Retrospective Analysis of Atypical Chest Pain Presentations in Older Adults and Their Association With Missed Acute Coronary Syndrome Diagnosis in the Emergency Department: National Hospital Ambulatory Medical Care Survey (NHAMCS)-Based Study
Bibliographic record
Abstract
Background Atypical symptom presentations of Acute Coronary Syndrome (ACS) are common in older adults and may contribute to diagnostic delays or missed recognition in emergency departments (EDs). National-level data examining this relationship remains limited. Objective To evaluate whether atypical chest pain presentations are associated with reduced likelihood of ACS diagnosis among U.S. adults aged 65 years and older during ED visits. Methods We conducted a retrospective cross-sectional study using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS) from 2014 to 2020. ED visits by adults aged ≥65 years were analyzed. Atypical presentations were defined using Reason for Visit (RFV) codes for symptoms such as weakness, dyspnea, dizziness, nausea, syncope, and abdominal pain. The primary outcome was an ED diagnosis of ACS based on ICD-9-CM codes. Multivariable logistic regression was used to assess associations. Results Among 2,470 eligible ED visits, only 15 (0.6%) were diagnosed with ACS. Of those, 7 (46.7%) presented with atypical symptoms. Atypical presentation was not significantly associated with ACS diagnosis (OR: 0.90; 95% CI: 0.32-2.49; p = 0.83). No significant associations were found with age, sex, race/ethnicity, or ED disposition. The variable "admitted to hospital from ED" was excluded due to collinearity. Conclusion Nearly half of older adults diagnosed with ACS presented atypically, yet atypical presentation was not significantly associated with missed ACS diagnosis in the ED. Given the limitations of administrative data and low ACS event rates, future research using richer clinical datasets and follow-up outcomes is needed to better understand diagnostic gaps in this high-risk population.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".