Simulation of Quality-Adjusted Survival in Chronic Diseases

2011 ◽  
Vol 31 (4) ◽  
pp. 559-570 ◽  
Author(s):  
Alison J. Hayes ◽  
Philip M. Clarke ◽  
Merryn Voysey ◽  
Anthony Keech
Keyword(s):  
Quality Of Life ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Health Utility ◽  
Clinical Risk Factors ◽  
Patient Level ◽  
Clinical Risk ◽  
Life Years

Background. Recent studies have demonstrated that measures of health-related quality of life can predict complications and mortality in patients with diabetes, even after adjustment for clinical risk factors. Methods. The authors developed a simulation model of disease progression in type 2 diabetes to investigate the impact of patient quality of life on lifetime outcomes and its potential response to therapy. Changes in health utility over time are captured as a result of complications and aging. All risk equations, model parameter estimates, and input data were derived from patient-level data from the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) trial. Results. Healthier patients with type 2 diabetes enjoy more life years, quality-adjusted life years (QALYs), and more life years free of complications. A 65-year-old patient at full health (utility = 1) can expect to live approximately 2 years longer and achieve 6 more QALYs than a patient at average health (utility = 0.8), given similar clinical risk factors. For patients with higher EQ-5D utility, the additional years lived without complications contribute more to longer life expectancy than years lived with complications. Conclusions. The authors have developed a model for progression of disease in diabetes that has a number of novel features; it captures the observed relationships between measures of quality of life and future outcomes, the number of states have been minimized, and it can be parameterized with just 4 risk equations. Underlying the simple model structure is important patient-level heterogeneity in health and outcomes. The simulations suggest that differences in patients’ EQ-5D utility can account for large differences in QALYs, which could be relevant in cost-utility analyses.

scholarly journals Investigating Factors Associated with Depressive Symptoms of Chronic Kidney Diseases in China with Type 2 Diabetes

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Xu Wang ◽  
Biyu Shen ◽  
Xun Zhuang ◽  
Xueqin Wang ◽  
Weiqun Weng
Keyword(s):  
Quality Of Life ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Regression Analysis ◽  
Depressive Symptoms ◽  
Kidney Diseases ◽  
Chronic Kidney Diseases ◽  
Factors Associated

Aim.To assess the depressive symptoms status of chronic kidney diseases in Nantong, China, with type 2 diabetes and to identify factors associated with depressive symptoms.Methods.In this cross-sectional analytic study, 210 type 2 diabetic patients were recruited from the Second Affiliated Hospital of Nantong University. Depressive symptoms were assessed with the depression subscale of the Hospital Anxiety and Depression Scale (HAD-D). The quality of life was measured with the RAND 36-Item Health Survey (SF-36). And the independent risk factors of depressive symptoms were assessed by using a stepwise forward model of logistic regression analysis.Results.The mean age of the study subjects was 57.66 years (SD: 11.68). Approximately 21.4% of subjects reported depressive symptoms (n=45). Forward stepwise logistic regression analysis showed that female gender (P=0.010), hypertension (P=0.022), Stage IV (P=0.003), and Stage V (P<0.001) were significant risk factors for depressive symptoms. The quality of life of individuals with HAD-D score <11 was significantly better compared with individuals with HAD-D score ≥ 11.Conclusions.These results indicate that clinicians should be aware that female patients with chronic kidney diseases with T2DM in their late stage with hypertension are at a marked increased risk of depressive symptoms. Providing optimal care for the psychological health of this population is vital.

Clinical risk factors for depressive syndrome in Type 2 diabetes: the Fremantle Diabetes Study

2018 ◽  
Vol 35 (7) ◽  
pp. 903-910 ◽  
Author(s):  
D. G. Bruce ◽  
W. A. Davis ◽  
S. E. Starkstein ◽  
T. M. E. Davis
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Depressive Syndrome ◽  
Clinical Risk Factors ◽  
Clinical Risk

scholarly journals Genetic risk score for risk prediction of diabetic nephropathy in Han Chinese type 2 diabetes patients

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Li-Na Liao ◽  
Tsai-Chung Li ◽  
Chia-Ing Li ◽  
Chiu-Shong Liu ◽  
Wen-Yuan Lin ◽  
...  
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Prediction Model ◽  
Risk Prediction ◽  
Genetic Risk Score ◽  
Clinical Risk Factors ◽  
Clinical Risk ◽  
Conventional Risk Factors ◽  
Diabetes Patients

AbstractWe evaluated whether genetic information could offer improvement on risk prediction of diabetic nephropathy (DN) while adding susceptibility variants into a risk prediction model with conventional risk factors in Han Chinese type 2 diabetes patients. A total of 995 (including 246 DN cases) and 519 (including 179 DN cases) type 2 diabetes patients were included in derivation and validation sets, respectively. A genetic risk score (GRS) was constructed with DN susceptibility variants based on findings of our previous genome-wide association study. In derivation set, areas under the receiver operating characteristics (AUROC) curve (95% CI) for model with clinical risk factors only, model with GRS only, and model with clinical risk factors and GRS were 0.75 (0.72–0.78), 0.64 (0.60–0.68), and 0.78 (0.75–0.81), respectively. In external validation sample, AUROC for model combining conventional risk factors and GRS was 0.70 (0.65–0.74). Additionally, the net reclassification improvement was 9.98% (P = 0.001) when the GRS was added to the prediction model of a set of clinical risk factors. This prediction model enabled us to confirm the importance of GRS combined with clinical factors in predicting the risk of DN and enhanced identification of high-risk individuals for appropriate management of DN for intervention.

Clinical risk factors and the role of VDR gene polymorphisms in diabetic retinopathy in Polish type 2 diabetes patients

2006 ◽  
Vol 43 (4) ◽  
pp. 114-119 ◽  
Author(s):  
K. Cyganek ◽  
B. Mirkiewicz-Sieradzka ◽  
M. T. Malecki ◽  
P. Wolkow ◽  
J. Skupien ◽  
...  
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Diabetic Retinopathy ◽  
Gene Polymorphisms ◽  
Clinical Risk Factors ◽  
Vdr Gene ◽  
Clinical Risk ◽  
Vdr Gene Polymorphisms

Risk Factors and Coping Style Affect Health Outcomes in Adults With Type 2 Diabetes

2015 ◽  
Vol 18 (1) ◽  
pp. 82-89 ◽  
Author(s):  
Chiung-Yu Huang ◽  
Hui-Ling Lai ◽  
Yung-Chuan Lu ◽  
Wen-Kuei Chen ◽  
Shu-Ching Chi ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Coping Style ◽  
Coping Styles ◽  
Equation Modeling ◽  
Duration Of Diabetes ◽  
And Coping

Objective: Most psychosocial interventions among individuals with Type 2 diabetes mellitus (T2DM) target depressive symptoms (DSs) rather than causal antecedents that lead to DSs or affect health-related quality of life (HrQoL). This research investigated a conceptual model of the effects of risk factors and coping styles on HrQoL and DSs in patients with T2DM. Method: A descriptive, correlational design was used with a convenience sample of 241 adults with T2DM aged ≥ 20 years recruited from a hospital metabolic outpatient department. Data were collected using a demographic questionnaire, the modified Ways of Coping Checklist, the Center for Epidemiological Studies Depression Scale, the Short Form 36 Health Survey, and physiological examination. HbA1C was collected from participants’ medical records. Structural equation modeling techniques were used to analyze relationships among risk factors, mediators, and HrQoL. Results: Younger age, more education, and longer duration of diabetes predicted better physical quality of life. Duration of diabetes and three coping styles predicted DSs. Longer duration of diabetes and lower fasting glucose predicted better mental quality of life. Three coping styles acted as mediators between risk factors and health, that is, active and minimizing styles promoted positive outcomes, while avoidance promoted negative outcomes. Conclusions: This integrated model provides a holistic picture of how risk factors and coping style influence HrQoL and DSs in individuals with T2DM. Nurses could use active coping strategies in cognitive behavioral therapy to enhance glycemic control in patients with T2DM.

scholarly journals The association of ADIPOQ gene polymorphisms and clinical risk factors with nephropathy progression in type 2 diabetes

2012 ◽  
Vol 26 (S1) ◽  
Author(s):  
Hsin-Fang Chung ◽  
Pao-Shan Chen ◽  
Kurt Long ◽  
Chih-Cheng Hsu ◽  
Meng-Chuan Huang
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Gene Polymorphisms ◽  
Clinical Risk Factors ◽  
Adipoq Gene ◽  
Clinical Risk

Common Type 2 Diabetes Genetic Risk Variants Improve the Prediction of Gestational Diabetes

2019 ◽  
Vol 51 (10) ◽  
pp. 655-660 ◽  
Author(s):  
Violetta Dziedziejko ◽  
Krzysztof Safranow ◽  
Maciej Tarnowski ◽  
Andrzej Pawlik
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Gestational Diabetes ◽  
Glucose Tolerance ◽  
Pregnant Women ◽  
Genetic Polymorphisms ◽  
Roc Curves ◽  
Clinical Risk Factors ◽  
Clinical Risk

AbstractGestational diabetes mellitus (GDM) is a carbohydrate intolerance that occurs in women during pregnancy. The aims of this study were to develop a model to predict the risk of GDM development using common clinical parameters and selected genetic polymorphisms and to analyse the performance of the model using receiver operator characteristic (ROC) curves. ROC analysis was used to examine whether the evaluation of genetic polymorphisms may enhance the accuracy of GDM prediction in comparison to using common clinical risk factors only. This study included 204 pregnant women with GDM and 207 pregnant women with normal glucose tolerance. The diagnosis of GDM was based on a 75 g oral glucose tolerance test at 24–28 weeks gestation. The difference between the AUC of ROC curves for the model 1 including only age and BMI and the model 2 also including 8 genetic polymorphisms was highly significant (p=0.0001) in favour of model 2 (0.090±0.023). Moreover, the additional use of 8 genetic polymorphisms may increase both the sensitivity and specificity of GDM prediction by 10%. The results of this study indicate that the use of 8 genetic polymorphisms associated with carbohydrate and lipid metabolism and type 2 diabetes [PTGS2 (COX2) rs6681231, FADS1 rs174550, HNF1B rs4430796, ADIPOQ rs266729, IL18 rs187238, CCL2 rs1024611, HHEX rs5015480 and CDKN2A/2B rs10811661] together with clinical risk factors (BMI and age) may significantly improve the prediction of GDM.

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