Diabetes risk score in Oman: a tool to identify prevalent type 2 diabetes among Arabs of the Middle East

Development of type 2 diabetes risk assessment model for Turkish society

Purpose

The study was conducted to develop a risk assessment tool to determine the Turkish population's risk of undiagnosed type 2 diabetes.

Methods

The study was carried out in a methodological design. A total of 610 individuals, including those diagnosed with diabetes (321) and not diagnosed with diabetes (289), who applied to the internal medicine and diabetes outpatient clinics of a public hospital, were included in the study. The sample of patients with diabetes was created with the individuals who applied to diabetes outpatient clinics, were 40 years of age and older, and had the values of FPG ≥ 126 mg/dl and HbA1C ≥ 6.5%. The sample of healthy individuals consisted of people over the age of 40 who were not diagnosed with diabetes or prediabetes. Logistic regression and random forest algorithms were used to evaluate the diabetes risk of individuals. The performance of the models was evaluated with sensitivity, specificity, accuracy, and area under the ROC (AUC).

Result

In the study, the variables of exercise in daily routines, presence of prediabetes, getting angry, feeling hungry frequently, and excessive thirst formed the diabetes risk assessment model with Sensitivity 0.983 and Specificity 0.984 according to the logistic regression model obtained. Body mass index, physical activity, age, gender, and family history of diabetes were not found to be significant in differentiating cases with diabetes (0.05 < p).

Conclusion

This diabetes risk assessment tool is a reliable tool for Turkish society to identify individuals at high risk for diabetes and to raise awareness of the importance of modifiable risk factors.

Prevalence and Factors Associated with Metabolic Syndrome among Non-Diabetic Saudi Adults: A Cross-Sectional Study

(1) Introduction: given the high prevalence of metabolic syndrome (MetS) in Saudi Arabia, especially in Jeddah, this study aims to understand the dietary and lifestyle-related risk factors among Jeddah's non-diabetic adults. (2) Material and Methods: Employing a cross-sectional design, non-diabetic adults were sourced from public healthcare centers. Demographics, lifestyle, and dietary habits were surveyed. Blood pressure, anthropometrics, and fasting blood samples measuring plasma glucose, serum triglycerides, and HDL cholesterol were collected. The age cut-off for MetS was ascertained using the receiver operating characteristic curve. Variables influencing MetS were evaluated using univariate logistic regression, and consequential factors underwent multivariate analysis, adjusted for age and sex. (3) Results: Among 1339 participants, 16% had MetS, with age being the strongest predictor (p < 0.001). The optimal age cut-off was 32 years. For those <32, elevated BP in men and waist circumference (WC) in women were most prevalent. For those >32, elevated WC was dominant in both sexes. Univariate logistic regression revealed that higher income and education correlated with lower MetS prevalence, while marriage and smoking were risk factors. Adjusting for age and sex, only very high income had a significant low-risk association (p = 0.034). (4) Conclusion: MetS is notable in the studied group, with age as the pivotal predictor. High income reduces MetS risk, while marital status and smoking could increase it. Since this was a cross-sectional study, cohort studies are needed to validate our findings.

Validation of prevalent diabetes risk scores based on non-invasively measured predictors in Ghanaian migrant and non-migrant populations - The RODAM study

Background

Non-invasive diabetes risk models are a cost-effective tool in large-scale population screening to identify those who need confirmation tests, especially in resource-limited settings.

Aims

This study aimed to evaluate the ability of six non-invasive risk models (Cambridge, FINDRISC, Kuwaiti, Omani, Rotterdam, and SUNSET model) to identify screen-detected diabetes (defined by HbA1c) among Ghanaian migrants and non-migrants.

Study design

A multicentered cross-sectional study.

Methods

This analysis included 4843 Ghanaian migrants and non-migrants from the Research on Obesity and Diabetes among African Migrants (RODAM) Study. Model performance was assessed using the area under the receiver operating characteristic curves (AUC), Hosmer-Lemeshow statistics, and calibration plots.

Results

All six models had acceptable discrimination (0.70 ≤ AUC <0.80) for screen-detected diabetes in the overall/combined population. Model performance did not significantly differ except for the Cambridge model, which outperformed Rotterdam and Omani models. Calibration was poor, with a consistent trend toward risk overestimation for screen-detected diabetes, but this was substantially attenuated by recalibration through adjustment of the original model intercept.

Conclusion

Though acceptable discrimination was observed, the original models were poorly calibrated among populations of African ancestry. Recalibration of these models among populations of African ancestry is needed before use.

External validation of non-invasive diabetes score in a 15-year prospective study

BACKGROUND

A novel scoring system called Non-invasive Diabetes Score (NDS) was developed. The model showed prominent discrimination and calibration in the original study population. However, before a new model could be adopted in clinical practice and acquire widespread use, it is necessary to confirm that it also performs well in external validations in different settings of people. The aim of this study was to investigate whether the novel user-friendly score predicting diabetes mellitus (DM) could have satisfying performance in predicting DM in Southwest China in a 15-year prospective cohort study.

METHODS

This prospective cohort study was carried out based on a general Chinese population of 711 individuals from 1992 to 2007. We excluded 24 of them at baseline because they were diabetics. The end point was DM, and the risk was calculated using the model formula.

RESULTS

During a follow-up of 15 years, 74 (10.77%) patients reached the end point. Evaluation of this model in our cohort, with Harrell's C-index of 0.662 (95% CI: 0.600-0.723) for the whole cohort and 0.695 (95% CI: 0.635-0.756) in sensitivity analysis, indicated the possibly helpful discrimination. The calibration capability in our cohort was useful that the observed incidence of diabetes mellitus was near the predicted.

CONCLUSIONS

Our external validation suggested NDS had possibly helpful discrimination and satisfying calibration for predicting DM during 15-year follow-up.

Non-invasive type 2 diabetes risk scores do not identify diabetes when the cause is β-cell failure: The Africans in America study

Background

Emerging data suggests that in sub-Saharan Africa β-cell-failure in the absence of obesity is a frequent cause of type 2 diabetes (diabetes). Traditional diabetes risk scores assume that obesity-linked insulin resistance is the primary cause of diabetes. Hence, it is unknown whether diabetes risk scores detect undiagnosed diabetes when the cause is β-cell-failure.

Aims

In 528 African-born Blacks living in the United States [age 38 ± 10 (Mean ± SE); 64% male; BMI 28 ± 5 kg/m²] we determined the: (1) prevalence of previously undiagnosed diabetes, (2) prevalence of diabetes due to β-cell-failure vs. insulin resistance; and (3) the ability of six diabetes risk scores [Cambridge, Finnish Diabetes Risk Score (FINDRISC), Kuwaiti, Omani, Rotterdam, and SUNSET] to detect previously undiagnosed diabetes due to either β-cell-failure or insulin resistance.

Methods

Diabetes was diagnosed by glucose criteria of the OGTT and/or HbA1c ≥ 6.5%. Insulin resistance was defined by the lowest quartile of the Matsuda index (≤ 2.04). Diabetes due to β-cell-failure required diagnosis of diabetes in the absence of insulin resistance. Demographics, body mass index (BMI), waist circumference, visceral adipose tissue (VAT), family medical history, smoking status, blood pressure, antihypertensive medication, and blood lipid profiles were obtained. Area under the Receiver Operator Characteristics Curve (AROC) estimated sensitivity and specificity of each continuous score. AROC criteria were: Outstanding: >0.90; Excellent: 0.80-0.89; Acceptable: 0.70-0.79; Poor: 0.50-0.69; and No Discrimination: 0.50.

Results

Prevalence of diabetes was 9% (46/528). Of the diabetes cases, β-cell-failure occurred in 43% (20/46) and insulin resistance in 57% (26/46). The β-cell-failure group had lower BMI (27 ± 4 vs. 31 ± 5 kg/m² P < 0.001), lower waist circumference (91 ± 10 vs. 101 ± 10cm P < 0.001) and lower VAT (119 ± 65 vs. 183 ± 63 cm³, P < 0.001). Scores had indiscriminate or poor detection of diabetes due to β-cell-failure (FINDRISC AROC = 0.49 to Cambridge AROC = 0.62). Scores showed poor to excellent detection of diabetes due to insulin resistance, (Cambridge AROC = 0.69, to Kuwaiti AROC = 0.81).

Conclusions

At a prevalence of 43%, β-cell-failure accounted for nearly half of the cases of diabetes. All six diabetes risk scores failed to detect previously undiagnosed diabetes due to β-cell-failure while effectively identifying diabetes when the etiology was insulin resistance. Diabetes risk scores which correctly classify diabetes due to β-cell-failure are urgently needed.

COVID-19 risk score as a public health tool to guide targeted testing: A demonstration study in Qatar

We developed a Coronavirus Disease 2019 (COVID-19) risk score to guide targeted RT-PCR testing in Qatar. The Qatar national COVID-19 testing database, encompassing a total of 2,688,232 RT-PCR tests conducted between February 5, 2020-January 27, 2021, was analyzed. Logistic regression analyses were implemented to derive the COVID-19 risk score, as a tool to identify those at highest risk of having the infection. Score cut-off was determined using the ROC curve based on maximum sum of sensitivity and specificity. The score’s performance diagnostics were assessed. Logistic regression analysis identified age, sex, and nationality as significant predictors of infection and were included in the risk score. The ROC curve was generated and the area under the curve was estimated at 0.63 (95% CI: 0.63–0.63). The score had a sensitivity of 59.4% (95% CI: 59.1%-59.7%), specificity of 61.1% (95% CI: 61.1%-61.2%), a positive predictive value of 10.9% (95% CI: 10.8%-10.9%), and a negative predictive value of 94.9% (94.9%-95.0%). The concept and utility of a COVID-19 risk score were demonstrated in Qatar. Such a public health tool can have considerable utility in optimizing testing and suppressing infection transmission, while maximizing efficiency and use of available resources.

Developing a risk score for undiagnosed prediabetes or type 2 diabetes among Saharawi refugees in Algeria

AIMS

To prevent type 2 diabetes mellitus (T2D) and reduce the risk of complications, early identification of people at risk of developing T2D, preferably through simple diabetes risk scores, is essential. The aim of this study was to create a risk score for identifying subjects with undiagnosed prediabetes or T2D among Saharawi refugees in Algeria and compare the performance of this score to the Finnish diabetes risk score (FINDRISC).

METHODS

A cross-sectional survey was carried out in five Saharawi refugee camps in Algeria in 2014. A total of 180 women and 175 men were included. HbA1c and cut-offs proposed by the American Diabetes Association (ADA) were used to define cases. Variables to include in the risk score were determined by backwards elimination in logistic regression. Simplified scores were created based on beta coefficients from the multivariable model after internal validation with bootstrapping and shrinkage. The empirical cut-off value for the simplified score and FINDRISC was determined by Area Under the Receiver Operating Curve (AUROC) analysis.

RESULTS

Variables included in the final risk score were age, body mass index (BMI), and waist circumference. The area under the curve (AUC) (C.I) was 0.82 (0.76, 0.88). The sensitivity, specificity, and positive and negative predictive values were 89, 65, 28, and 97%, respectively. AUC and sensitivity were slightly higher and specificity somewhat lower than for FINDRISC.

CONCLUSIONS

The risk score developed is a helpful tool to decide who should be screened for prediabetes or T2D by blood sample analysis. The performance of the risk score was adequate based on internal validation with bootstrap analyses, but should be confirmed in external validation studies.

Association between anthropometric indices and non-anthropometric components of the metabolic syndrome in Saudi adults

Context

Waist circumference (WC) is used in screening for metabolic syndrome (MetS) based on its association with cardiometabolic risk. This might apply differently in ethnically different populations. Associations with other measures are also unclear.

Objective

This work aimed to investigate the association between neck circumference (NC), WC, WC:hip circumference, WC:height (WC:Ht), NC:Ht, fat percentage, body mass index (BMI), conicity index, abdominal volume index, and weight-adjusted waist index with nonanthropometric components of MetS in nondiabetic Saudi adults.

Methods

This cross-sectional study took place in public health centers in Jeddah, comprising 1365 Saudi adults (772 men and 593 women) aged 18 years or older not previously diagnosed with diabetes.

Main outcome measures included

the presence of 2 or more nonanthropometric components of the MetS were used to define clinical metabolic abnormality (CMA). The predictive ability of studied anthropometric indices for CMA was determined using the area under receiver operating characteristics (AUC) curve and binary logistic regression.

Results

A total of 157 men and 83 women had CMA. NC and NC:Ht had the highest predictive ability for CMA in men (odds ratio [OR]_NC = 1.79, P < .001 and OR_NC:Ht = 1.68, P < .001; AUC_NC = 0.69 [95% CI, 0.64-0.74] and AUC_NC:Ht = 0.69 [95% CI, 0.64-0.73]). In women, WC had the highest predictive ability OR_WC = 1.81, P < .001; AUC_WC = 0.75 [95% CI, 0.69-0.80]).

Conclusion

Upper-body anthropometric indicators that were associated with subcutaneous fat had the highest predictive ability for CMA in men whereas abdominal obesity indictors had the best predictive ability in women, suggesting that fat distribution might contribute to CMA in a sex-specific manner.

A systematic review of diabetes risk assessment tools in sub-Saharan Africa

Objectives

To systematically review all current studies on diabetes risk assessment tools used in SSA to diagnose diabetes in symptomatic and asymptomatic patients.

Methods

Tools were identified through a systematic search of PubMed, Ovid, Google Scholar, and the Cochrane Library for articles published from January 2010 to January 2020. The search included articles reporting the use of diabetes risk assessment tool to detect individuals with type 2 diabetes in SSA. A standardized protocol was used for data extraction (registry #177726).

Results

Of the 825 articles identified, 39 articles met the inclusion criteria, and three articles reported tools used in SSA population but developed for the Western population. None was validated in SSA population. All but three articles were observational studies (136 and 58,657 study participants aged between the ages of 15 and 85 years). The Finnish Medical Association risk tool, World Health Organization (WHO) STEPS instrument, General Practice Physical Activity Questionnaire (GPPAQ), Rapid Eating and Activity Assessment for Patients (REAP), and an anthropometric tool were the most frequently used non-invasive tools in SSA. The accuracy of the tools was measured using sensitivity, specificity, or area under the receiver operating curve. The anthropometric predictor variables identified included age, body mass index, waist circumference, positive family of diabetes, and activity levels.

Conclusions

This systematic review demonstrated a paucity of validated diabetes risk assessment tools for SSA. There remains a need for the development and validation of a tool for the rapid identification of diabetes for targeted interventions.

Screening for diabetes and impaired glucose metabolism in Qatar: Models' development and validation

AIM

To establish two scoring models for identifying individuals at risk of developing Impaired Glucose Metabolism (IGM) or Type two Diabetes Mellitus (T2DM) in Qatari.

MATERIALS AND METHODS

A sample of 2000 individuals, from Qatar BioBank, was evaluated to determine features predictive of T2DM and IGM. Another sample of 1000 participants was obtained for external validation of the models. Several scoring models screening for T2DM were evaluated and compared to the model proposed by this study.

RESULTS

Age, gender, waist-to-hip-ratio, history of hypertension and hyperlipidemia, and levels of educational were statistically associated with the risk of T2DM and constituted the Qatar diabetes mellitus risk score (QDMRISK). Along with, the 6 aforementioned variables, the IGM model showed that BMI was statistically significant. The QDMRISK performed well with area under the curve (AUC) 0.870 and .815 in the development and external validation cohorts, respectively. The QDMRISK showed overall better accuracy and calibration compared to other evaluated scores. The IGM model showed good accuracy and calibration, with AUCs .796 and .774 in the development and external validation cohorts, respectively.

CONCLUSIONS

This study developed Qatari-specific diabetes and IGM risk scores to identify high risk individuals and can guide the development of a nationwide primary prevention program.

Performance of Risk Assessment Models for Prevalent or Undiagnosed Type 2 Diabetes Mellitus in a Multi-Ethnic Population-The Helius Study

Background:

Most risk assessment models for type 2 diabetes (T2DM) have been developed in Caucasians and Asians; little is known about their performance in other ethnic groups.

Objective(s):

We aimed to identify existing models for the risk of prevalent or undiagnosed T2DM and externally validate them in a multi-ethnic population currently living in the Netherlands.

Methods:

A literature search to identify risk assessment models for prevalent or undiagnosed T2DM was performed in PubMed until December 2017. We validated these models in 4,547 Dutch, 3,035 South Asian Surinamese, 4,119 African Surinamese, 2,326 Ghanaian, 3,598 Turkish, and 3,894 Moroccan origin participants from the HELIUS (Healthy LIfe in an Urban Setting) cohort study performed in Amsterdam. Model performance was assessed in terms of discrimination (C-statistic) and calibration (Hosmer-Lemeshow test). We identified 25 studies containing 29 models for prevalent or undiagnosed T2DM. C-statistics varied between 0.77–0.92 in Dutch, 0.66–0.83 in South Asian Surinamese, 0.70–0.82 in African Surinamese, 0.61–0.81 in Ghanaian, 0.69–0.86 in Turkish, and 0.69–0.87 in the Moroccan populations. The C-statistics were generally lower among the South Asian Surinamese, African Surinamese, and Ghanaian populations and highest among the Dutch. Calibration was poor (Hosmer-Lemeshow p < 0.05) for all models except one.

Conclusions:

Generally, risk models for prevalent or undiagnosed T2DM show moderate to good discriminatory ability in different ethnic populations living in the Netherlands, but poor calibration. Therefore, these models should be recalibrated before use in clinical practice and should be adapted to the situation of the population they are intended to be used in.

The Risk of Type 2 Diabetes Mellitus in a Russian Population Cohort According to Data from the HAPIEE Project

The aim of this study is to investigate the 14-year risk of type 2 diabetes mellitus (T2DM) and develop a risk score for T2DM in the Siberian cohort. A random population sample (males/females, 45-69 years old) was examined at baseline in 2003-2005 (Health, Alcohol, and Psychosocial Factors in Eastern Europe (HAPIEE) project, n = 9360, Novosibirsk) and re-examined in 2006-2008 and 2015-2017. After excluding those with baseline T2DM, the final analysis included 7739 participants. The risk of incident T2DM during a 14-year follow-up was analysed using Cox regression. In age-adjusted models, male and female hazard ratios (HR) of incident T2DM were 5.02 (95% CI 3.62; 6.96) and 5.13 (95% CI 3.56; 7.37) for BMI ≥ 25 kg/m2; 4.38 (3.37; 5.69) and 4.70 (0.27; 6.75) for abdominal obesity (AO); 3.31 (2.65; 4.14) and 3.61 (3.06; 4.27) for fasting hyperglycaemia (FHG); 2.34 (1.58; 3.49) and 3.27 (2.50; 4.26) for high triglyceride (TG); 2.25 (1.74; 2.91) and 2.82 (2.27; 3.49) for hypertension (HT); and 1.57 (1.14; 2.16) and 1.69 (1.38; 2.07) for family history of diabetes mellitus (DM). In addition, secondary education, low physical activity (PA), and history of cardiovascular disease (CVD) were also significantly associated with T2DM in females. A simple T2DM risk calculator was generated based on non-laboratory parameters. A scale with the best quality included waist circumference >95 cm, HT history, and family history of T2DM (area under the curve (AUC) = 0.71). The proposed 10-year risk score of T2DM represents a simple, non-invasive, and reliable tool for identifying individuals at a high risk of future T2DM.

A diabetes risk score for Qatar utilizing a novel mathematical modeling approach to identify individuals at high risk for diabetes

We developed a diabetes risk score using a novel analytical approach and tested its diagnostic performance to detect individuals at high risk of diabetes, by applying it to the Qatari population. A representative random sample of 5,000 Qataris selected at different time points was simulated using a diabetes mathematical model. Logistic regression was used to derive the score using age, sex, obesity, smoking, and physical inactivity as predictive variables. Performance diagnostics, validity, and potential yields of a diabetes testing program were evaluated. In 2020, the area under the curve (AUC) was 0.79 and sensitivity and specificity were 79.0% and 66.8%, respectively. Positive and negative predictive values (PPV and NPV) were 36.1% and 93.0%, with 42.0% of Qataris being at high diabetes risk. In 2030, projected AUC was 0.78 and sensitivity and specificity were 77.5% and 65.8%. PPV and NPV were 36.8% and 92.0%, with 43.0% of Qataris being at high diabetes risk. In 2050, AUC was 0.76 and sensitivity and specificity were 74.4% and 64.5%. PPV and NPV were 40.4% and 88.7%, with 45.0% of Qataris being at high diabetes risk. This model-based score demonstrated comparable performance to a data-derived score. The derived self-complete risk score provides an effective tool for initial diabetes screening, and for targeted lifestyle counselling and prevention programs.

Development and validation of screening scores of non-alcoholic fatty liver disease in middle-aged and elderly Chinese

AIM

Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease and also closely related to cardiometabolic disease. Its prevalence was estimated at over one-fourth in the general population in China. We aimed to develop effective score tools for detecting NAFLD.

METHODS

A total of 17,212 participants aged 45-70 years old were surveyed in Shanghai between 2013 and 2014, and 13,293 participants were included in this analysis. All participants were randomly classified into the exploratory group or the validation group. Candidate categorical variables were selected using a logistic regression model. The score points were generated according to the β-coefficients.

RESULTS

We developed the Shanghai Nicheng NAFLD Score I (SHNC NAFLD Score I), which included body mass index and waist circumference with an area under the receiver-operating characteristic curve (AUC) of 0.802 (95% CI 0.792-0.811) in the exploratory group and 0.802 (95% CI 0.793-0.812) in the validation group. We further developed the SHNC NAFLD Score II by adding fasting plasma glucose, triglyceride, and alanine aminotransferase/aspartate aminotransferase ratio to the SHNC NAFLD Score I, achieving an AUC of 0.852 (95% CI 0.843-0.861) in the exploratory group and 0.843 (95% CI 0.834-0.852) in the validation group. The two score tools also performed well in subjects with normal alanine aminotransferase (ALT) levels.

CONCLUSIONS

Based on anthropometric and clinical categorical variables, our two scores are effective tools for detecting NAFLD in both this southern Chinese population and their subpopulation with normal ALT levels.

The Association Between Dietary Habits and Other Lifestyle Indicators and Dysglycemia in Saudi Adults Free of Previous Diagnosis of Diabetes

Objective:

Study the association of dietary habits and other indicators of lifestyle with dysglycemia in Saudi adults.

Methods:

In a cross-sectional design, data were obtained from 1403 Saudi adults (⩾20 years), not previously diagnosed with diabetes. Demographics, lifestyle variables and dietary habits were obtained using a predesigned questionnaire. Fasting plasma glucose, glycated hemoglobin and 1-hour oral glucose tolerance test were used to identify dysglycemia. Regression analysis was performed to determine the associations of dietary factors and other indicators of lifestyle with dysglycemia.

Results:

A total 1075 adults (596 men, and 479 women) had normoglycemia, and 328 (195 men, and 133 women) had dysglycemia. Following adjustment for age, BMI and waist circumference, in men the weekly intake of 5 portions or more of red meat and Turkish coffee were associated with decreased odds of having dysglycemia odds ratio (OR) 0.444 (95% CI: 0.223, 0.881; P = .02) and 0.387 (95% CI: 0.202, 0.74; P = .004), respectively. In women, the intake of fresh juice 1 to 4 portions per week and 5 portions or more were associated with OR 0.603 (95% CI: 0.369, 0.985; P = .043) and OR 0.511 (95% CI: 0.279, 0.935; P = .029) decreased odds of having dysglycemia, respectively compared with women who did not drink fresh juice. The intake of 5 times or more per week of hibiscus drink was associated with increased odds of having dysglycemia, OR 5.551 (95% CI: 1.576, 19.55, P = .008) compared with women not using such a drink. Other lifestyle factors were not associated with dysglycemia.

Conclusion:

Dietary practices by studied Saudis have some impact on risk of dysglycemia, with obvious sex differences.

The Association between Dyslipidemia, Dietary Habits and Other Lifestyle Indicators among Non-Diabetic Attendees of Primary Health Care Centers in Jeddah, Saudi Arabia

Diet and other lifestyle habits have been reported to contribute to the development of dyslipidemia in various populations. Therefore, this study investigated the association between dyslipidemia and dietary and other lifestyle practices among Saudi adults. Data were collected from adults (≥20 years) not previously diagnosed with diabetes in a cross-sectional design. Demographic, anthropometric, and clinical characteristics, as well as lifestyle and dietary habits were recorded using a predesigned questionnaire. Fasting blood samples were drawn to estimate the serum lipid profile. Out of 1385 people, 858 (62%) (491 men, 367 women) had dyslipidemia. After regression analysis to adjust for age, body mass index, and waist circumference, an intake of ≥5 cups/week of Turkish coffee, or carbonated drinks was associated with increased risk of dyslipidemia in men (OR (95% CI), 2.74 (1.53, 4.89) p = 0.001, and 1.53 (1.04, 2.26) p = 0.03 respectively), while the same intake of American coffee had a protective effect (0.53 (0.30, 0.92) p = 0.025). Sleep duration <6 h, and smoking were also associated with increased risk in men (1.573 (1.14, 2.18) p = 0.006, and 1.41 (1.00, 1.99) p = 0.043 respectively). In women, an increased intake of fresh vegetables was associated with increased risk (2.07 (1.09, 3.94) p = 0.026), which could be attributed to added salad dressing. Thus, there are sex differences in response to dietary and lifestyle practices.

Performance and costs of multiple screening strategies for type 2 diabetes: two population-based studies in Shanghai, China

INTRODUCTION

To compare the performance and the costs of various assumed screening strategies for type 2 diabetes mellitus (T2DM) among Chinese adults, and identify an optimal one for the population.

RESEARCH DESIGN AND METHODS

Two multistage-sampling surveys were conducted in Shanghai, China, in 2009 and 2017. All participants were interviewed, had anthropometry, measured fasting plasma glucose (FPG), hemoglobin A1c (A1c) and/or postprandial glucose. The 1999 WHO diagnostic criteria was used to identify undiagnosed T2DM. A previously developed Chinese risk assessment system and a specific risk assessment system developed in this study were applied to calculate diabetes risk score (DRS) 1 and 2. Optimal screening strategies were selected based on the sensitivity, Youden index and the costs using the 2009 survey data as the training set and the 2017 survey data as the validation set. A twofold cross-validation was also performed.

RESULTS

Of numerous assumed strategies, FPG ≥5.6 mmol/L alone performed well (Youden index of 71.8%) and cost least (US$18.4 for each case detected), followed by the strategy of DRS2 ≥8 combining with FPG ≥5.6 mmol/L (Youden index of 71.7% and US$20.2 per case detected) and the strategy of DRS1 ≥17 combining with FPG ≥5.6 mmol/L (Youden index of 72.0% and US$21.6 per case detected). However, FPG alone resulted in more subjects requiring oral glucose tolerance test (OGTT) than did combining with DRS. The strategy of FPG ≥5.6 mmol/L combining with A1c ≥4.7% achieved a Youden index of 72.1%, but had a cost as high as US$48.8 for each case identified. Twofold cross-validation also supported the use of FPG alone, but with an optimal cut-off of 6.1 mmol/L.

CONCLUSIONS

Our results support the use of FPG alone in T2DM screening in Chinese adults. DRS may be used combining with FPG in populations with available electronic health records to reduce the number of OGTT and save costs of screening.

Diabetes mellitus risk among Jordanians in a dental setting: a cross-sectional study

BACKGROUND

Dental offices can be useful to screen and identify patients at risk of developing diabetes mellitus (DM) using risk prediction tools. The Finnish Diabetes Risk Score (FINDRISC) is a validated, questionnaire-based tool used to predict the 10-year risk of developing type II DM.

OBJECTIVES

To determine the 10-year DM risk among Jordanians using the FINDRISC questionnaire in a dental setting.

MATERIALS AND METHODS

Participants attending two university dental teaching centres between March 2017 and February 2018 were interviewed using an Arabic translated version of the FINDRISC questionnaire. Anthropometrics including weight, height, waist circumference (WC) and body mass index (BMI) were recorded. Random capillary blood glucose level was measured for each participant. Statistical analysis was done using Chi-square and independent t-tests.

RESULTS

A total of 1,247 (436 males and 811 females) participants were included. As defined by BMI, 1,012 (81.2%) participants were either overweight or obese. Abdominal adiposity as determined by WC was seen in 738 (59.2%) participants. The mean (± SD) FINDRISC score for females (11.3 ± 4.3) was significantly higher (P = 0.001) than males (10.4 ± 4.9). After age adjustment, more females were in the high-risk categories (FINDRISC ≥ 15) compared with males. This trend was seen among all age groups, but was statistically significant in the older age groups; 55-64 years (P = 0.037) and ≥ 65 years (P = 0.004).

CONCLUSION

In a developing Middle Eastern country such as Jordan, almost half of Jordanians attending university dental clinics are at a moderate to high risk of developing type II DM in 10 years. The risk of DM should be considered in dental patients, particularly older females.

Dysglycemia risk score in Saudi Arabia: A tool to identify people at high future risk of developing type 2 diabetes

AIMS/INTRODUCTION

To develop a non-invasive risk score to identify Saudis having prediabetes or undiagnosed type 2 diabetes.

METHODS

Adult Saudis without diabetes were recruited randomly using a stratified two-stage cluster sampling method. Demographic, dietary, lifestyle variables, personal and family medical history were collected using a questionnaire. Blood pressure and anthropometric measurements were taken. Body mass index was calculated. The 1-h oral glucose tolerance test was carried out. Glycated hemoglobin, fasting and 1-h plasma glucose were measured, and obtained values were used to define prediabetes and type 2 diabetes (dysglycemia). Logistic regression models were used for assessing the association between various factors and dysglycemia, and Hosmer-Lemeshow summary statistics were used to assess the goodness-of-fit.

RESULTS

A total of 791 men and 612 women were included, of whom 69 were found to have diabetes, and 259 had prediabetes. The prevalence of dysglycemia was 23%, increasing with age, reaching 71% in adults aged ≥65 years. In univariate analysis age, body mass index, waist circumference, use of antihypertensive medication, history of hyperglycemia, low physical activity, short sleep and family history of diabetes were statistically significant. The final model for the Saudi Diabetes Risk Score constituted sex, age, waist circumference, history of hyperglycemia and family history of diabetes, with the score ranging from 0 to 15. Its fit based on assessment using the receiver operating characteristic curve was good, with an area under the curve of 0.76 (95% confidence interval 0.73-0.79). The proposed cut-point for dysglycemia is 5 or 6, with sensitivity and specificity being approximately 0.7.

CONCLUSION

The Saudi Diabetes Risk Score is a simple tool that can effectively distinguish Saudis at high risk of dysglycemia.

Agreement between Type 2 Diabetes Risk Scales in a Caucasian Population: A Systematic Review and Report

Early detection of people with undiagnosed type 2 diabetes (T2D) is an important public health concern. Several predictive equations for T2D have been proposed but most of them have not been externally validated and their performance could be compromised when clinical data is used. Clinical practice guidelines increasingly incorporate T2D risk prediction models as they support clinical decision making. The aims of this study were to systematically review prediction scores for T2D and to analyze the agreement between these risk scores in a large cross-sectional study of white western European workers. A systematic review of the PubMed, CINAHL, and EMBASE databases and a cross-sectional study in 59,042 Spanish workers was performed. Agreement between scores classifying participants as high risk was evaluated using the kappa statistic. The systematic review of 26 predictive models highlights a great heterogeneity in the risk predictors; there is a poor level of reporting, and most of them have not been externally validated. Regarding the agreement between risk scores, the DETECT-2 risk score scale classified 14.1% of subjects as high-risk, FINDRISC score 20.8%, Cambridge score 19.8%, the AUSDRISK score 26.4%, the EGAD study 30.3%, the Hisayama study 30.9%, the ARIC score 6.3%, and the ITD score 3.1%. The lowest agreement was observed between the ITD and the NUDS study derived score (κ = 0.067). Differences in diabetes incidence, prevalence, and weight of risk factors seem to account for the agreement differences between scores. A better agreement between the multi-ethnic derivate score (DETECT-2) and European derivate scores was observed. Risk models should be designed using more easily identifiable and reproducible health data in clinical practice.

Comparative analysis of screening models for undiagnosed diabetes in Mexico

BACKGROUND

It is estimated that 37% of Mexican adults have undiagnosed diabetes, and are therefore at high risk of developing the severe and devastating complications associated to it. In recent years, a variety of screening tools based on the characteristics of the adult Mexican population have been proposed in order to reduce the negative effects of the disease.

OBJECTIVES

To assess the performance of screening models to diagnose diabetes in the Mexican adult population and to propose a screening model based on HbA1c measurements.

MATERIALS AND METHODS

Data from the 2016 Halfway National Health and Nutrition Survey (NHNS) were used to assess the screening models and to develop and validate the proposed 2016 NHNS model, built using a multivariate logistic regression model. Explanatory variables included in the 2016 NHNS 2016 model were selected through a stepwise backward procedure, using sensitivity and specificity as performance indicators.

RESULTS

Of the screening models assessed, only the model based on the 2006 NHNS survey showed a performance consistent with previous reports. The proposed 2016 NHNS model included age, waist circumference, and systolic blood pressure as explanatory variables and showed a sensitivity of 0.72 and a specificity of 0.80 in the validation data set.

CONCLUSIONS

Age, waist circumference, and systolic blood pressure are variables of special importance for early detection of undiagnosed diabetes in Mexican adults. Based on the consistent performance of the 2006 NHNS model in different data sets, its use as a screening tool for adults with undiagnosed diabetes in Mexico is recommended.

Assessment of risk of diabetes by using Indian Diabetic risk score (IDRS) in Indian population

AIMS

To screen the Indian population for Type 2 Diabetes Mellitus (DM) based on Indian Diabetes Risk Score. Our main question was; Does Indian Diabetic risk score (IDRS) effectively screen diabetic subjects in Indian population?

METHODS

Multi-centric nationwide screening for DM and its risk in all populous states and Union territories of India in 2017. It is the first pan India DM screening study conducted on 240,000 subjects in a short period of 3 months based on IDRS. This was a stratified translational research study in randomly selected cluster populations from all zones of rural and urban India. Two non-modifiable (age, family history) and two modifiable (waist circumference & physical activity) were used to obtain the score. High, moderate and low risk groups were selected based on scores.

RESULTS

In this study 40.9% subjects were detected to be high risk, known or newly diagnosed DM subjects in urban and rural regions. IDRS could detect 78.1% known diabetic subjects as high risk group. Age group 50-59 (17.4%); 60-69 (22%); 70-79 (22.8%); >80 (19.2%) revealed high percentage of subjects. ROC was found to be 0.763 at CI 95% of 0.761-0.765 with statistical significance of p < 0.0001. At >50 cut off, youden index showed the sensitivity of 78.05 and specificity of 62.68. Regression analysis revealed that IDRS and Diabetes are significantly positively associated.

CONCLUSIONS

Data reveals that IDRS is a good indicator of high risk diabetic subjects.

A Deep Learning Model for Estimation of Patients with Undiagnosed Diabetes

A screening model for undiagnosed diabetes mellitus (DM) is important for early medical care. Insufficient research has been carried out developing a screening model for undiagnosed DM using machine learning techniques. Thus, the primary objective of this study was to develop a screening model for patients with undiagnosed DM using a deep neural network. We conducted a cross-sectional study using data from the Korean National Health and Nutrition Examination Survey (KNHANES) 2013–2016. A total of 11,456 participants were selected, excluding those with diagnosed DM, an age < 20 years, or missing data. KNHANES 2013–2015 was used as a training dataset and analyzed to develop a deep learning model (DLM) for undiagnosed DM. The DLM was evaluated with 4444 participants who were surveyed in the 2016 KNHANES. The DLM was constructed using seven non-invasive variables (NIV): age, waist circumference, body mass index, gender, smoking status, hypertension, and family history of diabetes. The model showed an appropriate performance (area under curve (AUC): 80.11) compared with existing previous screening models. The DLM developed in this study for patients with undiagnosed diabetes could contribute to early medical care.

Which clinical and biochemical predictors should be used to screen for diabetes in patients with serious mental illness receiving antipsychotic medication? A large observational study

OBJECTIVE

We aimed to investigate which clinical and metabolic tests offer optimal accuracy and acceptability to help diagnose diabetes among a large sample of people with serious mental illness in receipt of antipsychotic medication.

METHODS

A prospective observational study design of biochemical and clinical factors was used. Biochemical measures were fasting glucose, insulin and lipids, oral glucose tolerance testing (OGTT), hemoglobin A1c, and insulin resistance assessed with the homeostatic model (HOMA-IR) were determined in a consecutive cohort of 798 adult psychiatric inpatients receiving antipsychotics. Clinical variables were gender, age, global assessment of functioning (GAF), mental health clinicians' global impression (CGI), duration of severe mental illness, height, weight, BMI and waist/hip ratio. In addition, we calculated the risk using combined clinical predictors using the Leicester Practice Risk Score (LPRS) and the Topics Diabetes Risk Score (TDRS). Diabetes was defined by older criteria (impaired fasting glucose (IFG) or OGTT) as well as2010 criteria (IFG or OGTT or Glycated haemoglobin (HBA1c)) at conventional cut-offs.

RESULTS

Using the older criteria, 7.8% had diabetes (men: 6.3%; women: 10.3%). Using the new criteria, 10.2% had diabetes (men: 8.2%, women: 13.2%), representing a 30.7% increase (p = 0.02) in the prevalence of diabetes. Regarding biochemical predictors, conventional OGTT, IFG, and HbA1c thresholds used to identify newly defined diabetes missed 25%, 50% and 75% of people with diabetes, respectively. The conventional HBA1c cut-point of ≥6.5% (48 mmol/mol) missed 7 of 10 newly defined cases of diabetes while a cut-point of ≥5.7% improved sensitivity from 44.4% to up to 85%. Specific algorithm approaches offered reasonable accuracy. Unfortunately no single clinical factor was able to accurately rule-in a diagnosis of diabetes. Three clinical factors were able to rule-out diabetes with good accuracy namely: BMI, waist/hip ratio and height. A BMI < 30 had a 92% negative predictive value in ruling-out diabetes. Of those not diabetic, 20% had a BMI ≥ 30. However, for complete diagnosis a specific biochemical protocol is still necessary.

CONCLUSIONS

Patients with SMI maintained on antipsychotic medication cannot be reliably screened for diabetes using clinical variables alone. Accurate assessment requires a two-step algorithm consisting of HBA1c ≥5.7% followed by both FG and OGTT which does not require all patients to have OGTT and FG.

Effect of model updating strategies on the performance of prevalent diabetes risk prediction models in a mixed-ancestry population of South Africa

Background

Prediction model updating methods are aimed at improving the prediction performance of a model in a new setting. This study sought to critically assess the impact of updating techniques when applying existent prevalent diabetes prediction models to a population different to the one in which they were developed, evaluating the performance in the mixed-ancestry population of South Africa.

Methods

The study sample consisted of 1256 mixed-ancestry individuals from the Cape Town Bellville-South cohort, of which 173 were excluded due to previously diagnosed diabetes and 162 individuals had undiagnosed diabetes. The primary outcome, undiagnosed diabetes, was based on an oral glucose tolerance test. Model updating techniques and prediction models were identified via recent systematic reviews. Model performance was assessed using the C-statistic and expected/observed (E/O) events rates ratio.

Results

Intercept adjustment and logistic calibration improved calibration across all five models (Cambridge, Kuwaiti, Omani, Rotterdam and Simplified Finnish diabetes risk models). This was improved further by model revision, where likelihood ratio tests showed that the effect of body mass index, waist circumference and family history of diabetes required additional adjustment (Omani, Rotterdam and Finnish models). However, discrimination was poor following internal validation of these models. Re-estimation of the regression coefficients did not increase performance, while the addition of new variables resulted in the highest discriminatory and calibration performance combination for the models it was undertaken in.

Conclusions

While the discriminatory performance of the original existent models during external validation were higher, calibration was poor. The highest performing models, based on discrimination and calibration, were the Omani diabetes model following model revision, and the Cambridge diabetes risk model following the addition of waist circumference as a predictor. However, while more extensive methods incorporating development population information were superior over simpler methods, the increase in model performance was not great enough for recommendation.

Development and validation of a non-invasive assessment tool for screening prevalent undiagnosed diabetes in middle-aged and elderly Chinese

To develop a non-invasive assessment tool and compare it to other assessment tools among middle-aged and elderly Shanghainese, 15,309 individuals, who were 45-70 years old, not previously diagnosed with diabetes, and from a cross-sectional survey conducted between April 2013 and August 2014 in Shanghai, were selected into this study. The participants were randomly assigned to either the exploratory group or the validation group. Undiagnosed diabetes was defined according to the American Diabetes Association diagnostic criteria, and score points were generated according to the logistic regression coefficients. Age, family history of diabetes, hypertension, overweight/obesity, and central obesity all contributed to the constructed model, the Shanghai Nicheng diabetes screening score, with the area under the receiver-operating characteristic curve (AUC) being 0.654 (95% CI 0.637-0.670) in the exploratory group and 0.669 (95% CI 0.653-0.686) in the validation group. The score value of 6 was the optimal cut-point with the largest Youden's index. When applied to the validation group, our model had a similar discriminative ability to the New Chinese Diabetes Risk Score (AUC: 0.669 vs. 0.662, p = 0.187), and performed better than other screening scores for Chinese. However, our model was inferior to fasting plasma glucose, 2-hour plasma glucose, and glycosylated hemoglobin in detecting prevalent undiagnosed diabetes (AUC: 0.669 (0.653-0.686) vs. 0.881 (0.868-0.894), 0.934 (0.923-0.944), and 0.834 (0.819-0.848), all p < 0.001). Although non-invasive models, based on demographic and clinical information, are advisable in resource-scarce developing areas, regular blood glucose screening is still necessary among those aged 45 or older.

Care providers' perceptions towards challenges and opportunities for service improvement at diabetes management clinics in public primary health care in Muscat, Oman: a qualitative study

Background

The literature has described several challenges related to the quality of diabetes management clinics in public primary health care centres in Oman. These clinics continue to face challenges due to the continuous growth of individuals diagnosed with type 2 diabetes. We sought to explore the challenges faced in these clinics and discuss opportunities for improvement in Oman.

Methods

This qualitative study was designed to include non-participant observations of diabetic patients and care providers during service provision at diabetes management clinics, as well as semi-structured interviews with care providers, at five purposively selected public primary health care centres. Care providers included physicians, nurses, dieticians, health educators, pharmacists, an assistant pharmacist, a psychologist, and a medical orderly. The data were analysed using qualitative content analysis.

Results

The study disclosed three different models of service delivery at diabetes management clinics, which, to varying degrees, face challenges related to health centre infrastructure, technical and pharmaceutical support, and care providers’ interests, knowledge, and skills. Challenges related to the community were also found in terms of cultural beliefs, traditions, health awareness, and public transportation.

Conclusion

The challenges encountered in diabetes management clinics fall within two contexts: health care centres and community. Although many challenges exist, opportunities for improvement are available. However, improvements in the quality of diabetic clinics in primary health care centres might take time and require extensive involvement, shared responsibilities, and implications from the government, health care centres, and community.

A Review of Diabetes Prediction Equations in African Descent Populations

Background: Predicting undiagnosed diabetes is a critical step toward addressing the diabetes epidemic in populations of African descent worldwide. Objective: To review characteristics of equations developed, tested, or modified to predict diabetes in African descent populations. Methods: Using PubMed, Scopus, and Embase databases, a scoping review yielded 585 research articles. After removal of duplicates (n = 205), 380 articles were reviewed. After title and abstract review 328 articles did not meet inclusion criteria and were excluded. Fifty-two articles were retained. However, full text review revealed that 44 of the 52 articles did not report findings by AROC or C-statistic in African descent populations. Therefore, eight articles remained. Results: The 8 articles reported on a total of 15 prediction equation studies. The prediction equations were of two types. Prevalence prediction equations (n = 9) detected undiagnosed diabetes and were based on non-invasive variables only. Non-invasive variables included demographics, blood pressure and measures of body size. Incidence prediction equations (n = 6) predicted risk of developing diabetes and used either non-invasive variables or both non-invasive and invasive. Invasive variables required blood tests and included fasting glucose, high density lipoprotein-cholesterol (HDL), triglycerides (TG), and A1C. Prevalence prediction studies were conducted in the United States, Africa and Europe. Incidence prediction studies were conducted only in the United States. In all these studies, the performance of diabetes prediction equations was assessed by area under the receiver operator characteristics curve (AROC) or the C-statistic. Therefore, we evaluated the efficacy of these equations based on standard criteria, specifically discrimination by either AROC or C-statistic were defined as: Poor (0.50 - 0.69); Acceptable (0.70 - 0.79); Excellent (0.80 - 0.89); or Outstanding (0.90 - 1.00). Prediction equations based only on non-invasive variables reported to have poor to acceptable detection of diabetes with AROC or C-statistic 0.64 - 0.79. In contrast, prediction equations which were based on both non-invasive and invasive variables had excellent diabetes detection with AROC or C-statistic 0.80 - 0.82. Conclusion: Equations which use a combination of non-invasive and invasive variables appear to be superior in the prediction of diabetes in African descent populations than equations that rely on non-invasive variables alone.

Validity of the FINDRISC as a prediction tool for diabetes in a contemporary Norwegian population: a 10-year follow-up of the HUNT study

OBJECTIVE

The Finnish Diabetes Risk Score (FINDRISC) is a recommended tool for type 2 diabetes prediction. There is a lack of studies examining the performance of the current 0-26 point FINDRISC scale. We examined the validity of FINDRISC in a contemporary Norwegian risk environment.

RESEARCH DESIGN AND METHODS

We followed 47 804 participants without known diabetes and aged ≥20 years in the HUNT3 survey (2006-2008) by linkage to information on glucose-lowering drug dispensing in the Norwegian Prescription Database (2004-2016). We estimated the C-statistic, sensitivity and specificity of FINDRISC as predictor of incident diabetes, as indicated by incident use of glucose-lowering drugs. We estimated the 10-year cumulative diabetes incidence by categories of FINDRISC.

RESULTS

The C-statistic (95% CI) of FINDRISC in predicting future diabetes was 0.77 (0.76 to 0.78). FINDRISC ≥15 (the conventional cut-off value) had a sensitivity of 38% and a specificity of 90%. The 10-year cumulative diabetes incidence (95% CI) was 4.0% (3.8% to 4.2%) in the entire study population, 13.5% (12.5% to 14.5%) for people with FINDRISC ≥15 and 2.8% (2.6% to 3.0%) for people with FINDRISC <15. Thus, FINDRISC ≥15 had a positive predictive value of 13.5% and a negative predictive value of 97.2% for diabetes within the next 10 years. To approach a similar sensitivity as in the study in which FINDRISC was developed, we would have to lower the cut-off value for elevated FINDRISC to ≥11. This would yield a sensitivity of 73%, specificity of 67%, positive predictive value of 7.7% and negative predictive value of 98.5%.

CONCLUSIONS

The validity of FINDRISC and the risk of diabetes among people with FINDRISC ≥15 is substantially lower in the contemporary Norwegian population than assumed in official guidelines. To identify ~3/4 of those developing diabetes within the next 10 years, we would have to lower the threshold for elevated FINDRISC to ≥11, which would label ~1/3 of the entire adult population as having an elevated FINDRISC necessitating a glycemia assessment.

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.

Determining type 2 diabetes risk factors for the adults: A community based study from Turkey

AIMS

This study aimed to determine risk factors for type 2 diabetes among adults who were not diagnosed with diabetes.

METHODS

Adults were included in this study within the public activities performed on World Diabetes Day (n=1872). Data were collected using the FINDRISC questionnaire and a short questionnaire.

RESULTS

Participants' mean age was 39.35±10.40. The mean FINDRISC score was 7.46±4.62, women's mean score was higher than that for men. The FINDRISC score indicates that 7.4% of the participants were in the highrisk group. Among participants, BMI value of 65.1% was 25kg/m² and higher, waist circumference of 58% was over the threshold value; and 50.7% did not engage in sufficient physical activity. Of the participants, 9.5% had a history of high blood glucose, families of 38.9% had a history of diabetes. The mean FINDRISC score was in the slightly high category, 121 participants were found likely to be diagnosed with diabetes within ten years if no action was taken.

CONCLUSIONS

It is recommended the risk screening studies to be conducted and the FINDRISC tool to be used in Turkey, where diabetes prevalence is increasing rapidly, to determine diabetes risks in the early period and to raise social awareness for diabetes.

Accuracy of diabetes screening methods used for people with tuberculosis, Indonesia, Peru, Romania, South Africa

Objective

To evaluate the performance of diagnostic tools for diabetes mellitus, including laboratory methods and clinical risk scores, in newly-diagnosed pulmonary tuberculosis patients from four middle-income countries.

Methods

In a multicentre, prospective study, we recruited 2185 patients with pulmonary tuberculosis from sites in Indonesia, Peru, Romania and South Africa from January 2014 to September 2016. Using laboratory-measured glycated haemoglobin (HbA1c) as the gold standard, we measured the diagnostic accuracy of random plasma glucose, point-of-care HbA1c, fasting blood glucose, urine dipstick, published and newly derived diabetes mellitus risk scores and anthropometric measurements. We also analysed combinations of tests, including a two-step test using point-of-care HbA1cwhen initial random plasma glucose was ≥ 6.1 mmol/L.

Findings

The overall crude prevalence of diabetes mellitus among newly diagnosed tuberculosis patients was 283/2185 (13.0%; 95% confidence interval, CI: 11.6–14.4). The marker with the best diagnostic accuracy was point-of-care HbA1c (area under receiver operating characteristic curve: 0.81; 95% CI: 0.75–0.86). A risk score derived using age, point-of-care HbA1c and random plasma glucose had the best overall diagnostic accuracy (area under curve: 0.85; 95% CI: 0.81–0.90). There was substantial heterogeneity between sites for all markers, but the two-step combination test performed well in Indonesia and Peru.

Conclusion

Random plasma glucose followed by point-of-care HbA1c testing can accurately diagnose diabetes in tuberculosis patients, particularly those with substantial hyperglycaemia, while reducing the need for more expensive point-of-care HbA1c testing. Risk scores with or without biochemical data may be useful but require validation.

Screening models for undiagnosed diabetes in Mexican adults using clinical and self-reported information

BACKGROUND

Prevalence of diabetes in Mexico has constantly increased since 1993. Since type 2 diabetes may remain undiagnosed for many years, identification of subjects at high risk of diabetes is very important to reduce its impact and to prevent its associated complications.

OBJECTIVE

To develop easily implementable screening models to identify subjects with undiagnosed diabetes based on the characteristics of Mexican adults.

SUBJECTS AND METHODS

Screening models were developed using datasets from the 2006 and 2012 National Health and Nutrition Surveys (NHNS). Variables used to develop the multivariate logistic regression models were selected using a backward stepwise procedure. Final models were validated using data from the 2000 National Health Survey (NHS).

RESULTS

The model based on the 2006 NHNS included age, waist circumference, and systolic blood pressure as explanatory variables, while the model based on the 2012 NHNS included age, waist circumference, height, and family history of diabetes. The sensitivity and specificity values obtained from the external validation procedure were 0.74 and 0.62 (2006 NHNS model) and 0.76 and 0.55 (2012 NHNS model) respectively.

CONCLUSIONS

Both models were equally capable of identifying subjects with undiagnosed diabetes (∼75%), and performed satisfactorily when compared to other models developed for other regions or countries.

High -density lipoprotein cholesterol as a predictor for diabetes mellitus

Background:

Diabetes is a prevalent chronic disease around the world. To evaluate the risk of diabetes comprehensively, we developed a score model for risk prediction with HDL-C as a protective factor.

Methods:

We extracted physical examination data of 2728 individuals. The data contain 18 demographic and clinical variables. To identify the statistical significant feature variables, the backward stepwise logistic regression was used based on the data of the “exploratory population”. To ascertain the cutoff value of the selected variables, we used the Youden index. Then we assigned each variable level a score according to the estimated regression model coefficients and then calculated the individual’s total score. We gained the cutoff value for the total score through the Youden Index and stratified the total score into four levels. We employed the data of “validation population” to test the performance of the score model based on the area under the ROC curve.

Results:

Age, LDL-C, HDL-C, BMI, family history of diabetes, diastolic blood pressure and TCHO were selected as statistically significant variables. The diabetes risk score range varied from 0 to 17. The risk level categorized by the total score was low, middle, high and extremely high, with a score range of 0-2, 3-7, 8-12 and 13-17, respectively.

Conclusions:

The score model based on physical examination data is an efficient and valuable tool to evaluate and monitor the potential diabetes risk for both healthy and unhealthy people at an individual level.

Diabetes risk score in the United Arab Emirates: a screening tool for the early detection of type 2 diabetes mellitus

OBJECTIVE

The objective of this study was to develop a simple non-invasive risk score, specific to the United Arab Emirates (UAE) citizens, to identify individuals at increased risk of having undiagnosed type 2 diabetes mellitus.

RESEARCH DESIGN AND METHODS

A retrospective analysis of the UAE National Diabetes and Lifestyle data was conducted. The data included demographic and anthropometric measurements, and fasting blood glucose. Univariate analyses were used to identify the risk factors for diabetes. The risk score was developed for UAE citizens using a stepwise forward regression model.

RESULTS

A total of 872 UAE citizens were studied. The overall prevalence of diabetes in the UAE adult citizens in the Northern Emirates was 25.1%. The significant risk factors identified for diabetes were age (≥35 years), a family history of diabetes mellitus, hypertension, body mass index ≥30.0 and waist-to-hip ratio ≥0.90 for males and ≥0.85 for females. The performance of the model was moderate in terms of sensitivity (75.4%, 95% CI 68.3 to 81.7) and specificity (70%, 95% CI 65.8 to 73.9). The area under the receiver-operator characteristic curve was 0.82 (95% CI 0.78 to 0.86).

CONCLUSIONS

A simple, non-invasive risk score model was developed to help to identify those at high risk of having diabetes among UAE citizens. This score could contribute to the efficient and less expensive earlier detection of diabetes in this high-risk population.

Validity of Madras Diabetes Research Foundation: Indian Diabetes Risk Score for Screening of Diabetes Mellitus among Adult Population of Urban Field Practice Area, Indira Gandhi Medical College, Shimla, Himachal Pradesh, India

INTRODUCTION

IDRS is based on four simple parameters derived from known risk factors for diabetes; two modifiable risk factors (waist circumference and physical inactivity) and two non-modifiable risk factors (age and family history of diabetes), which may be amenable to intervention. The present study has been planned as the region specific validation is important before it can be used for screening in this part of the country.

AIMS

The aim of the present study was to validate MDRF-IDRS for screening of diabetes mellitus among adult population of urban field practice area, IGMC, Shimla, Himachal Pradesh, India.

METHODS

The present community based cross sectional study was conducted among 417 adults fulfilling the eligibility criteria using a two stage sampling design.

RESULTS

In the present study IDRS value ≥70 had an optimum sensitivity of 61.33% and specificity of 56.14% for detecting undiagnosed type 2 diabetes in the community. At an IDRS score of ≥70, the PPV was 23.47%, NPV as 86.88%, the diagnostic accuracy as 57.07%, LR for positive test as 1.398, LR for negative test as 0.69 and Youden's index as 0.17. However Youden's index was 0.19 at a cut of ≥60 i.e. higher than what was at ≥70. Higher IDRS scores increased the specificity but the sensitivity dramatically decreased. Conversely, lower IDRS values increased the sensitivity but the specificity drastically decreased. Area under the curve = 0.630 and a P value < 0.001.

CONCLUSIONS

MDRF IDRS is user friendly screening tool but the criteria of including the parameter of physical activity for the calculation of the risk score needs to be clearly defined. In the present study the maximum sensitivity of 100% was seen at a cut off of ≥30. Hence we would recommend that all those in the medium and high risk group should be screened for type 2 Diabetes.

Risk score model of type 2 diabetes prediction for rural Chinese adults: the Rural Deqing Cohort Study

OBJECTIVE

Risk score (RS) model is a cost-effective tool to identify adults who are at high risk for diabetes. This study was to develop an RS model of type 2 diabetes (T2DM) prediction specifically for rural Chinese adults.

METHODS

A prospective whole cohort study (n = 28,251) and a sub-cohort study (n = 3043) were conducted from 2006-2014 and 2006-2008 to 2015 in rural Deqing, China. All participants were free of T2DM at baseline. Incident T2DM cases were identified through electronic health records, self-reported and fasting plasma glucose testing for the sub-cohort, respectively. RS models were constructed with coefficients (β) of Cox regression. Receiver-operating characteristic curves were plotted and the area under the curve (AUC) reflected the discriminating accuracy of an RS model.

RESULTS

By 2015, the incidence of T2DM was 3.3 and 7.7 per 1000 person-years in the whole cohort and the sub-cohort, respectively. Based on data from the whole cohort, the non-invasive RS model included age (4 points), overweight (2 points), obesity (4 points), family history of T2DM (3 points), meat diet (3 points), and hypertension (2 points). The plus-fasting plasma glucose (FPG) model added impaired fasting glucose (4 points). The AUC was 0.705 with a positive predictive value of 2.5% for the non-invasive model, and for the plus-FPG model the AUC was 0.754 with a positive predictive value of 2.5%. These models performed better as compared with 12 existing RS models for the study population.

CONCLUSIONS

Our non-invasive RS model can be used to identify individuals who are at high risk of T2DM as a simple, fast, and cost-effective tool for rural Chinese adults.

Evaluation of the modified FINDRISC to identify individuals at high risk for diabetes among middle-aged white and black ARIC study participants

OBJECTIVE

To evaluate a modified Finnish Diabetes Risk Score (FINDRISC) for predicting the risk of incident diabetes among white and black middle-aged participants from the Atherosclerosis Risk in Communities (ARIC) study.

RESEARCH DESIGN AND METHODS

We assessed 9754 ARIC cohort participants who were free of diabetes at baseline. Logistic regression and receiver operator characteristic (ROC) curves were used to evaluate a modified FINDRISC for predicting incident diabetes after 9 years of follow-up, overall and by race/gender group. The modified FINDRISC used comprised age, body mass index, waist circumference, blood pressure medication and family history.

RESULTS

The mean FINDRISC (range, 2 [lowest risk] to 17 [highest risk]) for black women was higher (9.9 ± 3.6) than that for black men (7.6 ± 3.9), white women (8.0 ± 3.6) and white men (7.6 ± 3.5). The incidence of diabetes increased generally across deciles of FINDRISC for all 4 race/gender groups. ROC curve statistics for the FINDRISC showed the highest area under the curve for white women (0.77) and the lowest for black men (0.70).

CONCLUSIONS

We used a modified FINDRISC to predict the 9-year risk of incident diabetes in a biracial US population. The modified risk score can be useful for early screening of incident diabetes in biracial populations, which may be helpful for early interventions to delay or prevent diabetes.

Development and evaluation of a risk score for type 2 diabetes mellitus among middle-aged Chinese rural population based on the RuralDiab Study

The purpose of this study was to establish a simple and effective risk score for type 2 diabetes mellitus (T2DM) in middle-aged rural Chinese. Total of 5453 participants aged 30–59 years from the Rural Diabetes, Obesity and Lifestyle (RuralDiab) study were recruited for establishing the RuralDiab risk score by using logistic regression analysis. The RuralDiab risk score was validated in a prospective study from Henan Province of China, and compared with previous risk scores by using the receiver-operating characteristics cure. Ultimately, sex, age, family history of diabetes, physical activity, waist circumference, history of dyslipidemia, diastolic blood pressure, body mass index were included in the RuralDiab risk score (range from 0 to 36), and the optimal cutoff value was 17 with 67.9% sensitivity and 67.8% specificity. The area under the cures (AUC) of the RuralDiab risk score was 0.723(95%CI: 0.710–0.735) for T2DM in validation population, which was significant higher than the American Diabetes Association score (AUC: 0.636), the Inter99 score (AUC: 0.669), the Oman risk score (AUC: 0.675). The RuralDiab risk score was established and demonstrated an appropriate performance for predicting T2DM in middle-aged Chinese rural population. Further studies for validation should be implemented in different populations.

A community-based primary prevention programme for type 2 diabetes mellitus integrating identification and lifestyle intervention for prevention: a cluster randomised controlled trial

Background

Prevention of type 2 diabetes mellitus (T2DM) is a global priority; however, there is a lack of evidence investigating how to effectively translate prevention research into a primary care setting.

Objectives

(1) To develop and validate a risk score to identify individuals at high risk of T2DM in the UK; and (2) to establish whether or not a structured education programme targeting lifestyle and behaviour change was clinically effective and cost-effective at preventing progression to T2DM in people with prediabetes mellitus (PDM), identified through a risk score screening programme in primary care.

Design

A targeted screening study followed by a cluster randomised controlled trial (RCT), with randomisation at practice level. Participants were followed up for 3 years.

Setting

A total of 44 general practices across Leicestershire, UK. The intervention took place in the community.

Participants

A total of 17,972 individuals from 44 practices identified through the risk score as being at high risk of T2DM were invited for screening; of these, 3449 (19.2%) individuals attended. All received an oral glucose tolerance test. PDM was detected in 880 (25.5%) of those screened. Those with PDM were included in the trial; of these, 36% were female, the average age was 64 years and 16% were from an ethnic minority group.

Intervention

Practices were randomised to receive either standard care or the intervention. The intervention consisted of a 6-hour group structured education programme, with an annual refresher and regular telephone contact.

Main outcome measures

The primary outcome was progression to T2DM. The main secondary outcomes were changes in glycated haemoglobin concentrations, blood glucose levels, cardiovascular risk, the presence of metabolic syndrome, step count and the cost-effectiveness of the intervention.

Results

A total of 22.6% of the intervention group did not attend the education and 29.1% attended all sessions. A total of 131 participants developed T2DM (standard care,n = 67; intervention,n = 64). There was a 26% reduced risk of T2DM in the intervention arm compared with standard care, but this did not reach statistical significance (hazard ratio 0.74, 95% confidence interval 0.48 to 1.14;p = 0.18). There were statistically significant improvements in glycated haemoglobin concentrations, low-density lipoprotein cholesterol levels, psychosocial well-being, sedentary time and step count in the intervention group. The intervention was found to result in a net gain of 0.046 quality-adjusted life-years over 3 years at a cost of £168 per patient, with an incremental cost-effectiveness ratio of £3643 and a probability of 0.86 of being cost-effective at a willingness-to-pay threshold of £20,000.

Conclusions

We developed and validated a risk score for detecting those at high risk of undiagnosed PDM/T2DM. We screened > 3400 people using a two-stage screening programme. The RCT showed that a relatively low-resource pragmatic programme may lead to a reduction in T2DM and improved biomedical and psychosocial outcomes, and is cost-effective.

Limitations

Only 19% of those invited to screening attended, which may limit generalisability. The variation in cluster size in the RCT may have limited the power of the study.

Future work

Future work should focus on increasing attendance to both screening and prevention programmes and offering the programme in different modalities, such as web-based modalities. A longer-term follow-up of the RCT participants would be valuable.

Trial registration

Current Controlled Trials ISRCTN80605705.

Funding

The National Institute for Health Research Programme Grants for Applied Research programme.

A competing-risk-based score for predicting twenty-year risk of incident diabetes: the Beijing Longitudinal Study of Ageing study

Few risk tools have been proposed to quantify the long-term risk of diabetes among middle-aged and elderly individuals in China. The present study aimed to develop a risk tool to estimate the 20-year risk of developing diabetes while incorporating competing risks. A three-stage stratification random-clustering sampling procedure was conducted to ensure the representativeness of the Beijing elderly. We prospectively followed 1857 community residents aged 55 years and above who were free of diabetes at baseline examination. Sub-distribution hazards models were used to adjust for the competing risks of non-diabetes death. The cumulative incidence function of twenty-year diabetes event rates was 11.60% after adjusting for the competing risks of non-diabetes death. Age, body mass index, fasting plasma glucose, health status, and physical activity were selected to form the score. The area under the ROC curve (AUC) was 0.76 (95% Confidence Interval: 0.72-0.80), and the optimism-corrected AUC was 0.78 (95% Confidence Interval: 0.69-0.87) after internal validation by bootstrapping. The calibration plot showed that the actual diabetes risk was similar to the predicted risk. The cut-off value of the risk score was 19 points, marking mark the difference between low-risk and high-risk patients, which exhibited a sensitivity of 0.74 and specificity of 0.65.

Development and validation of a Diabetes Risk Score for screening undiagnosed diabetes in Sri Lanka (SLDRISK)

BACKGROUND

Screening for undiagnosed diabetes is not widely undertaken due to the high costs and invasiveness of blood sampling. Simple non-invasive tools to identify high risk individuals can facilitate screening. The main objectives of this study are to develop and validate a risk score for screening undiagnosed diabetes among Sri Lankan adults and to compare its performance with the Cambridge Risk Score (CRS), the Indian Diabetes Risk Score (IDRS) and three other Asian risk scores.

METHODS

Data were available from a representative sample of 4276 adults without diagnosed diabetes. In a jack-knife approach two thirds of the sample was used for the development of the risk score and the remainder for the validation. Age, waist circumference, BMI, hypertension, balanitis or vulvitis, family history of diabetes, gestational diabetes, physical activity and osmotic symptoms were significantly associated with undiagnosed diabetes (age most to osmotic symptoms least). Individual scores were generated for these factors using the beta coefficient values obtained in multiple logistic regression. A cut-off value of sum = 31 was determined by ROC curve analysis.

RESULTS

The area under the ROC curve of the risk score for prevalent diabetes was 0.78 (CI 0.73-0.82). In the sample 36.3 % were above the cut-off of 31. A risk score above 31 gave a sensitivity, specificity, positive predictive value and negative predictive value of 77.9, 65.6, 9.4 and 98.3 % respectively. For Sri Lankans the AUC for the CRS and IDRS were 0.72 and 0.66 repectively.

CONCLUSIONS

This simple non-invasive screening tool can identify 80 % of undiagnosed diabetes by selecting 40 % of Sri Lankan adults for confirmatory blood investigations.

Evaluation of Non-Laboratory and Laboratory Prediction Models for Current and Future Diabetes Mellitus: A Cross-Sectional and Retrospective Cohort Study

BACKGROUND

Various diabetes risk scores composed of non-laboratory parameters have been developed, but only a few studies performed cross-validation of these scores and a comparison with laboratory parameters. We evaluated the performance of diabetes risk scores composed of non-laboratory parameters, including a recently published Korean risk score (KRS), and compared them with laboratory parameters.

METHODS

The data of 26,675 individuals who visited the Seoul National University Hospital Healthcare System Gangnam Center for a health screening program were reviewed for cross-sectional validation. The data of 3,029 individuals with a mean of 6.2 years of follow-up were reviewed for longitudinal validation. The KRS and 16 other risk scores were evaluated and compared with a laboratory prediction model developed by logistic regression analysis.

RESULTS

For the screening of undiagnosed diabetes, the KRS exhibited a sensitivity of 81%, a specificity of 58%, and an area under the receiver operating characteristic curve (AROC) of 0.754. Other scores showed AROCs that ranged from 0.697 to 0.782. For the prediction of future diabetes, the KRS exhibited a sensitivity of 74%, a specificity of 54%, and an AROC of 0.696. Other scores had AROCs ranging from 0.630 to 0.721. The laboratory prediction model composed of fasting plasma glucose and hemoglobin A1c levels showed a significantly higher AROC (0.838, P < 0.001) than the KRS. The addition of the KRS to the laboratory prediction model increased the AROC (0.849, P = 0.016) without a significant improvement in the risk classification (net reclassification index: 4.6%, P = 0.264).

CONCLUSIONS

The non-laboratory risk scores, including KRS, are useful to estimate the risk of undiagnosed diabetes but are inferior to the laboratory parameters for predicting future diabetes.

Development and Validation of a Risk-Score Model for Type 2 Diabetes: A Cohort Study of a Rural Adult Chinese Population

Some global models to predict the risk of diabetes may not be applicable to local populations. We aimed to develop and validate a score to predict type 2 diabetes mellitus (T2DM) in a rural adult Chinese population. Data for a cohort of 12,849 participants were randomly divided into derivation (n = 11,564) and validation (n = 1285) datasets. A questionnaire interview and physical and blood biochemical examinations were performed at baseline (July to August 2007 and July to August 2008) and follow-up (July to August 2013 and July to October 2014). A Cox regression model was used to weigh each variable in the derivation dataset. For each significant variable, a score was calculated by multiplying β by 100 and rounding to the nearest integer. Age, body mass index, triglycerides and fasting plasma glucose (scores 3, 12, 24 and 76, respectively) were predictors of incident T2DM. The model accuracy was assessed by the area under the receiver operating characteristic curve (AUC), with optimal cut-off value 936. With the derivation dataset, sensitivity, specificity and AUC of the model were 66.7%, 74.0% and 0.768 (95% CI 0.760–0.776), respectively. With the validation dataset, the performance of the model was superior to the Chinese (simple), FINDRISC, Oman and IDRS models of T2DM risk but equivalent to the Framingham model, which is widely applicable in a variety of populations. Our model for predicting 6-year risk of T2DM could be used in a rural adult Chinese population.

Validation of the Finnish diabetes risk score (FINDRISC) for the Caucasian population of Siberia

Aim. A validation of the Finnish diabetes risk score (FINDRISC) was conducted among the Siberian population. FINDRISC was used to study the prevalence of risk factors for type 2 diabetes mellitus (T2DM) and to estimate the incidence of T2DM in high-risk groups during a 10-year observation period. Materials and methods. A total of 9,360 subjects aged between 45 and 69 years were enrolled in this cross-sectional, population-based study. FINDRISC was used to group 8,050 people without diabetes according to their risk for T2DM. Statistical analysis was performed using SPSS. Results. When a cutoff point of 11 was used to identify those with diabetes, sensitivity was 76. 0% and specificity was 60. 2%. The area under the receiver operating curve for diabetes was 0. 73 (0. 73 for men and 0. 70 for women). More than one-third (31. 7%) of the adult population of Novosibirsk was estimated to have medium, high or very high risk of developing T2DM in the next 10 years. Cases of T2DM estimated to occur during the 10 years of follow-up had significantly higher incidence of risk factors such as BMI ≥30 kg/m2, waist circumference 102 cm in men and 88 cm in women and a family history of T2DM and were more likely to take drugs to lower blood pressure. Conclusion. FINDRISC provided good results in our sample, and we recommend its use in the Siberian population. 

Simple risk score to detect rural Asian Indian (Bangladeshi) adults at high risk for type 2 diabetes

AIMS/INTRODUCTION

To develop and evaluate a simple, non-invasive, diabetes risk score for detecting individuals at high risk for type 2 diabetes in rural Bangladesh.

MATERIALS AND METHODS

Data from 2,293 randomly selected individuals aged ≥20 years from a cross-sectional study in a rural community of Bangladesh (2009 Chandra Rural Study) was used for model development. The validity of the model was assessed in another rural cross-sectional study (2009 Thakurgaon Rural Study). The logistic regression model used included age, sex, body mass index, waist-to-hip ratio and hypertension status to predict individuals who were at high risk for type 2 diabetes.

RESULTS

On applying the developed model to both cohorts, the area under the receiver operating characteristic curve was 0.70 (95% confidence interval 0.68-0.72) for the Chandra cohort and 0.71 (95% confidence interval 0.68-0.74) for the Thakurgaon cohort. The risk score of >9 was shown to have the optimal cut-point to detect diabetes. This score had a sensitivity of 62.4 and 75.7%, and specificity of 67.4 and 61.6% in the two cohorts, respectively. This risk score was shown to have improved sensitivity and specificity to detect type 2 diabetes cases compared with the Thai, Indian, Omani, UK, Dutch, Portuguese and Pakistani diabetes risk scores.

CONCLUSIONS

This simple, non-invasive risk score can be used to detect individuals at high risk for type 2 diabetes in rural Bangladesh. Subjects with a score of 9 or above (out of 15) should undergo an oral glucose tolerance test for definitive diagnosis of diabetes.

Toward Big Data Analytics: Review of Predictive Models in Management of Diabetes and Its Complications

Diabetes is one of the top priorities in medical science and health care management, and an abundance of data and information is available on these patients. Whether data stem from statistical models or complex pattern recognition models, they may be fused into predictive models that combine patient information and prognostic outcome results. Such knowledge could be used in clinical decision support, disease surveillance, and public health management to improve patient care. Our aim was to review the literature and give an introduction to predictive models in screening for and the management of prevalent short- and long-term complications in diabetes. Predictive models have been developed for management of diabetes and its complications, and the number of publications on such models has been growing over the past decade. Often multiple logistic or a similar linear regression is used for prediction model development, possibly owing to its transparent functionality. Ultimately, for prediction models to prove useful, they must demonstrate impact, namely, their use must generate better patient outcomes. Although extensive effort has been put in to building these predictive models, there is a remarkable scarcity of impact studies.