scholarly journals The survival rate of tuberculosis patients in HIV-treated cohort of 2008-2018 in Almaty, Kazakhstan

2020 ◽  
Vol 14 (11.1) ◽  
pp. 116S-121S
Author(s):  
Ainur Zhandybayeva ◽  
Nune Truzyan ◽  
Elina Shahumyan ◽  
Aizat Kulzhabaeva ◽  
Zhamilya Nugmanova ◽  
...  
Keyword(s):  
Regression Model ◽  
Treatment Outcomes ◽  
Proportional Hazards ◽  
Cox Regression ◽  
Survival Function ◽  
Cox Proportional Hazards ◽  
Cd4 Count ◽  
Medical Decision ◽  
Life Duration ◽  
Tb Treatment

Introduction: HIV/TB comorbidity is responsible for 1.6 million deaths worldwide. HIV/TB control and patients’ survival are still among priorities of the national HIV and TB programs. We aimed to evaluate the HIV/TB survival in connection with TB treatment outcomes and factors influencing life duration of the cohort 2008-2018 in Almaty, Kazakhstan. Methodology: This retrospective cohort study extracted data for all HIV and pulmonary TB adults coinfected during 2008-2018 in Almaty from national registries to apply descriptive, Kaplan-Meier estimation, and Cox proportional hazards regression model. Survival function for the TB treatment outcomes and factors predicting the probability of survival were tested and described. Results: The cohort population (n = 521) mean age was 37.4 years with 405 (77.7%) males and 210 (40.3%) marrieds. More than one TB treatment had 181 (34.7%) patients, 291 (55.9%) were smear-positive (SS+), and 423 (81.2%) were on antiretroviral therapy with mean CD4 count 254.22cells/µL. Probability to live longer was higher (128 versus 37 months, p = 0.003; 95% confidence interval (CI) 71.65, 184.35) for those who succeeded in TB treatment compared to “lost to follow-up” and “failed” treatment outcomes. Adjusted Cox regression model death hazard showed association with missing ART treatment (HR: 1.699, 95%CI 1.164, 2.481, p = 0.006) and having CD4 count < 499 (HR 2.398, 95%CI 1.191, 4.830, p < 0.014). Conclusion: TB treatment outcomes, ART treatment, and the CD4 count of HIV/TB coinfected population substantially influence their life duration. The medical decision- and policy-makers should take this into consideration when implementing targeted improvements in the national HIV and TB programs.

scholarly journals Factors associated with the duration of hospitalisation among COVID-19 patients in Vietnam: A survival analysis

2020 ◽  
Vol 148 ◽  
Author(s):  
Pham Quang Thai ◽  
Do Thi Thanh Toan ◽  
Dinh Thai Son ◽  
Hoang Thi Hai Van ◽  
Luu Ngoc Minh ◽  
...  
Keyword(s):  
Regression Model ◽  
Median Duration ◽  
Symptom Onset ◽  
Proportional Hazards ◽  
Cox Regression ◽  
Length Of Hospital Stay ◽  
Cox Proportional Hazards ◽  
Factors Associated ◽  
Hospitalised Patients ◽  
Hospital Stays

Abstract Background The median duration of hospital stays due to COVID-19 has been reported in several studies on China as 10−13 days. Global studies have indicated that the length of hospitalisation depends on different factors, such as the time elapsed from exposure to symptom onset, and from symptom onset to hospital admission, as well as specificities of the country under study. The goal of this paper is to identify factors associated with the median duration of hospital stays of COVID-19 patients during the second COVID-19 wave that hit Vietnam from 5 March to 8 April 2020. Method We used retrospective data on 133 hospitalised patients with COVID-19 recorded over at least two weeks during the study period. The Cox proportional-hazards regression model was applied to determine the potential risk factors associated with length of hospital stay. Results There were 65 (48.9%) females, 98 (73.7%) patients 48 years old or younger, 15 (11.3%) persons with comorbidities, 21 (16.0%) severely ill patients and 5 (3.8%) individuals with life-threatening conditions. Eighty-two (61.7%) patients were discharged after testing negative for the SARS-CoV-2 virus, 51 were still in the hospital at the end of the study period and none died. The median duration of stay in a hospital was 21 (IQR: 16–34) days. The multivariable Cox regression model showed that age, residence and sources of contamination were significantly associated with longer duration of hospitalisation. Conclusion A close look at how long COVID-19 patients stayed in the hospital could provide an overview of their treatment process in Vietnam, and support the country's National Steering Committee on COVID-19 Prevention and Control in the efficient allocation of resources over the next stages of the COVID-19 prevention period.

scholarly journals Incidence and maternal-fetal risk factors of stillbirth: A population-based historical cohort and a nested case-control study

2019 ◽  
Author(s):  
Zahra Maleki ◽  
Haleh Ghaem ◽  
Mozhgan Seif ◽  
Sedigheh Foruhari
Keyword(s):  
Risk Factors ◽  
Regression Model ◽  
Proportional Hazards ◽  
Cox Regression ◽  
Gestational Hypertension ◽  
Population Based ◽  
Cox Proportional Hazards ◽  
Historical Cohort ◽  
Significance Level ◽  
Fetal Risk

Abstract Background: For parents, stillbirth is a disappointing phenomenon; thus, identifying the associated risk factors can be beneficial in order to prevent this event. This study aimed to investigate the incidence and risk factors associated with stillbirth.Methods: In this historical cohort study, a total of 18129 birth records were investigated. For each case of stillbirth, three live birth infants on the same day and same hospital were selected as the controls, which were matched for gestational age. The data was collected using a researcher-made checklist. Finally, data were analyzed using STATA, 13.0 with Cox proportional hazards regression model at the significance level of 0.05.Results: The cumulative incidence of still birth was 9.48 per 1000 live births. Based on multivariate Cox regression model, five risk factors for stillbirth were identified, including male gender, fetal diseases, gestational hypertension, gestational diabetes, and hypothyroidism, (all hazard ratios > 1 and p<0.05).Conclusion: For the first time, maternal hypothyroidism, oligohydramnios and polyhydramnios were shown as risk factors for stillbirth, which were not evaluated in any previous study. The findings of this study suggest that some maternal and fetal risk factors can be recognized as predictors of stillbirth, which might help to prevent and detect high-risk parents at early stages in order to avoid adverse health consequences in the mother and her neonate.

scholarly journals Incorporating sampling weights into robust estimation of Cox proportional hazards regression model, with illustration in the Multi-Ethnic Study of Atherosclerosis

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Colleen M. Sitlani ◽  
Thomas Lumley ◽  
Barbara McKnight ◽  
Kenneth M. Rice ◽  
Nels C. Olson ◽  
...  
Keyword(s):  
Cohort Study ◽  
Regression Model ◽  
Proportional Hazards ◽  
Cox Regression ◽  
Cox Proportional Hazards ◽  
Robust Methods ◽  
Time To Event ◽  
Sampling Weights ◽  
Cox Proportional Hazards Regression ◽  
Proportional Hazards Regression

Abstract Background Cox proportional hazards regression models are used to evaluate associations between exposures of interest and time-to-event outcomes in observational data. When exposures are measured on only a sample of participants, as they are in a case-cohort design, the sampling weights must be incorporated into the regression model to obtain unbiased estimating equations. Methods Robust Cox methods have been developed to better estimate associations when there are influential outliers in the exposure of interest, but these robust methods do not incorporate sampling weights. In this paper, we extend these robust methods, which already incorporate influence weights, so that they also accommodate sampling weights. Results Simulations illustrate that in the presence of influential outliers, the association estimate from the weighted robust method is closer to the true value than the estimate from traditional weighted Cox regression. As expected, in the absence of outliers, the use of robust methods yields a small loss of efficiency. Using data from a case-cohort study that is nested within the Multi-Ethnic Study of Atherosclerosis (MESA) longitudinal cohort study, we illustrate differences between traditional and robust weighted Cox association estimates for the relationships between immune cell traits and risk of stroke. Conclusions Robust weighted Cox regression methods are a new tool to analyze time-to-event data with sampling, e.g. case-cohort data, when exposures of interest contain outliers.

scholarly journals Gene selection in Cox regression model based on a new adaptive penalized method

2020 ◽  
Vol 8 (1) ◽  
pp. 16
Author(s):  
Oday Isam Alskal ◽  
Zakariya Yahya Algamal
Keyword(s):  
Regression Model ◽  
Survival Data ◽  
Proportional Hazards ◽  
Gene Selection ◽  
Cox Regression ◽  
Area Under The Curve ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Regression ◽  
Proportional Hazards Regression ◽  
Selection Operator

The common issues of high dimensional gene expression data for survival analysis are that many of genes may not be relevant to their diseases. Gene selection has been proved to be an effective way to improve the result of many methods. The Cox proportional hazards regression model is the most popular model in regression analysis for censored survival data. In this paper, an adaptive penalized Cox proportional hazards regression model is proposed, with the aim of identification relevant genes and provides high classification accuracy, by combining the Cox proportional hazards regression model with the weighted least absolute shrinkage and selection operator (LASSO) method. Experimental results show that the proposed method significantly outperforms two competitor methods in terms of the area under the curve and the number of the selected genes.  

scholarly journals Testing the proportional hazards assumption in cox regression and dealing with possible non-proportionality in total joint arthroplasty research: methodological perspectives and review

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ilari Kuitunen ◽  
Ville T. Ponkilainen ◽  
Mikko M. Uimonen ◽  
Antti Eskelinen ◽  
Aleksi Reito
Keyword(s):  
Survival Analysis ◽  
Regression Model ◽  
Total Joint Arthroplasty ◽  
Full Text ◽  
Proportional Hazards ◽  
Cox Regression ◽  
Joint Arthroplasty ◽  
Central Research ◽  
Cox Regression Model ◽  
Pubmed Database

Abstract Background Survival analysis and effect of covariates on survival time is a central research interest. Cox proportional hazards regression remains as a gold standard in the survival analysis. The Cox model relies on the assumption of proportional hazards (PH) across different covariates. PH assumptions should be assessed and handled if violated. Our aim was to investigate the reporting of the Cox regression model details and testing of the PH assumption in survival analysis in total joint arthroplasty (TJA) studies. Methods We conducted a review in the PubMed database on 28th August 2019. A total of 1154 studies were identified. The abstracts of these studies were screened for words “cox and “hazard*” and if either was found the abstract was read. The abstract had to fulfill the following criteria to be included in the full-text phase: topic was knee or hip TJA surgery; survival analysis was used, and hazard ratio reported. If all the presented criteria were met, the full-text version of the article was then read. The full-text was included if Cox method was used to analyze TJA survival. After accessing the full-texts 318 articles were included in final analysis. Results The PH assumption was mentioned in 114 of the included studies (36%). KM analysis was used in 281 (88%) studies and the KM curves were presented graphically in 243 of these (87%). In 110 (45%) studies, the KM survival curves crossed in at least one of the presented figures. The most common way to test the PH assumption was to inspect the log-minus-log plots (n = 59). The time-axis division method was the most used corrected model (n = 30) in cox analysis. Of the 318 included studies only 63 (20%) met the following criteria: PH assumption mentioned, PH assumption tested, testing method of the PH assumption named, the result of the testing mentioned, and the Cox regression model corrected, if required. Conclusions Reporting and testing of the PH assumption and dealing with non-proportionality in hip and knee TJA studies was limited. More awareness and education regarding the assumptions behind the used statistical models among researchers, reviewers and editors are needed to improve the quality of TJA research. This could be achieved by better collaboration with methodologists and statisticians and introducing more specific reporting guidelines for TJA studies. Neglecting obvious non-proportionality undermines the overall research efforts since causes of non-proportionality, such as possible underlying pathomechanisms, are not considered and discussed.

Female hormonal exposures and risk of rheumatoid arthritis in the French E3N-EPIC cohort study

Rheumatology ◽  
2021 ◽  
Author(s):  
Carine Salliot ◽  
Yann Nguyen ◽  
Gaëlle Gusto ◽  
Amandine Gelot ◽  
Juliette Gambaretti ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Prospective Cohort ◽  
Regression Models ◽  
Proportional Hazards ◽  
Cox Regression ◽  
Cox Proportional Hazards ◽  
Smoking History ◽  
Nulliparous Women ◽  
Hormonal Exposures ◽  
Incident Cases

Abstract Objective To assess the relationships between female hormonal exposures and risk of rheumatoid arthritis (RA), in a prospective cohort of French women. Methods E3N is an on-going French prospective cohort that included 98 995 women aged 40–65 years in 1990. Every 2–3 years, women completed mailed questionnaires on their lifestyles, reproductive factors, and health conditions. Cox proportional-hazards regression models were used to determine factors associated with risk of incident RA, with age as the time scale, adjusted for known risk factors of RA, and considering endogenous and exogenous hormonal factors. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated. Effect modification by smoking history was investigated. Results A total of 698 incident cases of RA were ascertained among 78 452 women. In multivariable-adjusted Cox regression models, risk of RA was increased with early age at first pregnancy (&lt;22 vs ≥27 years; HR = 1.34; 95%CI 1.0–1.7) and menopause (≤45 vs ≥53 years; HR = 1.40; 95%CI 1.0–1.9). For early menopause, the association was of similar magnitude in ever and never smokers, although the association was statistically significant only in ever smokers (HR = 1.54; 95%CI 1.0–2.3). We found a decreased risk in nulliparous women never exposed to smoking (HR = 0.44; 95%CI 0.2–0.8). Risk of RA was inversely associated with exposure to progestogen only in perimenopause (&gt;24 vs 0 months; multi-adjusted HR = 0.77; 95%CI 0.6–0.9). Conclusions These results suggest an effect of both endogenous and exogenous hormonal exposures on RA risk and phenotype that deserves further investigation.

Abstract 328: Cardiovascular Disease Associated with Ability to Achieve Renal Transplant and Post-Transplant Adverse Events

2015 ◽  
Vol 8 (suppl_2) ◽  
Author(s):  
Sahar J Ismail ◽  
Meet Patel ◽  
Ryan Gindi ◽  
Ahmad Salah ◽  
Ignatius Tang ◽  
...  
Keyword(s):  
Renal Transplant ◽  
Regression Model ◽  
Graft Failure ◽  
Logistic Regression Model ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Post Transplant ◽  
History Of

Introduction: Patients with end stage renal disease suffer from a high burden of cardiovascular disease (CVD). Renal transplant offers mortality and morbidity benefits. Hypothesis: We predict that patients with CVD are less likely to obtain a renal transplant after being listed and that CVD may be associated with post-transplant adverse events. Methods: We conducted a retrospective analysis of all adult patients listed for first time renal transplantation at the University Of Illinois Chicago from 2002 till 2006. We defined Coronary Artery Disease (CAD) as a history of myocardial infarction or coronary revascularization. We defined reduced ejection fraction (rEF) as an EF less than or equal to 40%. CAD equivalents were defined as a history of diabetes, stroke or peripheral vascular disease. We assessed the outcome of achieving transplantation in a multivariate logistic regression model. We assessed post-transplant events of death or graft failure in a Cox proportional hazards regression model. Results: Of the 460 patients studied African-Americans accounted for 52% and men for 58%. CAD was present in 10.9% of patients and rEF was present in 9.6%. Pre-operative revascularization occurred in 8.9% of patients (74% percutaneous coronary intervention, 26% bypass surgery. Patients with CAD or a CAD equivalent were older (54.7 vs. 43.2 years old, p <0.01), had higher systolic blood pressure (147.2 vs. 140.6 mmHg, p<0.01) and lower diastolic blood pressure (79.3 vs. 83.6 mmHg, p<0.01). Beta-blocker (63% vs. 54%, p = 0.06) statin (45% vs. 11%, p<0.01) and aspirin (40% vs 12%, p<0.01) use was more common in those with CAD or equivalent. In a multivariate logistic regression model controlling for sex, medications, pre-operative revascularization, and comorbidities, age (OR 0.975, 95% CI 0.954 to 0.997, p = 0.03) and history of CAD (OR 0.385 95% CI 0.159 to 0.932, p= 0.03) were associated with lower odds of receiving transplant. In a Cox proportional hazards model controlling for age, sex, pre-operative revascularization, type of transplant, and comorbidities, CAD (HR 2.56 95% CI 1.08 - 6.10, p = 0.03) and rEF (HR 2.37 95% CI 1.06 - 5.35, p = 0.03) were associated with an increased hazard of graft failure or death. Of 337 patients that received transplant only 4 peri-operative myocardial infarcts and 1 stroke occurred. Conclusions: CVD is common in patients listed for renal transplant. CAD is independently associated with lower odds of receiving a transplant. CAD and rEF are independently associated with increased hazard of post-transplant death or graft failure. Future efforts should focus measures to optimize outcomes in patients with CVD awaiting transplant.

scholarly journals Relationship Between Creatinine and Body Weight Ratio and Diabetes Mellitus in a Chinese Cohort Study

2021 ◽  
Author(s):  
Xinyu Wang ◽  
Zhuangsen Chen ◽  
Fan Yang ◽  
Xiaohan Ding ◽  
Changchun Cao ◽  
...  
Keyword(s):  
Body Weight ◽  
Missing Values ◽  
Proportional Hazards ◽  
Cox Regression ◽  
Weight Ratio ◽  
Cox Proportional Hazards ◽  
Additive Models ◽  
Cox Regression Analysis ◽  
Non Linear ◽  
The Relationship

Abstract Background: Research on the relationship between Creatinine to Body Weight Ratios (Cre/BW ratios) and the prevalence of diabetes is still lacking. The aim of this study was to investigate the potential association between Cre/BW ratios and incident of diabetes in Chinese adults.Methods: This retrospective study was conducted in 199,526 patients from Rich Healthcare Group in China from 2010 to 2016. The participants were divided into quartiles of the Cre/BW ratios. Multivariate multiple imputation and dummy variables were used to handle missing values. Cox proportional-hazards regression was used to investigate the association of Cre/BW and diabetes. Generalized additive models(GAM) were used to identify non-linear relationships.Results: Of all participants,after handling missing values and adjustment for potential confounders, the multivariate Cox regression analysis results showed that Cre/BW ratios was inversely associated with diabetes risk( HR: 0.268; 95% CI:0.229 to 0.314, P < 0.00001).For men, the hazard ratios(HRs) of incident diabetes was 0.255(95%CI: 0.212-0.307);and for women HR= 0.297 (95%CI: 0.218-0.406).Moreover, sensitivity analysis confirmed the stability of the results. Furthermore, GAM revealed a saturation effect on the independent association between Cre/BW and incident of diabetes.Conclusions: This study demonstrated that increased Cre/BW is negatively correlated with incident of diabetes in Chinese for the first time. And we found that the relationship between Cre/BW and incident of diabetes was non-linear.

scholarly journals Cox proportion hazard model for patients with hepatitis disease in Iraq

2009 ◽  
Vol 6 (3) ◽  
pp. 612-617
Author(s):  
Baghdad Science Journal
Keyword(s):  
Regression Model ◽  
Survival Time ◽  
Cox Regression ◽  
Survival Function ◽  
Hazard Model ◽  
Cox Regression Model ◽  
Hepatic Diseases ◽  
Kaplan Meier

Cox regression model have been used to estimate proportion hazard model for patients with hepatitis disease recorded in Gastrointestinal and Hepatic diseases Hospital in Iraq for (2002 -2005). Data consists of (age, gender, survival time terminal stat). A Kaplan-Meier method has been applied to estimate survival function and hazerd function.

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