Preoperative cardiopulmonary exercise testing in England – a national survey
Bibliographic record
Abstract
BACKGROUND: Cardiopulmonary exercise testing (CPET) has become well established in the preoperative assessment of patients presenting for major surgery in the United Kingdom. There is evidence supporting its use in risk-stratifying patients prior to major high-risk surgical procedures.We set out to establish how CPET services in England have developed since the only survey on this subject was undertaken in 2008 (J Intensive Care Soc 2009, 10:275-278). METHODS: Availability of preoperative CPET and contact details were collected via a telephone survey and email invites to complete the online survey were sent to all contacts. The survey was live during March and April 2011. RESULTS: We received 123 (74%) responses from the 166 emails that were sent out. In total, 32% (53/166) of all adult anesthetic departments in England have access to preoperative CPET services and a further 4% (6) were in the process of setting up services. The number of departments offering preoperative CPET, including those in the process of setting up services, has risen from 42 in 2008 to 59 in 2011, a rise of over 40%. Only 61% of the clinics are run by anesthetists and 39% of clinics have trained cardiorespiratory technicians assisting in the performance of the test. Most of the clinics (55%) rely solely on a bicycle ergometer. Vascular surgical patients are the largest group of patients tested, and the majority of tests are run to a symptom-limited maximum. We estimate that 15,000 tests are performed annually for preoperative assessment in England. Only 37% of respondents were confident that the tests performed were being billed for. CONCLUSIONS: CPET is increasing in popularity as a preoperative risk assessment tool. There remains a lack of consistency in the way tests are reported and utilized. The results highlight the extent and diversity of the use of preoperative CPET and the potential for further research into its use in unstudied patient groups.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".