Validity of algorithm for estimating left sided filling pressures on echocardiography in a population referred for pulmonary arterial hypertension
Bibliographic record
Abstract
The determination of LV filling pressure is integral to the diagnosis of pulmonary arterial hypertension (PAH). The American Society of Echocardiography (ASE) has devised algorithms for their estimation. We aimed to test these algorithms in a population referred for suspected PAH. In our retrospective study, we evaluated the accuracy of the ASE Algorithms compared to right heart catheterization done within three months, in patients seen during 2006-2014. All echocardiograms were classified as showing normal, elevated or indeterminate filling pressures. Those with indeterminate pressures were excluded. We evaluated the diagnostic properties of this algorithm to predict a pulmonary artery wedge pressure (PAWP) and left ventricular end diastolic pressure (LVEDP) >15 mmHg. A total of 94 patients were included. The ASE algorithms yielded indeterminate results in 50 (53.2%) patients. This occurred more commonly in older patients and patients with cardiovascular comorbidities. The algorithm had a high sensitivity for predicting an elevated PAWP at 89.5% (95% confidence interval [CI] = 66.9-98.7) and an elevated LVEDP at 100% (95% CI = 76.8-100). The algorithm had a negative predictive value of 81.8% and 100% for predicting an elevated PAWP (95% CI = 52.4-94.8) and LVEDP, respectively, but a poor positive predictive value. The ASE algorithm for predicting LV filling pressures often cannot be applied in populations with suspected PAH. When they are interpretable, they have a high negative predictive value for elevated PAWP and LVEDP. We recommend caution when using these algorithms in populations with suspected PAH.
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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 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".