Redo Ileocolic Resection Is Not an Independent Risk Factor for Anastomotic Leak in Recurrent Crohn's Disease

A nomogram to predict stoma creation in elective surgical resection for penetrating Crohn's disease

BACKGROUNDS

The decision to perform a stoma during surgical resection of penetrating Crohn's disease (CD) is a critical consideration. The objective of this study was to identify factors that influence stoma creation and develop a predictive nomogram model to assist surgeons in making optimal surgical decisions.

METHODS

A retrospective study was conducted at a tertiary center from December 1, 2012, to December 1, 2021, involving consecutive patients with penetrating CD who underwent elective surgical resection. The LASSO regression method was used to select preoperative predictors, and a nomogram was constructed using multivariate logistic regression. The performance of the nomogram was validated in an internal cohort by assessing its discrimination, calibration, and clinical usefulness.

RESULTS

The study included 405 cases in the training group and 135 cases in the validation group. Nine risk factors for stoma formation were identified, including disease location, fistula resulted from previous anastomosis, absence of preoperative exclusive enteral nutrition support, albumin levels below 35 g/L, C-reactive protein levels above 10 mg/L, hemoglobin levels below 100 g/L, perianal disease, internal fistula, and surgical approach. These risk factors were selected using the LASSO regression method, and a nomogram was developed based on them. The area under the curve and the coefficient of determination (R2) of the nomogram were 0.821 and 0.394, respectively. And the nomogram demonstrated good performance in the validation cohort.

CONCLUSIONS

The nomogram exhibited good predictive ability for stoma formation during elective surgical resection for penetrating CD, which can assist surgeons in making informed clinical decisions.

Does Kono-S Anastomosis Reduce Recurrence in Crohn's Disease Compared with Conventional Ileocolonic Anastomosis? A Nationwide Propensity Score-matched Study from GETAID Chirurgie Group [KoCoRICCO Study]

BACKGROUND AND AIMS

Postoperative recurrence is a major concern in Crohn's disease. The Kono-S anastomosis has been described to reduce the rate of recurrence. However, the level of evidence for its effectiveness remains low. The KoCoRICCO study aimed to compare outcomes between Kono-S anastomosis and conventional anastomosis in two nationwide, prospective cohorts.

METHODS

Adult patients with Crohn's disease, who underwent ileocolonic resection with Kono-S anastomosis, were prospectively included in seven referral centres between 2020 and 2022. Patients with conventional side-to-side anastomosis were enrolled from a previously published cohort. A propensity score analysis was performed to compare recurrence at first endoscopy in a matched 1:2 ratio population.

RESULTS

A total of 433 patients with ileocolonic anastomosis were enrolled, of whom 155 had a Kono-S anastomosis. Before matching, both groups were unbalanced for preoperative, intraoperative, and postoperative characteristics. After matching patients with available endoscopic follow-up, endoscopic recurrence ≥i2 was found in 47.5% of the Kono-S group and 44.3% of the conventional side-to-side group [p = 0.6745].

CONCLUSIONS

The KoCoRICCO study suggests that Kono-S anastomosis does not reduce the risk of endoscopic recurrence in Crohn's disease compared with conventional side-to-side anastomosis. Further research with a longer follow-up is necessary to determine whether there is a potential benefit on surgical recurrence.

Predicting short-term major postoperative complications in intestinal resection for Crohn's disease: A machine learning-based study

BACKGROUND

Due to the complexity and numerous comorbidities associated with Crohn's disease (CD), the incidence of postoperative complications is high, significantly impacting the recovery and prognosis of patients. Consequently, additional studies are required to precisely predict short-term major complications following intestinal resection (IR), aiding surgical decision-making and optimizing patient care.

AIM

To construct novel models based on machine learning (ML) to predict short-term major postoperative complications in patients with CD following IR.

METHODS

A retrospective analysis was performed on clinical data derived from a patient cohort that underwent IR for CD from January 2017 to December 2022. The study participants were randomly allocated to either a training cohort or a validation cohort. The logistic regression and random forest (RF) were applied to construct models in the training cohort, with model discrimination evaluated using the area under the curves (AUC). The validation cohort assessed the performance of the constructed models.

RESULTS

Out of the 259 patients encompassed in the study, 5.0% encountered major postoperative complications (Clavien-Dindo ≥ III) within 30 d following IR for CD. The AUC for the logistic model was 0.916, significantly lower than the AUC of 0.965 for the RF model. The logistic model incorporated a preoperative CD activity index (CDAI) of ≥ 220, a diminished preoperative serum albumin level, conversion to laparotomy surgery, and an extended operation time. A nomogram for the logistic model was plotted. Except for the surgical approach, the other three variables ranked among the top four important variables in the novel ML model.

CONCLUSION

Both the nomogram and RF exhibited good performance in predicting short-term major postoperative complications in patients with CD, with the RF model showing more superiority. A preoperative CDAI of ≥ 220, a diminished preoperative serum albumin level, and an extended operation time might be the most crucial variables. The findings of this study can assist clinicians in identifying patients at a higher risk for complications and offering personalized perioperative management to enhance patient outcomes.