Identifying Risk Indicators of Cardiovascular Disease in Fasa Cohort Study (FACS): An Application of Generalized Linear Mixed-Model Tree.

BACKGROUND: Today, cardiovascular disease (CVD) is the most important cause of death around the world. In this study, our main aim was to predict CVD using some of the most important indicators of this disease and present a tree-based statistical framework for detecting CVD patients according to these indicators. METHODS: We used data from the baseline phase of the Fasa Cohort Study (FACS). The outcome variable was the presence of CVD. The ordinary Tree and generalized linear mixed models (GLMM) were fitted to the data and their predictive power for detecting CVD was compared with the obtained results from the GLMM tree. Statistical analysis was performed using the RStudio software. RESULTS: Data of 9499 participants aged 35‒70 years were analyzed. The results of the multivariable mixed-effects logistic regression model revealed that participants' age, total cholesterol, marital status, smoking status, glucose, history of cardiac disease or myocardial infarction (MI) in first- and second-degree relatives, and presence of other diseases (like hypertension, depression, chronic headaches, and thyroid disease) were significantly related to the presence of CVD (P<0.05). Fitting the ordinary tree, GLMM, and GLMM tree resulted in area under the curve (AUC) values of 0.58 (0.56, 0.61), 0.81 (0.77, 0.84), and 0.80 (0.76, 0.83), respectively, among the study population. In addition, the tree model had the best specificity at 81% but the lowest sensitivity at 65% compared to the other models. CONCLUSION: Given the superior performance of the GLMM tree compared with the standard tree and the lack of significant difference with the GLMM, using this model is suggested due to its simpler interpretation and fewer assumptions. Using updated statistical models for more accurate CVD prediction can result in more precise frameworks to aid in proactive patient detection planning.

Six-Decade Trend Analysis of Life Expectancy at Birth in Asia, Regions of Asia and Iran: A Joinpoint Analysis.

Background: Life expectancy is one of the key indicators for investigating the overall health status of a population. Thus, analyzing the trend of this demographic measure is of great importance for planning health and social services in different societies. In this study, we aimed to model the trends of life expectancy in Asia, regions of Asia, and Iran over the past six decades. Methods: The annual life expectancy at birth data sets were extracted for Iran and the total Asia population between 1960 and 2020 from the database provided by the Our World in Data website. The trend analysis was performed using the joinpoint regression model. Results: During the study period, Iranians and Asians have, respectively, experienced about 32 and 28.6 years increase in life expectancy. The results from joinpoint regression showed that the average annual percent change (AAPC) of life expectancy was positive for all regions of Asia, and ranged from 0.4% for Central Asia to 0.9% for Southern Asia. In addition, the estimated AAPC in Iranian people was about 0.1 higher than the total Asian population (0.9% vs. 0.8%). Conclusion: Despite protracted wars, poverty, and social inequality in some parts of Asia, life expectancy has drastically increased in this continent over the last decades. However, life expectancy in Asia (and Iran) is still remarkably lower than in more developed parts of the world. To elevate life expectancy to a higher level, the policymakers in Asian countries should put more effort into improving the standards of living and access to health facilities in their societies.

Assessing sex differential in COVID-19 mortality rate by age and polymerase chain reaction test results: an Iranian multi-center study.

BACKGROUND: The aim of this study is to evaluate the sex differential effect in the COVID-19 mortality by different age groups and polymerase chain reaction (PCR) test results. RESEARCH DESIGN: In a multicenter cross-sectional study from 55 hospitals in Tehran, Iran, patients were categorized as positive, negative, and suspected cases. RESULTS: A total of 25,481 cases (14,791 males) were included in the study with a mortality rate of 12.0%. The mortality rates in positive, negative, and suspected cases were 20.55%, 9.97%, and 7.31%, respectively. Using a Cox regression model, sex had a significant effect on the hazard of death due to COVID-19 in adult and senior male patients having positive and suspected PCR test results. However, sex was not found as significant factor for mortality in patients with a negative PCR test in different age groups. CONCLUSIONS: Regardless of other risk factors, we found that the effect of sex on COVID-19 mortality varied significantly in different age groups. Therefore, appropriate strategies should be designed to protect adult and senior males from this deadly infectious disease. Furthermore, owing to the considerable death rate of COVID-19 patients with negative test results, new policies should be launched to increase the accuracy of diagnosis tests.

Trend analysis of disability adjusted life years due to cardiovascular diseases: results from the global burden of disease study 2019.

BACKGROUND: Cardiovascular diseases (CVDs) are the number one cause of global mortality representing about one third of all deaths across the world. The objective of the present study was to model the global trend in disability-adjusted life years (DALY) and its components due to CVD over the past three decades. We also aimed to evaluate the longitudinal relationship between CVD DALY and Human Development Index (HDI) in this period of time. METHODS: The age-standardized rates of years lost due to disability (YLD), years of life lost (YLL) and DALY were extracted for cardiovascular diseases from the Global Burden of Disease (GBD) Study 2019 in years 1990 to 2019. Additionally, the United Nations Development Programme (UNDP) database was used to retrieve HDI values for all world countries at the same period time. The trend analysis was performed using the joinpoint regression model. RESULTS: The obtained revealed a significant downward trend for DALY and its components with the average annual percent change of - 1.0, - 0.3 and - 1.1 per 100,000 population, respectively for DALY, YLD and YLL. We also found that countries with high/very high levels of HDI have remarkably experienced steeper declining slope of trend than those in lower levels of HDI over the study period. CONCLUSIONS: Although the observed decreasing trend of CVD burden is a hopeful message for all world countries, the considerable gap in slope of trend between richer and poorer parts of the world is a serious alarm for health policy makers. Regarding this, there is an urgent need to put more efforts on implementing preventive programs, improving the level of patients' care and providing efficient treatment, especially in regions with lower levels of HDI.

Assessment of prognostic factors in long-term survival of male and female patients with colorectal cancer using non-mixture cure model based on the Weibull distribution.

OBJECTIVE: Colorectal cancer (CRC) is known as one of the malignant form of cells growing in the inner lining of colon and rectum which could seriously affect the cure rate of patients. We aimed to evaluate the effect of prognostic factors on cure fraction of CRC patients. METHODS: A total of 1043 CRC patients were included to the study from December 2001 to January 2007 at the Research Center of Gastroenterology and Liver Disease in Shahid Beheshti University of Medical Sciences, Tehran, Iran. Patients' information was extracted from their medical records, then they were followed to identify their death status via phone-call. Weibull non-mixture cure model was used to evaluate the effect of the risk factors on cure fraction of CRC patients. RESULTS: The five-years survival rate was 0.66 (males: 0.64 and female: 0.69). The median survival time for non-cured CRC patients were 3.45 years (males: 3.46; females = 3.45 years). In the single Weibull model, BMI≥30 (OR = 4.61, p-value = 0.033), poorly differentiated tumor grade (OR = 0.36, p-value = 0.036), tumor size≥25 mm (OR = 0.22, p-value = 0.046), and N1-stage (OR = 0.42, p-value = 0.005) had significant effect on females' cure fraction. Also, cure fraction of male CRC patients significantly affected by BMI (levels:25.0-29.9-OR = 12.13-p-value<0.001; ≥30-OR = 7.00-p-value = 0.017), T1-stage (OR = 0.52, p-value = 0.021), M1-stage (OR = 0.45, p-value = 0.007), IV-staging (OR = 0.36, p-value = 0.041) and IBD (OR = 0.26, p-value = 0.017). In multiple Weibull model, females were associated with tumor size≥25 mm (OR = 0.20, p-value = 0.044) and N1-stage (OR = 0.45, p-value = 0.013) and males were affected by M1-stage (OR = 0.41, p-value = 0.011) and IBD (OR = 0.20, p-value = 0.022).The cure fraction of males and females CRC patients was 64% and 69%, respectively. CONCLUSIONS: The prognostic factors for cure fraction of patients with CRC may be different among males and females. Further multicenter studies are required to assess the effect of common prognostic factors between males and females.

The Predictive Value of Serum Cytokines for Distinguishing Celiac Disease from Non-Celiac Gluten Sensitivity and Healthy Subjects

Background: It has been established that the level of some inflammatory cytokines increases in celiac disease (CD) and non-celiac gluten sensitivity (NCGS) in comparison with healthy subjects. Therefore, the primary interest in our research was proposing an accurate tool to diagnose patients with CD and NCGS from healthy individuals in an Iranian population. Methods: The serum samples were examined in 171 participants, including 110 CD patients, 46 healthy individuals, and 15 NCGS. The commercial ELISA kits were used to detect the level of the following cytokines: IL-1, IL-6, IL-8, IL-15, and IFN-γ. The receiver operating characteristic (ROC) curve analysis was applied to determine the optimal thresholds for high sensitivity, specificity, positive and negative predictive values of cytokines, as the indicators of CD, NCGS, and healthy control groups. Results: In NCGS group, the values of area under the ROC curve for IL-1, IL-8, and IFN-γ were 71%, 78%, and 70%, respectively. To differentiate the CD and NCGS groups from the control group, IL-15 had the highest sensitivity (82.70%), specificity (56.50%), positive predictive value (81.98%), and negative predictive value (57.78%), followed by IL-8 with the highest sensitivity of 74.50%, specificity of 73.30%, and positive and negative predictive values of 95.35% and 30.21%, respectively. Conclusion: The obtained results demonstrate that IL-15 and IL-8 could be proposed as potential markers in their optimal cut-off points for distinguishing CD from the NCGS and the healthy control. Based on our findings, the evaluation of cytokine levels can be recommended as a useful tool for the diagnosis of CD and NCGS in a clinical practice.

Evaluation of Neutrophil Gelatinase-Associated Lipocalin and Cystatin C in Early Diagnosis of Chronic Kidney Disease in the Absence of the Gold Standard.

BACKGROUND: Glomerular filtration rate (GFR) is considered as a gold standard of kidney function. However, using GFR as the gold standard is not common in clinical practice, because its direct measurement is usually expensive, cumbersome, and invasive. In the present study, we assessed the predictive power of two other biomarkers, Cystatin-C (Cys-C) and Neutrophil Gelatinase-Associated Lipocalin (NGAL) for early detection of chronic kidney diseases (CKD) in the absence of a gold standard. MATERIALS AND METHODS: In this study, 72 patients who referred to the Shohadaye Tajrish Hospital of Tehran, Iran, for measuring their kidney function were studied. The ELISA method was utilized for measuring plasma NGAL (PNGAL) and serum Cys-C (SCys-C). The Bayesian latent class modeling approach was applied to asses the predictive power of these biomarkers. RESULTS: While both the biomarkers had rather high sensitivities (PNGAL=91%, SCys-C= 89%), the specificity of SCys-C biomarker was very lower than the one of PNGAL (SCys-C=56%, PNGAL=94%). The estimated area under the receiver operating characteristic (ROC) curve for SCys-C as the single biomarker for the diagnosis of CKD was about 0.76, while a similar estimate for PNGAL was 0.93. The added value of PNGAL to SCys-C for the diagnosis of CKD in terms of the ROC curve was about 0.19, while the added value of SCys-C to PNGAL was less than 0.02. CONCLUSION: In general, our findings suggest that PNGAL can be utilized as a single reliable biomarker for early detection of CKD. In addition, results showed that when a perfect gold standard is not available, Bayesian approaches to latent class models could lead to more precise sensitivity and specificity estimates of imperfect tests.

The Optimal Cut-off Score of the Nijmegen Questionnaire for Diagnosing Hyperventilation Syndrome Using a Bayesian Model in the Absence of a Gold Standard.

BACKGROUND: The Nijmegen questionnaire is one of the most common tools for diagnosing hyperventilation syndrome (HVS). However, there is no precise cut-off score for differentiating patients with HVS from those without HVS. This study was conducted to evaluate the accuracy of Nijmegen questionnaire for detecting patients with HVS and to provide the best cut-off score for differentiating patients with HVS from normal individuals using a Bayesian model in the absence of a gold standard. MATERIALS AND METHODS: A total of 490 students from a rehabilitation center in Tehran, Iran, were asked to participate in this case study of HVS from January to August 2018. RESULTS: A total of 215 students (40% male and 60% female) completed the Nijmegen questionnaire. The area under the receiver operating characteristic curve (AUC) was 0.93 (male: 0.95; female: 94) for all of the cut-off scores. The optimal cut-off score of ≥20 could predict HVS with sensitivity of 0.91 (male: 0.99; female: 91) and specificity of 0.92 (male: 96; female: 89). CONCLUSION: Accurate differentiation of HVS patients from individuals without HVS can be accomplished by estimating the cut-off score of Nijmegen questionnaire based on a non-parametric Bayesian model.