CHAID model for JOA score <25 points showed spontaneous numbness/pain as the first decision node. For the presence of spontaneous numbness/pain, sagittal vertical axis ≥70 mm was selected as the second decision node. Then lateral wedging, ≥6° and pelvic incidence minus lumbar lordosis (PI-LL) ≥30° followed as the third decision node. For the absence of spontaneous numbness/pain, sex and lateral olisthesis, ≥3mm and American Society of Anesthesiologists physical status classification system score were selected as the second and third decision nodes. The sensitivity, specificity, and the positive predictive value of this CHAID model was 65.1, 69.8, and 64.7% respectively.

CONCLUSIONS

The CHAID model incorporating basic information and functional and radiologic factors is useful for predicting surgical outcomes.

Risk evaluation of carbapenem-induced liver injury based on machine learning analysis

INTRODUCTION

Information regarding carbapenem-induced liver injury is limited, and the rate of liver injury caused by meropenem (MEPM) and doripenem (DRPM) remains unknown. Decision tree (DT) analysis, a machine learning method, has a flowchart-like model where users can easily predict the risk of liver injury. Thus, we aimed to compare the rate of liver injury between MEPM and DRPM and construct a flowchart that can be used to predict carbapenem-induced liver injury.

METHODS

We investigated patients treated with MEPM (n = 310) or DRPM (n = 320) and confirmed liver injury as the primary outcome. We used a chi-square automatic interaction detection algorithm to construct DT models. The dependent variable was set as liver injury from a carbapenem (MEPM or DRPM), and factors including alanine aminotransferase (ALT), albumin-bilirubin (ALBI) score, and concomitant use of acetaminophen were used as explanatory variables.

RESULTS

The rates of liver injury were 22.9% (71/310) and 17.5% (56/320) in the MEPM and DRPM groups, respectively; no significant differences in the rate were observed (95% confidence interval: 0.710-1.017). Although the DT model of MEPM could not be constructed, DT analysis showed that the incidence of introducing DRPM in patients with ALT >22 IU/L and ALBI scores > -1.87 might be high-risk.

CONCLUSIONS

The risk of developing liver injury did not differ significantly between the MEPM and DRPM groups. Since ALT and ALBI score are evaluated in clinical settings, this DT model is convenient and potentially useful for medical staff in assessing liver injury before DRPM administration.

Prediction of Catheter-Associated Urinary Tract Infections Among Neurosurgical Intensive Care Patients: A Decision Tree Analysis

BACKGROUND

Catheter-associated urinary tract infections (CAUTIs) are the most common device-associated infections in hospitals and can be prevented. To identify the risk factors and develop a risk prediction model for CAUTIs among neurosurgical intensive care unit (NICU) patients.

METHODS

All patients admitted to the NICU of a tertiary hospital between January 2019 and January 2020 were enrolled. Two decision tree models were applied to analyze the risk factors associated with CAUTIs in NICU patients. The performance of the decision tree model was evaluated.

RESULTS

A total of 537 patients admitted to the NICU with indwelling catheters were recruited for this study. The rate of CAUTIs was 4.44 per 1000 catheter days, and Escherichia coli was the predominant pathogen causing CAUTIs among indwelling catheter patients. The classification and regression tree model displayed good power of prediction (area under the curve : 0.920). Nine CAUTI risk factors (age ≥60 years (P = 0.004), Glasgow Coma Scale score ≤8 (P = 0.009), epilepsy at admission (P = 0.007), admission to the hospital during the summer (P < 0.001), ventilators use (P = 0.007), receiving less than 2 types of antibiotics (P < 0.001), albumin level <35 g/L (P = 0.002), female gender (P = 0.002), and having an indwelling catheter for 7-14 days (P = 0.001) were also identified.

CONCLUSION

We developed a novel scoring model for predicting the risk of CAUTIs in patients with neuro-critical illness in daily clinical practice. This model identified several risk factors for CAUTI among NICU patients, novel factors including epilepsy and admission during the summer, can be used to help providers prevent and reduce the risk of CAUTI among vulnerable groups.

Conditional inference tree models to perceive depth of invasion in T1 colorectal cancer

BACKGROUND AND AIM

Accurate diagnosis of invasion depth for T1 colorectal cancer is of critical importance as it decides optimal resection technique. Few reports have previously covered the effects of endoscopic morphology on depth assessment. We developed and validated a novel diagnostic algorithm that accurately predicts the depth of early colorectal cancer.

METHODS

We examined large pathological and endoscopic databases compiled between Jan 2015 and Dec 2018. Training and validation data cohorts were derived and real-world diagnostic performance of two conditional interference tree algorithms (Models 1 and 2) was evaluated against that of the Japan NBI-Expert Team (JNET) classification used by both expert and non-expert endoscopists.

RESULTS

Model 1 had higher sensitivity in deep submucosal invasion than that of JNET alone in both training (45.1% vs. 28.6%, p < 0.01) and validation sets (52.3% vs. 40.0%, p < 0.01). Model 2 demonstrated higher sensitivity than Model 1 (66.2% vs. 52.3%, p < 0.01) in excluding deeper invasion of suspected Tis/T1a lesions.

CONCLUSION

We discovered that machine-learning classifiers, including JNET and macroscopic features, provide the best non-invasive screen to exclude deeper invasion for suspected Tis/T1 lesions. Adding this algorithm improves depth diagnosis of T1 colorectal lesions for both expert and non-expert endoscopists.

A simple algorithm to predict disability in community-dwelling older Japanese adults

BACKGROUND

With a worldwide aging population, the prevention of disability in older adults has become an important issue. Therefore, the purpose of this study was to develop a model for predicting disability risk in older adults based on multiple factors, using a decision tree analysis. This model may be used with a mobile application when it is difficult to interview older adults, and to obtain individualized information for prioritizing interventions.

METHODS

We examined the data from a cohort study conducted by the National Center for Geriatrics and Gerontology-Study of Geriatric Syndromes. We included 12,000 older adults without a disability and performed a decision tree analysis using the Chi-square automatic interaction detection (CHAID) algorithm.

RESULTS

Among the 12,000 participants without a disability, 11,503 and 497 participants remained disability-free and developed disability, respectively. The CHAID analysis identified 24 end nodes with five levels of partition and 16 partitioning variables for 34 questionnaire variables, with incident disability probabilities ranging from 0.0% to 96.7%. The classification accuracy and area under the curve of the CHAID model were 73.4% and 0.76, respectively. We found that maintaining mental health was important for older adults in their 80s and older, and that lifestyles and geriatric syndromes were important factors for those in their 70s.

CONCLUSIONS

The magnitude of the influences on the risk of developing a disability differ by age group. The results of this study may provide useful information for the development of mobile applications that predict the risk of developing disability and create tailor-made interventions.

Chi-square automatic interaction detector decision tree analysis model: Predicting cefmetazole response in intra-abdominal infection

BACKGROUND

Cefmetazole is used as the first-line treatment for intra-abdominal infections. However, only a few studies have investigated the risk factors for cefmetazole treatment failure.

AIMS

This study aimed to develop a decision tree-based predictive model to assess the effectiveness of cefmetazole in initial intra-abdominal infection treatment to improve the clinical treatment strategies.

METHODS

This retrospective cohort study included adult patients who were unexpectedly hospitalized due to intra-abdominal infections between 2003 and 2020 and initially treated with cefmetazole. The primary outcome was clinical intra-abdominal infection improvement. The chi-square automatic interaction detector decision tree analysis was used to create a predictive model for clinical improvement after cefmetazole treatment.

RESULTS

Among 2,194 patients, 1,807 (82.4%) showed clinical improvement post-treatment; their mean age was 48.7 (standard deviation: 18.8) years, and 1,213 (55.3%) patients were men. The intra-abdomせinal infections were appendicitis (n = 1,186, 54.1%), diverticulitis (n = 334, 15.2%), and pancreatitis (n = 285, 13.0%). The chi-square automatic interaction detector decision tree analysis identified the intra-abdominal infection type, C-reactive protein level, heart rate, and body temperature as predictive factors by categorizing patients into seven groups. The area under the receiver operating characteristic curve was 0.71 (95% confidence interval: 0.68-0.73).

CONCLUSION

This predictive model is easily understandable visually and may be applied in clinical practice.

Gradually shifting clinical phenomics in migraine spectrum: a cross-sectional, multicenter study of 5438 patients

Background

The aim of the study was to investigate whether MwoA and MwA are different manifestations of a single disease, distinct clinical entities, or located at two poles of a spectrum.

Methods

In this cross-sectional study, 5438 patients from 10 hospitals in China were included: 4651 were diagnosed with migraine without aura (MwoA) and 787 with migraine with aura (MwA). We used a validated standardized electronic survey to collect multidimensional data on headache characteristics and evaluated the similarities and differences between migraine subtypes. To distinguish migraine subtypes, we employed correlational analysis, factor analysis of mixed data (FAMD), and decision tree analysis.

Results

Compared to MwA, MwoA had more severe headaches, predominantly affected females, were more easily produced by external factors, and were more likely to have accompanying symptoms and premonitory neck stiffness. Patients with MwA are heterogeneous, according to correlation analysis; FAMD divided the subjects into three clear clusters. The majority of the differences between MwoA and MwA were likewise seen when typical aura with migraine headache (AWM) and typical aura with non-migraine headache (AWNM) were compared. Furthermore, decision trees analysis revealed that the chaotic MwA data reduced the decision tree’s accuracy in distinguishing MwoA from MwA, which was significantly increased by splitting MwA into AWM and AWNM.

Conclusions

The clinical phenomics of headache phenotype varies gradually from MwoA to AWM and AWNM, and AWM is a mid-state between MwoA and AWNM. We tend to regard migraine as a spectrum disorder, and speculate that different migraine subtypes have different “predominant regions” that generate attacks.

Supplementary Information

The online version contains supplementary material available at 10.1186/s10194-022-01461-5.

Characterizing Organizational Lifecycle through Strategic and Structural Flexibility: Insights from MSMEs in Mexico

Today's lifespan of companies tends to be low in the so-called micro-, small-, and medium-sized enterprises (MSMEs). Organizational life cycle (OLC) theory indicates that organizational aging is related, but not determined, by the firm chronological age or its size. Therefore, a firm's aging should be analyzed by other factors such as flexibility. The literature considers flexibility as an essential capability, a source of competitive advantage, and an enabler of long-term growth for MSMEs. However, little attention in emerging economies has been paid to examine the nuances of this concept in relation to the OLC in this type of companies. Additionally, studies tend to analyze flexibility as a general term, ignoring that it is a polymorphic concept. That is why there is a need to research the different categories of flexibility. Drawing on a quantitative approach conducting a factor analysis, a two-step cluster, and decision tree analysis to interrogate data from 257 MSMEs in Mexico, this study provides evidence of different dimensions of strategic and structural flexibility that help to characterize and predict the growth, maturity, and declining stages of MSMEs. Our results show that mature firms present more strategic and structural flexible characteristics than those involved in growth or decline stages. The flexible factors that help classify and predict an MSME in the maturity stage include open communication, decentralized decision making, and formalization. We provide a model with these results to illuminate unaddressed issues regarding the broad term of flexibility and its relationship to OLC.

The Relationship Between Visual Function and Performance in Para Swimming

BACKGROUND

Paralympic swimmers with vision impairment (VI) currently compete in one of the three classes depending on their visual acuity (VA) and/or visual field. However, there is no evidence to suggest that a three-class system is the most legitimate approach for classification in swimming, or that the tests of VA and visual field are the most suitable. An evidence-based approach is required to establish the relationship between visual function and performance in the sport. Therefore, the aim of this study was to establish the relationship between visual function and performance in VI Para swimming. The swimming performance of 45 elite VI swimmers was evaluated during international competitions by measuring the total race time, start time, clean swim velocity, ability to swim in a straight line, turn time, and finish time. Visual function was measured using a test battery that included VA, contrast sensitivity, light sensitivity, depth perception, visual search, and motion perception.

RESULTS

Results revealed that VA was the best predictor of total race time (r = 0.40, p < 0.01), though the relationship was not linear. Decision tree analysis suggested that only two classes were necessary for legitimate competition in VI swimming, with a single cut-off between 2.6 and 3.5 logMAR. No further significant association remained between visual function and performance in either of the two resulting classes (all |rs|< 0.11 and ps > 0.54).

CONCLUSIONS

Results suggest that legitimate competition in VI swimming requires one class for partially sighted and another for functionally blind athletes.

Development of a clinical prediction rule for patients with cervical spinal cord injury who have difficulty in obtaining independent living

BACKGROUND CONTEXT

A simple and easy to use clinical prediction rule (CPR) to detect patients with a cervical spinal cord injury (SCI) who would have difficulty in obtaining independent living status is vital for providing the optimal rehabilitation and education in both care recipients and caregivers. A machine learning approach was recently applied to the field of rehabilitation and has the possibility to develop an accurate and useful CPR.

PURPOSE

The aim of this study was to develop and assess a CPR using a decision tree algorithm for predicting which patients with a cervical SCI would have difficulty in obtaining an independent living.

STUDY DESIGN

The present study was a cohort study.

PATIENT SAMPLE

In the present study, the data was obtained from the nationwide Japan Rehabilitation Database (JRD). The data on the SCIs was collected from 10 hospitals and the data was collected from the registries obtained between 2005 and 2015. The severity of SCI can vary, and patient prognosis differs depending on the damage site. In this study, the patients with cervical SCI were included.

OUTCOME MEASURES

In this study, the degree of the independent living at discharge was investigated. The degree of the independent living was classified and listed as below: independent in social, independent at home, need care at home, independent at facility, need care at facility. In this study, the independent in social and independent at home were defined as "independent," and the other situations were defined as "non-independent."

METHODS

We performed a classification and regression tree (CART) analysis to develop the CPR to predict whether the cervical SCI patients obtain an independent living at discharge. The area under the curve, the classification accuracy, sensitivity, specificity, and positive predictive value were used for model evaluation.

RESULTS

A total of 4181 patients with SCI were registered in the JRD and the CART analysis was performed for 1282 patients with the cervical SCI. The Functional Independence Measure (FIM) total score and the American Spinal Injury Association impairment scale were identified as the first and second discriminators for predicting the degree of the independence, respectively. Subsequently, the CART model identified FIM eating, the residual function level, and the FIM bed to chair transfer as next discriminators. Each parameter for evaluating the CART model were the area under the curve 0.813, the classification accuracy 78.6%, the sensitivity 80.7%, the specificity 75.1%, and the positive predictive value 84.5%.

CONCLUSIONS

In this study, we developed a clinically useful CPR with moderate accuracy to predict whether the cervical SCI patients obtain independent living at the discharge.

Development of a decision flowchart to identify the patients need high-dose vancomycin in early phase of treatment

Background

The standard dose of vancomycin (VCM, 2 g/day) sometimes fails to achieve therapeutic concentration in patients with normal renal function. In this study, we aimed to identify factors to predict patients who require high-dose vancomycin (> 2 g/day) to achieve a therapeutic concentration and to develop a decision flowchart to select these patients prior to VCM administration.

Methods

Patients who had an estimated creatinine clearance using the Cockcroft–Gault equation (eCCr) of ≥50 mL/min and received intravenous VCM were divided into 2 cohorts: an estimation set (n = 146, from April to September 2016) and a validation set (n = 126, from October 2016 to March 2017). In each set, patients requiring ≤2 g/day of VCM to maintain the therapeutic trough concentration (10–20 μg/mL) were defined as standard-dose patients, while those who needed > 2 g/day were defined as high-dose patients. Univariate and multivariate logistic regression analysis was performed to identify the predictive factors for high-dose patients and decision tree analysis was performed to develop decision flowchart to identify high-dose patients.

Results

Among the covariates analyzed, age and eCCr were identified as independent predictors for high-dose patients. Further, the decision tree analysis revealed that eCCr (cut off value = 81.3 mL/min) is the top predictive factor and is followed by age (cut off value = 58 years). Based on these findings, a decision flowchart was constructed, in which patients with eCCr ≥81.3 mL/min and age < 58 years were designated as high-dose patients and other patients were designated as standard-dose patients. Subsequently, we applied this decision flowchart to the validation set and obtained good predictive performance (positive and negative predictive values are 77.6 and 84.4%, respectively).

Conclusion

These results suggest that the decision flowchart constructed in this study provides an important contribution for avoiding underdosing of VCM in patients with eCCr of ≥50 mL/min.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40780-021-00231-w.

A multi-echelon dynamic cold chain for managing vaccine distribution

While cold chain management has been part of healthcare systems, enabling the efficient administration of vaccines in both urban and rural areas, the COVID-19 virus has created entirely new challenges for vaccine distributions. With virtually every individual worldwide being impacted, strategies are needed to devise best vaccine distribution scenarios, ensuring proper storage, transportation and cost considerations. Current models do not consider the magnitude of distribution efforts needed in our current pandemic, in particular the objective that entire populations need to be vaccinated. We expand on existing models and devise an approach that considers the needed extensive distribution capabilities and special storage requirements of vaccines, while at the same time being cognizant of costs. As such, we provide decision support on how to distribute the vaccine to an entire population based on priority. We do so by conducting predictive analysis for three different scenarios and dividing the distribution chain into three phases. As the available vaccine doses are limited in quantity at first, we apply decision tree analysis to find the best vaccination scenario, followed by a synthetic control analysis to predict the impact of the vaccination programme to forecast future vaccine production. We then formulate a mixed-integer linear programming (MILP) model for locating and allocating cold storage facilities for bulk vaccine production, followed by the proposition of a heuristic algorithm to solve the associated objective functions. The application of the proposed model is evaluated by implementing it in a real-world case study. The optimized numerical results provide valuable decision support for healthcare authorities.

A decision tree analysis applied to women aged 43-45: who should be referred for ovum donation?

RESEARCH QUESTION

In women at the advanced age of 43-45 years undergoing repeated IVF cycles with autologous oocytes, who has the highest chance for birth and who should be referred early to receive donor oocytes?

DESIGN

A retrospective cohort study was conducted at a university hospital reproductive centre. The computerized database of 394 women aged 43-45 years undergoing 1528 non-donor IVF or intracytoplasmic sperm injection cycles between 2010 and 2019 was analysed. A decision tree was developed, enabling a comprehensive study of a set of clinical parameters and the expected outcomes.

RESULTS

The cumulative clinical pregnancy rate was 15.0% (59/394) and the cumulative live birth rate was 8.4% (33/394). The decision tree developed to predict women who should be offered egg donation included age, poor ovarian response to stimulation, the number of top-quality embryos, dominant follicles, previous pregnancy or live birth, fertilized oocytes and body mass index. The model showed that a good ovarian response in the first cycle was the best predictor for live birth (13.3% gave birth). However, among women with poor responses, 7.1% of those who were younger than 43.5 years gave birth, and none of the women who were older than 43.5 years did.

CONCLUSIONS

Women over 43.5 years old with fewer than four oocytes collected in their first IVF cycle should be offered ovum donation, since their live birth rate in subsequent cycles is negligible.

Predictors of progression through the cascade of care to a cure for hepatitis C patients using decision trees and random forests

BACKGROUND

This study uses machine learning techniques to identify sociodemographic and clinical predictors of progression through the hepatitis C (HCV) cascade of care for patients in the 1945-1965 birth cohort in the Southern United States.

METHODS

We compared sociodemographic and clinical variables between groups of patients for three care outcomes: linkage to care, initiation of antiviral treatment, and virologic cure. A decision tree model and random forest model were built for each outcome.

RESULTS

Patients were primarily male, African American/Black or Caucasian/White, non-Hispanic or Latino, and insured. The average age at first HCV screening was 60 years old, and common medical diagnoses included chronic kidney disease, fibrosis and/or cirrhosis, transplanted liver, diabetes mellitus, and liver cell carcinoma. Variables used in predicting linkage to care included age at first HCV screening, insurance at first HCV screening, race, fibrosis and/or cirrhosis, other liver disease, ascites, and transplanted liver. Variables used in predicting initiation of antiviral treatment included insurance at first HCV screening, gender, other liver cancer, steatosis, and liver cell carcinoma. Variables used in predicting virologic cure included insurance at first HCV screening, transplanted liver, and ethnicity.

CONCLUSION

These patients have a high hepatic health burden, likely reflecting complications of untreated HCV and highlighting the urgency to cure HCV in this birth cohort. We found differences in HCV care outcomes based on sociodemographic and clinical variables. More work is needed to understand the mechanisms of these differences in care outcomes and to improve HCV care.

Identifying factors that conjointly influence nicotine vaping product relative harm perception among smokers and recent ex-smokers: Findings from the 2016 ITC Four Country Smoking and Vaping Survey

BACKGROUND

Use of nicotine vaping products (NVPs) to replace smoking is often influenced by perceived harmfulness of these products relative to smoking. This study aimed to identify factors that conjointly influenced NVP relative harm perception among smokers and ex-smokers.

METHODS

Data (n = 11,838) from adult smokers and ex-smokers (quit < 2 years) who participated in the 2016 ITC 4 Country Smoking and Vaping Surveys in Australia, Canada, England and the US were analyzed. Decision tree models were used to classify respondents into those who perceived vaping as less harmful than smoking ("correct" perception) versus otherwise ("incorrect" perception) based on their socio-demographic, smoking and vaping related variables.

RESULTS

Decision tree analysis identified nicotine replacement therapy (NRT) harmfulness perceptions relative to smoking, perceived vaping portrayal in the media and other sources as positive, negative or balanced, recency of seeking online vaping information, and age as the key variables that interacted conjointly to classify respondents into those with "correct" versus "incorrect" harm perceptions of vaping relative to smoking (model performance accuracy = 0.70-0.74). In all countries, NRT relative harmfulness perception and vaping portrayal perception were consistently the two most important classifying variables, with other variables showing some country differences.

CONCLUSIONS

In all four countries, perception of NVP relative harmfulness among smokers and recent ex-smokers is strongly influenced by a combination of NRT relative harmfulness perception and vaping portrayal in the media and other sources. These conjoint factors can serve as useful markers for identifying subgroups more vulnerable to misperception about NVP relative harmfulness to benefit from corrective intervention.

Cardiac Screening for High Risk Sudden Cardiac Death in School-Aged Children

Background

Sudden cardiac death (SCD) is an uncommon but significant cause of death in the young. Citywide cardiac screening of school-aged children has been performed in Taipei since 1989. In this study, we investigate the efficacy of this screening method for identifying those at high risk of SCD.

Methods

This study analyzed the data from the results of cardiac screening for school-aged children in Taipei from 2003 to 2014. The cardiac screening included: Stage I, questionnaire surveys, simplified phonocardiography test and simplified electrocardiography (ECG) test; Stage II, physical examination and auscultation by a pediatric cardiologist for all children who had abnormal findings in stage I screening; Stage III, referral to a pediatric cardiologist for further examinations. Logistic regression and decision tree analyses were performed.

Results

A total of 566,447 students were screened, of whom 685 were identified as being at high risk of SCD. The most common causes of being at high risk of SCD included Wolff-Parkinson-White syndrome, long QT syndrome, cardiomyopathy and Marfan's syndrome. Using logistic regression analysis, the simplified ECG test was identified as being the most effective tool (odds ratio = 16.4, p < 0.001) and past history as the second most crucial factor (odds ratio = 3.95, p < 0.001) for detecting a high risk of SCD. Decision tree analysis showed that serial studies with a past history and the simplified ECG test could accurately identify those at high risk of SCD.

Conclusions

Questionnaire survey and simplified electrocardiography test-based cardiovascular screening in school-aged children can identify those at high risk of SCD.

Canadian children's and youth's adherence to the 24-h movement guidelines during the COVID-19 pandemic: A decision tree analysis

PURPOSE

The purpose of this study was to use decision tree modeling to generate profiles of children and youth who were more or less likely to meet the Canadian 24-h movement guidelines during the coronavirus disease-19 (COVID-19) outbreak.

METHODS

Data for this study were from a nationally representative sample of 1472 Canadian parents (Mean_age = 45.12, SD = 7.55) of children (5-11 years old) or youth (12-17 years old). Data were collected in April 2020 via an online survey. Survey items assessed demographic, behavioral, social, micro-environmental, and macro-environmental characteristics. Four decision trees of adherence and non-adherence to all movement recommendations combined and each individual movement recommendation (physical activity [PA], screen time, and sleep) were generated.

RESULTS

Results revealed specific combinations of adherence and non-adherence characteristics. Characteristics associated with adherence to the recommendation(s) included high parental perceived capability to restrict screen time, annual household income of ≥ $100,000, increases in children's and youth's outdoor PA/sport since the COVID-19 outbreak began, being a boy, having parents younger than 43 years old, and small increases in children's and youth's sleep duration since the COVID-19 outbreak began. Characteristics associated with non-adherence to the recommendation(s) included low parental perceived capability to restrict screen time, youth aged 12-17 years, decreases in children's and youth's outdoor PA/sport since the COVID-19 outbreak began, primary residences located in all provinces except Quebec, low parental perceived capability to support children's and youth's sleep and PA, and annual household income of ≤ $99,999.

CONCLUSION

Our results show that specific characteristics interact to contribute to (non)adherence to the movement behavior recommendations. Results highlight the importance of targeting parents' perceived capability for the promotion of children's and youth's movement behaviors during challenging times of the COVID-19 pandemic, paying particular attention to enhancing parental perceived capability to restrict screen time.

Identifying smoker subgroups with high versus low smoking cessation attempt probability: A decision tree analysis approach

BACKGROUND

Regression-based research has successfully identified independent predictors of smoking cessation, both its initiation and maintenance. However, it is unclear how these various independent predictors interact with each other and conjointly influence smoking behaviour. As a proof-of-concept, this study used decision tree analysis (DTA) to identify the characteristics of smoker subgroups with high versus low smoking cessation initiation probability based on the conjoint effects of four predictor variables, and determine any variations by socio-economic status (SES).

METHODS

Data come from the Australian arm of the ITC project, a longitudinal cohort study of adult smokers followed up approximately annually. Reported wanting to quit smoking, worries about smoking negative health impact, quitting self-efficacy and quit intentions assessed in 2005 were used as predictors and reported quit attempts at the 2006 follow-up survey were used as the outcome for the initial model calibration and validation analyses (n = 1475), and further cross-validated using the 2012-2013 data (n = 787).

RESULTS

DTA revealed that while all four predictor variables conjointly contributed to the identification of subgroups with high versus low smoking cessation initiation probability, quit intention was the most important predictor common across all SES strata. The relative importance of the other predictors showed differences by SES.

CONCLUSIONS

Modifiable characteristics of smoker subgroups associated with making a quit attempt and any variations by SES can be successfully identified using a decision tree analysis approach, to provide insights as to who might benefit from targeted intervention, thus, underscoring the value of this approach to complement the conventional regression-based approach.

Patient decision aids: a content analysis based on a decision tree structure

INTRODUCTION

This paper presents the preliminary results of a decision-tree analysis of Patient Decision Aids (PDA). PDAs are online or offline tools used to structure health information, elicit relevant values and emphasize the decision as a process, in ways that help patients make more informed health decisions individually or with relevant others.

METHOD

Twenty PDAs are randomly selected from the International Patient Decision Aids Standards (IPDAS) ( https://decisionaid.ohri.ca/AZlist.html ) approved list. An evaluation tool is built bottom-up and top-down and results are described in terms of communicating uncertainty, completeness of the decision tree, ambiguous or misleading phrasing, overall strategies suggested within personal stories.

RESULTS

Twelve of the analyzed PDAs had branches of the decision tree which were not discussed in the tool and 6 had logically ambiguous phrasing. Many tools included dichotomous options, when the option range was wider. Several options were clustered within the "Do not take/Do not do" option and thus the PDA failed to provide all comparisons necessary to make a decision. Some tools employ expressions that do not differentiate between lack of information and known negative effects. Other tools provide unequal amounts or non-comparable bits of information about the options.

CONCLUSION

These results indicate a very loose range of interpretations of what constitutes an option, a treatment, and a treatment option. It thus emphasizes a gap between theory and practice in the evaluation of PDAs. Future developments of PDA evaluation tools should keep track of missing decision tree branches, accurate communication of uncertainty, ambiguity, and lack of knowledge and consider using measures for evaluating the completeness of the option spectrum at an agreed period in time.

Types of problematic smartphone use based on psychiatric symptoms

To provide appropriate solutions for problematic smartphone use, we need to first understand its types. This study aimed to identify types of problematic smartphone use based on psychiatric symptoms, using the decision tree method. We recruited 5,372 smartphone users from online surveys conducted between February 3 and February 22, 2016. Based on scores on the Korean Smartphone Addiction Proneness Scale for Adults (S-Scale), 974 smartphone users were assigned to the smartphone-dependent group and 4398 users were assigned to the normal group. The data-mining technique of C5.0 decision tree was applied. We used 15 input variables, including demographic and psychological factors. Four psychiatric variables emerged as the most important predictors: self-control (Sc; 66%), anxiety (Anx; 25%), depression (Dep; 7%), and dysfunctional impulsivities (Imp; 3%). We identified the following five types of problematic smartphone use: (1) non-comorbid, (2) self-control, (3) Sc + Anx, (4) Sc + Anx + Dep, and (5) Sc + Anx + Dep + Imp. We found that 74% of smartphone-dependent users had psychiatric symptoms. The ratio of participants belonging to the non-comorbid and self-control types was 64%. We proposed that these types of problematic smartphone use may be used for the development of an appropriate service for controlling and preventing such behaviors in adults.

Costs of outpatient and inpatient MRSA screening and treatment strategies for patients at elective hospital admission - a decision tree analysis

Background

Nosocomial infections are among the most common complications in hospitals. A major part is caused by multidrug-resistant organisms (MDRO). MRSA is still the most prominent and frequent MDRO. The early detection of carriers of multidrug-resistant bacteria is an effective measure to reduce nosocomial infections caused by MDRO. For patients who are planning to go to the hospital, an outpatient screening for MDRO and pre-hospital decolonization is recommended. However, the effectiveness of such pre-admission MDRO management in preparation for a planned hospital stay has not yet been sufficiently scientifically examined from an economic perspective.

Methods

A decision tree will be used to develop scenarios for MDRO screening and treatment in the context of the outpatient and inpatient sectors using MRSA-positive patients as an example. Subsequently, the expected costs for the respective strategy are presented.

Results

The decision tree analysis shows that the expected costs of outpatient MRSA management are €8.24 and that of inpatient MRSA management are €672.51.

Conclusion

The forward displacement of the MRSA screening to the ambulatory sector and any subsequent outpatient decolonization for patients with a planned hospitalization is the most cost-effective strategy and should become a standard benefit. Excluding opportunity costs, the expected costs of inpatient MRSA management are €54.94.

Identification of task demands and usability issues in police use of mobile computing terminals

Crash reports from various states in the U.S. have shown high numbers of emergency vehicle crashes, especially in law enforcement situations. This study identified the perceived importance and frequency of police mobile computing terminal (MCT) tasks, quantified the demands of different tasks using a cognitive performance modeling methodology, identified usability violations of current MCT interface designs, and formulated design recommendations for an enhanced interface. Results revealed that "access call notes", "plate number check" and "find location on map" are the most important and frequently performed tasks for officers. "Reading plate information" was also found to be the most visually and cognitively demanding task-method. Usability principles of "using simple and natural dialog" and "minimizing user memory load" were violated by the current MCT interface design. The enhanced design showed potential for reducing cognitive demands and task completion time. Findings should be further validated using a driving simulation study.

Diagnostic assessment of intraoperative cytology for papillary thyroid carcinoma: using a decision tree analysis

PURPOSE

The aim of this study was to elucidate the cytological characteristics and the diagnostic usefulness of intraoperative cytology (IOC) for papillary thyroid carcinoma (PTC). In addition, using decision tree analysis, effective features for accurate cytological diagnosis were sought.

METHODS

We investigated cellularity, cytological features and diagnosis based on the Bethesda System for Reporting Thyroid Cytopathology in IOC of 240 conventional PTCs. The cytological features were evaluated in terms of nuclear score with nuclear features, and additional figures such as presence of swirling sheets, psammoma bodies, and multinucleated giant cells. The nuclear score (range 0-7) was made via seven nuclear features, including (1) enlarged, (2) oval or irregularly shaped nuclei, (3) longitudinal nuclear grooves, (4) intranuclear cytoplasmic pseudoinclusion, (5) pale nuclei with powdery chromatin, (6) nuclear membrane thickening, and (7) marginally placed micronucleoli.

RESULTS

Nuclear scores in PTC, suspicious for malignancy, and atypia of undetermined significance cases were 6.18 ± 0.80, 4.48 ± 0.82, and 3.15 ± 0.67, respectively. Additional figures more frequent in PTC than in other diagnostic categories were identified. Cellularity of IOC significantly correlated with tumor size, nuclear score, and presence of additional figures. Also, IOCs with higher nuclear scores (4-7) significantly correlated with larger tumor size and presence of additional figures. In decision tree analysis, IOCs with nuclear score >5 and swirling sheets could be considered diagnostic for PTCs.

CONCLUSIONS

Our study suggests that IOCs using nuclear features and additional figures could be useful with decreasing the likelihood of inconclusive results.

Factors delaying management of acute stroke: An Indian scenario

Background and Purpose:

The purpose of this study was to assess factors causing delay in treatment of acute stroke in a tertiary care institute in South India.

Methods:

All clinically suspected cases of acute stroke presenting to the emergency department over a period of 1 year were prospectively followed up and data collected as per a preset pro forma. The various time intervals from stroke onset to definitive management and other pertinent data were collected. The time delays have been evaluated in the decision tree model: Chi-squared Automatic Interaction Detection. Significance was assessed at 5% level of significance (P < 0.05).

Results:

The mean prehospital time delay for all clinically suspected stroke (n = 361) in our institute was 716 min and the median time 190 min. The mean total in-hospital delay was 94.17 ± 54.5 min and median time being 82 min. The onset of symptoms to first medical contact was the main interval that influenced the prehospital delay. Computed tomographic (CT) diagnosis to stroke unit admission influenced the in-hospital delay the most.

Conclusions:

Lack of awareness regarding stroke leads to delayed seeking of treatment for the same. The factors that contribute to the in-hospital delay included patient admission procedure delay, lack of staff to transport the patient, and the distance between the stroke unit and CT room. Educating the community with regard to “stroke” and implementation of a better pre- and in-hospital stroke care system is a need of the hour in the country.

The value of decision tree analysis in planning anaesthetic care in obstetrics

The use of decision tree analysis is discussed in the context of the anaesthetic and obstetric management of a young pregnant woman with joint hypermobility syndrome with a history of insensitivity to local anaesthesia and a previous difficult intubation due to a tongue tumour. The multidisciplinary clinical decision process resulted in the woman being delivered without complication by elective caesarean section under general anaesthesia after an awake fibreoptic intubation. The decision process used is reviewed and compared retrospectively to a decision tree analytical approach. The benefits and limitations of using decision tree analysis are reviewed and its application in obstetric anaesthesia is discussed.

Decision tree analysis as a supplementary tool to enhance histomorphological differentiation when distinguishing human from non-human cranial bone in both burnt and unburnt states: A feasibility study

This feasibility study was undertaken to describe and record the histological characteristics of burnt and unburnt cranial bone fragments from human and non-human bones. Reference series of fully mineralized, transverse sections of cranial bone, from all variables and specimen states, were prepared by manual cutting and semi-automated grinding and polishing methods. A photomicrograph catalogue reflecting differences in burnt and unburnt bone from human and non-humans was recorded and qualitative analysis was performed using an established classification system based on primary bone characteristics. The histomorphology associated with human and non-human samples was, for the main part, preserved following burning at high temperature. Clearly, fibro-lamellar complex tissue subtypes, such as plexiform or laminar primary bone, were only present in non-human bones. A decision tree analysis based on histological features provided a definitive identification key for distinguishing human from non-human bone, with an accuracy of 100%. The decision tree for samples where burning was unknown was 96% accurate, and multi-step classification to taxon was possible with 100% accuracy. The results of this feasibility study strongly suggest that histology remains a viable alternative technique if fragments of cranial bone require forensic examination in both burnt and unburnt states. The decision tree analysis may provide an additional but vital tool to enhance data interpretation. Further studies are needed to assess variation in histomorphology taking into account other cranial bones, ontogeny, species and burning conditions.

Impact of different diagnostic technologies for MRSA admission screening in hospitals - a decision tree analysis

BACKGROUND

Hospital infections with multiresistant bacteria, e.g., Methicillin-resistant Staphylococcus aureus (MRSA), cause heavy financial burden worldwide. Rapid and precise identification of MRSA carriage in combination with targeted hygienic management are proven to be effective but incur relevant extra costs. Therefore, health care providers have to decide which MRSA screening strategy and which diagnostic technology should be applied according to economic criteria.

AIM

The aim of this study was to determine which MRSA admission screening and infection control management strategy causes the lowest expected cost for a hospital. Focus was set on the Point-of-Care Testing (PoC).

METHODS

A decision tree analytic cost model was developed, primarily based on data from peer-reviewed literature. In addition, univariate sensitivity analyses of the different input parameters were conducted to study the robustness of the results.

FINDINGS

In the basic analysis, risk-based PoC screening showed the highest mean cost savings with 14.98 € per admission in comparison to no screening. Rapid universal screening methods became favorable at high MRSA prevalence, while in situations with low MRSA transmission rates omission of screening may be favorable.

CONCLUSION

Early detection of MRSA by rapid PoC or PCR technologies and consistent implementation of appropriate hygienic measures lead to high economic efficiency of MRSA management. Whether general or targeted screening is more efficient depends mainly on epidemiological and infrastructural parameters.

Decision tree analyses of key patient characteristics in Middle Eastern/North African and Latin American men treated with long-acting and short-acting PDE5 inhibitors for erectile dysfunction

BACKGROUND

Phosphodiesterase type 5 (PDE5) inhibitors have discontinuation rates as high as 60% in men with erectile dysfunction. Treatment satisfaction has been significantly associated with treatment continuation. Understanding key characteristics in terms of treatment preference, relationship, and lifestyle issues could provide direction on how to improve compliance with PDE5 inhibitor treatment globally.

OBJECTIVE

The objective was to identify subgroups of interest in the pooled database of two observational studies conducted in Latin America (LA) and Middle East/North Africa (MENA) exploring patient characteristics and prescription of either a long- or short-acting PDE5 inhibitor at baseline.

METHODS

Two identical prospective, non-interventional, observational, studies in MENA (N = 493) and LA (N = 511) treated men with an 'on demand' (pro re nata, PRN) PDE5 inhibitor (sildenafil, tadalafil, vardenafil, or lodenafil) during 6 months. In this post-hoc meta-analysis of two observational studies with equal design, pooled data were analyzed to determine patient characteristics and PDE5 inhibitor prescribed/used most likely to be associated with patient expectations, satisfaction, self-esteem, and patient-partner relationships. Decision tree analyses, with and without weighting, were used to identify and describe key features.

RESULTS

In each analysis of patient expectations, patient-partner relationship, and self-esteem, we describe the two major subgroups at baseline for each decision tree. Analyses of patient expectations and sexual self-esteem revealed that patients prescribed long-acting PDE5 inhibitors (59%) highlighted the importance of treatment effect duration, second to partner satisfaction with treatment, while patients prescribed short-acting PDE5 inhibitors (32%) placed less importance on treatment effect duration but considerable importance on treatment effect lasting until intercourse completion. Further insights regarding patients, partner relationship characteristics, and treatment expectations were identified.

CONCLUSION

Our analyses have described key characteristics, such as self- and partner perceptions, sexual attitudes, and treatment expectations in relation to the patients' country and prescribed treatment, which might guide treatment decisions in MENA and LA men with ED.

Adaptive Flood Risk Management Under Climate Change Uncertainty Using Real Options and Optimization

It is well recognized that adaptive and flexible flood risk strategies are required to account for future uncertainties. Development of such strategies is, however, a challenge. Climate change alone is a significant complication, but, in addition, complexities exist trying to identify the most appropriate set of mitigation measures, or interventions. There are a range of economic and environmental performance measures that require consideration, and the spatial and temporal aspects of evaluating the performance of these is complex. All these elements pose severe difficulties to decisionmakers. This article describes a decision support methodology that has the capability to assess the most appropriate set of interventions to make in a flood system and the opportune time to make these interventions, given the future uncertainties. The flood risk strategies have been explicitly designed to allow for flexible adaptive measures by capturing the concepts of real options and multiobjective optimization to evaluate potential flood risk management opportunities. A state-of-the-art flood risk analysis tool is employed to evaluate the risk associated to each strategy over future points in time and a multiobjective genetic algorithm is utilized to search for the optimal adaptive strategies. The modeling system has been applied to a reach on the Thames Estuary (London, England), and initial results show the inclusion of flexibility is advantageous, while the outputs provide decisionmakers with supplementary knowledge that previously has not been considered.