Pulmonary hypertension misdiagnosis due to preventable errors in echocardiography and right heart catheterization
Bibliographic record
Abstract
Abstract Objectives We hypothesized that preventable human errors in performance and reporting of transthoracic echocardiograms (TTEs) and right RHCs are common and may lead to misdiagnosis of pulmonary hypertension (PH) subgroups. Background PH is a common disease, however PH subgroups have vastly different mortality and treatment. This is particularly the case for pulmonary arterial hypertension (PAH) versus PH secondary to heart failure with preserved ejection fraction (HFpEF). TTE) and RHC are needed to differentiate these two diseases. Diagnosis requires specific cut-offs for mean pulmonary artery pressure (mPAP) and pulmonary artery wedge pressure (PAWP), which can only be measured by RHC. However, TTE first identifies PH, triggering referral to specialized PH centres. Methods We re-analyzed TTEs and RHCs of 252 PH program referrals over 5 years. We also compared the inferred diagnosis from the original reports to the diagnosis made after error correction. Results We identified numerous preventable errors in the performance and reporting of both tests, and subsequently there was a poor correlation between the parameters measured by both tests. The referral TTE reports missed or overcalled PH in 44 patients. The RHC, mostly by PAWP mistakes, led to misdiagnosis in 41 patients. Conclusion TTE errors may delay referrals, while RHC errors may lead to misdiagnosis and applying wrong therapies to patients. As PAH therapies are extremely expensive, this also impacts the health care system. Primary care physicians need to be on alert for such errors and referral centres need to promote quality improvement programs to mitigate these errors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".