Observational studyBMJ open2022
Development and validation of a diabetes mellitus and prediabetes risk prediction function for case finding in primary care in Hong Kong: a cross-sectional study and a prospective study protocol paper.
Observational study in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04881383 (The Development and Validation of a DM and Pre-DM Risk Prediction Function for Case Finding in Primary Care in Hong Kong), which is not on this map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
The Development and Validation of a DM and Pre-DM Risk Prediction Function for Case Finding in Primary Care in Hong Kong
Who cites it
2 citing papers in PubMed, 4 citations in OpenAlex.
- External validation of the Hong Kong Chinese non-laboratory risk models and scoring algorithm for case finding of prediabetes and diabetes mellitus in primary care.Journal of diabetes investigation · 2024Article
- 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 healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionDiabetes mellitus (DM) is a major non-communicable disease with an increasing prevalence. Undiagnosed DM is not uncommon and can lead to severe complications and mortality. Identifying high-risk individuals at an earlier disease stage, that is, pre-diabetes (pre-DM), is crucial in delaying progression. Existing risk models mainly rely on non-modifiable factors to predict only the DM risk, and few apply to Chinese people. This study aims to develop and validate a risk prediction function that incorporates modifiable lifestyle factors to detect DM and pre-DM in Chinese adults in primary care. METHODS AND ANALYSIS: A cross-sectional study to develop DM/Pre-DM risk prediction functions using data from the Hong Kong's Population Health Survey (PHS) 2014/2015 and a 12-month prospective study to validate the functions in case finding of individuals with DM/pre-DM. Data of 1857 Chinese adults without self-reported DM/Pre-DM will be extracted from the PHS 2014/2015 to develop DM/Pre-DM risk models using logistic regression and machine learning methods. 1014 Chinese adults without a known history of DM/Pre-DM will be recruited from public and private primary care clinics in Hong Kong. They will complete a questionnaire on relevant risk factors and blood tests on Oral Glucose Tolerance Test (OGTT) and haemoglobin A1C (HbA1c) on recruitment and, if the first blood test is negative, at 12 months. A positive case is DM/pre-DM defined by OGTT or HbA1c in any blood test. Area under receiver operating characteristic curve, sensitivity, specificity, positive predictive value and negative predictive value of the models in detecting DM/pre-DM will be calculated. ETHICS AND DISSEMINATION: Ethics approval has been received from The University of Hong Kong/Hong Kong Hospital Authority Hong Kong West Cluster (UW19-831) and Hong Kong Hospital Authority Kowloon Central/Kowloon East Cluster (REC(KC/KE)-21-0042/ER-3). The study results will be submitted for publication in a peer-reviewed journal. TRIAL REGISTRATION NUMBER: US ClinicalTrial.gov: NCT04881383; HKU clinical trials registry: HKUCTR-2808; Pre-results.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.