Performance of DETECT Pulmonary Arterial Hypertension Algorithm According to the Hemodynamic Definition of Pulmonary Arterial Hypertension in the 2022 European Society of Cardiology and the European Respiratory Society Guidelines
Bibliographic record
Abstract
OBJECTIVE: The evidence-based DETECT pulmonary arterial hypertension (PAH) algorithm is frequently used in patients with systemic sclerosis (SSc) to help clinicians screen for PAH by using noninvasive data to recommend patient referral to echocardiography and, if applicable, for a diagnostic right-sided heart catheterization. However, the hemodynamic definition of PAH was recently updated in the 2022 European Society of Cardiology (ESC)/European Respiratory Society (ERS) guidelines. The performance of DETECT PAH in identifying patients with a high risk of PAH according to this new definition was assessed. METHODS: In this post hoc analysis of DETECT, which comprised 466 patients with SSc, the performance of the DETECT PAH algorithm in identifying patients with a high risk of PAH as defined in the 2022 ESC/ERS guidelines (mean pulmonary arterial pressure [mPAP] >20 mm Hg, pulmonary capillary wedge pressure [PCWP] ≤15 mm Hg, and pulmonary vascular resistance >2 Wood units) was assessed using summary statistics and was descriptively compared to the known performance of DETECT PAH as defined in 2014, when it was developed (mPAP ≥25 mm Hg and PCWP ≤15 mm Hg). RESULTS: The sensitivity of DETECT PAH in identifying patients with a high risk of PAH according to the 2022 ESC/ERS definition was lower (88.2%) compared to the 2014 definition (95.8%). Specificity improved from 47.8% to 50.8%. CONCLUSION: The performance of the DETECT algorithm to screen for PAH in patients with SSc is maintained when PAH is defined according to the 2022 ESC/ERS hemodynamic definition, indicating that DETECT remains applicable to screen for PAH in patients with SSc.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 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".