ArticleJournal of primary care & community health
Recalibration of a Non-Laboratory-Based Risk Model to Estimate Pre-Diabetes/Diabetes Mellitus Risk in Primary Care in Hong Kong.
Article in Journal of primary care & community health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
INTRODUCTION/
objectivesA 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.
methodsWe 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.
resultsRecalibrating 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.
conclusionThe 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.