Patient Health Utility Equations for a Type 2 Diabetes Model

Objective: To estimate the health utility impact of diabetes-related complications in a large, longitudinal U.S. sample of people with type 2 diabetes. Research Design and Methods: We combined Health Utility Index-3 data on patients with type 2 diabetes from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) and Action for Health in Diabetes (Look AHEAD) trials and their follow-on studies. Complications were classified as events if they occurred in the year preceding the utility measurement; otherwise, they were classified as a history of the complication. We estimated utility decrements associated with complications using a fixed-effects regression model. Results: Our sample included 15,252 persons with an average follow-up of 8.2 years and a total of 128,873 person-visit observations. The largest, statistically significant (p < 0.05) health utility decrements were for stroke (event: −0.109; history: −0.051), amputation (event: −0.092; history: −0.150), congestive heart failure (CHF; event: −0.051; history: −0.041), dialysis (event: −0.039), estimated glomerular filtration rate (eGFR) < 30 mL/min/1.73 m2 (event: −0.043; history: −0.025), angina (history: −0.028), and myocardial infarction (MI) (event: −0.028). There were smaller effects for laser photocoagulation, and eGFR < 60 mL/min/1.73 m2. Decrements for dialysis history, angina event, MI history, revascularization event, revascularization history, laser photocoagulation event, and hypoglycemia were not significant (p >= 0.05) Conclusions: Using a large study sample and a longitudinal design, our estimated health utility scores are expected to be largely unbiased. Estimates can be used to describe the health utility impact of diabetes complications, improve cost-effectiveness models, and inform diabetes policies.

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Patient Health Utility Equations for a Type 2 Diabetes Model

10.2337/figshare.13166159.v1 ◽

2020 ◽

Author(s):

Simon J. Neuwahl ◽

Ping Zhang ◽

Haiying Chen ◽

Hui Shao ◽

Michael Laxy ◽

...

Keyword(s):

Type 2 Diabetes ◽

Fixed Effects ◽

Laser Photocoagulation ◽

Longitudinal Design ◽

Health Utility Index ◽

Health Utility ◽

Stroke Event ◽

Look Ahead ◽

Fixed Effects Regression

Objective: To estimate the health utility impact of diabetes-related complications in a large, longitudinal U.S. sample of people with type 2 diabetes. Research Design and Methods: We combined Health Utility Index-3 data on patients with type 2 diabetes from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) and Action for Health in Diabetes (Look AHEAD) trials and their follow-on studies. Complications were classified as events if they occurred in the year preceding the utility measurement; otherwise, they were classified as a history of the complication. We estimated utility decrements associated with complications using a fixed-effects regression model. Results: Our sample included 15,252 persons with an average follow-up of 8.2 years and a total of 128,873 person-visit observations. The largest, statistically significant (p < 0.05) health utility decrements were for stroke (event: −0.109; history: −0.051), amputation (event: −0.092; history: −0.150), congestive heart failure (CHF; event: −0.051; history: −0.041), dialysis (event: −0.039), estimated glomerular filtration rate (eGFR) < 30 mL/min/1.73 m2 (event: −0.043; history: −0.025), angina (history: −0.028), and myocardial infarction (MI) (event: −0.028). There were smaller effects for laser photocoagulation, and eGFR < 60 mL/min/1.73 m2. Decrements for dialysis history, angina event, MI history, revascularization event, revascularization history, laser photocoagulation event, and hypoglycemia were not significant (p >= 0.05) Conclusions: Using a large study sample and a longitudinal design, our estimated health utility scores are expected to be largely unbiased. Estimates can be used to describe the health utility impact of diabetes complications, improve cost-effectiveness models, and inform diabetes policies.

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Within trial cost-effectiveness of a structured lifestyle intervention in adults with overweight/obesity and type 2 diabetes: Results from the Action for Health in Diabetes (Look AHEAD) Study

10.2337/figshare.13084802 ◽

2020 ◽

Author(s):

Ping Zhang ◽

Karen M. Atkinson ◽

George Bray ◽

Haiying Chen ◽

Jeanne M. Clark ◽

...

Keyword(s):

Type 2 Diabetes ◽

Cost Effectiveness ◽

Lifestyle Intervention ◽

Health Utility Index ◽

Health Benefit ◽

Cost Effective ◽

Health Utility ◽

Health Care Expenditures ◽

Quality Adjusted Life Year

OBJECTIVE To assess the cost-effectiveness (CE) of an intensive lifestyle intervention (ILI) compared to standard diabetes support and education (DSE) in adults with overweight/obesity and type 2 diabetes, as implemented in the Action for Health in Diabetes study. RESEARCH DESIGN AND METHODS Data were from 4,827 participants during the first 9 years of the study from 2001 to 2012. Information on Health Utility Index-2 and -3, SF-6D, and Feeling Thermometer [FT]), cost of delivering the interventions, and health expenditures were collected during the study. CE was measured by incremental cost-effectiveness ratios (ICERs) in costs per quality-adjusted life year (QALY). Future costs and QALYs were discounted at 3% annually. Costs were in 2012 US dollars. RESULTS <a>Over the </a>9 years studied, the mean cumulative intervention costs and mean cumulative health care expenditures were $11,275 and $64,453 per person for ILI and $887 and $68,174 for DSE. Thus, ILI cost $6,666 more per person than DSE. Additional QALYs gained by ILI were not statistically significant measured by the HUIs and were 0.17 and 0.16, respectively, measured by SF-6D and FT. The ICERs ranged from no health benefit with a higher cost based on HUIs, to $96,458/QALY and $43,169/QALY, respectively, based on SF-6D and FT. Conclusions Whether ILI was cost-effective over the 9-year period is unclear because different health utility measures led to different conclusions.

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Within trial cost-effectiveness of a structured lifestyle intervention in adults with overweight/obesity and type 2 diabetes: Results from the Action for Health in Diabetes (Look AHEAD) Study

10.2337/figshare.13084802.v1 ◽

2020 ◽

Author(s):

Ping Zhang ◽

Karen M. Atkinson ◽

George Bray ◽

Haiying Chen ◽

Jeanne M. Clark ◽

...

Keyword(s):

Type 2 Diabetes ◽

Cost Effectiveness ◽

Lifestyle Intervention ◽

Health Utility Index ◽

Health Benefit ◽

Cost Effective ◽

Health Utility ◽

Health Care Expenditures ◽

Quality Adjusted Life Year

OBJECTIVE To assess the cost-effectiveness (CE) of an intensive lifestyle intervention (ILI) compared to standard diabetes support and education (DSE) in adults with overweight/obesity and type 2 diabetes, as implemented in the Action for Health in Diabetes study. RESEARCH DESIGN AND METHODS Data were from 4,827 participants during the first 9 years of the study from 2001 to 2012. Information on Health Utility Index-2 and -3, SF-6D, and Feeling Thermometer [FT]), cost of delivering the interventions, and health expenditures were collected during the study. CE was measured by incremental cost-effectiveness ratios (ICERs) in costs per quality-adjusted life year (QALY). Future costs and QALYs were discounted at 3% annually. Costs were in 2012 US dollars. RESULTS <a>Over the </a>9 years studied, the mean cumulative intervention costs and mean cumulative health care expenditures were $11,275 and $64,453 per person for ILI and $887 and $68,174 for DSE. Thus, ILI cost $6,666 more per person than DSE. Additional QALYs gained by ILI were not statistically significant measured by the HUIs and were 0.17 and 0.16, respectively, measured by SF-6D and FT. The ICERs ranged from no health benefit with a higher cost based on HUIs, to $96,458/QALY and $43,169/QALY, respectively, based on SF-6D and FT. Conclusions Whether ILI was cost-effective over the 9-year period is unclear because different health utility measures led to different conclusions.

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1526-P: Time-Dependent Effect of Kidney Disease on All-Cause and CVD Mortality in Adults with Type 2 Diabetes: A Secondary Analysis of the Look AHEAD Clinical Trial

Diabetes ◽

10.2337/db20-1526-p ◽

2020 ◽

Vol 69 (Supplement 1) ◽

pp. 1526-P

Author(s):

ANDREW C. WILLS ◽

WILLIAM C. KNOWLER

Keyword(s):

Type 2 Diabetes ◽

Clinical Trial ◽

Secondary Analysis ◽

Time Dependent ◽

Dependent Effect ◽

Look Ahead ◽

Time Dependent Effect ◽

The Look ◽

Cvd Mortality

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170-OR: Association of Timing of Physical Activity Bouts with Cardiorespiratory Fitness in Adults with Type 2 Diabetes in the Look AHEAD Study

Diabetes ◽

10.2337/db20-170-or ◽

2020 ◽

Vol 69 (Supplement 1) ◽

pp. 170-OR

Author(s):

JINGYI QIAN ◽

MICHAEL P. WALKUP ◽

SHYH-HUEI CHEN ◽

PETER H. BRUBAKER ◽

DALE BOND ◽

...

Keyword(s):

Physical Activity ◽

Type 2 Diabetes ◽

Cardiorespiratory Fitness ◽

Look Ahead ◽

The Look

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Does Lifestyle Intervention Improve Health of Adults with Overweight/Obesity and Type 2 Diabetes: Findings from the Look AHEAD Randomized Trial

Obesity ◽

10.1002/oby.23158 ◽

2021 ◽

Author(s):

Rena R. Wing ◽

Keyword(s):

Type 2 Diabetes ◽

Randomized Trial ◽

Lifestyle Intervention ◽

Look Ahead ◽

The Look ◽

Improve Health

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Abstract MP003: Development and Validation of a Prediction Model of Weight Loss Maintenance or Regain at 4 Years in the Look AHEAD Trial

Circulation ◽

10.1161/circ.135.suppl_1.mp003 ◽

2017 ◽

Vol 135 (suppl_1) ◽

Author(s):

Samantha E Berger ◽

Gordon S Huggins ◽

Jeanne M McCaffery ◽

Alice H Lichtenstein

Keyword(s):

Machine Learning ◽

Type 2 Diabetes ◽

Weight Loss ◽

Logistic Regression ◽

Prediction Model ◽

Weight Regain ◽

Initial Weight ◽

Initial Weight Loss ◽

Look Ahead

Introduction: The development of type 2 diabetes is strongly associated with excess weight gain and can often be partially ameliorated or reversed by weight loss. While many lifestyle interventions have resulted in successful weight loss, strategies to maintain the weight loss have been considerably less successful. Prior studies have identified multiple predictors of weight regain, but none have synthesized them into one analytic stream. Methods: We developed a prediction model of 4-year weight regain after a one-year lifestyle-induced weight loss intervention followed by a 3 year maintenance intervention in 1791 overweight or obese adults with type 2 diabetes from the Action for Health in Diabetes (Look AHEAD) trial who lost ≥3% of initial weight by the end of year 1. Weight regain was defined as regaining <50% of the weight lost during the intervention by year 4. Using machine learning we integrated factors from several domains, including demographics, psychosocial metrics, health status and behaviors (e.g. physical activity, self-monitoring, medication use and intervention adherence). We used classification trees and stochastic gradient boosting with 10-fold cross validation to develop and internally validate the prediction model. Results: At the end of four years, 928 individuals maintained ≥50% of their initial weight lost (maintainers), whereas 863 did not met that criterion (regainers). We identified an interaction between age and several variables in the model, as well as percent initial weight loss. Several factors were significant predictors of weight regain based on variable importance plots, regardless of age or initial weight loss, such as insurance status, physical function score, baseline BMI, meal replacement use and minutes of exercise recorded during year 1. We also identified several factors that were significant predictors depending on age group (45-55y/ 56-65y/66-76y) and initial weight loss (lost 3-9% vs. ≥10% of initial weight). When the variables identified from machine learning were added to a logistic regression model stratified by age and initial weight loss groups, the models showed good prediction (3-9% initial weight loss, ages 45-55y (n=293): ROC AUC=0.78; ≥10% initial weight loss, ages 45-55y (n=242): ROC AUC=0.78; (3-9% initial weight loss, ages 56-65y (n=484): ROC AUC=0.70; ≥10% initial weight loss, ages 56-65y (n=455): ROC AUC = 0.74; 3-9% initial weight loss, ages 66-76y (n=150): ROC AUC=0.84; ≥10% initial weight loss, ages 66-76y (n=167): ROC AUC=0.86). Conclusion: The combination of machine learning methodology and logistic regression generates a prediction model that can consider numerous factors simultaneously, can be used to predict weight regain in other populations and can assist in the development of better strategies to prevent post-loss regain.

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The Role of Hypertension and Dyslipidaemia in the Progression of Diabetic Retinopathy in Type 2 Diabetes

International Journal of Diabetes & Metabolic Disorders ◽

10.33140/ijdmd.04.04.01 ◽

2019 ◽

Vol 4 (4) ◽

Cited By ~ 1

Keyword(s):

Quality Of Life ◽

Diabetes Mellitus ◽

Type 2 Diabetes ◽

Diabetic Retinopathy ◽

Laser Photocoagulation ◽

Vitreoretinal Surgery ◽

Additional Burden

Despite the introduction of retinal laser photocoagulation and vitreoretinal surgery, diabetic retinopathy (DR) remains a significant source of sight disorders and blindness amongst individuals with Type 2 Diabetes Mellitus (T2DM) [1]. Visual impairment and blindness can add an additional burden to individuals with T2DM, thereby, affecting their quality of life and ability to self-manage their diabetes [2]. The number of people registered blind and those with moderate to severe sight complications due to DR rose from 0.2 million to 0.4 and 1.4 million to 2.6 million respectively between 1990 to 2015 [3].

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Association Between Change in Accelerometer-Measured and Self-Reported Physical Activity and Cardiovascular Disease in the Look AHEAD Trial

10.2337/figshare.17157926 ◽

2022 ◽

Author(s):

John M. Jakicic ◽

Robert I. Berkowitz ◽

Paula Bolin ◽

George A. Bray ◽

Jeanne M. Clark ◽

...

Keyword(s):

Physical Activity ◽

Type 2 Diabetes ◽

Cardiovascular Disease ◽

Proportional Hazards ◽

Secondary Analysis ◽

Cox Proportional Hazards ◽

Self Report ◽

Look Ahead ◽

The Look

OBJECTIVE: To conduct post-hoc secondary analysis examining the association between change in physical activity (PA), measured with self-report and accelerometry, from baseline to 1 and 4 years and cardiovascular disease (CVD) outcomes in the Look AHEAD Trial. RESEARCH DESIGN AND METHODS: Participants were adults with overweight/obesity and type 2 diabetes with PA data at baseline and year 1 or 4 (n = 1,978). Participants were randomized to diabetes support and education or intensive lifestyle intervention. Measures included accelerometry-measured moderate-to-vigorous PA (MVPA), self-reported PA, and composite (morbidity and mortality) CVD outcomes. RESULTS: In pooled analyses of all participants, using Cox proportional hazards models, each 100 MET-min/wk increase in accelerometry-measured MVPA from baseline to 4 years was associated with decreased risk of the subsequent primary composite outcome of CVD. Results were consistent for changes in total MVPA [HR=0.97 (95% CI: 0.95, 0.99)] and MVPA accumulated in >10-minute bouts [HR=0.95 (95% CI: 0.91, 0.98)], with a similar pattern for secondary CVD outcomes. Change in accelerometry-measured MVPA at 1 year and self-reported change in PA at 1 and 4 years were not associated with CVD outcomes. CONCLUSIONS: Increased accelerometry-measured MVPA from baseline to year 4 is associated with decreased risk of CVD outcomes. This suggests the need for long-term engagement in MVPA to reduce the risk of CVD in adults with overweight/obesity and type 2 diabetes.

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