Development and validation of prediabetes risk score for predicting prediabetes among Indonesian adults in primary care: Cross-sectional diagnostic study
To develop and validate a risk score model for recognizing prediabetes among Indonesian adults in primary care. This was a cross-sectional diagnostic study. After excluding subjects with diabetes from Indonesian National Basic Health Survey (INBHS) data set, 21,720 subjects who have completed fastin...
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Veröffentlicht in: | Interventional medicine and applied science 2017-06, Vol.9 (2), p.76-85 |
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creator | Fujiati, Isti Ilmiati Damanik, Harun Alrasyid Bachtiar, Adang Nurdin, Andi Armyn Ward, Paul |
description | To develop and validate a risk score model for recognizing prediabetes among Indonesian adults in primary care.
This was a cross-sectional diagnostic study. After excluding subjects with diabetes from Indonesian National Basic Health Survey (INBHS) data set, 21,720 subjects who have completed fasting plasma glucose test and aged >18 years were selected for development stage. About 6,933 subjects were selected randomly from INBHS for validation stage in different diagnostic criteria of prediabetes-based random plasma glucose. Logistic regression was used to determine significant diagnostic variable and the receiver operating characteristic analysis was used to calculate area under the curve (AUC), cutoff point, sensitivity, specificity, and predictive values.
Age, sex, education level, family history of diabetes, smoking habit, physical activity, body mass index, and hypertension were significant variables for Indonesian Prediabetes Risk Score (INA-PRISC). The scoring range from 0 to 24, the AUC was 0.623 (95% CI 0.616-0.631) and cutoff point of 12 yielded sensitivity/specificity (50.03%/67.19%, respectively). The validation study showed the AUC was 0.646 (95% CI 0.623-0.669) and cutoff point of 12 yielded sensitivity/specificity (55.11%/65.81%, respectively).
INA-PRISC, which consists of eight demographical and clinical variables, is a valid and a simple prediabetes risk score in primary care. |
doi_str_mv | 10.1556/1646.9.2017.18 |
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This was a cross-sectional diagnostic study. After excluding subjects with diabetes from Indonesian National Basic Health Survey (INBHS) data set, 21,720 subjects who have completed fasting plasma glucose test and aged >18 years were selected for development stage. About 6,933 subjects were selected randomly from INBHS for validation stage in different diagnostic criteria of prediabetes-based random plasma glucose. Logistic regression was used to determine significant diagnostic variable and the receiver operating characteristic analysis was used to calculate area under the curve (AUC), cutoff point, sensitivity, specificity, and predictive values.
Age, sex, education level, family history of diabetes, smoking habit, physical activity, body mass index, and hypertension were significant variables for Indonesian Prediabetes Risk Score (INA-PRISC). The scoring range from 0 to 24, the AUC was 0.623 (95% CI 0.616-0.631) and cutoff point of 12 yielded sensitivity/specificity (50.03%/67.19%, respectively). The validation study showed the AUC was 0.646 (95% CI 0.623-0.669) and cutoff point of 12 yielded sensitivity/specificity (55.11%/65.81%, respectively).
INA-PRISC, which consists of eight demographical and clinical variables, is a valid and a simple prediabetes risk score in primary care.</description><identifier>ISSN: 2061-1617</identifier><identifier>EISSN: 2061-5094</identifier><identifier>DOI: 10.1556/1646.9.2017.18</identifier><identifier>PMID: 28932501</identifier><language>eng</language><publisher>Hungary: Akademiai Kiado</publisher><subject>Health risk assessment ; Methods ; Prediabetic state ; Risk factors</subject><ispartof>Interventional medicine and applied science, 2017-06, Vol.9 (2), p.76-85</ispartof><rights>COPYRIGHT 2017 Akademiai Kiado</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c369t-b6a8198a2324af8550528f70d3c505a90c1f5ff28c73b049b4a986e00fd28bdb3</citedby><cites>FETCH-LOGICAL-c369t-b6a8198a2324af8550528f70d3c505a90c1f5ff28c73b049b4a986e00fd28bdb3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,864,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28932501$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Fujiati, Isti Ilmiati</creatorcontrib><creatorcontrib>Damanik, Harun Alrasyid</creatorcontrib><creatorcontrib>Bachtiar, Adang</creatorcontrib><creatorcontrib>Nurdin, Andi Armyn</creatorcontrib><creatorcontrib>Ward, Paul</creatorcontrib><title>Development and validation of prediabetes risk score for predicting prediabetes among Indonesian adults in primary care: Cross-sectional diagnostic study</title><title>Interventional medicine and applied science</title><addtitle>Interv Med Appl Sci</addtitle><description>To develop and validate a risk score model for recognizing prediabetes among Indonesian adults in primary care.
This was a cross-sectional diagnostic study. After excluding subjects with diabetes from Indonesian National Basic Health Survey (INBHS) data set, 21,720 subjects who have completed fasting plasma glucose test and aged >18 years were selected for development stage. About 6,933 subjects were selected randomly from INBHS for validation stage in different diagnostic criteria of prediabetes-based random plasma glucose. Logistic regression was used to determine significant diagnostic variable and the receiver operating characteristic analysis was used to calculate area under the curve (AUC), cutoff point, sensitivity, specificity, and predictive values.
Age, sex, education level, family history of diabetes, smoking habit, physical activity, body mass index, and hypertension were significant variables for Indonesian Prediabetes Risk Score (INA-PRISC). The scoring range from 0 to 24, the AUC was 0.623 (95% CI 0.616-0.631) and cutoff point of 12 yielded sensitivity/specificity (50.03%/67.19%, respectively). The validation study showed the AUC was 0.646 (95% CI 0.623-0.669) and cutoff point of 12 yielded sensitivity/specificity (55.11%/65.81%, respectively).
INA-PRISC, which consists of eight demographical and clinical variables, is a valid and a simple prediabetes risk score in primary care.</description><subject>Health risk assessment</subject><subject>Methods</subject><subject>Prediabetic state</subject><subject>Risk factors</subject><issn>2061-1617</issn><issn>2061-5094</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNptkU9rHSEUxaWkNCHNtssgdD0TdcYZzS68pO2DQDbtWu7452E7ow_1BfJR-m3r8F4ChejCy_V3rhwPQl8oaSnnww0d-qGVLSN0bKn4gC4YGWjDiezPTjUd6HiOrnL-Terq2CiI_ITOmZAd44ReoL_39tnOcb_YUDAEg59h9gaKjwFHh_fJGg-TLTbj5PMfnHVMFruYjle6-LD7j4Il1s42mBhs9hAwmMNcMvahYn6B9II1JHuLNynm3GSr17dgxnXCLsRcvMa5HMzLZ_TRwZzt1em8RL--Pfzc_Ggen75vN3ePje4GWZppAEGlANaxHpzgnHAm3EhMp2sJkmjquHNM6LGbSC-nHqQYLCHOMDGZqbtEX49zdzBb5YOLJYFefNbqrpejqD9IeKXad6i6jV28rl6dr_33BHr1maxTJ_uKErWmp9b0lFRreoqKKrg-CvaHabHmDX_NqvsHNhmWzw</recordid><startdate>20170601</startdate><enddate>20170601</enddate><creator>Fujiati, Isti Ilmiati</creator><creator>Damanik, Harun Alrasyid</creator><creator>Bachtiar, Adang</creator><creator>Nurdin, Andi Armyn</creator><creator>Ward, Paul</creator><general>Akademiai Kiado</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20170601</creationdate><title>Development and validation of prediabetes risk score for predicting prediabetes among Indonesian adults in primary care: Cross-sectional diagnostic study</title><author>Fujiati, Isti Ilmiati ; Damanik, Harun Alrasyid ; Bachtiar, Adang ; Nurdin, Andi Armyn ; Ward, Paul</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c369t-b6a8198a2324af8550528f70d3c505a90c1f5ff28c73b049b4a986e00fd28bdb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Health risk assessment</topic><topic>Methods</topic><topic>Prediabetic state</topic><topic>Risk factors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fujiati, Isti Ilmiati</creatorcontrib><creatorcontrib>Damanik, Harun Alrasyid</creatorcontrib><creatorcontrib>Bachtiar, Adang</creatorcontrib><creatorcontrib>Nurdin, Andi Armyn</creatorcontrib><creatorcontrib>Ward, Paul</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><jtitle>Interventional medicine and applied science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fujiati, Isti Ilmiati</au><au>Damanik, Harun Alrasyid</au><au>Bachtiar, Adang</au><au>Nurdin, Andi Armyn</au><au>Ward, Paul</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Development and validation of prediabetes risk score for predicting prediabetes among Indonesian adults in primary care: Cross-sectional diagnostic study</atitle><jtitle>Interventional medicine and applied science</jtitle><addtitle>Interv Med Appl Sci</addtitle><date>2017-06-01</date><risdate>2017</risdate><volume>9</volume><issue>2</issue><spage>76</spage><epage>85</epage><pages>76-85</pages><issn>2061-1617</issn><eissn>2061-5094</eissn><abstract>To develop and validate a risk score model for recognizing prediabetes among Indonesian adults in primary care.
This was a cross-sectional diagnostic study. After excluding subjects with diabetes from Indonesian National Basic Health Survey (INBHS) data set, 21,720 subjects who have completed fasting plasma glucose test and aged >18 years were selected for development stage. About 6,933 subjects were selected randomly from INBHS for validation stage in different diagnostic criteria of prediabetes-based random plasma glucose. Logistic regression was used to determine significant diagnostic variable and the receiver operating characteristic analysis was used to calculate area under the curve (AUC), cutoff point, sensitivity, specificity, and predictive values.
Age, sex, education level, family history of diabetes, smoking habit, physical activity, body mass index, and hypertension were significant variables for Indonesian Prediabetes Risk Score (INA-PRISC). The scoring range from 0 to 24, the AUC was 0.623 (95% CI 0.616-0.631) and cutoff point of 12 yielded sensitivity/specificity (50.03%/67.19%, respectively). The validation study showed the AUC was 0.646 (95% CI 0.623-0.669) and cutoff point of 12 yielded sensitivity/specificity (55.11%/65.81%, respectively).
INA-PRISC, which consists of eight demographical and clinical variables, is a valid and a simple prediabetes risk score in primary care.</abstract><cop>Hungary</cop><pub>Akademiai Kiado</pub><pmid>28932501</pmid><doi>10.1556/1646.9.2017.18</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
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source | DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals; PubMed Central |
subjects | Health risk assessment Methods Prediabetic state Risk factors |
title | Development and validation of prediabetes risk score for predicting prediabetes among Indonesian adults in primary care: Cross-sectional diagnostic study |
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