Utilizing shared frailty with the Cox proportional hazards regression: Post discharge survival analysis of CHF patients.

Understanding patients' survival probability as well as the factors affecting it constitute a significant concern for researchers and practitioners, in particular for patients with severe chronic illnesses such as congestive heart failure (CHF). CHF is a clinical syndrome characterized by comorbidities and adverse medical events. Risk stratification to identify patients most likely to die shortly after hospital discharge can improve the quality of care by better allocating organizational resources and personalized interventions. Probability assessment improves clinical decision-making, contributes to personalized care, and saves costs. Although one of the most informative indices is the time to an adverse event for each patient, commonly analyzed using survival analysis methods, these are often challenging to implement due to the complexity of the medical data. Numerous studies have used the Cox proportional hazards (PH) regression method to generate the survival distribution pattern and factors affecting survival. This model, although advantageous for survival analysis, assumes the homogeneity of the hazard ratio across patients and independence of the observations in terms of survival time. These assumptions are often violated in real-world data, especially when the dataset is composed of readmission data for chronically ill patients, since these recurring observations are inherently dependent. This study ran the Cox PH regression on a feature set selected by machine learning algorithms from a rich hospital dataset. The event modeled here was patient mortality within 90 days post-hospital discharge. The sample was composed of medical records of patients hospitalized in the Israeli Sheba Medical Center more than once, with CHF as the primary diagnosis. We modeled the survival of CHF patients using the Cox PH regression with and without the shared frailty correction that addresses the shortcomings of the Cox Model. The results of the two models of the Cox PH regression - with and without the shared frailty correction were compared. The results demonstrate that the shared frailty correction, which was statistically significant in our analysis, improved the performance of the basic Cox PH model. While this is the main contribution, we also show that this model outperforms two commonly used measures (ADHERE and EFFECT) for predicting early mortality of CHF patients. Thus, the results illustrate how applying advanced analytics can outperform traditional methods. An additional contribution is the feature set selected using machine-learning methods that is different from those used in the extant literature.

Return visits to the emergency department: An analysis using group based curve models.

Stratification modeling in health services is useful to identify differential patient risk groups, or latent classes. Given the frequency and costs, repeated emergency department (ED) may be an appropriate candidate for risk stratification modeling. We applied a method called group-based trajectory modeling (GBTM) to a sample of 37,416 patients who visited an urban, safety-net ED between 2006 and 2016. Patients had up to 10 ED visits during the study period. Data sources included the hospital's electronic health record (EHR), the state-wide health information exchange system, and area-level social determinants of health factors. Results revealed three distinct trajectory groups. Trajectories with a higher risk of revisit were marked by more patients with behavioral diagnoses, injuries, alcohol & substance abuse, stroke, diabetes, and other factors. The application of advanced computational techniques, like GBTM, provides opportunities for health care organizations to better understand the underlying risks of their large patient populations. Identifying those patients who are likely to be members of high-risk trajectories allows healthcare organizations to stratify patients by level of risk and develop early targeted interventions.

Measuring the cost-effectiveness of using telehealth for diabetes management: A narrative review of methods and findings.

INTRODUCTION: Diabetes is a chronic metabolic disease characterized by high levels of blood glucose, which can lead over time to severe impairment to the heart, blood vessels, eyes, kidneys, nerves and premature death. Diabetes is prone to complications such as kidney failure, vision loss and nerve damage. The total assessed cost of diagnosed diabetes is growing rapidly; hence, harnessing telehealth for diabetes management may be cost-effective. A few previous publications have pointed to the effectiveness of telehealth but more numerous articles indicate that the results are inconsistent and economic models are lacking. This narrative review surveys the recent literature on the implementation of telehealth for diabetes management that incorporates cost-effectiveness analyses. MATERIALS AND METHODS: This paper follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [25]. RESULTS: The vast majority of articles dealing with managing Type 2 diabetes have primarily used the telephone for telehealth monitoring (followed by teleophalmology and telemonitoring). Most publications report that the telehealth solution was cost effective. The leading cost-effectiveness method was the Markov model; however, only a small number of papers extend the Markov model to critical sensitivity analyses of their outcomes. The main goal of telehealth in general is diabetes management or monitoring, followed by ophthalmology, depression management, weight loss and other goals. CONCLUSION: This work summarizes the literature on recent trends in telehealth options, and analyzes successes and failures in relation to both effectiveness and costs, which may be valuable to both scholars and practitioners.

Stratifying individuals into non-alcoholic fatty liver disease risk levels using time series machine learning models.

Non-alcoholic fatty liver disease (NAFLD) affects 25% of the population worldwide, and its prevalence is anticipated to increase globally. While most NAFLD patients are asymptomatic, NAFLD may progress to fibrosis, cirrhosis, cardiovascular disease, and diabetes. Research reports, with daunting results, show the challenge that NAFLD's burden causes to global population health. The current process for identifying fibrosis risk levels is inefficient, expensive, does not cover all potential populations, and does not identify the risk in time. Instead of invasive liver biopsies, we implemented a non-invasive fibrosis assessment process calculated from clinical data (accessed via EMRs/EHRs). We stratified patients' risks for fibrosis from 2007 to 2017 by modeling the risk in 5579 individuals. The process involved time-series machine learning models (Hidden Markov Models and Group-Based Trajectory Models) profiled fibrosis risk by modeling patients' latent medical status resulted in three groups. The high-risk group had abnormal lab test values and a higher prevalence of chronic conditions. This study can help overcome the inefficient, traditional process of detecting fibrosis via biopsies (that are also medically unfeasible due to their invasive nature, the medical resources involved, and costs) at early stages. Thus longitudinal risk assessment may be used to make population-specific medical recommendations targeting early detection of high risk patients, to avoid the development of fibrosis disease and its complications as well as decrease healthcare costs.

Body Mass Index and Caries: Machine Learning and Statistical Analytics of the Dental, Oral, Medical Epidemiological (DOME) Nationwide Big Data Study.

The objectives of the research were to analyze the association between Body Mass Index (BMI) and dental caries using novel approaches of both statistical and machine learning (ML) models while adjusting for cardiovascular risk factors and metabolic syndrome (MetS) components, consequences, and related conditions. This research is a data-driven analysis of the Dental, Oral, Medical Epidemiological (DOME) big data repository, that integrates comprehensive socio-demographic, medical, and dental databases of a nationwide sample of dental attendees to military dental clinics for 1 year aged 18−50 years. Obesity categories were defined according to the World Health Organization (WHO): under-weight: BMI < 18.5 kg/m2, normal weight: BMI 18.5 to 24.9 kg/m2, overweight: BMI 25 to 29.9 kg/m2, and obesity: BMI ≥ 30 kg/m2. General linear models were used with the mean number of decayed teeth as the dependent variable across BMI categories, adjusted for (1) socio-demographics, (2) health-related habits, and (3) each of the diseases comprising the MetS definition MetS and long-term sequelae as well as associated illnesses, such as hypertension, diabetes, hyperlipidemia, cardiovascular disease, obstructive sleep apnea (OSA) and non-alcoholic fatty liver disease (NAFLD). After the statistical analysis, we run the XGBoost machine learning algorithm on the same set of clinical features to explore the features' importance according to the dichotomous target variable of decayed teeth as well as the obesity category. The study included 66,790 subjects with a mean age of 22.8 ± 7.1. The mean BMI score was 24.2 ± 4.3 kg/m2. The distribution of BMI categories: underweight (3113 subjects, 4.7%), normal weight (38,924 subjects, 59.2%), overweight (16,966, 25.8%), and obesity (6736, 10.2%). Compared to normal weight (2.02 ± 2.79), the number of decayed teeth was statistically significantly higher in subjects with obesity [2.40 ± 3.00; OR = 1.46 (1.35−1.57)], underweight [2.36 ± 3.04; OR = 1.40 (1.26−1.56)] and overweight [2.08 ± 2.76, OR = 1.05 (1.01−1.11)]. Following adjustment, the associations persisted for obesity [OR = 1.56 (1.39−1.76)] and underweight [OR = 1.29 (1.16−1.45)], but not for overweight [OR = 1.11 (1.05−1.17)]. Features important according to the XGBoost model were socioeconomic status, teeth brushing, birth country, and sweetened beverage consumption, which are well-known risk factors of caries. Among those variables was also our main theory independent variable: BMI categories. We also performed clinical features importance based on XGBoost with obesity set as the target variable and received an AUC of 0.702, and accuracy of 0.896, which are considered excellent discrimination, and the major features that are increasing the risk of obesity there were: hypertension, NAFLD, SES, smoking, teeth brushing, age as well as our main theory dependent variable: caries as a dichotomized variable (Yes/no). The study demonstrates a positive association between underweight and obesity BMI categories and caries, independent of the socio-demographic, health-related practices, and other systemic conditions related to MetS that were studied. Better allocation of resources is recommended, focusing on populations underweight and obese in need of dental care.

Non-alcoholic Fatty Liver and Liver Fibrosis Predictive Analytics: Risk Prediction and Machine Learning Techniques for Improved Preventive Medicine.

Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease worldwide, with a prevalence of 20%-30% in the general population. NAFLD is associated with increased risk of cardiovascular disease and may progress to cirrhosis with time. The purpose of this study was to predict the risks associated with NAFLD and advanced fibrosis on the Fatty Liver Index (FLI) and the 'NAFLD fibrosis 4' calculator (FIB-4), to enable physicians to make more optimal preventive medical decisions. A prospective cohort of apparently healthy volunteers from the Tel Aviv Medical Center Inflammation Survey (TAMCIS), admitted for their routine annual health check-up. Data from the TAMCIS database were subjected to machine learning classification models to predict individual risk after extensive data preparation that included the computation of independent variables over several time points. After incorporating the time covariates and other key variables, this technique outperformed the predictive power of current popular methods (an improvement in AUC above 0.82). New powerful factors were identified during the predictive process. The findings can be used for risk stratification and in planning future preventive strategies based on lifestyle modifications and medical treatment to reduce the disease burden. Interventions to prevent chronic disease can substantially reduce medical complications and the costs of the disease. The findings highlight the value of predictive analytic tools in health care environments. NAFLD constitutes a growing burden on the health system; thus, identification of the factors related to its incidence can make a strong contribution to preventive medicine.

The patient-physician relationship: an account of the physician's perspective.

BACKGROUND: The issue of patient-physician relationships in general, and particularly the trust of patients in their primary care physician has gained much interest in academia and with practitioners in recent years. Most research on this important topic, however, focused on how patients view the relationship and not how the physicians see it. This research strives to bridge this gap, with the resolution of leading to an improved appreciation of this multifaceted relationship. METHODS: A survey of 328 actively practicing physicians from all four health maintenance organizations (HMOs) in Israel resulted in a hierarchical formation of components, indicating both the relative as well as absolute importance of each component in the formation of the patient-physician relationship. The sample conducted was a convenience one. Methodologically, we used two different complementary methods of analysis, with the primary emphasis on the Analytic Hierarchical Processing (AHP), a unique and advanced statistical method. RESULTS: The results provide a detailed picture of physicians' attitudes toward the patient-physician relationship. Research indicates that physicians tend to consider the relationship with the patient in a rather pragmatic manner. To date, this attitude was mostly referred to intuitively, without the required rigorous investigation provided by this paper. Specifically, the results indicate that physicians tend to consider the relationship with the patient in a rather pragmatic manner. Namely, while fairness, reliability, devotion, and serviceability received high scores from physicians, social interaction, friendship, familial, as well as appreciation received the lowest scores, indicating low priority for warmth and sociability in the trust relationship from the physician's perspective. The results showed good consistency between the AHP results and the ANOVA comparable analyses. CONCLUSIONS: In contrast to patients who traditionally stress the importance of interpersonal skills, physicians stress the significance of the technical expertise and knowledge of health providers, emphasizing the role of competence and performance. Physicians evaluate the relationship on the basis of their ability to solve problems through devotion, serviceability, reliability, and trustworthiness and disregard the "softer" interpersonal aspects such as caring, appreciation, and empathy that have been found to be important to their patients. This illustrates a mismatch in the important components of relationship building that can lead to a loss of trust, satisfaction, and repeat purchase. POLICY IMPLICATIONS: We study the impact physicians' incentives have on the tangible relationship and discuss the significance of physician-patient relationship on satisfaction with the health service given. As a result policies leading to a more dynamic role must be given to the patient, who being well informed by the physician, can help in the decision making process. Policy schemes need to be implemented as a way of changing physicians' behavior, forcing them to better construct and utilize this dyadic relationship.

Fear of Hospital-Acquired Infections: The Combined Impact of Patient's Hygiene Sensitivity and Perceived Staff Preventive Behavior.

Over the years, the public has paid growing attention to hospital-acquired infections (HAIs). Currently, infection prevention and control are considered a number one national priority in leading developed countries. However, while some hospital visitors are knowledgeable of the topic, others may be ignorant or careless as regards sterility and hygiene-related matters. This study, conducted in Israel, compared people cognizant of hygiene-related issues to those who are less so, in an attempt to account for differences in terms of attitudes and perceptions regarding the hospital environment. Based on Endsley's (in: Proceedings of the IEEE 1988 national aerospace and electronics conference, IEEE, 1988, 1995) situation awareness concept, we hypothesized that people attending the hospital with different hygiene schema would react differently when faced with HAI-related triggers. Based on a survey of 208 respondents, the results support the hypotheses, and showed a significant moderating effect of hygiene-sensitivity on the relationship between the staffs' hospital acquired infection-related proactive behavior and avoidance tendencies among hospital visitors. Theoretical as well as practical recommendations are discussed.

Exploring trajectories of emergency department visits using a laboratory-based indicator of serious illness.

Repeated emergency department visits have become a serious challenge worldwide. Despite prior research indicating that laboratory results may provide early alerts about such patients on their upcoming adverse events, few studies have examined their role as a critical indicator of the stability of a patient's medical condition over time. We model and analyze the developmental trajectories of patients' creatinine levels, a key laboratory marker of serious illness, as a potential risk stratification mechanism across many emergency department visits. We apply group-based statistical methodology to electronic health record data of 70,385 patients, with 3-15 emergency department visits, to identify and profile these trajectories for the entire population, for males and for females. Results reveal three distinct creatinine-based trajectory groups over time with significantly differing characteristics that may enable targeted interventions for each group. Future research will incorporate additional disease markers to identify longitudinal factors leading to repeated emergency department visits.

The use of analytic hierarchy process for measuring the complexity of medical diagnosis.

Diagnostic complexity is an important contextual factor affecting a variety of medical outcomes. Existing measurements of diagnosis complexity either rely on crude proxies or use fine-grained measures that employ indicators from proprietary data that are not readily available. Hence, the study of this important construct in fields such as medical informatics has been hampered by the difficulty of measuring diagnostic complexity. This article presents a novel approach for conceptualizing and operationalizing diagnostic task complexity as a multi-dimensional construct, which employs the readily available International Classification of Diseases codes from medical encounters in hospitals and uses Analytic Hierarchical Process methodology. We demonstrate the reliability of the proposed approach and show that despite using a relatively simple procedure, it is able to predict readmission rates just as well as (or even better) than some of the sophisticated measures that have been used in recent studies (namely, the LaCE score index).

A case study of applying text analysis to identify possible adverse drug interactions: The case of Adalat (Nifedipine).

Adalat (Nifedipine) is a calcium-channel blocker that is also used as an antihypertensive drug. The drug was approved by the US Food and Drug Administration in 1985 but was discontinued in 1996 on account, among other things, of interactions with other medications. Nonetheless, Adalat is still used in other countries to treat congestive heart failure. We examine all the congestive heart failure electronic health records of the largest medical center in Israel to discover whether, possibly, taking Adalat with other medications is associated with patient death. This study examines a semantic space built by running latent semantic analysis on the entire corpus of congestive heart failure electronic health records of that medical center, encompassing 8 years of data on almost 12,000 patients. Through this semantic space, the most highly correlated medications and medical conditions that co-occurred with Adalat were identified. This was done separately for men and women. The results show that Adalat is correlated with different medications and conditions across genders. The data also suggest that taking Adalat with Captopril (angiotensin-converting enzyme inhibitor) or Rulid (antibiotic) might be dangerous in both genders. The study thus demonstrates the potential of applying latent semantic analysis to identify potentially dangerous drug interactions that may have otherwise gone under the radar.

Data mining techniques utilizing latent class models to evaluate emergency department revisits.

BACKGROUND: The use of machine learning techniques is especially pertinent to the composite and challenging conditions of emergency departments (EDs). Repeat ED visits (i.e. revisits) are an example of potentially inappropriate utilization of resources that can be forecasted by these techniques. OBJECTIVE: To track the ED revisit risk over time using the hidden Markov model (HMM) as a major latent class model. Given the HMM states, we carried out forecasting of future ED revisits with various data mining models. METHODS: Information integrated from four distributed sources (e.g. electronic health records and health information exchange) was integrated into four HMMs which capture the relationships between an observed and a hidden progression that shift over time through a series of hidden states in an adult patient population. RESULTS: Assimilating a pre-analysis of the various patients by applying latent class models and directing them to well-known classifiers functioned well. The performance was significantly better than without utilizing pre-analysis of HMM for all prediction models (classifiers(. CONCLUSIONS: These findings suggest that one prospective approach to advanced risk prediction is to leverage the longitudinal nature of health care data by exploiting patients' between state variation.

Prediction of emergency department revisits using area-level social determinants of health measures and health information exchange information.

INTRODUCTION: Interoperable health information technologies, like electronic health records (EHR) and health information exchange (HIE), provide greater access to patient information from across multiple organizations. Also, an increasing number of public data sources exist to describe social determinant of health factors. These data may help better inform risk prediction models, but the relative importance or value of these data has not been established. This study assessed the performance of different classes of information individually, and in combination, in predicting emergency department (ED) revisits. METHODS: In a sample of 279,611 adult ED encounters. We compared the performance of Two-Class Boosted Decision Trees machine learning algorithm using 5 classes of information: 1) social determinants of health measures only, 2) current visit EHR information only, 3) current and historical EHR information, 4) HIE information only, and 5) all available information combined. RESULTS: The social determinants of health measure only model had the overall worst performance with an area under the curve AUC of 0.61. The model using all information classes together had the best performance (AUC = 0.732). The model using HIE information only performed better than all other single information class models. CONCLUSIONS: Broad information sources, which are reflective of patients' reliance on multiple organizations for care, better support risk prediction modeling in the emergency department.

Visual Analytics for Congestive Heart Failure Mortality Prediction.

Several indices exist to classify Congestive Heart Failure (CHF) patients' propensity for early mortality; however, they are primarily based on limited data and are not intuitive to use at the point of care. We investigate a novel, data-driven, risk assessment and visualization approach to investigate mortality prediction of CHF patients using data retrieved from an intensively digitized hospital's data repository. Combining well-known, computationally efficient, dimensionality reduction (DR) methods with 2-d information visualization, the method classifies and visualizes CHF patients into high and low risk groups, contextualized by the factors driving their classification. The DR method performed similar to logistic regression (LR), but visualized the classification and its significant factors at the population level, individual level and the potential impact of interventions for an individual patient. These are encouraging results in favor of the proposed visualization approach, and contributes to the current focus on advancing patient care via large-scale visual analytics.

Low serum vitamin D is independently associated with unexplained elevated ALT only among non-obese men in the general population.

INTRODUCTION AND OBJECTIVES: There are inconsistent findings on the association between human non-alcoholic fatty liver disease (NAFLD) and vitamin D, perhaps due to insufficient specificity for gender and obesity status. We aimed to assess whether serum levels of 25-hydroxyvitamin D are associated with unexplained elevated alanine aminotransferase (ALT) in general population across gender and body mass index (BMI) levels. MATERIALS AND METHODS: A cross-sectional analysis of a population-based cohort with a nationwide-distribution using electronic medical database. The population consisted of individuals aged 20-60 years who underwent blood tests for ALT and vitamin D. RESULTS: A total of 82,553 subjects were included (32.5% men, mean age 43.91±10.15 years). The prevalence of elevated ALT was higher among men and women with vitamin D insufficiency or deficiency, but in multivariate analysis, adjusting for: age, BMI, serum levels of glucose, total cholesterol, triglycerides, statin use and season, only the association among men remained significant for the vitamin D deficiency category (OR=1.16, 95%CI 1.04-1.29, P=0.010). Stratification by BMI revealed that only among normal weight and overweight men vitamin D deficiency was associated with elevated ALT (OR=1.27, 95%CI 1.01-1.59, P=0.041 and OR=1.27, 95%CI 1.08-1.50, P=0.003, respectively). No independent association was shown among women at all BMI categories. CONCLUSIONS: In a "real-life" general population, the association between vitamin D deficiency and unexplained elevated ALT is specific for non-obese men. The clinical significance of vitamin D for human NAFLD should be further elucidated with attention for a modifying effect of gender and adiposity.

Personal health records, global policy and regulation review.

Personal health records (PHR) have been endorsed as a promising tool for the self-management of an individual's medical information, affording benefits to both the individual patient and the healthcare system as a whole. Nevertheless, adoption rates have been relatively slow and widespread acceptance has yet to be achieved. A significant obstacle often cited as delaying the implementation of these systems has been concern regarding the ability to properly ensure the security and privacy of this sensitive information. This article reviews the current legislative landscape in various countries, examining the degree to which they address these issues and support the implementation of PHR's. This review compares in particular a number of prominent components of health data security and privacy across five different legislative jurisdictions in order to allow for a closer examination of regulatory approaches and measures. Of the legislation reviewed the EU's GDPR stands out as seemingly providing the most comprehensive and stringent protection measures, yet nonetheless appears to leave significant room for interpretation and a degree of ambiguity in key areas. The results of this comparison, demonstrate considerable variances with regards to legal terminology and the degree of compliance required from entities offering PHR services across various jurisdictions. The paper ends with a discussion of specific policy implications and recommendations stemming from the current legislative state of affairs.

Analysing repeated hospital readmissions using data mining techniques.

Few studies have examined how to identify future readmission of patients with a large number of repeat emergency department (ED) visits. We explore 30-day readmission risk prediction using Microsoft's AZURE machine learning software and compare five classification methods: Logistic Regression, Boosted Decision Trees (BDTs), Support Vector Machine (SVM), Bayes Point Machine (BPM), and Two-Class Neural Network (TCNN). We predict the last readmission visit of frequent ED patients extracted from the electronic health records of their 8455 penultimate visits. The methods show differential improvement, with the BDT indicating marginally better AUC (area under the ROC curve) than logistic regression and BPM, followed by the TCNN and SVM. A comparison of BDT and Logistic Regression results for correct and incorrect classification highlights the similarities and differences in the significant predictors identified by each method. Future research may incorporate time-varying covariates to identify other longitudinal factors that can lead to readmission risk reduction.

Analysing repeated hospital readmissions using data mining techniques.

Few studies have examined how to identify future readmission of patients with a large number of repeat emergency department (ED) visits. We explore 30-day readmission risk prediction using Microsoft's AZURE machine learning software and compare five classification methods: Logistic Regression, Boosted Decision Trees (BDTs), Support Vector Machine (SVM), Bayes Point Machine (BPM), and Two-Class Neural Network (TCNN). We predict the last readmission visit of frequent ED patients extracted from the electronic health records of their 8455 penultimate visits. The methods show differential improvement, with the BDT indicating marginally better AUC (area under the ROC curve) than logistic regression and BPM, followed by the TCNN and SVM. A comparison of BDT and Logistic Regression results for correct and incorrect classification highlights the similarities and differences in the significant predictors identified by each method. Future research may incorporate time-varying covariates to identify other longitudinal factors that can lead to readmission risk reduction.

Retrospective study of the associations between hepatitis C virus infection and metabolic factors.

AIM: To evaluate the bidirectional association between metabolic syndrome (MS) components and antiviral treatment response for chronic hepatitis C virus (HCV) infection. METHODS: This retrospective cohort study included 119 HCV + patients treated with pegylated-interferon-α and ribavirin. Metabolic characteristics and laboratory data were collected from medical records. Differences in baseline clinical and demographic risk factors between responders and non-responders were assessed using independent samples t-tests or χ2 tests. The effects of sustained viral response (SVR) to antiviral treatment on de novo impairments in MS components, including impaired fasting glucose (IFG) and type 2 diabetes mellitus (T2DM), were assessed using univariable and multivariable logistic regression analysis, while the effect of MS components on SVR was assessed using univariable logistic regression analysis. RESULTS: Of the 119 patients, 80 (67%) developed SVR over the average 54 ± 13 mo follow-up. The cumulative risks for de novo T2DM and IFG were 5.07- (95%CI: 1.261-20.4, P = 0.022) and 3.87-fold higher (95%CI: 1.484-10.15, P = 0.006), respectively for non-responders than responders, when adjusted for the baseline risk factors age, sex, HCV genotype, high viral load, and steatosis. Post-treatment triglyceride levels were significantly lower in non-responders than in responders (OR = 0.27; 95%CI: 0.069-0.962, P = 0.044). Age and HCV genotype 3 were significantly different between responders and non-responders, and MS components were not significantly associated with SVR. Steatosis tended to attenuate SVR (OR = 0.596; 95%CI: 0.331-1.073, P = 0.08). CONCLUSION: SVR was associated with lower de novo T2DM and IFG incidence and higher triglyceride levels. Patients infected with HCV should undergo T2DM screening and antidiabetic treatment.