Minimization of drug interactions in polypharmacy treatments of diabetes mellitus type 2 with cardiovascular comorbidities by using the decision support tool PM-TOM
Bibliographic record
Abstract
Combined polypharmacy treatments of multi-diseases like diabetes mellitus type 2 (DMT2) with its comorbidities could lead to serious adverse reactions (ADR) due to drug-drug interactions (DDIs). This study aimed to demonstrate that these DDI ADRs can be significantly reduced by carefully examining DDIs of recommended drugs and using advanced clinical decision support (CDS) tools, like PM-TOM (Personal Medicine: Therapy Optimization Method). DMT2 with heart failure (HF) and atherosclerotic cardiovascular disease (ASCVD) were taken for analysis. First, 20 drug classes were selected, recommended in relevant medical guidelines (US, European and Canadian); for example, biguanides, sodium-glucose transporter 2 inhibitors, glucagon-like peptide-1 receptor agonists, insulins, angiotensin 2 receptor blockers, angiotensin-converting enzyme inhibitors, beta-adrenergic blockers, diuretics, and statins. Next, these classes were combined into polypharmacy treatment cases, which were organized into three groups: Basic (combinations of three drug classes), Medial (five), and Advanced (eight). Then, the tool PM-TOM was used to find treatments with minimal and maximal drug interactions (MIN-DDI and MAX-DDI) for each case. Finally, these two treatments' minimal, average and maximal DDIs were calculated and statistically analyzed to examine the scope and effects of optimizing polypharmacy treatments in each case group. In the Basic group, 16 polypharmacy treatment cases were created; in the Medial 210 and the Advanced 736. The MIN-DDI and MAX-DDI treatments in each case group showed significant DDI differences; for example, in the Basic group, the average DDI count in the MIN-DDI treatments was 0.19 and in the MAX-DDI ones 1.75, while in the Medial and Advanced groups, these indicators were 1.66 and 7.66, and 5.76 and 20.52, respectively. Also, 87% of optimized treatments (MIN-DDI) in the Basic group showed no DDIs, 37% in the Medial, and 9% in the Advanced. In addition, 70% of cases in the Medial group had at most two DDIs, and 49% in the Advanced group at most five. These findings suggest that DDI ADRs in randomly selected (unoptimized) DMT2 polypharmacy treatments can be substantially reduced using specialized decision support tools, increasing patients' chances for successful treatment and decreasing health care costs. Similar findings can be expected for other multi-diseases as well.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".