A Simple Score Model to Assess Prediabetes Risk Status Based on the Medical Examination Data

Ouyang, Peng; Guo, Xitong; Shen, Yi-Ting; Lu, Naiji; Ma, Chenghua

doi:10.1016/j.jcjd.2016.02.013

Cited by 15 publications

(13 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared with other LR‐based prediabetes risk scores developed in other countries for adult populations, the discrimination capability of the PRISQ risk score, as measured by the AUC of the receiver operating characteristic curve, was 80%, higher than many other scores developed in other countries, such as Indonesia (AUC 62.3%) 38 , China (two scores: AUC 74% and AUC 70%) 36,39 and the USA (AUC 74%) 40 . According to our risk score, a person with a score between 17 and 27 would be considered as being at moderate risk of prediabetes, whereas a score >27 would be considered high risk.…”

Section: Discussionmentioning

confidence: 70%

Simple risk score to screen for prediabetes: A cross‐sectional study from the Qatar Biobank cohort

Abbas

Mall

Errafii

et al. 2020

J of Diabetes Invest

View full text Add to dashboard Cite

Aims/Introduction: The progression from prediabetes to type 2 diabetes is preventable by lifestyle intervention and/or pharmacotherapy in a large fraction of individuals with prediabetes. Our objective was to develop a risk score to screen for prediabetes in the Middle East, where diabetes prevalence is one of the highest in the world. Materials and Methods: In this cross-sectional, case-control study, we used data of 4,895 controls and 2,373 prediabetic adults obtained from the Qatar Biobank cohort. Significant risk factors were identified by logistic regression and other machine learning methods. The receiver operating characteristic was used to calculate the area under curve, cutoff point, sensitivity, specificity, positive and negative predictive values. The prediabetes risk score was developed from data of Qatari citizens, as well as long-term (≥15 years) residents. Results: The significant risk factors for the Prediabetes Risk Score in Qatar were age, sex, body mass index, waist circumference and blood pressure. The risk score ranges from 0 to 45. The area under the curve of the score was 80% (95% confidence interval 78-83%), and the cutoff point of 16 yielded sensitivity and specificity of 86.2% (95% confidence interval 82.7-89.2%) and 57.9% (95% confidence interval 65.5-71.4%), respectively. Prediabetes Risk Score in Qatar performed equally in Qatari nationals and long-term residents. Conclusions: Prediabetes Risk Score in Qatar is the first prediabetes screening score developed in a Middle Eastern population. It only uses risk factors measured non-invasively, is simple, cost-effective, and can be easily understood by the general public and health providers. Prediabetes Risk Score in Qatar is an important tool for early detection of prediabetes, and can help tremendously in curbing the diabetes epidemic in the region.

show abstract

Section: Discussionmentioning

confidence: 70%

Simple risk score to screen for prediabetes: A cross‐sectional study from the Qatar Biobank cohort

Abbas

Mall

Errafii

et al. 2020

J of Diabetes Invest

View full text Add to dashboard Cite

show abstract

“…Compared with interventions after the onset of diabetes, the related intervention measures started during the prediabetes stage are more efficient and sensible because they can prevent or delay the conversion from prediabetes to diabetes [12,16].…”

Section: Liberty and Kodimmentioning

confidence: 99%

Assess Prediabetes Risk, as a Golden Period for Prevention of Diabetes

Liberty

Kodim

2017

Asian J Pharm Clin Res

View full text Add to dashboard Cite

Objective: Prediabetes is a high-risk condition for diabetes development and several other health outcomes later in life, but little is known about the factors associated with this condition. On the other hand, by predicting the risk of prediabetes, it is also a golden period for prevent or delay the diabetes conversion. The aim here was to assess the prevalence, risk factor that associated, and build a model to assess prediabetes risk. Methods:A cross-sectional study was conducted in Palembang, Indonesia. Data were collected during January until May 2016. We recruited adult age >15 years from 16 districts in Palembang. Anthropometric, demographic, and clinical history data were measured by standard methods. Capillary blood glucose was measured by finger prick test, followed by confirmatory oral glucose tolerance tests. Results:Of a total of 1241 participants, the prevalence of prediabetes was 27.8% (345 participants) and 72.2% (896 participants) and those were normal blood glucose. Employment, age, exercise, alcohol consumption, body mass index, systolic pressure, diastolic pressure, waist circumference, and hypercholesterol history were screened out as independent factors to build the prediction risk model. Conclusion:The prediabetes prediction model can be used easily and understood by health-related users to assess prediabetes risk. The intervention program, designed based on our prediabetes model to prevent or delay the conversion of prediabetes to diabetes in the population. The discovery of pharmacological therapies to prevent further conversion is needed.

show abstract

“…But this can be reduced by the analysis of gender adjustment. No other factor was measured as a question item in the scoring method questionnaire such as central obesity, smoking status, and cholesterol levels 20,21,3 .…”

Section: Discussionmentioning

confidence: 99%

Prediabetes Screening with American Diabetes Association (ADA) Scoring in the Primary Health Care Yogyakarta (Development And Validation Of Scoring Systems)

Jeem

Koesnanto

Ikhsan

2020

rpcpe

View full text Add to dashboard Cite

Background: Numerous studies have shown the increasing of prediabetes incidence from the time being. Some of the prediabetes screening methods that can be performed at primary health care were American Diabetes Association (ADA) scoring for prediabetes. However, there was no data that describes the validity and applicability of the ADA scoring on prediabetes patients in Indonesia. Objective: To discribe prediabetes screening and to find out the applicability of the ADA scoring method in Yogyakarta primary health care. Method: The diagnostic test by scoring system of the ADA questionnaire was compared with OGTT (oral glucose tolerance test) as the gold standard. The subjects were patients of primary health care in Yogyakarta who fulfill the inclusion and exclusion criteria. Result: The subjects were 279 respondents with 227 female (81.4%) and 52 male patients (18.6%). The mean age of the study subjects was 50.4 years (SD 12.81). The sensitivity and specificity of the scoring method of ADA was 61% and 71%. This could be influenced by the difference in BMI standard as one of the scoring items. Conclusion: Prediabetes prevalence was 11.1% in the study population. The sensitivity and specificity of the scoring method of ADA is 61% and 71%. The scoring method of ADA could not be used in primary health care.

show abstract

A Simple Score Model to Assess Prediabetes Risk Status Based on the Medical Examination Data

Cited by 15 publications

References 24 publications

Simple risk score to screen for prediabetes: A cross‐sectional study from the Qatar Biobank cohort

Simple risk score to screen for prediabetes: A cross‐sectional study from the Qatar Biobank cohort

Assess Prediabetes Risk, as a Golden Period for Prevention of Diabetes

Prediabetes Screening with American Diabetes Association (ADA) Scoring in the Primary Health Care Yogyakarta (Development And Validation Of Scoring Systems)

Contact Info

Product

Resources

About