Evidence map›Paper›PMID 29725871›Full record

ArticlePharmacoEconomics2018

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

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

Erratum issuedOpen access · greenAbstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in PharmacoEconomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 46 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 5 pooled it
6.5field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

46 citing papers in PubMed, 5 syntheses or guidelines pooled it, 86 citations in OpenAlex.

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  4. Addressing Regional Differences in Diabetes Progression: Global Calibration for Diabetes Simulation Model.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2019
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Hui ShaoDepartment of Global Health Management and Policy, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal Street, Suite 1900, New Orleans, LA, 70112, USA.
Vivian FonsecaSchool of Medicine, Tulane University, New Orleans, LA, USA.
Charles StoeckerDepartment of Global Health Management and Policy, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal Street, Suite 1900, New Orleans, LA, 70112, USA.
Shuqian LiuDepartment of Global Health Management and Policy, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal Street, Suite 1900, New Orleans, LA, 70112, USA.
Lizheng ShiDepartment of Global Health Management and Policy, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal Street, Suite 1900, New Orleans, LA, 70112, USA. lshi1@tulane.edu.
Tulane University · US

Funding

Louisiana Clinical and Translational Science CenterU54GM104940 · LSU PENNINGTON BIOMEDICAL RESEARCH CTR · 2025 to 2025
$3.9M
NIGMS NIH HHS U54 GM104940
6 · The paper itself

Abstract

backgroundThere is an urgent need to update diabetes prediction, which has relied on the United Kingdom Prospective Diabetes Study (UKPDS) that dates back to 1970 s' European populations.

objectiveThe objective of this study was to develop a risk engine with multiple risk equations using a recent patient cohort with type 2 diabetes mellitus reflective of the US population.

methodsA total of 17 risk equations for predicting diabetes-related microvascular and macrovascular events, hypoglycemia, mortality, and progression of diabetes risk factors were estimated using the data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial (n = 10,251). Internal and external validation processes were used to assess performance of the Building, Relating, Assessing, and Validating Outcomes (BRAVO) risk engine. One-way sensitivity analysis was conducted to examine the impact of risk factors on mortality at the population level.

resultsThe BRAVO risk engine added several risk factors including severe hypoglycemia and common US racial/ethnicity categories compared with the UKPDS risk engine. The BRAVO risk engine also modeled mortality escalation associated with intensive glycemic control (i.e., glycosylated hemoglobin < 6.5%). External validation showed a good prediction power on 28 endpoints observed from other clinical trials (slope = 1.071, R

conclusionThe 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.

Indexed as

Models, StatisticalCardiovascular DiseasesComorbidityDecision Support Systems, ClinicalDiabetes Mellitus, Type 2Disease ProgressionFemaleHumansMaleRisk FactorsUnited States

Identifiers

PMID29725871
PMCPMC9115843
OpenAlexW2802297514

What Socratic holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.