Self-expandable Metallic Stents in Treatment of Colocutaneous Fistula due to Crohnʼs Disease
Bibliographic record
Abstract
Self-expandable metallic stents (SEMS) are used for management of large bowel obstruction (LBO) due to malignant strictures and often as a bridge to surgery. Crohn's disease is often associated with strictures, abscesses and enterocutaneous fistulas. Use of enteral stents as a long-term management option for colonic fistulas associated with Crohn's disease has not been evaluated. A 62-year-old male with history of recurrent spontaneous intra-abdominal abscesses s/p multiple laparotomies and loop ileostomy with reversal done at an outside institute, came to our institute year later with symptoms of partial colonic obstruction due to a transverse colon stricture. A colonoscopy confirmed anastomotic stricture with biopsies suggestive of active Crohn's disease (treated with infliximab and azathioprine). Subsequently over 6 months he developed spontaneous purulent discharge from his abdominal wall and a CECT showed an abscess with a colo-cutaneous fistula (CCF) (Figure 1). Serial endoscopic balloon dilatations (up to 19mm) in three different settings were performed, failing which an endoscopic Nano knife incision therapy was performed to treat the stricture. There was initial improvement in output from CCF with recurrence. A Wallflex uncovered through the scpe SEMS was placed (Figure 2, 3) across the stricture with the intent to relieve the obstruction due to stricture and ultimately closure of the fistula. There was complete resolution of CCF without ant recurrence at 9 months follow up. Primarily the management of CCF entails antibiotics, nutrition and fistula care; or an invasive surgical approach. Lately endoscopic techniques of percutaneous ethanol injection and over the scope clip closure (OTSC), endo-loops and rarely SEMS have been described. We report a case of successful management of colonic stricture associated CCF in patient with Crohn's disease treated with a colonic stent. SEMS can be used in the management of CCF allowing fecal diversion, leading to decrease wound spoilage, promoting wound healing ultimately leading to fistula healing. This technique can be useful especially in patients with significant co-morbidities who are poor surgical candidates. Long term follow-up and cohort studies are required to validate clinical role of SEMS in management of benign strictures and fistulas.Figure: Contrast enhanced CT showing Colo-Cutaneous Fistula.Figure: Contrast enhanced CT s/p colonic stent with the improvement of fistula.Figure: Colonoscopy showing colonic stricture and stent placement.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".