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Application of Indian Diabetic Risk Score (IDRS) and Community Based Assessment Checklist (CBAC) as Metabolic Syndrome prediction tools

BACKGROUND: Indian Diabetic Risk Score (IDRS) and Community Based Assessment Checklist (CBAC) are easy, inexpensive, and non-invasive tools that can be used to screen people for Metabolic Syndrome (Met S). The study aimed to explore the prediction abilities of IDRS and CBAC tools for Met S. METHODS:...

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Autores principales: Gupta, Manoj Kumar, Dutta, Gitashree, G., Sridevi, Raghav, Pankaja, Goel, Akhil Dhanesh, Bhardwaj, Pankaj, Saurabh, Suman, S., Srikanth, K. H., Naveen, T., Prasanna, Rustagi, Neeti, Sharma, Prem Prakash
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10042346/
https://www.ncbi.nlm.nih.gov/pubmed/36972242
http://dx.doi.org/10.1371/journal.pone.0283263
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author Gupta, Manoj Kumar
Dutta, Gitashree
G., Sridevi
Raghav, Pankaja
Goel, Akhil Dhanesh
Bhardwaj, Pankaj
Saurabh, Suman
S., Srikanth
K. H., Naveen
T., Prasanna
Rustagi, Neeti
Sharma, Prem Prakash
author_facet Gupta, Manoj Kumar
Dutta, Gitashree
G., Sridevi
Raghav, Pankaja
Goel, Akhil Dhanesh
Bhardwaj, Pankaj
Saurabh, Suman
S., Srikanth
K. H., Naveen
T., Prasanna
Rustagi, Neeti
Sharma, Prem Prakash
author_sort Gupta, Manoj Kumar
collection PubMed
description BACKGROUND: Indian Diabetic Risk Score (IDRS) and Community Based Assessment Checklist (CBAC) are easy, inexpensive, and non-invasive tools that can be used to screen people for Metabolic Syndrome (Met S). The study aimed to explore the prediction abilities of IDRS and CBAC tools for Met S. METHODS: All the people of age ≥30 years attending the selected rural health centers were screened for Met S. We used the International Diabetes Federation (IDF) criteria to diagnose the Met S. ROC curves were plotted by taking Met S as dependent variables, and IDRS and CBAC scores as independent/prediction variables. Sensitivity (SN), specificity (SP), Positive and Negative Predictive Value (PPV and NPV), Likelihood Ratio for positive and negative tests (LR(+) and LR(-)), Accuracy, and Youden’s index were calculated for different IDRS and CBAC scores cut-offs. Data were analyzed using SPSS v.23 and MedCalc v.20.111. RESULTS: A total of 942 participants underwent the screening process. Out of them, 59 (6.4%, 95% CI: 4.90–8.12) were found to have Met S. Area Under the Curve (AUC) for IDRS in predicting Met S was 0.73 (95%CI: 0.67–0.79), with 76.3% (64.0%-85.3%) sensitivity and 54.6% (51.2%-57.8%) specificity at the cut-off of ≥60. For the CBAC score, AUC was 0.73 (95%CI: 0.66–0.79), with 84.7% (73.5%-91.7%) sensitivity and 48.8% (45.5%-52.1%) specificity at the cut-off of ≥4 (Youden’s Index, 2.1). The AUCs of both parameters (IDRS and CBAC scores) were statistically significant. There was no significant difference (p = 0.833) in the AUCs of IDRS and CBAC [Difference between AUC = 0.00571]. CONCLUSION: The current study provides scientific evidence that both IDRS and CBAC have almost 73% prediction ability for Met S. Though CBAC holds relatively greater sensitivity (84.7%) than IDRS (76.3%), the difference in prediction abilities is not statistically significant. The prediction abilities of IDRS and CBAC found in this study are inadequate to qualify as Met S screening tools.
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spelling pubmed-100423462023-03-28 Application of Indian Diabetic Risk Score (IDRS) and Community Based Assessment Checklist (CBAC) as Metabolic Syndrome prediction tools Gupta, Manoj Kumar Dutta, Gitashree G., Sridevi Raghav, Pankaja Goel, Akhil Dhanesh Bhardwaj, Pankaj Saurabh, Suman S., Srikanth K. H., Naveen T., Prasanna Rustagi, Neeti Sharma, Prem Prakash PLoS One Research Article BACKGROUND: Indian Diabetic Risk Score (IDRS) and Community Based Assessment Checklist (CBAC) are easy, inexpensive, and non-invasive tools that can be used to screen people for Metabolic Syndrome (Met S). The study aimed to explore the prediction abilities of IDRS and CBAC tools for Met S. METHODS: All the people of age ≥30 years attending the selected rural health centers were screened for Met S. We used the International Diabetes Federation (IDF) criteria to diagnose the Met S. ROC curves were plotted by taking Met S as dependent variables, and IDRS and CBAC scores as independent/prediction variables. Sensitivity (SN), specificity (SP), Positive and Negative Predictive Value (PPV and NPV), Likelihood Ratio for positive and negative tests (LR(+) and LR(-)), Accuracy, and Youden’s index were calculated for different IDRS and CBAC scores cut-offs. Data were analyzed using SPSS v.23 and MedCalc v.20.111. RESULTS: A total of 942 participants underwent the screening process. Out of them, 59 (6.4%, 95% CI: 4.90–8.12) were found to have Met S. Area Under the Curve (AUC) for IDRS in predicting Met S was 0.73 (95%CI: 0.67–0.79), with 76.3% (64.0%-85.3%) sensitivity and 54.6% (51.2%-57.8%) specificity at the cut-off of ≥60. For the CBAC score, AUC was 0.73 (95%CI: 0.66–0.79), with 84.7% (73.5%-91.7%) sensitivity and 48.8% (45.5%-52.1%) specificity at the cut-off of ≥4 (Youden’s Index, 2.1). The AUCs of both parameters (IDRS and CBAC scores) were statistically significant. There was no significant difference (p = 0.833) in the AUCs of IDRS and CBAC [Difference between AUC = 0.00571]. CONCLUSION: The current study provides scientific evidence that both IDRS and CBAC have almost 73% prediction ability for Met S. Though CBAC holds relatively greater sensitivity (84.7%) than IDRS (76.3%), the difference in prediction abilities is not statistically significant. The prediction abilities of IDRS and CBAC found in this study are inadequate to qualify as Met S screening tools. Public Library of Science 2023-03-27 /pmc/articles/PMC10042346/ /pubmed/36972242 http://dx.doi.org/10.1371/journal.pone.0283263 Text en © 2023 Gupta et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Gupta, Manoj Kumar
Dutta, Gitashree
G., Sridevi
Raghav, Pankaja
Goel, Akhil Dhanesh
Bhardwaj, Pankaj
Saurabh, Suman
S., Srikanth
K. H., Naveen
T., Prasanna
Rustagi, Neeti
Sharma, Prem Prakash
Application of Indian Diabetic Risk Score (IDRS) and Community Based Assessment Checklist (CBAC) as Metabolic Syndrome prediction tools
title Application of Indian Diabetic Risk Score (IDRS) and Community Based Assessment Checklist (CBAC) as Metabolic Syndrome prediction tools
title_full Application of Indian Diabetic Risk Score (IDRS) and Community Based Assessment Checklist (CBAC) as Metabolic Syndrome prediction tools
title_fullStr Application of Indian Diabetic Risk Score (IDRS) and Community Based Assessment Checklist (CBAC) as Metabolic Syndrome prediction tools
title_full_unstemmed Application of Indian Diabetic Risk Score (IDRS) and Community Based Assessment Checklist (CBAC) as Metabolic Syndrome prediction tools
title_short Application of Indian Diabetic Risk Score (IDRS) and Community Based Assessment Checklist (CBAC) as Metabolic Syndrome prediction tools
title_sort application of indian diabetic risk score (idrs) and community based assessment checklist (cbac) as metabolic syndrome prediction tools
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10042346/
https://www.ncbi.nlm.nih.gov/pubmed/36972242
http://dx.doi.org/10.1371/journal.pone.0283263
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