Diagnostic prediction model for screening of elevated low-density and non-high-density lipoproteins in young Thai adults between 20 and 40 years of age
Background Low-density lipoprotein cholesterol (LDL-C) and non-high-density lipoprotein cholesterol (non-HDL-C) levels are paramount in atherosclerotic cardiovascular disease risk management. However, 94.4% of Thai young adult are unaware of their condition. A diagnostic prediction model may assist...
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BMJ Publishing Group
2025-01-01
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| Series: | BMJ Health & Care Informatics |
| Online Access: | https://informatics.bmj.com/content/32/1/e101180.full |
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| author | Phichayut Phinyo Wuttipat Kiratipaisarl Vithawat Surawattanasakul Wachiranun Sirikul |
| author_facet | Phichayut Phinyo Wuttipat Kiratipaisarl Vithawat Surawattanasakul Wachiranun Sirikul |
| author_sort | Phichayut Phinyo |
| collection | DOAJ |
| description | Background Low-density lipoprotein cholesterol (LDL-C) and non-high-density lipoprotein cholesterol (non-HDL-C) levels are paramount in atherosclerotic cardiovascular disease risk management. However, 94.4% of Thai young adult are unaware of their condition. A diagnostic prediction model may assist in screening and alleviating underdiagnosis.Objectives Development and internal validation of diagnostic prediction models on elevated LDL-C (≥160 mg/dL) and non-HDL-C (≥160 mg/dL).Methods Retrospective, single-centre, tertiary-care hospital annual health examination data from 29 March 2018 to 30 August 2023 was analysed. Two models with 11 predictors from anthropometry and bioimpedance are fitted with multivariable binary logistic regression predicting elevated LDL-C and non-HDL-C. Predictor selection used the backward stepwise elimination. Four performance metrics were quantified: discrimination using area under the receiver-operating characteristic curve (AuROC); calibration by calibration plot; utility by decision curve analysis and instability by performance instability plots. Internal validation was carried out using 500 repetitions of bootstrap-resampling.Results Dataset included 2222 LDL-C and 5149 non-HDL-C investigations, 303 were classed as elevated LDL-C (13.6%) and 1013 as elevated non-HDL-C cases (19.7%). Two predictors, gender and metabolic age, were identified in the LDL-C model with AuROC 0.639 (95% CI 0.617 to 0.661), poor calibration, and utility in the 7%–25% probability range. Three predictors—gender, diastolic blood pressure and metabolic age—were identified in the non-HDL-C model with AuROC 0.722 (95% CI 0.705 to 0.738), good calibration and utility in 9%–55% probability range.Discussion and conclusion Overall results demonstrated acceptable discrimination for non-HDL-C model but inadequate performance of LDL-C model for clinical practice. An external validation study should be planned for non-HDL-C model. |
| format | Article |
| id | doaj-art-1e40e61a2f6e49b39fbe04215577b48d |
| institution | Kabale University |
| issn | 2632-1009 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMJ Publishing Group |
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| series | BMJ Health & Care Informatics |
| spelling | doaj-art-1e40e61a2f6e49b39fbe04215577b48d2025-01-30T22:35:09ZengBMJ Publishing GroupBMJ Health & Care Informatics2632-10092025-01-0132110.1136/bmjhci-2024-101180Diagnostic prediction model for screening of elevated low-density and non-high-density lipoproteins in young Thai adults between 20 and 40 years of agePhichayut Phinyo0Wuttipat Kiratipaisarl1Vithawat Surawattanasakul2Wachiranun Sirikul3Center for Clinical Epidemiology and Clinical Statistics, Faculty of Medicine, Chiang Mai University, Suthep, ThailandDepartment of Community Medicine, Chiang Mai University Faculty of Medicine, Chiang Mai, ThailandDepartment of Community Medicine, Chiang Mai University Faculty of Medicine, Chiang Mai, ThailandDepartment of Community Medicine, Chiang Mai University Faculty of Medicine, Chiang Mai, ThailandBackground Low-density lipoprotein cholesterol (LDL-C) and non-high-density lipoprotein cholesterol (non-HDL-C) levels are paramount in atherosclerotic cardiovascular disease risk management. However, 94.4% of Thai young adult are unaware of their condition. A diagnostic prediction model may assist in screening and alleviating underdiagnosis.Objectives Development and internal validation of diagnostic prediction models on elevated LDL-C (≥160 mg/dL) and non-HDL-C (≥160 mg/dL).Methods Retrospective, single-centre, tertiary-care hospital annual health examination data from 29 March 2018 to 30 August 2023 was analysed. Two models with 11 predictors from anthropometry and bioimpedance are fitted with multivariable binary logistic regression predicting elevated LDL-C and non-HDL-C. Predictor selection used the backward stepwise elimination. Four performance metrics were quantified: discrimination using area under the receiver-operating characteristic curve (AuROC); calibration by calibration plot; utility by decision curve analysis and instability by performance instability plots. Internal validation was carried out using 500 repetitions of bootstrap-resampling.Results Dataset included 2222 LDL-C and 5149 non-HDL-C investigations, 303 were classed as elevated LDL-C (13.6%) and 1013 as elevated non-HDL-C cases (19.7%). Two predictors, gender and metabolic age, were identified in the LDL-C model with AuROC 0.639 (95% CI 0.617 to 0.661), poor calibration, and utility in the 7%–25% probability range. Three predictors—gender, diastolic blood pressure and metabolic age—were identified in the non-HDL-C model with AuROC 0.722 (95% CI 0.705 to 0.738), good calibration and utility in 9%–55% probability range.Discussion and conclusion Overall results demonstrated acceptable discrimination for non-HDL-C model but inadequate performance of LDL-C model for clinical practice. An external validation study should be planned for non-HDL-C model.https://informatics.bmj.com/content/32/1/e101180.full |
| spellingShingle | Phichayut Phinyo Wuttipat Kiratipaisarl Vithawat Surawattanasakul Wachiranun Sirikul Diagnostic prediction model for screening of elevated low-density and non-high-density lipoproteins in young Thai adults between 20 and 40 years of age BMJ Health & Care Informatics |
| title | Diagnostic prediction model for screening of elevated low-density and non-high-density lipoproteins in young Thai adults between 20 and 40 years of age |
| title_full | Diagnostic prediction model for screening of elevated low-density and non-high-density lipoproteins in young Thai adults between 20 and 40 years of age |
| title_fullStr | Diagnostic prediction model for screening of elevated low-density and non-high-density lipoproteins in young Thai adults between 20 and 40 years of age |
| title_full_unstemmed | Diagnostic prediction model for screening of elevated low-density and non-high-density lipoproteins in young Thai adults between 20 and 40 years of age |
| title_short | Diagnostic prediction model for screening of elevated low-density and non-high-density lipoproteins in young Thai adults between 20 and 40 years of age |
| title_sort | diagnostic prediction model for screening of elevated low density and non high density lipoproteins in young thai adults between 20 and 40 years of age |
| url | https://informatics.bmj.com/content/32/1/e101180.full |
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