Novel Risk Engine for Diabetes Progression and Mortality in USA: Building, Relating, Assessing, and Validating Outcomes (BRAVO)

Shao, Hui; Fonseca, Vivian; Stoecker, Charles; Liu, Shuqian; Shi, Lizheng

doi:10.1007/s40273-018-0662-1

Cited by 67 publications

(92 citation statements)

References 39 publications

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“…Along with early diagnosis, people with T2D now have a longer life expectancy compared with just a few decades ago, which emphasizes the need for a continued effort to ensure optimal glycaemic control to prevent diabetes‐related complications. By applying simulations and machine learning methods, trajectories of HbA1c have been predicted, but these models have been developed and validated using RCT data or country‐specific cohorts, thereby limiting their generalizability and application in a real‐world setting.…”

Section: Discussionmentioning

confidence: 99%

“…An increasing number of diagnostic and prognostic prediction models have been proposed in the area of diabetes medicine; most of them have focused on diabetes prevention and diagnosis or prediction of diabetes‐related complications . Few models have been developed for the prediction of glycaemic control in subjects with diabetes, and while some apply to people with type 1 diabetes (T1D), those for people with T2D focused on the trajectory of HbA1c predicted through simulation, or by using machine learning methods; these studies, however, used data from RCTs or a single‐country cohort, thus limiting the generalizability of the findings. Moreover, to our knowledge, no models have been developed to predict the durability of glycaemic control in people with T2D after metformin failure, thereby leaving the individual decision to the single healthcare professional.…”

Section: Introductionmentioning

confidence: 99%

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Durability of glycaemic control in patients with type 2 diabetes after metformin failure: Prognostic model derivation and validation using the DISCOVER study

Ling

Park

Zaccardi

et al. 2020

Diabetes Obesity Metabolism

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Aim: To develop and internally validate prognostic models on the long-term durability of glycaemic control in patients with type 2 diabetes after metformin failure.Materials and Methods: DISCOVER is a 3-year, prospective observational study across six continents investigating second-line glucose-lowering therapies. In this analysis from 35 countries, we included patients on metformin initiating secondline glucose-lowering medication(s) because of physician-defined lack of efficacy.The outcome was durability of glycaemic control, defined as three consecutive levels of HbA1c at 6-, 12-and 24-month follow-up at target (HbA1c equal to or lower than the level when the physician initiated the second-line therapy in patients with baseline HbA1c ≤7% [53 mmol/mol]; and equal to or lower than 7% in those with baseline HbA1c >7%). We developed and internally validated two prognostic models: a base model, which included age, sex, ethnicity, country income group, baseline HbA1c and second-line therapy, and an advanced model, established through statistical variable selections from a model including base variables and 13 additional predictors selected from a literature review. We used logistic regression to develop and 500 bootstrapping samples to internally validate the models; discrimination and calibration were used to assess model performance.Results: Overall, 896 out of 2995 participants (29.9%) had sustained glycaemic control. The base model performed well: Nagelkerke R 2 was 0.13, C-index 0.70 (95% CI: 0.68, 0.71) and bias-corrected C-index 0.69 after internal validation. Diabetes duration, insurance type, estimated glomerular filtration rate and glucose self-monitoring were additionally selected in the advanced model, which had only a slightly better performance compared with the base model: Nagelkerke R 2 0.20, C-index 0.71 (95% CI: 0.69, 0.73) and bias-corrected C-index 0.70. Calibration plots showed good calibrations of both validated models.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Durability of glycaemic control in patients with type 2 diabetes after metformin failure: Prognostic model derivation and validation using the DISCOVER study

Ling

Park

Zaccardi

et al. 2020

Diabetes Obesity Metabolism

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“…Several other models have been so far described to predict all-cause mortality in patients with type 2 diabetes (5,6,(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Some of them lack formal, independent and external validation (20-24).…”

Section: Discussionmentioning

confidence: 99%

“…Some other models were based on simulation studies, with the model's prognostic accuracy being, unfortunately, not reported (25)(26)(27)(28). In addition, many models have been built based on clinical trial data (25,28,30), which leaves the question open as to their transportability to real-life settings. Finally, several studies (5, 6, 21-23, 25, 28-30) included patients of different ethnicity, which makes it impossible to obtain population specific models.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Mortality Risk in Type 2 Diabetic Patients (ENFORCE): An Inexpensive and Parsimonious Prediction Model

Copetti

Shah

Fontana

et al. 2019

The Journal of Clinical Endocrinology &Amp; Metabolism

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Context. We previously developed and validated an inexpensive and parsimonious prediction model of 2-year all-cause mortality in real-life type 2 diabetic patients. Objective. This model, now named ENFORCE, was now investigated in terms of i) prediction performance at 6 years, a more clinically useful time-horizon; ii) further validation in an independent sample; iii) performance comparison in real-life versus clinical trial setting. Design. Observational prospective. Randomized clinical trial. Setting. White patients with type 2 diabetes. Patients. Gargano Mortality Study (GMS; n=1019), Foggia Mortality Study (FMS; n=1045), Pisa Mortality Study (PMS; n=972) as real-life samples and the standard glycemic arm of the ACCORD clinical trial (n=3150). Main Outcome Measure. The endpoint was all-cause mortality. Prediction accuracy and calibration were estimated to assess model's performances.Results. ENFORCE yielded a 6-year mortality C-statistics of 0.79, 0.78 and 0.75 in GMS, FMS and PMS, respectively (P heterogeneity=0.71). Pooling the three cohorts, a 6-year mortality C-statistic of 0.80 was observed. In the ACCORD trial, ENFORCE achieved a Cstatistic of 0.68, a value which is significantly lower than that obtained in the pooled real-life samples (P<0.0001). This difference resembles that observed with other models when comparing real-life vs. clinical trial settings, thus suggesting it is a true, replicable phenomenon.Conclusions. Time horizon of ENFORCE has been extended to 6 years and validated in three independent samples. ENFORCE is a free (http://www.operapadrepio.it/enforce/enforce.php) and user-friendly risk calculator of all-cause mortality in White type 2 diabetic patients from real-life setting.We extended and validated an inexpensive and parsimonious prediction model of 6-year all-cause mortality in White patients with type 2 diabetes from both real-life and clinical trial settings.

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“…In 2018, Shao et al participated the 9th Mt. Hood Challenge to demonstrate that their BRAVO microsimulation model of diabetes costs and outcomes, 29 based on the publicly available ACCORD patient-level clinical trial data, performs much better on many, if not all, dimensions than other advanced diabetes models, all of which are based on the much older UKPDS data. 30 In development of BRAVO, Shao et al devised a novel approach to estimate regional multipliers for diabetes models that are constructed using data from a single region, thereby improving prediction accuracy by reducing systematic bias and increasing explanation power.…”

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confidence: 99%

Characterizing the Validity and Real-World Utility of Health Technology Assessments in Healthcare: Future Directions Comment on "Problems and Promises of Health Technologies: The Role of Early Health Economic Modelling"

Zawadzki

Hay

2019

Int J Health Policy Manag

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With their article, Grutters et al raise an important question: What do successful health technology assessments (HTAs) look like, and what is their real-world utility in decision-making? While many HTAs are published in peer-reviewed journals, many are considered proprietary and their attributes remain confidential, limiting researchers’ ability to answer these questions. Models for economic evaluations like cost-effectiveness analyses (CEAs) synthesize a wide range of evidence, are often statistically and mathematically sophisticated, and require untestable assumptions. As such, there is nearly universal agreement among researchers that enhancing transparency is an important issue in health economic modeling. However, the definition of transparency and guidelines for its implementation vary. Model registration combined with a linked database of model-based economic evaluations has been proposed as a solution, whereby registered models and their accompanying technical and nontechnical documentation are sourced into a single publicly-available repository, ideally in a standardized format to ensure consistent and complete representation of features, code, data sources, results, validation exercises, and policy recommendations. When such a repository is ultimately created, modelers will not have to reinvent the wheel for every new drug launched or new treatment pathway. These more open and transparent approaches will have substantial implications for model accuracy, reliability, and validity, improving trust and acceptance by healthcare decision-makers.

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Novel Risk Engine for Diabetes Progression and Mortality in USA: Building, Relating, Assessing, and Validating Outcomes (BRAVO)

Abstract: The BRAVO risk engine for the US diabetes cohort provides an alternative to the UKPDS risk engine. It can be applied to assist clinical and policy decision making such as cost-effective resource allocation in USA.

Cited by 67 publications

References 39 publications

Durability of glycaemic control in patients with type 2 diabetes after metformin failure: Prognostic model derivation and validation using the DISCOVER study

Durability of glycaemic control in patients with type 2 diabetes after metformin failure: Prognostic model derivation and validation using the DISCOVER study

Estimation of Mortality Risk in Type 2 Diabetic Patients (ENFORCE): An Inexpensive and Parsimonious Prediction Model

Characterizing the Validity and Real-World Utility of Health Technology Assessments in Healthcare: Future Directions Comment on "Problems and Promises of Health Technologies: The Role of Early Health Economic Modelling"

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