Javascript must be enabled to continue!
Diabetes Risk Calculator
View through CrossRef
OBJECTIVE—The objective of this study was to develop a simple tool for the U.S. population to calculate the probability that an individual has either undiagnosed diabetes or pre-diabetes.
RESEARCH DESIGN AND METHODS—We used data from the Third National Health and Nutrition Examination Survey (NHANES) and two methods (logistic regression and classification tree analysis) to build two models. We selected the classification tree model on the basis of its equivalent accuracy but greater ease of use.
RESULTS—The resulting tool, called the Diabetes Risk Calculator, includes questions on age, waist circumference, gestational diabetes, height, race/ethnicity, hypertension, family history, and exercise. Each terminal node specifies an individual's probability of pre-diabetes or of undiagnosed diabetes. Terminal nodes can also be used categorically to designate an individual as having a high risk for 1) undiagnosed diabetes or pre-diabetes, 2) pre-diabetes, or 3) neither undiagnosed diabetes or pre-diabetes. With these classifications, the sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic area for detecting undiagnosed diabetes are 88%, 75%, 14%, 99.3%, and 0.85, respectively. For pre-diabetes or undiagnosed diabetes, the results are 75%, 65%, 49%, 85%, and 0.75, respectively. We validated the tool using v-fold cross-validation and performed an independent validation against NHANES 1999–2004 data.
CONCLUSIONS—The Diabetes Risk Calculator is the only currently available noninvasive screening tool designed and validated to detect both pre-diabetes and undiagnosed diabetes in the U.S. population.
American Diabetes Association
Title: Diabetes Risk Calculator
Description:
OBJECTIVE—The objective of this study was to develop a simple tool for the U.
S.
population to calculate the probability that an individual has either undiagnosed diabetes or pre-diabetes.
RESEARCH DESIGN AND METHODS—We used data from the Third National Health and Nutrition Examination Survey (NHANES) and two methods (logistic regression and classification tree analysis) to build two models.
We selected the classification tree model on the basis of its equivalent accuracy but greater ease of use.
RESULTS—The resulting tool, called the Diabetes Risk Calculator, includes questions on age, waist circumference, gestational diabetes, height, race/ethnicity, hypertension, family history, and exercise.
Each terminal node specifies an individual's probability of pre-diabetes or of undiagnosed diabetes.
Terminal nodes can also be used categorically to designate an individual as having a high risk for 1) undiagnosed diabetes or pre-diabetes, 2) pre-diabetes, or 3) neither undiagnosed diabetes or pre-diabetes.
With these classifications, the sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic area for detecting undiagnosed diabetes are 88%, 75%, 14%, 99.
3%, and 0.
85, respectively.
For pre-diabetes or undiagnosed diabetes, the results are 75%, 65%, 49%, 85%, and 0.
75, respectively.
We validated the tool using v-fold cross-validation and performed an independent validation against NHANES 1999–2004 data.
CONCLUSIONS—The Diabetes Risk Calculator is the only currently available noninvasive screening tool designed and validated to detect both pre-diabetes and undiagnosed diabetes in the U.
S.
population.
Related Results
ACS-NSQIP – Surgical risk calculator accurately predicts outcomes of laparotomy in a prospective study at a tertiary hospital in Tanzania
ACS-NSQIP – Surgical risk calculator accurately predicts outcomes of laparotomy in a prospective study at a tertiary hospital in Tanzania
Introduction: The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) surgical risk calculator is excellent in predicting postoperative complicat...
Evaluating the ARF diagnosis calculator: A survey and content analysis. (Preprint)
Evaluating the ARF diagnosis calculator: A survey and content analysis. (Preprint)
BACKGROUND
Acute Rheumatic Fever (ARF) is a critically important condition for which there is no diagnostic test. Diagnosis requires the use of a set of cri...
Undiagnosed Diabetes in Acute Coronary Syndrome: A Silent Threat in Pakistan
Undiagnosed Diabetes in Acute Coronary Syndrome: A Silent Threat in Pakistan
Diabetes mellitus (DM) has emerged as one of the most pressing public health challenges globally, and Pakistan stands among the countries most severely affected. With rising urbani...
A Multi-Polygenic Risk Score Approach Incorporating Physical Activity Genotypes for Predicting Type 2 Diabetes and Associated Comorbidities: A FinnGen Study
A Multi-Polygenic Risk Score Approach Incorporating Physical Activity Genotypes for Predicting Type 2 Diabetes and Associated Comorbidities: A FinnGen Study
ABSTRACT
Aims/hypothesis
Genetic prediction of type 2 diabetes risk has proven difficult using current methods. Recent studies ...
Application Peter Chew Rule in Calculator Design
Application Peter Chew Rule in Calculator Design
Mathematics has always been a challenging topic for high school and university students around the world. Literature revealed that use of technological tool have had a major i...
PENURUNAN KADAR GULA DARAH DAN RESIKO ULKUS PADA PENDERITA DIABETES MELLITUS DENGAN SENAM KAKI DIABETES
PENURUNAN KADAR GULA DARAH DAN RESIKO ULKUS PADA PENDERITA DIABETES MELLITUS DENGAN SENAM KAKI DIABETES
ABSTRAKDiabetes mellitus adalah suatu penyakit dengan peningkatan glukosa darah di atas normal. Indonesia merupakan negara menempati urutan ke 7 dengan penderita diabetes mellitus ...
Diabetes Awareness Among High School Students in Qatar
Diabetes Awareness Among High School Students in Qatar
Diabetes is a disease that occurs when there is an abundance of glucose in the blood stream and the body cannot produce enough insulin in the pancreas to transfer the sugar from th...
Effect of Diabetes Online Community Engagement on Health Indicators: Cross-Sectional Study (Preprint)
Effect of Diabetes Online Community Engagement on Health Indicators: Cross-Sectional Study (Preprint)
BACKGROUND
Successful diabetes management requires ongoing lifelong self-care and can require that individuals with diabetes become experts in translating c...

