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Article: Simple Non-laboratory- and Laboratory-based Risk Assessment Algorithms and Nomogram for Detecting Undiagnosed Diabetes Mellitus
Title | Simple Non-laboratory- and Laboratory-based Risk Assessment Algorithms and Nomogram for Detecting Undiagnosed Diabetes Mellitus 检测糖尿病的简化非实验室和实验室风险评估公式和计算图 |
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Authors | |
Keywords | Nomogram Risk algorithm Undiagnosed diabetes Validation |
Issue Date | 2016 |
Publisher | Wiley-Blackwell Publishing, Inc. The Journal's web site is located at http://www.wiley.com/bw/journal.asp?ref=1753-0393 |
Citation | Journal of Diabetes, 2016, v. 8 n. 3, p. 414-421 How to Cite? |
Abstract | Background: To develop a simple nomogram which can be used to predict the risk of diabetes mellitus (DM) in asymptomatic non-diabetic general population based on non-laboratory-based and laboratory-based risk algorithms. Methods: Anthropometric data, plasma fasting glucose, full lipid profile, exercise habit and family history of DM were collected from Chinese non-diabetic subjects aged 18-70. Logistic regression analysis was performed on the data of a random sample of 2518 subjects to construct non-laboratory-based and laboratory-based risk assessment algorithms for the detection of undiagnosed DM; both algorithms were validated on the data of the remaining sample (n=839). Hosmer-Lemeshow χ2 statistic and area under the receiver-operating characteristic curve (AUC) were employed to assess the calibration and discrimination of the different DM risk algorithms. Results: Of 3357 subjects recruited, 271 (8.1%) had undiagnosed DM defined by fasting glucose≥7.0mmol/L or 2-hour post-load plasma glucose≥11.1mmol/L after oral glucose tolerance test. The non-laboratory-based risk algorithm, with score ranging from 0 to 33, included age, body mass index, family history of DM, regular exercise and uncontrolled blood pressure; the laboratory-based risk algorithm, with score ranging from 0 to 37, added triglyceride level to the risk factors. Both algorithms demonstrated acceptable calibration (Hosmer-Lemeshow test: P=0.229 and P=0.483, respectively) and discrimination (AUC: 0.709 and 0.711, respectively) for the detection of undiagnosed DM. The optimal cutoff point on the receiver-operating characteristic curve was 18 for the detection of undiagnosed DM in both algorithms. Conclusions: Simple-to-use nomogram for detecting undiagnosed DM has been developed using the validated non-laboratory-based and laboratory-based risk algorithms. |
Persistent Identifier | http://hdl.handle.net/10722/209798 |
ISSN | 2023 Impact Factor: 3.0 2023 SCImago Journal Rankings: 0.951 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Wong, CKH | - |
dc.contributor.author | Siu, SC | - |
dc.contributor.author | Wan, EYF | - |
dc.contributor.author | Jiao, F | - |
dc.contributor.author | Yu, EYT | - |
dc.contributor.author | Fung, CSC | - |
dc.contributor.author | Wong, KW | - |
dc.contributor.author | Leung, AYM | - |
dc.contributor.author | Lam, CLK | - |
dc.date.accessioned | 2015-05-18T03:24:16Z | - |
dc.date.available | 2015-05-18T03:24:16Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | Journal of Diabetes, 2016, v. 8 n. 3, p. 414-421 | - |
dc.identifier.issn | 1753-0393 | - |
dc.identifier.uri | http://hdl.handle.net/10722/209798 | - |
dc.description.abstract | Background: To develop a simple nomogram which can be used to predict the risk of diabetes mellitus (DM) in asymptomatic non-diabetic general population based on non-laboratory-based and laboratory-based risk algorithms. Methods: Anthropometric data, plasma fasting glucose, full lipid profile, exercise habit and family history of DM were collected from Chinese non-diabetic subjects aged 18-70. Logistic regression analysis was performed on the data of a random sample of 2518 subjects to construct non-laboratory-based and laboratory-based risk assessment algorithms for the detection of undiagnosed DM; both algorithms were validated on the data of the remaining sample (n=839). Hosmer-Lemeshow χ2 statistic and area under the receiver-operating characteristic curve (AUC) were employed to assess the calibration and discrimination of the different DM risk algorithms. Results: Of 3357 subjects recruited, 271 (8.1%) had undiagnosed DM defined by fasting glucose≥7.0mmol/L or 2-hour post-load plasma glucose≥11.1mmol/L after oral glucose tolerance test. The non-laboratory-based risk algorithm, with score ranging from 0 to 33, included age, body mass index, family history of DM, regular exercise and uncontrolled blood pressure; the laboratory-based risk algorithm, with score ranging from 0 to 37, added triglyceride level to the risk factors. Both algorithms demonstrated acceptable calibration (Hosmer-Lemeshow test: P=0.229 and P=0.483, respectively) and discrimination (AUC: 0.709 and 0.711, respectively) for the detection of undiagnosed DM. The optimal cutoff point on the receiver-operating characteristic curve was 18 for the detection of undiagnosed DM in both algorithms. Conclusions: Simple-to-use nomogram for detecting undiagnosed DM has been developed using the validated non-laboratory-based and laboratory-based risk algorithms. | - |
dc.language | eng | - |
dc.publisher | Wiley-Blackwell Publishing, Inc. The Journal's web site is located at http://www.wiley.com/bw/journal.asp?ref=1753-0393 | - |
dc.relation.ispartof | Journal of Diabetes | - |
dc.rights | This is the accepted version of the following article: Journal of Diabetes, 2016, v. 8 n. 3, p. 414-421, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/1753-0407.12310 | - |
dc.subject | Nomogram | - |
dc.subject | Risk algorithm | - |
dc.subject | Undiagnosed diabetes | - |
dc.subject | Validation | - |
dc.title | Simple Non-laboratory- and Laboratory-based Risk Assessment Algorithms and Nomogram for Detecting Undiagnosed Diabetes Mellitus | - |
dc.title | 检测糖尿病的简化非实验室和实验室风险评估公式和计算图 | - |
dc.type | Article | - |
dc.identifier.email | Wong, CKH: carlosho@hku.hk | - |
dc.identifier.email | Wan, EYF: yfwan@hku.hk | - |
dc.identifier.email | Yu, EYT: ytyu@hku.hk | - |
dc.identifier.email | Fung, CSC: cfsc@hku.hk | - |
dc.identifier.email | Leung, AYM: angleung@hku.hk | - |
dc.identifier.email | Lam, CLK: clklam@hku.hk | - |
dc.identifier.authority | Wong, CKH=rp01931 | - |
dc.identifier.authority | Wan, EYF=rp02518 | - |
dc.identifier.authority | Yu, EYT=rp01693 | - |
dc.identifier.authority | Fung, CSC=rp01330 | - |
dc.identifier.authority | Leung, AYM=rp00405 | - |
dc.identifier.authority | Lam, CLK=rp00350 | - |
dc.description.nature | postprint | - |
dc.identifier.doi | 10.1111/1753-0407.12310 | - |
dc.identifier.pmid | 25952330 | - |
dc.identifier.scopus | eid_2-s2.0-84933556241 | - |
dc.identifier.hkuros | 243284 | - |
dc.identifier.volume | 8 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 414 | - |
dc.identifier.epage | 421 | - |
dc.identifier.isi | WOS:000373948100014 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 1753-0407 | - |