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Development and Validation of a Patient Self-assessment Score for Diabetes Risk

Heejung Bang, PhD; Alison M. Edwards, MStat; Andrew S. Bomback, MD, MPH; Christie M. Ballantyne, MD; David Brillon, MD; Mark A. Callahan, MD; Steven M. Teutsch, MD, MPH; Alvin I. Mushlin, MD, ScM; and Lisa M. Kern, MD, MPH
[+] Article and Author Information

From Weill Medical College of Cornell University, Columbia University College of Physicians and Surgeons, and FOJP Service Corporation, New York, New York; Baylor College of Medicine, and Center for Cardiovascular Disease Prevention, Methodist DeBakey Heart Center, Houston, Texas; and Los Angeles County Department of Public Health, Los Angeles, California.


Disclaimer: The NHANES were supported by the Centers for Disease Control and Prevention. The ARIC and CHS studies were supported by the National Heart, Lung and Blood Institute. This manuscript was prepared by using public-use data sets (for NHANES) and limited-access data sets (for ARIC and CHS) and does not necessarily reflect the opinions or views of these studies or agencies.

Acknowledgment: The authors thank the staff and participants of NHANES, ARIC, and CHS for their important contributions to research and data sharing.

Grant Support: By the Clinical and Translational Science Center at Weill Cornell Medical College (UL1-RR024996).

Potential Conflicts of Interest:Employment: S.M. Teutsch (Merck & Co.). Honoraria: L.M. Kern (Lifetime Healthcare). Stock ownership or options (other than mutual funds): S.M. Teutsch (Merck & Co.).

Reproducible Research Statement:Study protocol: Not available. Statistical code: Available from Dr. Bang (e-mail, heb2013@med.cornell.edu). Data set: Data for NHANES are publicly available (www.cdc.gov/nchs/nhanes.htm), and data for ARIC and CHS are available through a limited-access distribution agreement (www.nhlbi.nih.gov/resources/deca/datasets_obv.htm).

Requests for Single Reprints: Heejung Bang, PhD, Department of Public Health, Weill Cornell Medical College, 402 East 67th Street, New York, NY 10065; e-mail, heb2013@med.cornell.edu.

Current Author Addresses: Drs. Bang, Mushlin, and Kern and Ms. Edwards: Department of Public Health, Weill Cornell Medical College, 402 East 67th Street, New York, NY 10065.

Dr. Bomback: Division of Nephrology, Department of Medicine, Columbia University Medical Center, 622 West 168th Street, PH 4-124, New York, NY 10032.

Dr. Ballantyne: Section of Atherosclerosis and Lipoprotein Research, Department of Medicine, Baylor College of Medicine, 6565 Fannin, M.S. A-60, Houston, TX 77030.

Dr. Brillon: Department of Medicine, Weill Cornell Medical College, Baker Pavilion, 20th Floor, 525 East 68th Street, New York, NY 10065.

Dr. Callahan: FOJP Service Corporation, 28 East 28th Street, New York, NY 10016.

Dr. Teutsch: Los Angeles County Public Health, 313 North Figueroa Street, Room 708, Los Angeles, CA 90012.

Author Contributions: Conception and design: H. Bang, D. Brillon, M.A. Callahan, S.M. Teutsch, A.I. Mushlin, L.M. Kern.

Analysis and interpretation of the data: H. Bang, A.S. Bomback, D. Brillon, M.A. Callahan, S.M. Teutsch, A.I. Mushlin, L.M. Kern.

Drafting of the article: H. Bang, A.M. Edwards, A.S. Bomback, D. Brillon, A.I. Mushlin.

Critical revision of the article for important intellectual content: H. Bang, A.S. Bomback, C.M. Ballantyne, D. Brillon, A.I. Mushlin, L.M. Kern.

Final approval of the article: H. Bang, A.M. Edwards, A.S. Bomback, C.M. Ballantyne, M.A. Callahan, S.M. Teutsch, A.I. Mushlin, L.M. Kern.

Statistical expertise: H. Bang, A.M. Edwards, S.M. Teutsch.

Obtaining of funding: H. Bang, M.A. Callahan, S.M. Teutsch, A.I. Mushlin.

Collection and assembly of data: A.M. Edwards.


Ann Intern Med. 2009;151(11):775-783. doi:10.7326/0003-4819-151-11-200912010-00005
Text Size: A A A

Background: National guidelines disagree on who should be screened for undiagnosed diabetes. No existing diabetes risk score is highly generalizable or widely followed.

Objective: To develop a new diabetes screening score and compare it with other available screening instruments (Centers for Disease Control and Prevention, American Diabetes Association, and U.S. Preventive Services Task Force guidelines; 2 American Diabetes Association risk questionnaires; and the Rotterdam model).

Design: Cross-sectional data.

Setting: NHANES (National Health and Nutrition Examination Survey) 1999 to 2004 for model development and 2005 to 2006, plus a combined cohort of 2 community studies, ARIC (Atherosclerosis Risk in Communities) Study and CHS (Cardiovascular Health Study), for validation.

Participants: U.S. adults aged 20 years or older.

Measurements: A risk-scoring algorithm for undiagnosed diabetes, defined as fasting plasma glucose level of 7.0 mmol/L (126 mg/dL) or greater without known diabetes, was developed in the development data set. Logistic regression was used to determine which participant characteristics were independently associated with undiagnosed diabetes. The new algorithm and other methods were evaluated by standard diagnostic and feasibility measures.

Results: Age, sex, family history of diabetes, history of hypertension, obesity, and physical activity were associated with undiagnosed diabetes. In NHANES (ARIC/CHS), the cut-point of 5 or more points selected 35% (40%) of persons for diabetes screening and yielded a sensitivity of 79% (72%), specificity of 67% (62%), positive predictive value of 10% (10%), and positive likelihood ratio of 2.39 (1.89). In contrast, the comparison scores yielded a sensitivity of 44% to 100%, specificity of 10% to 73%, positive predictive value of 5% to 8%, and positive likelihood ratio of 1.11 to 1.98.

Limitation: Data during pregnancy were not available.

Conclusion: This easy-to-implement diabetes screening score seems to demonstrate improvements over existing methods. Studies are needed to evaluate it in diverse populations in real-world settings.

Primary Funding Source: Clinical and Translational Science Center at Weill Cornell Medical College.

Figures

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Appendix Figure.
Estimated prevalence of undiagnosed diabetes, by screening score.

NHANES 1999 to 2006 has 6898 participants, and ARIC/CHS has 19 728 participants. Proportions of persons with scores of −1, 0, 1, 2, 3, 4, 5, 6, 7, 8, or 9 correspond to 1%, 4%, 10%, 14%, 16%, 17%, 16%, 13%, 7%, 2%, and 0.1% in NHANES and 2%, 5%, 11%, 19%, 24%, 22%, 13%, 4%, 0.4%, and 0.01% in ARIC/CHS, respectively. ARIC = Atherosclerosis Risk in Communities; CHS = Cardiovascular Health Study; NHANES = National Health and Nutrition Examination Survey.

Grahic Jump Location
Grahic Jump Location
Figure.
Self-assessment screening score for undiagnosed diabetes or prediabetes.
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Diabetes Risk and Screening Instruments
Posted on December 7, 2009
Enrique J. Sanchez-Delgado
Hospital Metropolitano Vivan Pellas, Managua, Nicaragua
Conflict of Interest: None Declared

Diabetes Risk and Screening Instruments

Heejung Bang et al (Ann Intern Med December 1, 2009) developed a simple risk score to identify persons at high risk of Diabetes or Pre- Diabetes. This instrument is very important in view of the trend of increased number of obese population and younger persons with diabetes or hypertension, which threatens to stop or even reverse the great advances in cardiovascular diseases prevention already achieved.

It is therefore of the highest importance to identify the younger patients, in their twenties or thirties, who are diabetics or pre- diabetics, hypertensive or pre-hypertensive, with metabolic syndrome, that could benefit with more aggressive preventive measures or appropriate medications, as well as cessation of cigarette smoking at age 30 years or earlier, which adds about 10 years of life expectancy that they would lose if they continued to smoke and do not improve their cardio-metabolic risk.

Ten years ago (Lancet, 13 March 1999), we published that the Pulse Mass Index is a simple way for the preliminary identification of young patient with a high cardiovascular risk, when their Pulse Mass Index is over 1.2 and specially if over 1.3

The Pulse Mass Index is calculated as follows: Pulse or Resting Heart Rate (after at least 2 hours fasting) multiplied by the Body Mass Index and divided by 1730 as common denominator. Normal is a Pulse Mass Index of 1.0 or less (0.7 to 1.0).

A Pulse Mass Index over 1.2 or 1.3 has a correlation with the Framingham Risk Score of 95% in people over 40 years old, but is more sensitive in younger persons, despite their Framingham RS being apparently normal because of their young age.

The practical usefulness of the Pulse Mass Index helps us to identify the persons with high risk that could need more intensive preventive measures or appropriate drugs, after confirmation with the Framingham Risk Score and other appropriate tests.

The Pulse Mass Index can also help us to identify younger patients with pre-diabetes, pre-hypertension, or metabolic syndrome that could also benefit from intensive prevention, including medication.

There is a close association of the PMI with the Metabolic Syndrome and it is also frequently elevated in overweight or obese persons with pre -diabetes, or pre-hypertensive.

The Pulse Mass Index is also the most simple and economical first clinical approach, followed by the Framingham RS for the risk evaluation in a large population of both men and women, and more so in the developing countries, where around 80% of all cardiovascular deaths occurs.

In the last few years, almost a decade after our original publication, several studies have confirmed the importance of the pulse or resting heart rate, as well as the body mass index, the two components of the Pulse Mass Index, as cardiovascular risk factors of first range. Among others the studies: BEAUTIFUL, EUROPA, QRISK, Women Health Initiative (WHI), the Framingham Heart Study and the Framingham Offspring Study.

The Pulse Mass Index is useful in two ways:

1.- For a preliminary, rapid, inexpensive, clinical evaluation of the cardiovascular risk, especially practical in younger people that could require an early prevention or medications, and

2.- To predict the potential benefit or risks of new cardiovascular, metabolic or other drugs.

Lately, we are studying the idea to improve the Pulse Mass Index to form the PULSE MASS PRESSURE PRODUCT (PMPP), an extension of both, the Pulse Mass Index and the Pulse Pressure Product.

I think that the PULSE MASS PRESSURE PRODUCT could give us as clinicians, a more exact initial evaluation of the global cardiovascular risk, of pre-diabetes, pre-hypertension and metabolic syndrome.

These patients should be screened, either with fasting plasma glucose or with GlycoHbA1c or both, and lipids profile.

They should be treated early, either with intensive lifestyle interventions, or appropriate drugs, according to the patients characteristics, to prevent cardiovascular events or complications of diabetes or hypertension in young pre-diabetics or pre-hypertensive people.

For instance, the PULSE MASS PRESSURE PRODUCT (PMPP) can be:

72 (RHR) x 24 (BMI) x 115 (SBP), which totals 198720 or round 200000 (or 200 k) as normal or basic values.

In the case of a young pre-diabetic or pre-hypertensive patient, if he had a PMPP of round 240 k (PMPP x 1.2), he could be a candidate for intensive lifestyle interventions.

If he had a PMPP of round 260k (PMPP x 1.3), he could be a serious candidate for prevention with lifestyle intervention and/or appropriate drugs.

If he had a PMPP of round 300k (PMPP x 1.5), he could be a definite candidate for treatment with appropriate drugs.

Something similar could apply for the metabolic syndrome and global cardiovascular risk.

I invite interested colleagues to evaluate this idea and contact with me to collaborate in this study.

Conflict of Interest:

None declared

When to clinically suspect Diabetes Mellitus
Posted on December 31, 2009
Emmanel M Bhaskar
Sri Ramachandra Medical College and Research Institute,Chennai,India
Conflict of Interest: None Declared

The prediction score for Diabetes Mellitus developed by Bang and colleagues is commendable[1].However it is less suitable for a near perfect application at a general practice clinic . Lets consider a 35 year old male with history of smoking presenting to a general practitioner for a simple upper respiratory infection. Apart from addressing his present complaint it is a moral responsibility of the clinician to quickly assess additional risk factors for chronic or occult disease.In this regard the patient may have additional symptoms which he may be ignoring and not revealing to the clinician.

Any practicing family physician or primary care physician will probably aggree that a significant number of patients who are ultimately diagnosed with diabetes have symptoms suggestive of chronic complications of diabetes or frequently associated diseases with diabetes for variable periods of time. These symptoms include the classical symptoms of hyperglycemia (increased thirst, increased frequency of urination and loss of weight), symptoms of coronary artery disease, peripheral neuropathy, retinopathy, chronic infections like dental caries, cutaneus fungal infection,recurrent urinary tract infection,etc.Should these symptoms be considered befor a decision is made about testing for diabetes? This is what conventional teaching advices us , but is backed with inadequate evidence .

It will be interesting to see these features in the baseline characteristics of the study cohort.It will also enable a practicing clinician to understand the relevance of asking for these symptoms in an patient who fails to put forth chronic symptoms as a result of mistaken self assumption.

References: 1. Heejung Bang,Alison M. Edwards,Andrew S. Bomback, Christie M. Ballantyne,David Brillon,Mark A et al. Development and Validation of a Patient Self-assessment Score for Diabetes Risk.Ann Intern Med 2009;151:775-783

Conflict of Interest:

None declared

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Summary for Patients

A New Diabetes Screening Score

The summary below is from the full report titled “Development and Validation of a Patient Self-assessment Score for Diabetes Risk.” It is in the 1 December 2009 issue of Annals of Internal Medicine (volume 151, pages 775-783). The authors are H. Bang, A.M. Edwards, A.S. Bomback, C.M. Ballantyne, D. Brillon, M.A. Callahan, S.M. Teutsch, A.I. Mushlin, and L.M. Kern.

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