Effectiveness of electronic medical record-based strategies for death and hospital admission endpoint capture in pragmatic clinical trials
Bibliographic record
Abstract
OBJECTIVE: Event capture in clinical trials is resource-intensive, and electronic medical records (EMRs) offer a potential solution. This study develops algorithms for EMR-based death and hospitalization capture and compares them with traditional event capture methods. MATERIALS AND METHODS: We compared the effectiveness of EMR-based event capture and site-captured events adjudicated by a clinical endpoint committee in the multi-center INfluenza Vaccine to Effectively Stop cardio Thoracic Events and Decompensated heart failure (INVESTED) trial for participants from the Veterans Affairs healthcare system. Varying time windows around event dates were used to optimize events matching. The algorithms were externally validated for heart failure hospitalizations in the Medical Information Mart for Intensive Care (MIMIC)-IV database. RESULTS: We observed 100% sensitivity for death events with a 1-day window. Sensitivity for cardiovascular, heart failure, pulmonary, and nonspecific cardiopulmonary hospitalizations using discharge diagnosis codes varied between 75% and 95%. Including Centers for Medicare & Medicaid Services data improved sensitivity with no meaningful decrease in specificity. The MIMIC-IV analysis showed 82% sensitivity and 99% specificity for heart failure hospitalizations. DISCUSSION: EMR-based method accurately identifies all-cause mortality and demonstrates high accuracy for cardiopulmonary hospitalizations. This study underscores the importance of optimal time windows, data completeness, and domain variability in EMR systems. CONCLUSION: EMR-based methods are effective strategies for capturing death and hospitalizations in clinical trials; however, their effectiveness may be influenced by the complexity of events and domain variability across different EMR systems. Nonetheless, EMR-based methods can serve as a valuable complement to traditional methods.
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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 | 0.591 | 0.754 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".