Recalibration of a Non-Laboratory-Based Risk Model to Estimate Pre-Diabetes/Diabetes Mellitus Risk in Primary Care in Hong Kong
Introduction/Objectives: A non-laboratory-based pre-diabetes/diabetes mellitus (pre-DM/DM) risk prediction model developed from the Hong Kong Chinese population showed good external discrimination in a primary care (PC) population, but the estimated risk level was significantly lower than the observ...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2024-04-01
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| Series: | Journal of Primary Care & Community Health |
| Online Access: | https://doi.org/10.1177/21501319241241188 |
| _version_ | 1797221371798880256 |
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| author | Will H. G. Cheng Weinan Dong Emily T. Y. Tse Linda Chan Carlos K. H. Wong Weng Y. Chin Laura E. Bedford Wai Kit Ko David V. K. Chao Kathryn C. B. Tan Cindy L. K. Lam |
| author_facet | Will H. G. Cheng Weinan Dong Emily T. Y. Tse Linda Chan Carlos K. H. Wong Weng Y. Chin Laura E. Bedford Wai Kit Ko David V. K. Chao Kathryn C. B. Tan Cindy L. K. Lam |
| author_sort | Will H. G. Cheng |
| collection | DOAJ |
| description | Introduction/Objectives: A non-laboratory-based pre-diabetes/diabetes mellitus (pre-DM/DM) risk prediction model developed from the Hong Kong Chinese population showed good external discrimination in a primary care (PC) population, but the estimated risk level was significantly lower than the observed incidence, indicating poor calibration. This study explored whether recalibrating/updating methods could improve the model’s accuracy in estimating individuals’ risks in PC. Methods: We performed a secondary analysis on the model’s predictors and blood test results of 919 Chinese adults with no prior DM diagnosis recruited from PC clinics from April 2021 to January 2022 in HK. The dataset was randomly split in half into a training set and a test set. The model was recalibrated/updated based on a seven-step methodology, including model recalibrating, revising and extending methods. The primary outcome was the calibration of the recalibrated/updated models, indicated by calibration plots. The models’ discrimination, indicated by the area under the receiver operating characteristic curves (AUC-ROC), was also evaluated. Results: Recalibrating the model’s regression constant, with no change to the predictors’ coefficients, improved the model’s accuracy (calibration plot intercept: −0.01, slope: 0.69). More extensive methods could not improve any further. All recalibrated/updated models had similar AUC-ROCs to the original model. Conclusion: The simple recalibration method can adapt the HK Chinese pre-DM/DM model to PC populations with different pre-test probabilities. The recalibrated model can be used as a first-step screening tool and as a measure to monitor changes in pre-DM/DM risks over time or after interventions. |
| first_indexed | 2024-04-24T13:04:23Z |
| format | Article |
| id | doaj.art-b60a8a8a82464c27ad4e7f1ca7373e75 |
| institution | Directory Open Access Journal |
| issn | 2150-1327 |
| language | English |
| last_indexed | 2024-04-24T13:04:23Z |
| publishDate | 2024-04-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Journal of Primary Care & Community Health |
| spelling | doaj.art-b60a8a8a82464c27ad4e7f1ca7373e752024-04-05T09:04:00ZengSAGE PublishingJournal of Primary Care & Community Health2150-13272024-04-011510.1177/21501319241241188Recalibration of a Non-Laboratory-Based Risk Model to Estimate Pre-Diabetes/Diabetes Mellitus Risk in Primary Care in Hong KongWill H. G. Cheng0Weinan Dong1Emily T. Y. Tse2Linda Chan3Carlos K. H. Wong4Weng Y. Chin5Laura E. Bedford6Wai Kit Ko7David V. K. Chao8Kathryn C. B. Tan9Cindy L. K. Lam10The University of Hong Kong, Hong Kong SAR, ChinaThe University of Hong Kong, Hong Kong SAR, ChinaThe University of Hong Kong-Shenzhen Hospital, Shenzhen, ChinaThe University of Hong Kong-Shenzhen Hospital, Shenzhen, ChinaHong Kong Science and Technology Park, Sha Tin, Hong Kong SAR, ChinaThe University of Hong Kong, Hong Kong SAR, ChinaThe University of Hong Kong, Hong Kong SAR, ChinaHospital Authority, Hong Kong SAR, ChinaHospital Authority, Hong Kong SAR, ChinaThe University of Hong Kong, Hong Kong SAR, ChinaThe University of Hong Kong-Shenzhen Hospital, Shenzhen, ChinaIntroduction/Objectives: A non-laboratory-based pre-diabetes/diabetes mellitus (pre-DM/DM) risk prediction model developed from the Hong Kong Chinese population showed good external discrimination in a primary care (PC) population, but the estimated risk level was significantly lower than the observed incidence, indicating poor calibration. This study explored whether recalibrating/updating methods could improve the model’s accuracy in estimating individuals’ risks in PC. Methods: We performed a secondary analysis on the model’s predictors and blood test results of 919 Chinese adults with no prior DM diagnosis recruited from PC clinics from April 2021 to January 2022 in HK. The dataset was randomly split in half into a training set and a test set. The model was recalibrated/updated based on a seven-step methodology, including model recalibrating, revising and extending methods. The primary outcome was the calibration of the recalibrated/updated models, indicated by calibration plots. The models’ discrimination, indicated by the area under the receiver operating characteristic curves (AUC-ROC), was also evaluated. Results: Recalibrating the model’s regression constant, with no change to the predictors’ coefficients, improved the model’s accuracy (calibration plot intercept: −0.01, slope: 0.69). More extensive methods could not improve any further. All recalibrated/updated models had similar AUC-ROCs to the original model. Conclusion: The simple recalibration method can adapt the HK Chinese pre-DM/DM model to PC populations with different pre-test probabilities. The recalibrated model can be used as a first-step screening tool and as a measure to monitor changes in pre-DM/DM risks over time or after interventions.https://doi.org/10.1177/21501319241241188 |
| spellingShingle | Will H. G. Cheng Weinan Dong Emily T. Y. Tse Linda Chan Carlos K. H. Wong Weng Y. Chin Laura E. Bedford Wai Kit Ko David V. K. Chao Kathryn C. B. Tan Cindy L. K. Lam Recalibration of a Non-Laboratory-Based Risk Model to Estimate Pre-Diabetes/Diabetes Mellitus Risk in Primary Care in Hong Kong Journal of Primary Care & Community Health |
| title | Recalibration of a Non-Laboratory-Based Risk Model to Estimate Pre-Diabetes/Diabetes Mellitus Risk in Primary Care in Hong Kong |
| title_full | Recalibration of a Non-Laboratory-Based Risk Model to Estimate Pre-Diabetes/Diabetes Mellitus Risk in Primary Care in Hong Kong |
| title_fullStr | Recalibration of a Non-Laboratory-Based Risk Model to Estimate Pre-Diabetes/Diabetes Mellitus Risk in Primary Care in Hong Kong |
| title_full_unstemmed | Recalibration of a Non-Laboratory-Based Risk Model to Estimate Pre-Diabetes/Diabetes Mellitus Risk in Primary Care in Hong Kong |
| title_short | Recalibration of a Non-Laboratory-Based Risk Model to Estimate Pre-Diabetes/Diabetes Mellitus Risk in Primary Care in Hong Kong |
| title_sort | recalibration of a non laboratory based risk model to estimate pre diabetes diabetes mellitus risk in primary care in hong kong |
| url | https://doi.org/10.1177/21501319241241188 |
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