The CR-POSSUM Risk Calculator Predicts Failure of Enhanced Recovery after Colorectal Surgery
Bibliographic record
Abstract
AIMS: To determine predictors of failed enhanced recovery after surgery (ERAS) in patients after elective colorectal surgery. METHODS: A cohort of 55 patients undergoing elective colorectal surgery was monitored prospectively. Perioperative care was based on a previously established protocol for ERAS. Pre-, intra-, and postoperative parameters were analyzed to elicit predictors of ERAS failure. ERAS failure was defined as prolonged hospital stay (> 7 days). The risk calculator CR-POSSUM was evaluated for its clinical utility. RESULTS: Body mass index (BMI) or the American Society of Anesthesiologists score (ASA) was not associated with ERAS failure on univariate analysis, but patients that failed ERAS were significantly older (64 y vs 54 y; p = 0.023). Prolonged length of stay (>7 days) was also associated with an open approach (p = 0.009), intraoperative nasogastric tube placement (p = 0.005), blood loss > 500 ml (p = 0.008), stoma formation (p = 0.006) and insertion of more than one intraabdominal drain during surgery (p = 0.005). Postoperative continuation of intravenous fluids (p = 0.027), reinsertion of urinary catheter (p = 0.045) and postoperative ileus (p = 0.020) were also strongly associated with delayed discharge on univariate analysis. After multivariate analysis the preoperative parameters CR-POSSUM score (p = 0.022), increasing BMI (p = 0.014) and preoperative albumin level (p = 0.031) were all independently associated with failure of ERAS. CONCLUSIONS: A variety of perioperative factors contribute to failure of ERAS in routine practice. CR-POSSUM can help to identify patients at risk for possible failure of ERAS. This may help to optimize avoidable factors, or accommodate those patients likely to require a longer post-operative stay.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".