Digital Health Interventions in Patient Management Following Acute Coronary Syndrome: A Meta-Analysis of the Literature
Bibliographic record
Abstract
OBJECTIVE: Acute coronary syndrome patients should be closely followed-up to maintain optimal adherence to medical treatments and to reduce adverse events. Digital health interventions might provide improved outcomes for patient care by providing closer follow- up, compared to standard care. Thus, in this meta-analysis, we aimed to evaluate the effect of digital health interventions on follow-up in acute coronary syndrome patients. METHODS: We searched medical databases to obtain all relevant studies comparing digital health interventions with standard care in acute coronary syndrome patients. After reviewing all eligible studies, a meta-analysis was conducted with the remaining 11 randomized controlled studies and 2 non-randomized controlled studies. A modified Jadad scale and Newcastle-Ottawa scale were used to assess the quality of the publications for randomized controlled studies and non-randomized controlled studies, respectively. RESULTS: This meta-analysis consisted of 7657 patients. The all-cause mortality rate was 49% lower in the digital health intervention cases, compared to those who received standard care [relative risk (RR) = 0.51 (0.37; 0.70), P <.01]. There was a significant decrease in systolic blood pressure in the digital health interventions group, compared to the standard care group [mean difference = -5.28 (-9.47; -1.08), P =.01]. The rate of nonadherence to anti-aggregant drugs was 69% lower in the digital health interventions than in the standard care group [RR = 0.31 (0.20; 0.46), P <.01]. Also, nonadherence rates for statin and beta-blockers were lower in the digital health interventions group. The risk of rehospitalization was observed to be 55% less in the digital health interventions patients, compared to the standard care group [RR = 0.45 (0.30; 0.67), P <.01]. CONCLUSION: Digital health interventions can be effective in follow-up for secondary prevention in acute coronary syndrome patients.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.046 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".