Decision tree-based modelling for identification of potential interactions between type 2 diabetes risk factors: a decade follow-up in a Middle East prospective cohort study

Which Intervention Synergies Maximize AGYW's HIV Outcomes? A Classification and Regression Tree Analysis of Layered HIV Prevention Programming

INTRODUCTION

Intersecting behavioral, social, and structural factors increase adolescent girls' (AG) and young women's (YW) HIV vulnerability. Yet, understanding of optimal intervention synergies remains limited. We identified intervention combinations that statistically maximized reductions in AGYW's HIV-related risk.

METHODS

Using data collected in 2018 with Zambian AG (n = 487, aged 15-19 years) and YW (n = 505, aged 20-25 years) after 12-14 months exposure to Determined, Resilient, Empowered, AIDS-free, Mentored, and Safe (multisectoral HIV program), we used classification and regression trees to explore relationships between interventions (safe space/social asset building [SAB] and provision of/linkage to youth-friendly health services [YFHS], education social protection [Educ], economic social protection [Econ]) and HIV-related outcomes (HIV testing, consistent condom use, transactional sex, and sexual violence experience from partners and nonpartners).

RESULTS

Overall, 59.9% completed SAB and 81.5%, 35.4%, and 29.6% received YHFS, Educ, and Econ, respectively. For AG, HIV testing improved (from 73% to 83%) with exposure to all interventions, condom use improved with Econ (from 33% to 46%), transactional sex reduced with SAB + Educ, and sexual violence from partners and nonpartners reduced with Educ and SAB, respectively. For YW, HIV testing increased with Educ (from 77% to 91%), condom use increased with SAB + YFHS (from 36% to 52%), transactional sex reduced with combinations of all interventions, and sexual violence from partners reduced with YFHS and from nonpartners with SAB + Econ.

CONCLUSIONS

Tailored interventions might be more effective than uniform combination intervention packages in reducing AGYW's HIV risk. AG benefitted most from SAB and/or Educ while YFHS, Educ, and/or SAB reduced YW's HIV-related risk. Educational and asset-building interventions could have the greatest impact on AGYW's HIV risk.

A scoping review of artificial intelligence-based methods for diabetes risk prediction

The increasing prevalence of type 2 diabetes mellitus (T2DM) and its associated health complications highlight the need to develop predictive models for early diagnosis and intervention. While many artificial intelligence (AI) models for T2DM risk prediction have emerged, a comprehensive review of their advancements and challenges is currently lacking. This scoping review maps out the existing literature on AI-based models for T2DM prediction, adhering to the PRISMA extension for Scoping Reviews guidelines. A systematic search of longitudinal studies was conducted across four databases, including PubMed, Scopus, IEEE-Xplore, and Google Scholar. Forty studies that met our inclusion criteria were reviewed. Classical machine learning (ML) models dominated these studies, with electronic health records (EHR) being the predominant data modality, followed by multi-omics, while medical imaging was the least utilized. Most studies employed unimodal AI models, with only ten adopting multimodal approaches. Both unimodal and multimodal models showed promising results, with the latter being superior. Almost all studies performed internal validation, but only five conducted external validation. Most studies utilized the area under the curve (AUC) for discrimination measures. Notably, only five studies provided insights into the calibration of their models. Half of the studies used interpretability methods to identify key risk predictors revealed by their models. Although a minority highlighted novel risk predictors, the majority reported commonly known ones. Our review provides valuable insights into the current state and limitations of AI-based models for T2DM prediction and highlights the challenges associated with their development and clinical integration.

Using a decision tree algorithm to distinguish between repeated supra-therapeutic and acute acetaminophen exposures

BACKGROUND

This study aimed to compare clinical and laboratory characteristics of supra-therapeutic (RSTI) and acute acetaminophen exposures using a predictive decision tree (DT) algorithm.

METHODS

We conducted a retrospective cohort study using the National Poison Data System (NPDS). All patients with RSTI acetaminophen exposure (n = 4,522) between January 2012 and December 2017 were included. Additionally, 4,522 randomly selected acute acetaminophen ingestion cases were included. After that, the DT machine learning algorithm was applied to differentiate acute acetaminophen exposure from supratherapeutic exposures.

RESULTS

The DT model had accuracy, precision, recall, and F1-scores of 0.75, respectively. Age was the most relevant variable in predicting the type of acetaminophen exposure, whether RSTI or acute. Serum aminotransferase concentrations, abdominal pain, drowsiness/lethargy, and nausea/vomiting were the other most important factors distinguishing between RST and acute acetaminophen exposure.

CONCLUSION

DT models can potentially aid in distinguishing between acute and RSTI of acetaminophen. Further validation is needed to assess the clinical utility of this model.

Environmental exposures in machine learning and data mining approaches to diabetes etiology: A scoping review

BACKGROUND

Environmental exposures are implicated in diabetes etiology, but are poorly understood due to disease heterogeneity, complexity of exposures, and analytical challenges. Machine learning and data mining are artificial intelligence methods that can address these limitations. Despite their increasing adoption in etiology and prediction of diabetes research, the types of methods and exposures analyzed have not been thoroughly reviewed.

OBJECTIVE

We aimed to review articles that implemented machine learning and data mining methods to understand environmental exposures in diabetes etiology and disease prediction.

METHODS

We queried PubMed and Scopus databases for machine learning and data mining studies that used environmental exposures to understand diabetes etiology on September 19th, 2022. Exposures were classified into specific external, general external, or internal exposures. We reviewed machine learning and data mining methods and characterized the scope of environmental exposures studied in the etiology of general diabetes, type 1 diabetes, type 2 diabetes, and other types of diabetes.

RESULTS

We identified 44 articles for inclusion. Specific external exposures were the most common exposures studied, and supervised models were the most common methods used. Well-established specific external exposures of low physical activity, high cholesterol, and high triglycerides were predictive of general diabetes, type 2 diabetes, and prediabetes, while novel metabolic and gut microbiome biomarkers were implicated in type 1 diabetes.

DISCUSSION

The use of machine learning and data mining methods to elucidate environmental triggers of diabetes was largely limited to well-established risk factors identified using easily explainable and interpretable models. Future studies should seek to leverage machine learning and data mining to explore the temporality and co-occurrence of multiple exposures and further evaluate the role of general external and internal exposures in diabetes etiology.

Risk factor analysis and clinical decision tree model construction for diabetic retinopathy in Western China

BACKGROUND

Diabetic retinopathy (DR) is the driving force of blindness in patients with type 2 diabetes mellitus (T2DM). DR has a high prevalence and lacks effective therapeutic strategies, underscoring the need for early prevention and treatment. Yunnan province, located in the southwest plateau of China, has a high pre-valence of DR and an underdeveloped economy.

AIM

To build a clinical prediction model that will enable early prevention and treatment of DR.

METHODS

In this cross-sectional study, 1654 Han population with T2DM were divided into groups without (n = 826) and with DR (n = 828) based on fundus photography. The DR group was further subdivided into non-proliferative DR (n = 403) and proliferative DR (n = 425) groups. A univariate analysis and logistic regression analysis were conducted and a clinical decision tree model was constructed.

RESULTS

Diabetes duration ≥ 10 years, female sex, standing- or supine systolic blood pressure (SBP) ≥ 140 mmHg, and cholesterol ≥ 6.22 mmol/L were risk factors for DR in logistic regression analysis (odds ratio = 2.118, 1.520, 1.417, 1.881, and 1.591, respectively). A greater severity of chronic kidney disease (CKD) or hemoglobin A 1c increased the risk of DR in patients with T2DM. In the decision tree model, diabetes duration was the primary risk factor affecting the occurrence of DR in patients with T2DM, followed by CKD stage, supine SBP, standing SBP, and body mass index (BMI). DR classification outcomes were obtained by evaluating standing SBP or BMI according to the CKD stage for diabetes duration < 10 years and by evaluating CKD stage according to the supine SBP for diabetes duration ≥ 10 years.

CONCLUSION

Based on the simple and intuitive decision tree model constructed in this study, DR classification outcomes were easily obtained by evaluating diabetes duration, CKD stage, supine or standing SBP, and BMI.

Accuracy of Machine Learning Classification Models for the Prediction of Type 2 Diabetes Mellitus: A Systematic Survey and Meta-Analysis Approach

Soft-computing and statistical learning models have gained substantial momentum in predicting type 2 diabetes mellitus (T2DM) disease. This paper reviews recent soft-computing and statistical learning models in T2DM using a meta-analysis approach. We searched for papers using soft-computing and statistical learning models focused on T2DM published between 2010 and 2021 on three different search engines. Of 1215 studies identified, 34 with 136952 patients met our inclusion criteria. The pooled algorithm's performance was able to predict T2DM with an overall accuracy of 0.86 (95% confidence interval [CI] of [0.82, 0.89]). The classification of diabetes prediction was significantly greater in models with a screening and diagnosis (pooled proportion [95% CI] = 0.91 [0.74, 0.97]) when compared to models with nephropathy (pooled proportion = 0.48 [0.76, 0.89] to 0.88 [0.83, 0.91]). For the prediction of T2DM, the decision trees (DT) models had a pooled accuracy of 0.88 [95% CI: 0.82, 0.92], and the neural network (NN) models had a pooled accuracy of 0.85 [95% CI: 0.79, 0.89]. Meta-regression did not provide any statistically significant findings for the heterogeneous accuracy in studies with different diabetes predictions, sample sizes, and impact factors. Additionally, ML models showed high accuracy for the prediction of T2DM. The predictive accuracy of ML algorithms in T2DM is promising, mainly through DT and NN models. However, there is heterogeneity among ML models. We compared the results and models and concluded that this evidence might help clinicians interpret data and implement optimum models for their dataset for T2DM prediction.

Decision Tree Modeling for Osteoporosis Screening in Postmenopausal Thai Women

Osteoporosis is still a serious public health issue in Thailand, particularly in postmenopausal women; meanwhile, new effective screening tools are required for rapid diagnosis. This study constructs and confirms an osteoporosis screening tool-based decision tree (DT) model. Four DT algorithms, namely, classification and regression tree; chi-squared automatic interaction detection (CHAID); quick, unbiased, efficient statistical tree; and C4.5, were implemented on 356 patients, of whom 266 were abnormal and 90 normal. The investigation revealed that the DT algorithms have insignificantly different performances regarding the accuracy, sensitivity, specificity, and area under the curve. Each algorithm possesses its characteristic performance. The optimal model is selected according to the performance of blind data testing and compared with traditional screening tools: Osteoporosis Self-Assessment for Asians and the Khon Kaen Osteoporosis Study. The Decision Tree for Postmenopausal Osteoporosis Screening (DTPOS) tool was developed from the best performance of CHAID’s algorithms. The age of 58 years and weight at a cutoff of 57.8 kg were the essential predictors of our tool. DTPOS provides a sensitivity of 92.3% and a positive predictive value of 82.8%, which might be used to rule in subjects at risk of osteopenia and osteoporosis in a community-based screening as it is simple to conduct.

Utility of support vector machine and decision tree to identify the prognosis of metformin poisoning in the United States: analysis of National Poisoning Data System

BACKGROUND

With diabetes incidence growing globally and metformin still being the first-line for its treatment, metformin's toxicity and overdose have been increasing. Hence, its mortality rate is increasing. For the first time, we aimed to study the efficacy of machine learning algorithms in predicting the outcome of metformin poisoning using two well-known classification methods, including support vector machine (SVM) and decision tree (DT).

METHODS

This study is a retrospective cohort study of National Poison Data System (NPDS) data, the largest data repository of poisoning cases in the United States. The SVM and DT algorithms were developed using training and test datasets. We also used precision-recall and ROC curves and Area Under the Curve value (AUC) for model evaluation.

RESULTS

Our model showed that acidosis, hypoglycemia, electrolyte abnormality, hypotension, elevated anion gap, elevated creatinine, tachycardia, and renal failure are the most important determinants in terms of outcome prediction of metformin poisoning. The average negative predictive value for the decision tree and SVM models was 92.30 and 93.30. The AUC of the ROC curve of the decision tree for major, minor, and moderate outcomes was 0.92, 0.92, and 0.89, respectively. While this figure of SVM model for major, minor, and moderate outcomes was 0.98, 0.90, and 0.82, respectively.

CONCLUSIONS

In order to predict the prognosis of metformin poisoning, machine learning algorithms might help clinicians in the management and follow-up of metformin poisoning cases.

Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection

Diabetes mellitus is characterized by elevated blood glucose levels, however patients with diabetes may also develop hypoglycemia due to treatment. There is an increasing demand for non-invasive blood glucose monitoring and trends detection amongst people with diabetes and healthy individuals, especially athletes. Wearable devices and non-invasive sensors for blood glucose monitoring have witnessed considerable advances. This review is an update on recent contributions utilizing novel sensing technologies over the past five years which include electrocardiogram, electromagnetic, bioimpedance, photoplethysmography, and acceleration measures as well as bodily fluid glucose sensors to monitor glucose and trend detection. We also review methods that use machine learning algorithms to predict blood glucose trends, especially for high risk events such as hypoglycemia. Convolutional and recurrent neural networks, support vector machines, and decision trees are examples of such machine learning algorithms. Finally, we address the key limitations and challenges of these studies and provide recommendations for future work.

COVID-19 Phenotypes and Comorbidity: A Data-Driven, Pattern Recognition Approach Using National Representative Data from the United States

The aim of our study was to determine COVID-19 syndromic phenotypes in a data-driven manner using the survey results based on survey results from Carnegie Mellon University’s Delphi Group. Monthly survey results (>1 million responders per month; 320,326 responders with a certain COVID-19 test status and disease duration <30 days were included in this study) were used sequentially in identifying and validating COVID-19 syndromic phenotypes. Logistic Regression-weighted multiple correspondence analysis (LRW-MCA) was used as a preprocessing procedure, in order to weigh and transform symptoms recorded by the survey to eigenspace coordinates, capturing a total variance of >75%. These scores, along with symptom duration, were subsequently used by the Two Step Clustering algorithm to produce symptom clusters. Post-hoc logistic regression models adjusting for age, gender, and comorbidities and confirmatory linear principal components analyses were used to further explore the data. Model creation, based on August’s 66,165 included responders, was subsequently validated in data from March−December 2020. Five validated COVID-19 syndromes were identified in August: 1. Afebrile (0%), Non-Coughing (0%), Oligosymptomatic (ANCOS); 2. Febrile (100%) Multisymptomatic (FMS); 3. Afebrile (0%) Coughing (100%) Oligosymptomatic (ACOS); 4. Oligosymptomatic with additional self-described symptoms (100%; OSDS); 5. Olfaction/Gustatory Impairment Predominant (100%; OGIP). Our findings indicate that the COVID-19 spectrum may be undetectable when applying current disease definitions focusing on respiratory symptoms alone.

Decision tree outcome prediction of acute acetaminophen exposure in the United States: A study of 30,000 cases from the National Poison Data System

Acetaminophen is one of the most commonly used analgesic drugs in the United States. However, the outcomes of acute acetaminophen overdose might be very serious in some cases. Therefore, prediction of the outcomes of acute acetaminophen exposure is crucial. This study is a 6-year retrospective cohort study using National Poison Data System (NPDS) data. A decision tree algorithm was used to determine the risk predictors of acetaminophen exposure. The decision tree model had an accuracy of 0.839, an accuracy of 0.836, a recall of 0.72, a specificity of 0.86 and an F1_score of 0.76 for the test group and an accuracy of 0.848, a recall of 0.85, a recall of 0.74, a specificity of 0.87 and an F1_score of 0.78 for the training group. Our results showed that elevated serum levels of liver enzymes, other liver function test abnormality, anorexia, acidosis, electrolyte abnormality, increased bilirubin, coagulopathy, abdominal pain, coma, increased anion gap, tachycardia and hypotension were the most important factors in determining the outcome of acute acetaminophen exposure. Therefore, the decision tree model is a reliable approach in determining the prognosis of acetaminophen exposure cases and can be used in an emergency room or during hospitalization.

Data mining in clinical big data: the frequently used databases, steps, and methodological models

Many high quality studies have emerged from public databases, such as Surveillance, Epidemiology, and End Results (SEER), National Health and Nutrition Examination Survey (NHANES), The Cancer Genome Atlas (TCGA), and Medical Information Mart for Intensive Care (MIMIC); however, these data are often characterized by a high degree of dimensional heterogeneity, timeliness, scarcity, irregularity, and other characteristics, resulting in the value of these data not being fully utilized. Data-mining technology has been a frontier field in medical research, as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models. Therefore, data mining has unique advantages in clinical big-data research, especially in large-scale medical public databases. This article introduced the main medical public database and described the steps, tasks, and models of data mining in simple language. Additionally, we described data-mining methods along with their practical applications. The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.

Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making

BACKGROUND

Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision making.

METHODS

This systematic literature review was conducted to identify published observational research of employed machine learning to inform decision making at the patient-provider level. The search strategy was implemented and studies meeting eligibility criteria were evaluated by two independent reviewers. Relevant data related to study design, statistical methods and strengths and limitations were identified; study quality was assessed using a modified version of the Luo checklist.

RESULTS

A total of 34 publications from January 2014 to September 2020 were identified and evaluated for this review. There were diverse methods, statistical packages and approaches used across identified studies. The most common methods included decision tree and random forest approaches. Most studies applied internal validation but only two conducted external validation. Most studies utilized one algorithm, and only eight studies applied multiple machine learning algorithms to the data. Seven items on the Luo checklist failed to be met by more than 50% of published studies.

CONCLUSIONS

A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of machine learning methods to inform patient-provider decision making. There is a need to ensure that multiple machine learning approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that decisions for patient care are being made with the highest quality evidence. Future work should routinely employ ensemble methods incorporating multiple machine learning algorithms.

Analysis of risk factors for carotid intima-media thickness in patients with type 2 diabetes mellitus in Western China assessed by logistic regression combined with a decision tree model

BACKGROUND

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in patients with type 2 diabetes (T2DM). Carotid intima-media thickness (CIMT) is considered a preclinical stage of atherosclerosis. Therefore, it is necessary to identify the related risk factors for CIMT to facilitate the early prevention of CVD. Previous studies have shown that visceral fat area (VFA) is a risk factor for T2DM and CVD. However, few studies have focused on the effects of VFA on CIMT associated with T2DM. Moreover, considering that the body fat distribution shows regional and racial heterogeneity, the purpose of this study was to investigate the predictive value of VFA and other risk factors for CIMT associated with T2DM in Western China.

METHODS

In a cross-sectional study, a total of 1372 patients with T2DM were divided into the CIMT (-) group (n = 965) and the CIMT (+) group (n = 407) based on CIMT values. In addition to the univariate analyses, logistic regression analysis and a decision tree model were simultaneously performed to establish a correlation factor model for CIMT.

RESULTS

Univariate analyses showed that sex, smoking status, age, heart rate, systolic blood pressure (SBP), diastolic blood pressure (DBP), height, weight, body mass index (BMI), waist circumference, hip circumference, waist-hip ratio, VFA, subcutaneous fat area, and the levels of 2-h C-peptide, serum creatinine, urea nitrogen and uric acid were significantly different between the two groups (all p < 0.05). Smoking, increased VFA, female sex and increased BMI were risk factors in the logistic regression analyses (OR = 5.759, OR = 1.364, OR = 2.239, OR = 1.186, respectively). In the decision tree model, smoking was the root node, followed by sex, waist circumference, VFA and chronic kidney disease (CKD) in order of importance.

CONCLUSIONS

In addition to smoking, sex and BMI, VFA has a significant effect on CIMT associated with T2DM in the Chinese Han population in Western China. In addition, the decision tree model could help clinicians make more effective decisions, with its simplicity and intuitiveness, making it worth promoting in future medical research.Trial registration ChiCTR, ChiCTR1900027739. Registered 24 November 2019-Retrospectively registered, http://www.chictr.org.cn/index.aspx.

Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview

Health informatics is a vital technology that holds great promise in the healthcare setting. We describe two prominent health informatics tools relevant to emergency care, as well as the historical background and the current state of informatics. We also identify recent research findings and practice changes. The recent advances in machine learning and natural language processing (NLP) are a prominent development in health informatics overall and relevant in emergency medicine (EM). A basic comprehension of machine-learning algorithms is the key to understand the recent usage of artificial intelligence in healthcare. We are using NLP more in clinical use for documentation. NLP has started to be used in research to identify clinically important diseases and conditions. Health informatics has the potential to benefit both healthcare providers and patients. We cover two powerful tools from health informatics for EM clinicians and researchers by describing the previous successes and challenges and conclude with their implications to emergency care.

Diabetes Mellitus: Findings from 20 Years of the Tehran Lipid and Glucose Study

CONTEXT

We summarized findings from Tehran lipid and glucose study (TLGS) about different aspects of type 2 diabetes (T2D) over the span of nearly 2 decades.

EVIDENCE ACQUISITION

A review was undertaken to retrieve papers related to all aspects of T2D from the earliest date available up to January 30, 2018.

RESULTS

An annual crude incidence rate of 10 per 1000 person-years of follow-up was found for T2D in adult participants. Overall incidence rate of pre-diabetes/T2D was 36.3 per 1000 person-years or about 1% each year among youth. Diabetes was associated with increased risk of CVD [hazard ratio (HR): 1.86, 95% confidence interval (95% CI): 1.57 - 2.27] and mortality [HR: 2.56; 95% CI: 2.08 - 3.16] in the total population. Compared with non-diabetic men and women, their diabetic counterparts survived 1.4 and 0.7 years shorter, respectively, during 15 years of follow-up. Wrist circumference, hyperinsulinaemia, 25-hydroxy vitamin D and increase in alanin aminotranferase provided incremental prognostic information beyond the traditional risk factors for incident T2D in adults. Using decision tree algorithms, a number of high risk groups were found for incident T2D. A probability of 84% was found for incidence of T2D among a group of men with fasting plasma glucose (FPG) > 5.3 mmol/L and waist to height ratio (WHtR) > 0.56, and women with FPG > 5.2 mmol/L and WHtR > 0.56.

CONCLUSIONS

Original TLGS studies have contributed greatly to clarify important evidence regarding the epidemiology and risk factors for T2D among Iranian population.

Identifying Risk Factors for Drug Use in an Iranian Treatment Sample: A Prediction Approach Using Decision Trees

INTRODUCTION AND AIM

Substance abuse exacts considerable social and health care burdens throughout the world. The aim of this study was to create a prediction model to better identify risk factors for drug use.

DESIGN AND METHODS

A prospective cross-sectional study was conducted in South Khorasan Province, Iran. Of the total of 678 eligible subjects, 70% (n: 474) were randomly selected to provide a training set for constructing decision tree and multiple logistic regression (MLR) models. The remaining 30% (n: 204) were employed in a holdout sample to test the performance of the decision tree and MLR models. Predictive performance of different models was analyzed by the receiver operating characteristic (ROC) curve using the testing set. Independent variables were selected from demographic characteristics and history of drug use.

RESULTS

For the decision tree model, the sensitivity and specificity for identifying people at risk for drug abuse were 66% and 75%, respectively, while the MLR model was somewhat less effective at 60% and 73%. Key independent variables in the analyses included first substance experience, age at first drug use, age, place of residence, history of cigarette use, and occupational and marital status.

DISCUSSION AND CONCLUSION

While study findings are exploratory and lack generalizability they do suggest that the decision tree model holds promise as an effective classification approach for identifying risk factors for drug use. Convergent with prior research in Western contexts is that age of drug use initiation was a critical factor predicting a substance use disorder.

Application of survival tree analysis for exploration of potential interactions between predictors of incident chronic kidney disease: a 15-year follow-up study

Background

Chronic kidney disease (CKD) is a growing public health challenges worldwide. Various studies have investigated risk factors of incident CKD; however, a very few studies examined interaction between these risk factors. In an attempt to clarify the potential interactions between risk factors of CKD, we performed survival tree analysis.

Methods

A total of 8238 participants (46.1% men) aged > 20 years without CKD at baseline [(1999–2001) and (2002–2005)], were followed until 2014. The first occurrence of CKD, defined as the estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m², was set as the main outcome. Multivariable Cox proportional hazard (Cox PH) regression was used to identify significant independent predictors of CKD; moreover, survival tree analysis was performed to gain further insight into the potential interactions between predictors.

Results

The crude incidence rates of CKD were 20.2 and 35.2 per 1000 person-years in men and women, respectively. The Cox PH identified the main effect of significant predictors of CKD incidence in men and women. In addition, using a limited number of predictors, survival trees identified 12 and 10 subgroups among men and women, respectively, with different survival probability. Accordingly, a group of men with eGFR > 74 ml/min/1.73 m², age ≤ 46 years, low level of physical activity, waist circumference ≤ 100 cm and FPG ≤ 4.7 mmol/l had the lowest risk of CKD incidence; while men with eGFR ≤ 63.4 ml/min/1.73 m², age > 50 years had the highest risk for CKD compared to men in the lowest risk group [hazard ratio (HR), 70.68 (34.57–144.52)]. Also, a group of women aged ≤ 45 years and eGFR > 83.5 ml/min/1.73 m² had the lowest risk; while women with age > 48 years and eGFR ≤ 69 ml/min/1.73 m² had the highest risk compared to low risk group [HR 27.25 (19.88–37.34)].

Conclusion

In this post hoc analysis, we found the independent predictors of CKD using Cox PH; furthermore, by applying survival tree analysis we identified several numbers of homogeneous subgroups with different risk for incidence of CKD. Our study suggests that two methods can be used simultaneously to provide new insights for intervention programs and improve clinical decision making.

A new look at risk patterns related to coronary heart disease incidence using survival tree analysis: 12 Years Longitudinal Study

We identified risk patterns associated with incident coronary heart disease (CHD) using survival tree, and compared performance of survival tree versus Cox proportional hazards (Cox PH) in a cohort of Iranian adults. Data on 8,279 participants (3,741 men) aged ≥30 yr were used to analysis. Survival trees identified seven subgroups with different risk patterns using four [(age, non-HDL-C, fasting plasma glucose (FPG) and family history of diabetes] and five [(age, systolic blood pressure (SBP), non-HDL-C, FPG and family history of CVD] predictors in women and men, respectively. Additional risk factors were identified by Cox models which included: family history of CVD and waist circumference (in both genders); hip circumference, former smoking and using aspirin among men; diastolic blood pressure and lipid lowering drug among women. Survival trees and multivariate Cox models yielded comparable performance, as measured by integrated Brier score (IBS) and Harrell’s C-index on validation datasets; however, survival trees produced more parsimonious models with a minimum number of well recognized risk factors of CHD incidence, and identified important interactions between these factors which have important implications for intervention programs and improve clinical decision making.