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Value of risk stratification to increase the predictive validity of HbA1c in screening for undiagnosed diabetes in the US population.

ABSTRACT: BACKGROUND: Opportunistic screening using hemoglobin A1c (HbA1c) may improve detection of undiagnosed diabetes but remains controversial. OBJECTIVE: To evaluate the predictive validity of HbA1c as a screening test for undiagnosed diabetes in a risk-stratified sample of the US population. DESIGN: Weighted cross-sectional analysis of diabetes risk factors, HbA1c, and fasting plasma glucose (FPG) in National Health and Nutrition Examination Survey (NHANES), 1999-2004. SUBJECTS: Six thousand seven hundred and twenty-three NHANES participants from morning examination session, aged > or = 18 years and without prior physician-diagnosed diabetes. MEASUREMENTS: HbA1c and undiagnosed diabetes defined by FPG > or = 7.0 mmol/l (126 mg/dl). RESULTS: The estimated prevalence of undiagnosed diabetes in the US population was 2.8% (5.5 million people). HbA1c had strong correlation with undiagnosed diabetes, with an area under the receiver-operating characteristic curve of 0.93. Independent predictors of undiagnosed diabetes were older age, male sex, black race, hypertension, elevated waist circumference, elevated triglycerides, and low high-density lipoprotein cholesterol. We derived a risk score for undiagnosed diabetes and stratified participants into low (0.44% prevalence), moderate (4.1% prevalence), and high (11.1% prevalence) risk subgroups. In moderate and high risk groups, a threshold HbA1c value > or = 6.1% identified patients requiring confirmatory FPG; HbA1c < or = 5.4% identified patients for whom diabetes could be reliably excluded. Intermediate HbA1c (5.5-6.0%) may exclude diabetes in moderate, but not high risk groups). CONCLUSIONS: Risk stratification improves the predictive validity of HbA1c in screening for undiagnosed diabetes in the US population. Although opportunistic screening with HbA1c would improve detection of undiagnosed diabetes, cost-effectiveness studies are needed before implementation of specific screening strategies using HbA1c.

SUBMITTER: Ginde AA

PROVIDER: S-EPMC2517991 | biostudies-other | 2008 Sep

REPOSITORIES: biostudies-other

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Project description:BackgroundPrevalence of prediabetes and type 2 diabetes mellitus (T2DM) is increasing worldwide. The objective of this study was to determine the proportion of people in Northern Iceland with prediabetes, at risk of developing T2DM or with manifest undiagnosed T2DM, as this information is lacking in Iceland.MethodsA cross-sectional study. Clients of the three largest primary health care centres in the Health Care Institution of North Iceland (HSN) were invited to participate if fulfilling the following inclusion criteria: a) aged between 18 and 75 years, b) not diagnosed with diabetes, c) speaking and understanding Icelandic or English fluently and d) living in the included service area. Data collection took place via face-to-face interviews between 1 March 2020 and 15 May 2021. Participation included answering the Finnish Diabetes Risk Score (FINDRISC), measuring the HbA1c levels and background information.ResultsOf the 220 participants, 65.9% were women. The mean age was 52.1 years (SD ± 14.1) and FINDRISC scores were as follows: 47.3% scored ≤8 points, 37.2% scored between 9 and 14 points, and 15.5% scored between 15 and 26 points. The mean HbA1c levels in mmol/mol, were 35.5 (SD ± 3.9) for men and 34.4 (SD ± 3.4) for women, ranging from 24 to 47. Body mass index ≥30 kg/m2 was found in 32% of men and 35.9% of women. Prevalence of prediabetes in this cohort was 13.2%. None of the participants had undiagnosed T2DM. Best sensitivity and specificity for finding prediabetes was by using cut-off points of ≥11 on FINDRISC, which gave a ROC curve of 0.814.ConclusionsThe FINDRISC is a non-invasive and easily applied screening instrument for prediabetes. Used in advance of other more expensive and invasive testing, it can enable earlier intervention by assisting decision making, health promotion actions and prevention of the disease burden within primary health care.Trial registrationThis study is a pre-phase of the registered study "Effectiveness of Nurse-coordinated Follow up Program in Primary Care for People at risk of T2DM" at www.Clinicaltrialsgov (NCT01688359). Registered 30 December 2020.

Project description:In response to the type 2 diabetes epidemic, measuring HbA1c with the first-antenatal blood screen was recently recommended in NZ. This would enable prompt treatment of women with unrecognised type 2 diabetes, who may otherwise go undetected until the gestational diabetes (GDM) screen. We compare inter-ethnic antenatal screening practices to examine whether the HbA1c test would be accessed by ethnicities most at risk of diabetes, and we determined the prevalence of unrecognised type 2 diabetes and prediabetes in our pregnant population. This is an observational study of pregnancies in Christchurch NZ during 2008-2010. Utilising electronic databases, we matched maternal characteristics to first-antenatal bloods, HbA1c, and GDM screens (glucose challenge tests and oral glucose tolerance tests). Overall uptake of the first-antenatal bloods versus GDM screening was 83.1% and 53.8% respectively in 11,580 pregnancies. GDM screening was lowest in M?ori 39.3%, incidence proportion ratio (IPR) 0.77 (0.71, 0.84) compared with Europeans. By including HbA1c with the first-antenatal bloods, the number screened for diabetes increases by 28.5% in Europeans, 40.0% in M?ori, 28.1% in Pacific People, and 26.7% in 'Others' (majority of Asian descent). The combined prevalence of unrecognised type 2 diabetes and prediabetes by NZ criteria, HbA1c ?5.9% (41mmol/mol), was 2.1% in Europeans, M?ori 4.7% IPR 2.59 (1.71, 3.93), Pacific People 9.5% IPR 4.76 (3.10, 7.30), and 'Others' 6.2% IPR 2.99 (2.19, 4.07). Applying these prevalence data to 2013 NZ national births data, routine antenatal HbA1c testing could have identified type 2 diabetes in 0.44% and prediabetes in 3.96% of women. Routine HbA1c measurement in early pregnancy is an ideal screening opportunity, particularly benefitting vulnerable groups, reducing ethnic disparities in antenatal diabetes screening. This approach is likely to have world-wide relevance and applicability. Further research is underway to establish whether, as for type 2 diabetes, prompt treatment of prediabetes improves pregnancy and neonatal outcomes.

Project description:Diabetes mellitus is a group of metabolic diseases in which blood sugar levels are too high. About 8.8% of the world was diabetic in 2017. It is projected that this will reach nearly 10% by 2045. The major challenge is that when machine learning-based classifiers are applied to such data sets for risk stratification, leads to lower performance. Thus, our objective is to develop an optimized and robust machine learning (ML) system under the assumption that missing values or outliers if replaced by a median configuration will yield higher risk stratification accuracy. This ML-based risk stratification is designed, optimized and evaluated, where: (i) the features are extracted and optimized from the six feature selection techniques (random forest, logistic regression, mutual information, principal component analysis, analysis of variance, and Fisher discriminant ratio) and combined with ten different types of classifiers (linear discriminant analysis, quadratic discriminant analysis, naïve Bayes, Gaussian process classification, support vector machine, artificial neural network, Adaboost, logistic regression, decision tree, and random forest) under the hypothesis that both missing values and outliers when replaced by computed medians will improve the risk stratification accuracy. Pima Indian diabetic dataset (768 patients: 268 diabetic and 500 controls) was used. Our results demonstrate that on replacing the missing values and outliers by group median and median values, respectively and further using the combination of random forest feature selection and random forest classification technique yields an accuracy, sensitivity, specificity, positive predictive value, negative predictive value and area under the curve as: 92.26%, 95.96%, 79.72%, 91.14%, 91.20%, and 0.93, respectively. This is an improvement of 10% over previously developed techniques published in literature. The system was validated for its stability and reliability. RF-based model showed the best performance when outliers are replaced by median values.

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Value of risk stratification to increase the predictive validity of HbA1c in screening for undiagnosed diabetes in the US population.

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