Development and Validation of a Primary Care Tool to Identify Patients With Type 2 Diabetes Mellitus at High Risk of Hypoglycemia-Related Inpatient Admissions
Bibliographic record
Abstract
Background: Hypoglycemia inpatient admissions are costly and potentially preventable. Using established risk factors for hypoglycemia, we set out to develop a risk-scoring tool using the data from an Asian population. Methods: In this historical cohort study, we extracted the data of 47,404 type 2 diabetes mellitus (T2DM) patients with complete data based on their last visit in 2012 at selected National Healthcare Group Polyclinics in Singapore. The outcome variable is the occurrence of any hypoglycemia inpatient admission within 6 months from their last visit in 2012. We entered the following potential predictors into a logistic regression model: 1) Age; 2) Largest reduction in glycated hemoglobin within 1 year; 3) Last body mass index; 4) Last estimated glomerular filtration rate; 5) Usage of sulphonylurea and/or insulin; 6) Last glycated hemoglobin; 7) Any previous hypoglycemia inpatient admission in the past 1 year. The relative weightage of predictors were compared, and the model parameters were subsequently converted to a simple risk score (range: 0 to 100). Results: We found predictors 1 to 5 to be statistically significant for subsequent hypoglycemia inpatient admission. In our study population, based on a sensitivity of 73.8% and a specificity of 73.1%, a cut-off score of 38 was selected. The area under the receiver-operating characteristic curve was 0.809 (CI: 0.763 - 0.855). Conclusions: A risk score using commonly available clinical data can help to identify those at risk of hypoglycemia inpatient admission with satisfactory level of accuracy. This score needs to be further validated with randomized controlled studies. J Endocrinol Metab. 2019;9(3):43-50 doi: https://doi.org/10.14740/jem563
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".