The role of abdominal compliance, the neglected parameter in critically ill patients — a consensus review of 16. Part 2: measurement techniques and management recommendations
Bibliographic record
Abstract
The recent definitions on intra-abdominal pressure (IAP), intra-abdominal volume (IAV) and abdominal compliance (Cab) are a step forward in understanding these important concepts. They help our understanding of the pathophysiology, aetiology, prognosis, and treatment of patients with low Cab. However, there is still a relatively poor understanding of the different methods used to measure IAP, IAV and Cab and how certain conditions may affect the results. This review will give a concise overview of the different methods to assess and estimate Cab; it will list important conditions that may affect baseline values and suggest some therapeutic options. Abdominal compliance (Cab), defined as a measure of the ease of abdominal expansion, is measured differently than IAP. The compliance of the abdominal wall is only a part of the total abdominal pressure-volume (PV) relationship. Measurement or estimation of Cab is difficult at the bedside and can only be done in a case of change (removal or addition) in IAV. The different measurement techniques will be discussed in relation to decreases (ascites drainage, haematoma evacuation, gastric suctioning) or increases in IAV (gastric insufflation, laparoscopy with CO₂ pneumoperitoneum, peritoneal dialysis). More specific techniques using the interactions between the thoracic and abdominal compartment during positive pressure ventilation will also be discussed (low flow PV loop, respiratory IAP variations, respiratory abdominal variation test, mean IAP and abdominal pressure variation), together with the concept of the polycompartment model. The relation between IAV and IAP is linear at low IAV and becomes curvilinear and exponential at higher volumes. Specific conditions in relation to increased (previous pregnancy or laparoscopy, gynoid fat distribution, ellipse-shaped internal abdominal perimeter) or decreased Cab (obesity, fluid overload, android fat distribution, sphere-shaped internal abdominal perimeter) will be discussed as well as their impact on baseline IAV, IAP, reshaping capacity and abdominal workspace volume. Finally, we suggest possible treatment options in situations of unadapted IAV according to existing Cab, which results in high IAP. A large overlap exists between the treatment of patients with abdominal hypertension and those with low Cab. The Cab plays a key role in understanding the deleterious effects of unadapted IAV on IAP and end-organ perfusion and function. If we can identify patients with low Cab, we can anticipate and select the most appropriate surgical treatment to avoid complications such as IAH or ACS.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".