Cardiopulmonary Exercise Testing to Detect Chronic Thromboembolic Pulmonary Hypertension in Patients with Normal Echocardiography
Bibliographic record
Abstract
BACKGROUND: Chronic thromboembolic pulmonary hypertension (CTEPH) is a serious complication of pulmonary embolism (PE). Taking into account the reported incidence of CTEPH after acute PE, the number of patients with undiagnosed CTEPH may be high. OBJECTIVES: We aimed to determine if cardiopulmonary exercise testing (CPET) could serve as complementary tool in the diagnosis of CTEPH and can detect CTEPH in patients with normal echocardiography. METHODS: At diagnosis, we analyzed the data of CPET parameters in 42 patients with proven CTEPH and 51 controls, and evaluated the performance of two scores. RESULTS: VE/VCO2 slope, EQO2, EQCO2, P(A-a)O2, end-tidal partial pressure of CO2 at anaerobic threshold (PETCO2) and capillary to end-tidal carbon dioxide gradient [P(c-ET)CO2] were significantly different between patients with CTEPH and controls (p < 0.001). P(c-ET)CO2 was the single parameter with the highest sensitivity (85.7%) and specificity (88.2%). A score combining VE/VCO2 slope, P(A-a)O2, P(c-ET)CO2, PETCO2 [4-parameter-CPET (4-P-CPET) score] reached a sensitivity of 83.3% and a specificity of 92.2% after cross-validation. In 42 patients with CTEPH, echocardiography identified PH in 29 patients (69%), but it was normal in 13 patients (31%). All patients with normal or unmeasurable right ventricular systolic pressure had a pathological CPET. Twelve of the 13 patients (92%) were detected by both CPET scores. CONCLUSION: CPET is a useful noninvasive diagnostic tool for the detection of CTEPH in patients with suspected PH but normal echocardiography. The 4-P-CPET score provides a high sensitivity with the highest specificity.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".