<p>Sensitivity, specificity, positive and negative predictive values of identifying atrial fibrillation using administrative data: a systematic review and meta-analysis</p>
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: Atrial fibrillation (AF) is the commonest arrhythmia and a major cause of stroke and health care utilization. Researchers and administrators use electronic health data to assess disease burden, quality and variance in care, value of interventions and prognosis. We performed a systematic review and meta-analysis to assess the validity of AF case definitions in administrative databases. METHODS: Medline was searched from 2000 to 2018. Extracted information included sensitivity, specificity, positive and negative predictive values (PPV and NPV) for various AF case definitions. Estimates were pooled using random-effects models due to significant heterogeneity between studies. RESULTS: We identified 24 studies, including 21 from North America or Scandinavia. Hospital, ambulatory and mixed data sources were assessed in 10, 4 and 10 studies, respectively. Nine different AF case definitions were evaluated, most based on ICD-9 or 10 codes. Twenty-two studies assessed case definitions in patients diagnosed with AF and thus could generate PPV alone. Half the studies sampled unrestricted populations including a mix of those with and without AF to assess sensitivity. Only 13 studies included ECG confirmation as a gold standard. The pooled random effects estimates were: sensitivity 80% (95% CI 72-86%); specificity 98% (96-99%); PPV 88% (82-94%); NPV 97% (94-99%). Only 3 studies reported all accuracy parameters and included rhythm monitoring in the gold standard definition. CONCLUSION: Relatively few studies examined sensitivity, and fewer still included rhythm monitoring in the gold standard comparison. Administrative data may fail to identify a significant proportion of patients with AF. This, in turn, may bias estimates of quality of care and prognosis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.031 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it