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

Artificial Intelligence in Inflammatory Bowel Disease

Artificial intelligence (AI) is being increasingly studied and implemented in gastroenterology. In inflammatory bowel disease (IBD), numerous AI models are being developed to assist with IBD diagnosis, standardization of endoscopic and radiologic disease activity, and predicting outcomes. Further prospective, multicenter studies representing diverse populations and novel applications are needed prior to routine implementation in clinical practice and expected improved outcomes for clinicians and patients.

Advancing perioperative optimization in Crohn's disease surgery with machine learning predictions

This editorial offers commentary on the article which aimed to forecast the likelihood of short-term major postoperative complications (Clavien-Dindo grade ≥ III), including anastomotic fistula, intra-abdominal sepsis, bleeding, and intestinal obstruction within 30 days, as well as prolonged hospital stays following ileocecal resection in patients with Crohn's disease (CD). This prediction relied on a machine learning (ML) model trained on a cohort that integrated a nomogram predictive model derived from logistic regression analysis and a random forest (RF) model. Both the nomogram and RF showed good performance, with the RF model demonstrating superior predictive ability. Key variables identified as potentially critical include a preoperative CD activity index ≥ 220, low preoperative serum albumin levels, and prolonged operation duration. Applying ML approaches to predict surgical recurrence have the potential to enhance patient risk stratification and facilitate the development of preoperative optimization strategies, ultimately aiming to improve post-surgical outcomes. However, there is still room for improvement, particularly by the inclusion of additional relevant clinical parameters, consideration of medical therapies, and potentially integrating molecular biomarkers in future research efforts.

Influencing factors and preventive measures of infectious complications after intestinal resection for Crohn's disease

The incidence of Crohn's disease (CD) has increased in recent years, with most patients requiring intestinal resection. Complications after intestinal resection for CD can lead to poor prognosis and recurrence, among which infectious complications are the most common. This study aimed to investigate the common risk factors, including medications, preoperative nutritional status, surgery-related factors, microorganisms, lesion location and type, and so forth, causing infectious complications after intestinal resection for CD, and to propose corresponding preventive measures. The findings provided guidance for identifying susceptibility factors and the early intervention and prevention of infectious complications after intestinal resection for CD in clinical practice.

Can serious postoperative complications in patients with Crohn's disease be predicted using machine learning?

The routine introduction of novel anti-inflammatory therapies into the management algorithms of patients with Crohn's disease over the last 2 decades has not substantially changed the likelihood of ultimate surgery. Rather it has delayed the operative need and altered the presentation phenotype. The prospect of complications continues to remain high in this modern era but depending upon the cohort assessed, it remains difficult to make strict comparisons between individual specialist centres. Those patients who present rather late after their diagnosis with a septic complication like an intra-abdominal abscess and a penetrating/fistulizing pattern of disease are more likely to have a complicated course particularly if they have clinical features such as difficult percutaneous access to the collection or multilocularity both of which can make preoperative drainage unsuccessful. Equally, those cases with extensive adhesions where an initial laparoscopic approach needs open conversion and where there is an extended operative time, unsurprisingly will suffer more significant complications that impact their length of hospital stay. The need for a protective stoma also introduces its own derivative costs, utilizing a range of health resources as well as resulting in important alterations in quality of life outcomes. Having established the parameters of the problem can the statistical analysis of the available data identify high-risk cases, promote the notion of centralization of specialist services or improve the allocation of disease-specific health expenditure?

The emerging role of the gut microbiota and its application in inflammatory bowel disease

Inflammatory bowel disease (IBD), including Crohn's disease and ulcerative colitis, is a complex disorder with an unknown cause. However, the dysbiosis of the gut microbiome has been found to play a role in IBD etiology, including exacerbated immune responses and defective intestinal barrier integrity. The gut microbiome can also be a potential biomarker for several diseases, including IBD. Currently, conventional treatments targeting pro-inflammatory cytokines and pathways in IBD-associated dysbiosis do not yield effective results. Other therapies that directly target the dysbiotic microbiome for effective outcomes are emerging. We review the role of the gut microbiome in health and IBD and its potential as a diagnostic, prognostic, and therapeutic target for IBD. This review also explores emerging therapeutic advancements that target gut microbiome-associated alterations in IBD, such as nanoparticle or encapsulation delivery, fecal microbiota transplantation, nutritional therapies, microbiome/probiotic engineering, phage therapy, mesenchymal stem cells (MSCs), gut proteins, and herbal formulas.

Machine learning as a tool predicting short-term postoperative complications in Crohn's disease patients undergoing intestinal resection: What frontiers?

The recent study, "Predicting short-term major postoperative complications in intestinal resection for Crohn's disease: A machine learning-based study" investigated the predictive efficacy of a machine learning model for major postoperative complications within 30 days of surgery in Crohn's disease (CD) patients. Employing a random forest analysis and Shapley Additive Explanations, the study prioritizes factors such as preoperative nutritional status, operative time, and CD activity index. Despite the retrospective design's limitations, the model's robustness, with area under the curve values surpassing 0.8, highlights its clinical potential. The findings align with literature supporting preoperative nutritional therapy in inflammatory bowel diseases, emphasizing the importance of comprehensive assessment and optimization. While a significant advancement, further research is crucial for refining preoperative strategies in CD patients.

Machine learning in predicting postoperative complications in Crohn's disease

Crohn's disease (CD) is a chronic inflammatory bowel disease of unknown origin that can cause significant disability and morbidity with its progression. Due to the unique nature of CD, surgery is often necessary for many patients during their lifetime, and the incidence of postoperative complications is high, which can affect the prognosis of patients. Therefore, it is essential to identify and manage postoperative complications. Machine learning (ML) has become increasingly important in the medical field, and ML-based models can be used to predict postoperative complications of intestinal resection for CD. Recently, a valuable article titled "Predicting short-term major postoperative complications in intestinal resection for Crohn's disease: A machine learning-based study" was published by Wang et al. We appreciate the authors' creative work, and we are willing to share our views and discuss them with the authors.

From the mathematical model to the patient: The scientific and human aspects of artificial intelligence in gastrointestinal surgery

Recent medical literature shows that the application of artificial intelligence (AI) models in gastrointestinal pathology is an exponentially growing field, with promising models that show very high performances. Regarding inflammatory bowel disease (IBD), recent reviews demonstrate promising diagnostic and prognostic AI models. However, studies are generally at high risk of bias (especially in AI models that are image-based). The creation of specific AI models that improve diagnostic performance and allow the establishment of a general prognostic forecast in IBD is of great interest, as it may allow the stratification of patients into subgroups and, in turn, allow the creation of different diagnostic and therapeutic protocols for these patients. Regarding surgical models, predictive models of postoperative complications have shown great potential in large-scale studies. In this work, the authors present the development of a predictive algorithm for early post-surgical complications in Crohn's disease based on a Random Forest model with exceptional predictive ability for complications within the cohort. The present work, based on logical and reasoned, clinical, and applicable aspects, lays a solid foundation for future prospective work to further develop post-surgical prognostic tools for IBD. The next step is to develop in a prospective and multicenter way, a collaborative path to optimize this line of research and make it applicable to our patients.