Machine learning based preoperative analytics for the prediction of anastomotic insufficiency in colorectal surgery: a single-centre pilot study
Bibliographic record
Abstract
Abstract Introduction Anastomotic insufficiency (AI) is a relatively common but grave complication after colorectal surgery. This study aims to determine whether AI can be predicted from simple preoperative data using machine learning (ML) algorithms. Methods and analysis In this retrospective analysis, patients undergoing colorectal surgery with creation of a bowel anastomosis from the University Hospital of Basel were included. Data was split into a training set (80%) and a test set (20%). The group of patients with AI was oversampled to a ratio of 50:50 in the training set and missing values were imputed. Known predictors of AI were included as inputs: age, BMI, smoking status, the Charlson Comorbidity Index, the American Society of Anesthesiologists score, type of operation, indication, haemoglobin and albumin levels, and renal function. Results Of the 593 included patients, 88 experienced AI. At internal validation on unseen patients from the test set, area under the curve (AUC) was 0.61 (95% confidence interval [CI]: 0.44-0.79), calibration slope was 0.16 (95% CI: −0.06-0.39) and calibration intercept was 0.06 (95% CI: 0.02-0.11). We observed a specificity of 0.67 (95% CI: 0.58-0.76), sensitivity of 0.36 (95% CI: 0.08-0.67), and accuracy of 0.64 (95% CI: 0.55-0.72). Conclusion By using 10 patient-related risk factors associated with AI, we demonstrate the feasibility of ML-based prediction of AI after colorectal surgery. Nevertheless, it is crucial to include multicenter data and higher sample sizes to develop a robust and generalisable model, which will subsequently allow for deployment of the algorithm in a web-based application. Strengths and limitations of this study To the best of our knowledge, this is the first study to establish a risk prediction model for anastomotic insufficiency in a perioperative setting in colon surgery. Data from all patients that underwent colon surgery within 8 years at University Hospital Basel were included. We evaluated the feasibility of developing a machine learning model that predicts the outcome by using well-known risk factors for anastomotic insufficiency. Although our model showed promising results, it is crucial to validate our findings externally before clinical practice implications are possible.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".