A multivariate logistic regression equation to screen for dysglycaemia: development and validation

Cardiometabolic risk prediction algorithms for young people with psychosis: a systematic review and exploratory analysis

OBJECTIVE

Cardiometabolic risk prediction algorithms are common in clinical practice. Young people with psychosis are at high risk for developing cardiometabolic disorders. We aimed to examine whether existing cardiometabolic risk prediction algorithms are suitable for young people with psychosis.

METHODS

We conducted a systematic review and narrative synthesis of studies reporting the development and validation of cardiometabolic risk prediction algorithms for general or psychiatric populations. Furthermore, we used data from 505 participants with or at risk of psychosis at age 18 years in the ALSPAC birth cohort, to explore the performance of three algorithms (QDiabetes, QRISK3 and PRIMROSE) highlighted as potentially suitable. We repeated analyses after artificially increasing participant age to the mean age of the original algorithm studies to examine the impact of age on predictive performance.

RESULTS

We screened 7820 results, including 110 studies. All algorithms were developed in relatively older participants, and most were at high risk of bias. Three studies (QDiabetes, QRISK3 and PRIMROSE) featured psychiatric predictors. Age was more strongly weighted than other risk factors in each algorithm. In our exploratory analysis, calibration plots for all three algorithms implied a consistent systematic underprediction of cardiometabolic risk in the younger sample. After increasing participant age, calibration plots were markedly improved.

CONCLUSION

Existing cardiometabolic risk prediction algorithms cannot be recommended for young people with or at risk of psychosis. Existing algorithms may underpredict risk in young people, even in the face of other high-risk features. Recalibration of existing algorithms or a new tailored algorithm for the population is required.

Predicting Current Glycated Hemoglobin Values in Adults: Development of an Algorithm From the Electronic Health Record

Background

Electronic, personalized clinical decision support tools to optimize glycated hemoglobin (HbA_1c) screening are lacking. Current screening guidelines are based on simple, categorical rules developed for populations of patients. Although personalized diabetes risk calculators have been created, none are designed to predict current glycemic status using structured data commonly available in electronic health records (EHRs).

Objective

The goal of this project was to create a mathematical equation for predicting the probability of current elevations in HbA_1c (≥5.7%) among patients with no history of hyperglycemia using readily available variables that will allow integration with EHR systems.

Methods

The reduced model was compared head-to-head with calculators created by Baan and Griffin. Ten-fold cross-validation was used to calculate the bias-adjusted prediction accuracy of the new model. Statistical analyses were performed in R version 3.2.5 (The R Foundation for Statistical Computing) using the rms (Regression Modeling Strategies) package.

Results

The final model to predict an elevated HbA_1c based on 22,635 patient records contained the following variables in order from most to least importance according to their impact on the discriminating accuracy of the model: age, body mass index, random glucose, race, serum non–high-density lipoprotein, serum total cholesterol, estimated glomerular filtration rate, and smoking status. The new model achieved a concordance statistic of 0.77 which was statistically significantly better than prior models. The model appeared to be well calibrated according to a plot of the predicted probabilities versus the prevalence of the outcome at different probabilities.

Conclusions

The calculator created for predicting the probability of having an elevated HbA_1c significantly outperformed the existing calculators. The personalized prediction model presented in this paper could improve the efficiency of HbA_1c screening initiatives.

Screening for Type 2 Diabetes and Dysglycemia in Saudi Arabia: Development and Validation of Risk Scores

OBJECTIVE

The prevalence of type 2 diabetes in Saudi Arabia is the highest worldwide after excluding small island nations. We developed and validated a noninvasive screening test based on demographic and clinical data for identifying adults with undiagnosed diabetes and dysglycemia in Saudi Arabia.

RESEARCH DESIGN AND METHODS

Data from 1,485 nonpregnant Saudi adults ≥20 years of age without a current diagnosis of diabetes were obtained from urban and rural primary healthcare centers in 2009. Clinical and demographic data were obtained through physician-administered interviews. Oral glucose tolerance test data were used to define diabetes (fasting plasma glucose ≥7.0 mmol/L or 2-h post-load glucose ≥11.1 mmol/L) and dysglycemia (fasting plasma glucose ≥5.6 mmol/L or 2-h post-load glucose ≥7.8 mmol/L). Predictive models were developed using data from 1,435 individuals. Multivariable logistic regression and receiver operating characteristic curves were used to develop and evaluate a separate risk score for both diabetes and dysglycemia. Scores were validated on a hold-out sample of 50 individuals.

RESULTS

The risk score for undiagnosed diabetes contained age, history of gestational diabetes, smoking, family history of diabetes, and central obesity with a sensitivity of 76.6% and a specificity of 52.1%. The dysglycemia risk score contained age, gestational diabetes, hypertension, and central obesity with a sensitivity of 71.2% and a specificity of 54.0%. All performed equally well, if not better, in the hold-out sample.

CONCLUSIONS

These risk scores can identify Saudi adults with undiagnosed diabetes or dysglycemia and should be validated in prospective studies.

Guiding diabetes screening and prevention: rationale, recommendations and remaining challenges

Advances made in diabetes management are not sufficient to reduce morbidity, mortality and cost without making prevention efforts at various levels imperative for substantial impact. Research has demonstrated the efficacy of lifestyle intervention and medications in preventing type 2 diabetes among diverse high-risk groups commonly identified with oral glucose tolerance testing. Efficacy, sustainability and safety data are most comprehensive for lifestyle and metformin, with other medications also demonstrating efficacy and potential in the pharmacoprevention of diabetes. Subsequent implementation studies have demonstrated feasibility of lifestyle intervention programs at health centers, communities, and at local and national government levels. Challenges remain in widespread translation and reaching and engaging at-risk individuals and populations.

Screening for impaired fasting glucose and diabetes using available health plan data

AIMS

To develop and validate prediction equations to identify individuals at high risk for type 2 diabetes using existing health plan data.

METHODS

Health plan data from 2005 to 2009 from 18,527 members of a Midwestern HMO without diabetes, 6% of whom had fasting plasma glucose (FPG) ≥110mg/dL, and health plan data from 2005 to 2006 from 368,025 members of a West Coast-integrated delivery system without diabetes, 13% of whom had FPG ≥110mg/dL were analyzed. Within each health plan, we used multiple logistic regression to develop equations to predict FPG ≥110mg/dL for half of the population and validated the equations using the other half. We then externally validated the equations in the other health plan.

RESULTS

Areas under the curve for the most parsimonious equations were 0.665 to 0.729 when validated internally. Positive predictive values were 14% to 32% when validated internally and 14% to 29% when validated externally.

CONCLUSION

Multivariate logistic regression equations can be applied to existing health plan data to efficiently identify persons at higher risk for dysglycemia who might benefit from definitive diagnostic testing and interventions to prevent or treat diabetes.

Risk scores for diabetes and impaired glycaemia in the Middle East and North Africa

AIMS

To develop risk scores for diabetes and diabetes or impaired glycaemia for individuals living in the Middle East and North Africa region. In addition, to derive national risk scores for Algeria, Saudi Arabia and the United Arab Emirates and to compare the performance of the regional risk scores with the national risk scores.

METHODS

An opportunistic sample of 6588 individuals aged 30-75 years was screened. Screening consisted of a questionnaire and a clinical examination including measurement of HbA(1c). Two regional risk scores and national risk scores for each of the three countries were derived separately by stepwise backwards multiple logistic regression with diabetes [HbA(1c) ≥ 48 mmol/mol (≥ 6.5%)] and diabetes or impaired glycaemia [HbA(1c) ≥ 42 mmol/mol (≥ 6.0%)] as outcome. The performance of the regional and national risk scores was compared in data from each country by receiver operating characteristic analysis.

RESULTS

The eight risk scores all included age and BMI, while additional variables differed between the scores. The areas under the receiver operating characteristic curves were between 0.67 and 0.70, and for sensitivities approximately 75%; specificities varied between 50% and 57%. The regional and the national risk scores performed equally well in the three national samples.

CONCLUSIONS

Two regional risk scores for diabetes and diabetes or impaired glycaemia applicable to the Middle East and North Africa region were identified. The regional risk scores performed as well as the national risk scores derived in the same manner.

Can HbA1c and one-hour glucose concentration in standard OGTT be used for evaluation of glucose homeostasis in childhood?

OBJECTIVE

To investigate whether glycosylated hemoglobin (HbA1c) and 1-hour glucose level in oral glucose tolerance test (OGTT) are useful parameters for evaluation of glucose homeostasis in childhood.

METHODS

The medical records of 106 obese/overweight children aged from 7 to 18 years who underwent OGTT were evaluated retrospectively. The subjects were divided into 2 groups according to their one-hour glucose concentration. Group 1 consisted of subjects whose one-hour glucose level was <155 mg/dL, and Group 2 consisted of subjects whose one-hour glucose level was ≥155 mg/dL. The fasting and 2-hour glucose concentrations of the groups were compared. The sensitivity and specificity levels were determined using the ROC curve to assess the predictive value of HbA1c for impaired glucose tolerance (IGT).

RESULTS

The mean 2-hour glucose concentration of the subjects in Group 2 was significantly higher than that of the subjects in Group 1 (137.8±35.5 mg/dL versus 113.1±21.2 mg/dL, p<0.05). If a 5.5% cut-off value for HbA1c was accepted as predictor of IGT, the sensitivity was 63% and specificity was 70%. 31% of the subjects with HbA1c levels at or above 5.5% had IGT. This rate was significantly lower in subjects who had HbA1c levels below 5.5% (p<0.05).

CONCLUSIONS

Obese/overweight children and adolescents whose 1-hour glucose level is ≥155 mg/dL in the standard OGTT carry a high risk for IGT. Obese/overweight children and adolescents whose HbA1c level is at or above 5.5% may have IGT even though their fasting glucose level is normal, thus, OGTT is necessary to evaluate the glucose tolerance.

Survey of diabetes risk assessment tools: concepts, structure and performance

The objective of this study is to review the effectiveness and limitations of existing diabetes risk screening tools to assess the need for further developing of such tools. An electronic search of the EMBASE, MEDLINE, and Cochrane library supplemented by a manual search was performed from 1995-2010. The search retrieved a total of 2168 articles reporting diabetes risk assessment tools which, after culling, produced 41 tools developed in 22 countries, with the majority (n = 26) developed in North America and Europe. All are short questionnaires of 2-16 questions incorporating common variables including age, gender, waist circumference, BMI, family history of diabetes, history of hypertension or antihypertensive medications. While scoring format and cut-offs point are diverse between questionnaires, overall accuracy value range of 40-97%, 24-86% and 62-87% were reported for sensitivity, specificity and receiver operating characteristic curve respectively. In summary, there is a trend of increasing availability of diabetes prediction tools with the existing risk assessment tools being generally a short questionnaire aiming for ease of use in clinical practice. The overall performance of existing tools showed moderate to high accuracy in their predictive performance. However, further detailed comparison of existing questionnaires is needed to evaluate whether they can serve adequately as diabetes risk assessment tool in clinical practice.

Random plasma glucose values measured in community dental practices: findings from the Dental Practice-Based Research Network

OBJECTIVES

This study aimed to examine feasibility of testing and frequency of abnormal plasma glucose among dental patients in The Dental Practice-Based Research Network.

METHODS

Eligible dental patients were ≥19 years old and had at least one American Diabetes Association-defined risk factor for diabetes mellitus or an existing diagnosis of diabetes or pre-diabetes. Random (fasting not required) plasma glucose was measured in standardized fashion using a commercial glucometer. Readings <70 or >300 mg/dl triggered re-testing. Patients with glucose ≥126 mg/dl were referred for medical follow-up.

RESULTS

Of 498 subjects in 28 dental practices, 491 (98 %) consented and 418 (85.1 %) qualified for testing. Fifty-one patients (12.2 %) had diabetes; 24 (5.7 %) had pre-diabetes. Glucose ranged from 50 to 465 mg/dl. One hundred twenty-nine subjects (31 %) had readings outside the normal range; of these, 28 (6.7 %) had readings <80 mg/dl and 101 (24.2 %) had readings ≥126 mg/dl; in nine patients (seven with diabetes), glucose was >200 mg/dl.

CONCLUSIONS

A significant proportion of patients tested had abnormal blood glucose. Routine glucose testing in dental practice of populations at risk or diagnosed with diabetes may be beneficial and community dental practices hold promise as settings for diabetes and pre-diabetes screening and monitoring.

CLINICAL RELEVANCE

Results suggest that implementation of glucose measurement in dental practice may provide important clinical and health information for both patients and practitioners.

A globally applicable screening model for detecting individuals with undiagnosed diabetes

AIMS

Current risk scores for undiagnosed diabetes are additive in structure. We sought to derive a globally applicable screening model based on established non-invasive risk factors for diabetes but with a more flexible structure.

METHODS

Data from the DETECT-2 study were used, including 102,058 participants from 38 studies covering 8 geographical regions worldwide. A global screening model for undiagnosed diabetes was identified through tree-structured regression analysis. The performance of the global screening model was evaluated in each of the geographical regions by receiver operating characteristic (ROC) analysis.

RESULTS

The global screening model included age, height, body mass index, waist circumference and systolic- and diastolic blood pressure. Area under the ROC curve ranged between 0.64 in North America and 0.76 in Australia and New Zealand. Overall, to identify 75% of the undiagnosed diabetes cases, 49% required further diagnostic testing.

CONCLUSIONS

We identified a globally applicable screening model to detect individuals at high risk of undiagnosed diabetes. The model performed well in most geographical regions, is simple and requires no calculations. This global screening model may be particularly helpful in developing countries with no population based data with which to develop own screening models.

Screening for type 2 diabetes and dysglycemia

Type 2 diabetes mellitus (T2DM) and dysglycemia (impaired glucose tolerance and/or impaired fasting glucose) are increasingly contributing to the global burden of diseases. The authors reviewed the published literature to critically evaluate the evidence on screening for both conditions and to identify the gaps in current understanding. Acceptable, relatively simple, and accurate tools can be used to screen for both T2DM and dysglycemia. Lifestyle modification and/or medication (e.g., metformin) are cost-effective in reducing the incidence of T2DM. However, their application is not yet routine practice. It is unclear whether diabetes-prevention strategies, which influence cardiovascular risk favorably, will also prevent diabetic vascular complications. Cardioprotective therapies, which are cost-effective in preventing complications in conventionally diagnosed T2DM, can be used in screen-detected diabetes, but the magnitude of their effects is unknown. Economic modeling suggests that screening for both T2DM and dysglycemia may be cost-effective, although empirical data on tangible benefits in preventing complications or death are lacking. Screening for T2DM is psychologically unharmful, but the specific impact of attributing the label of dysglycemia remains uncertain. Addressing these gaps will inform the development of a screening policy for T2DM and dysglycemia within a holistic diabetes prevention and control framework combining secondary and high-risk primary prevention strategies.

Investigation of insulin resistance in narcoleptic patients: dependent or independent of body mass index?

BACKGROUND

Narcolepsy is a severe sleep-wake cycle disorder resulting in most cases from a lack of orexin, the energy balance-regulating hormone. Narcoleptic patients have been reported to suffer from an excess morbidity of Type 2 diabetes, even after correction for their often elevated body mass index.

METHODS

To explore whether narcolepsy is specifically associated with a propensity to develop insulin resistance, we measured fasting glucose, insulin, and intact proinsulin levels in 43 narcoleptic patients and 47 controls matched for body mass index and age. The proinsulin-to-insulin ratio was calculated. Insulin resistance was determined using the homeostatic model assessment method.

RESULTS

Narcoleptic patients did not show elevated insulin resistance parameters.

CONCLUSION

In contrast with earlier reports, we found no evidence that narcolepsy specifically elevates the risk of insulin resistance (and consequently of type 2 diabetes) independently of body mass index.

Improvements in ability to detect undiagnosed diabetes by using information on family history among adults in the United States

Family history is an independent risk factor for diabetes, but it is not clear how much adding family history to other known risk factors would improve detection of undiagnosed diabetes in a population. Using the National Health and Nutrition Examination Survey for 1999-2004, the authors compared logistic regression models with established risk factors (model 1) with a model (model 2) that also included familial risk of diabetes (average, moderate, and high). Adjusted odds ratios for undiagnosed diabetes, using average familial risk as referent, were 1.7 (95% confidence interval (CI): 1.2, 2.5) and 3.8 (95% CI: 2.2, 6.3) for those with moderate and high familial risk, respectively. Model 2 was superior to model 1 in detecting undiagnosed diabetes, as reflected by several significant improvements, including weighted C statistics of 0.826 versus 0.842 (bootstrap P = 0.001) and integrated discrimination improvement of 0.012 (95% CI: 0.004, 0.030). With a risk threshold of 7.3% (sensitivity of 40% based on model 1), adding family history would identify an additional 620,000 (95% CI: 221,100, 1,020,000) cases without a significant change in false-positive fraction. Study findings suggest that adding family history of diabetes can provide significant improvements in detecting undiagnosed diabetes in the US population. Further research is needed to validate the authors' findings.

Validation of a type 2 diabetes screening tool in rural Honduras

OBJECTIVE

To validate a low-cost tool for identifying diabetic patients in rural areas of Latin America.

RESEARCH DESIGN AND METHODS

A regression equation incorporating postprandial time and a random plasma glucose was used to screen 800 adults in Honduras. Patients with a probability of diabetes of > or =20% were asked to return for a fasting plasma glucose (FPG). A random fifth of those with a screener-based probability of diabetes <20% were also asked to return for follow-up. The gold standard was an FPG > or =126 mg/dl.

RESULTS

The screener had very good test characteristics (area under the receiver operating characteristic curve = 0.89). Using the screening criterion of > or =0.42, the equation had a sensitivity of 74.1% and specificity of 97.2%.

CONCLUSIONS

This screener is a valid measure of diabetes risk in Honduras and could be used to identify diabetic patients in poor clinics in Latin America.

Fine-tuning of prediction of isolated impaired glucose tolerance: a quantitative clinical prediction model

In this cross-sectional study, we evaluated results of oral glucose tolerance test (OGTT) from 4742 women and 3470 men, participated in the Tehran Lipid and Glucose Study, aged >or=20 years and without diabetes, to determine the diagnostic value of subjects' clinical traits with isolated impaired glucose tolerance (isolated-IGT) defined as fasting plasma glucose (FPG) <5.6 mmol/L and 2-h plasma glucose between 7.8 and 11.1 mmol/L. The overall prevalence of IGT was 13.6% (n=1120); of these subjects, 59.6% (n=668) had isolated-IGT. The adjusted odds ratios for having isolated-IGT among 7012 subjects with FPG <5.6 mmol/L were significant for age >or=40 years (2.5), hypertension (1.9), abnormal waist circumference (1.9), obesity (1.5), and family history of diabetes (1.3). Adding the lipid profiles to the clinical model increased the area under the ROC curve only slightly (73.2% vs. 72.1%, respectively; P=0.002). In summary, this study showed that in adults with FPG <5.6 mmol/L, older age, family history of diabetes, abnormal waist circumference and obesity, and hypertension were significantly associated with a higher likelihood of isolated-IGT; OGTT could hence be recommended in subjects who have most of these characteristics to find Isolated-IGT, especially if the findings are supported by appropriately designed clinical trials.

A new look at screening and diagnosing diabetes mellitus

OBJECTIVE

Diabetes is underdiagnosed. About one third of people with diabetes do not know they have it, and the average lag between onset and diagnosis is 7 yr. This report reconsiders the criteria for diagnosing diabetes and recommends screening criteria to make case finding easier for clinicians and patients.

PARTICIPANTS

R.M.B. invited experts in the area of diagnosis, monitoring, and management of diabetes to form a panel to review the literature and develop consensus regarding the screening and diagnosis of diabetes with particular reference to the use of hemoglobin A1c (HbA1c). Participants met in open session and by E-mail thereafter. Metrika, Inc. sponsored the meeting.

EVIDENCE

A literature search was performed using standard search engines.

CONSENSUS PROCESS

The panel heard each member's discussion of the issues, reviewing evidence prior to drafting conclusions. Principal conclusions were agreed on, and then specific cut points were discussed in an iterative consensus process.

CONCLUSIONS

The main factors in support of using HbA1c as a screening and diagnostic test include: 1) HbA1c does not require patients to be fasting; 2) HbA1c reflects longer-term glycemia than does plasma glucose; 3) HbA1c laboratory methods are now well standardized and reliable; and 4) errors caused by nonglycemic factors affecting HbA1c such as hemoglobinopathies are infrequent and can be minimized by confirming the diagnosis of diabetes with a plasma glucose (PG)-specific test. Specific recommendations include: 1) screening standards should be established that prompt further testing and closer follow-up, including fasting PG of 100 mg/dl or greater, random PG of 130 mg/dl or greater, or HbA1c greater than 6.0%; 2) HbA1c of 6.5-6.9% or greater, confirmed by a PG-specific test (fasting plasma glucose or oral glucose tolerance test), should establish the diagnosis of diabetes; and 3) HbA1c of 7% or greater, confirmed by another HbA1c- or a PG-specific test (fasting plasma glucose or oral glucose tolerance test) should establish the diagnosis of diabetes. The recommendations are offered for consideration of the clinical community and interested associations and societies.

Likelihood of having isolated postchallenge hyperglycemia in an Iranian urban population

To identify subjects who would most likely benefit from oral glucose tolerance test (OGTT) for diagnosis of diabetes mellitus (DM), namely isolated postchallenge hyperglycemia (IPH) (i.e. FPG<126mg/dl and 2h-PG>or=200mg/dl), we evaluated data and results of OGTT of 9745 participants of Tehran Lipid and Glucose Study (TLGS), aged >20 years and without previously diagnosed DM. The overall prevalence of IPH was 3.1% (95% CI: 2.8-3.4%, n=302). In the multivariate logistic regression analysis, the odds ratios (OR) for IPH were statistically significant for FPG>or=100mg/dl (OR 9.5; 95% CI: 7.1-12.5), age >or=40 years (OR 2.6; 95% CI: 1.8-3.7), triglycerides >or=200mg/dl (OR 2.1; 95% CI: 1.6-2.7), hypertension (OR 2.0; 95% CI: 1.5-2.6) and abnormal waist circumference (OR 1.9; 95% CI: 1.3-2.8). In subjects with FPG<126mg/dl, findings that best distinguished between IPH and non-diabetic subjects were FPG>or=100mg/dl [positive likelihood ratio (LR(+))=5.2], FPG>or=100mg/dl together with triglycerides >or=200mg/dl [LR(+)=9.7] and a combination of all the five factors [LR(+)=12.9]. This analysis showed that in Iranian urban subjects with FPG<126mg/dl, factors such as FPG>or=100mg/dl, older age, hypertriglyceridemia, hypertension and abnormal waist circumference were the best predictors of presence of IPH; OGTT would hence be recommended for opportunistic screening of IPH in subjects with above mentioned characteristics.

Clinician vs mathematical statistical models: which is better at predicting an abnormal chest radiograph finding in injured patients?

OBJECTIVE

The purpose of this study was to determine if statistical models for prediction of chest injuries would outperform the clinician's (MD) ability to identify injured patients at risk for a thoracic injury diagnosed by chest radiograph (CXR).

DESIGN

A prospective observational study was done during a 12-month period.

SETTING

The study was conducted in a level I trauma center.

PATIENTS

Injured patients meeting trauma team activation criteria were enrolled to the study.

INTERVENTIONS

Physical examination findings by a clinician were interpreted and CXR was performed.

OUTCOME MEASURES

The accuracy of 2 mathematical models is compared against the accuracy of clinician's clinical judgment in predicting an injury by CXR. Two newly constructed multivariate models, binary logistic regression (LR) and classification and regression tree (CaRT) analysis, are compared to previously published data of clinician clinical assessment of probability of thoracic injury identified by CXR.

RESULTS

Data for 757 patients were analyzed. Classification and regression tree analysis developed a stepwise decision tree to determine which signs/symptoms were indicative of an abnormal CXR finding. The sensitivity (CaRT, 36.6%; LR, 36.3%; MD, 58.7%), specificity (CaRT, 98.3%; LR, 98.2%; MD, 96.4%), and error rates (CaRT, 0.93; LR, 0.94; MD, 0.82) show that the mathematical decision aids are less sensitive and risk more misclassification compared to clinician judgment in predicting an injury by CXR.

CONCLUSION

Clinician judgment was superior to mathematical decision aids for predicting an abnormal CXR finding in injured patients with chest trauma.