Failure of Prospective Validation and Derivation of a Refined Clinical Decision Rule for Chest Radiography in Emergency Department Patients With Chest Pain and Possible Acute Coronary Syndrome
Bibliographic record
Abstract
OBJECTIVES: The authors previously derived a clinical decision rule (CDR) for chest radiography in patients with chest pain and possible acute coronary syndrome (ACS) consisting of the absence of three predictors: history of congestive heart failure, history of smoking, and abnormalities on lung auscultation. The aim of the investigation was to prospectively validate and refine the CDR for chest radiography in an independent patient population. METHODS: Patients over 24 years of age with a primary complaint of chest pain and possible ACS were prospectively enrolled from September 2008 to January 2010 at an academic emergency department (ED) with 73,000 annual patient visits. Physicians completed standardized data collection forms before ordering chest radiographs. Two investigators, blinded to the data collection forms, independently classified chest radiographs as "normal,""abnormal not requiring intervention," or "abnormal requiring intervention" (e.g., heart failure, infiltrates), based on review of the radiology report and medical record. Analyses included descriptive statistics, interrater reliability assessment (kappa), and recursive partitioning. RESULTS: Of 1,159 visits for possible ACS in which chest radiography was obtained, mean (±SD) age was 60.3 (±15.6) years, and 51% were female. Twenty-four percent had a history of acute myocardial infarction, 10% congestive heart failure, and 11% atrial fibrillation. Sixty-nine (6.0%, 95% confidence interval [CI] = 4.7% to 7.5%) patients had a radiographic abnormality requiring intervention. The kappa statistic for chest radiograph classification was 0.93 (95% CI = 0.88 to 0.97). The previously derived prediction rule (no history of congestive heart failure, no history of smoking, and no abnormalities on lung auscultation) was 78.3% sensitive (95% CI = 67.2% to 86.4%) and 45.1% specific (95% CI = 42.2% to 48.1%) and had a positive predictive value of 8.3% (95% CI = 6.4% to 10.7%) and a negative predictive value of 97.0% (95% CI = 95.2% to 98.2%). Due to suboptimal performance, the rule was refined. The refined rule (no shortness of breath, no history of smoking, no abnormalities on lung auscultation, and age < 55 years) was 100.0% sensitive (95% CI = 93.4% to 100.0%) and 11.5% specific (95% CI = 9.6% to 13.5%) and had a positive predictive value of 6.7% (95% CI = 5.3% to 8.4%) and a negative predictive value of 100.0% (95% CI = 96.3% to 100.0%). CONCLUSIONS: Prospective validation of our previously derived CDR for clinically important chest radiographic abnormalities was not successful. Derivation of a refined rule identified all clinically important radiographic abnormalities, but was insufficiently specific. No CDR with adequate sensitivity and specificity could be found.
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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 | 0.087 | 0.284 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".