Hypoglycemia and associated cardiovascular diseases, morbidity and mortality in patients with type 2 diabetes mellitus in university teaching hospitals in Rwanda
Bibliographic record
Abstract
Background: Hypoglycemia is a common yet underrecognized complication in patients with type 2 diabetes mellitus (T2DM), often linked with increased cardiovascular (CV) morbidity and mortality. Despite its clinical importance, there is a limited data on the association between hypoglycemia, CV events, and mortality among T2DM patients in Rwanda. This study investigated the occurrence of hypoglycemia and its association with CV diseases, morbidity, and mortality in T2DM patients attending two university teaching hospitals in Rwanda. Methods: A retrospective study was conducted using secondary data from 267 T2DM patients attending Kigali University Teaching Hospital (CHUK) and Butare University Teaching Hospital between 2015 and 2020. Socio-demographic and clinical data, including anti-diabetic medications, hypoglycemia episodes, CV events, and comorbidities, were extracted from medical records and analyzed using Python. Binary regression was used to determine significant predictors of hypoglycemia. Results: Hypoglycemia occurred in 112 (41.9%) patients during their hospitalization or hospital admissions. The use of insulin was significantly associated with hypoglycemia (OR = 1.590, CI: 1.100-2.290, p = 0.010). The mean age of patients who experienced hypoglycemia is 54.2 (±12.1) years. Hypoglycemia occurrence was higher in males (59.8%) group compared to females (40.2%) (p = 0.007). Cardiovascular conditions were common (73.8%), with hypertension being the most prevalent (85.4%). Insulin was the most frequently used anti-diabetic therapy (42.3%). A significant association was found between hypoglycemia and subsequent CV complications. Management of hypoglycemia predominantly involved the use of 50% dextrose solution. Conclusion: Hypoglycemia is a frequent and clinically significant occurrence among T2DM patients in Rwanda, particularly associated with insulin therapy and CV comorbidities. Enhanced clinical monitoring and individualized treatment regimens are essential to mitigate hypoglycemia-related complications and reduce mortality. It is important to conduct a larger studies to support the evidence based findings and address the current methodological constraints.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".