Predicting Risk of Postoperative Disease Recurrence in Crohn's Disease: Patients With Indolent Crohn's Disease Have Distinct Whole Transcriptome Profiles at the Time of First Surgery

Linking gene expression to clinical outcomes in pediatric Crohn's disease using machine learning

Pediatric Crohn's disease (CD) is characterized by a severe disease course with frequent complications. We sought to apply machine learning-based models to predict risk of developing future complications in pediatric CD using ileal and colonic gene expression. Gene expression data was generated from 101 formalin-fixed, paraffin-embedded (FFPE) ileal and colonic biopsies obtained from treatment-naïve CD patients and controls. Clinical outcomes including development of strictures or fistulas and progression to surgery were analyzed using differential expression and modeled using machine learning. Differential expression analysis revealed downregulation of pathways related to inflammation and extra-cellular matrix production in patients with strictures. Machine learning-based models were able to incorporate colonic gene expression and clinical characteristics to predict outcomes with high accuracy. Models showed an area under the receiver operating characteristic curve (AUROC) of 0.84 for strictures, 0.83 for remission, and 0.75 for surgery. Genes with potential prognostic importance for strictures (REG1A, MMP3, and DUOX2) were not identified in single gene differential analysis but were found to have strong contributions to predictive models. Our findings in FFPE tissue support the importance of colonic gene expression and the potential for machine learning-based models in predicting outcomes for pediatric CD.

Prognostic modelling in IBD

In the ideal world prognostication or predicting disease course in any chronic condition would allow the clinician to anticipate disease behaviour, providing crucial information for the patient and data regarding best use of resources. Prognostication also allows an understanding of likely response to treatment and the risk of adverse effects of a treatment leading to withdrawal in any individual patient. Therefore, the ability to predict outcomes from the onset of disease is the key step to developing precision personalised medicine, which is the design of medical care to optimise efficiency or therapeutic benefit based on careful profiling of patients. An important corollary is to prevent unnecessary healthcare costs. This paper outlines currently available predictors of disease outcome in IBD and looks to the future which will involve the use of artificial intelligence to interrogate big data derived from various important 'omes' to tease out a more holistic approach to IBD.

Integration and implementation of precision medicine in the multifaceted inflammatory bowel disease

Inflammatory bowel disease (IBD) is a complex disease with variability in genetic, environmental, and lifestyle factors affecting disease presentation and course. Precision medicine has the potential to play a crucial role in managing IBD by tailoring treatment plans based on the heterogeneity of clinical and temporal variability of patients. Precision medicine is a population-based approach to managing IBD by integrating environmental, genomic, epigenomic, transcriptomic, proteomic, and metabolomic factors. It is a recent and rapidly developing medicine. The widespread adoption of precision medicine worldwide has the potential to result in the early detection of diseases, optimal utilization of healthcare resources, enhanced patient outcomes, and, ultimately, improved quality of life for individuals with IBD. Though precision medicine is promising in terms of better quality of patient care, inadequacies exist in the ongoing research. There is discordance in study conduct, and data collection, utilization, interpretation, and analysis. This review aims to describe the current literature on precision medicine, its multiomics approach, and future directions for its application in IBD.

Prognostic models for predicting postoperative recurrence in Crohn's disease: a systematic review and critical appraisal

Background and Aims

Prophylaxis of postoperative recurrence is an intractable problem for clinicians and patients with Crohn's disease. Prognostic models are effective tools for patient stratification and personalised management. This systematic review aimed to provide an overview and critically appraise the existing models for predicting postoperative recurrence of Crohn's disease.

Methods

Systematic retrieval was performed using PubMed and Web of Science in January 2022. Original articles on prognostic models for predicting postoperative recurrence of Crohn's disease were included in the analysis. The risk of bias was assessed using the Prediction Model Risk of Bias Assessment (PROBAST) tool. This study was registered with the International Prospective Register of Systematic Reviews (PROSPERO; number CRD42022311737).

Results

In total, 1948 articles were screened, of which 15 were ultimately considered. Twelve studies developed 15 new prognostic models for Crohn's disease and the other three validated the performance of three existing models. Seven models utilised regression algorithms, six utilised scoring indices, and five utilised machine learning. The area under the receiver operating characteristic curve of the models ranged from 0.51 to 0.97. Six models showed good discrimination, with an area under the receiver operating characteristic curve of >0.80. All models were determined to have a high risk of bias in modelling or analysis, while they were at low risk of applicability concerns.

Conclusions

Prognostic models have great potential for facilitating the assessment of postoperative recurrence risk in patients with Crohn's disease. Existing prognostic models require further validation regarding their reliability and applicability.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022311737.

Mise au point de l’utilisation de l’intelligence artificielle dans la prise en charge des maladies inflammatoires chroniques de l’intestin

Complexity of inflammatory bowel diseases (IBD) lies on their management and their biology. Clinics, blood and fecal samples tests, endoscopy and histology are the main tools guiding IBD treatment, but they generate a large amount of data, difficult to analyze by clinicians. Because of its capacity to analyze large number of data, artificial intelligence is currently generating enthusiasm in medicine, and this technology could be used to improve IBD management. In this review, after a short summary on IBD management and artificial intelligence, we will report pragmatic examples of artificial intelligence utilisation in IBD. Lastly, we will discuss the limitations of this technology.

Contemporary Management of Postoperative Crohn's Disease after Ileocolonic Resection

Surgery remains an important treatment modality in the multidisciplinary management of patients with Crohn's disease (CD). To illustrate the recent advances in the management of postoperative CD we outline the contemporary approach to treatment: diagnosing disease recurrence using endoscopy or noninvasive methods and risk stratification underlying decisions to institute treatment. Endoscopic scoring indices are being refined to guide treatment decisions by accurately estimating the risk of recurrence based on endoscopic appearance. The original Rutgeerts score has been modified to separate anastomotic lesions from lesions in the neoterminal ileum. Two further indices, the REMIND score and the POCER index, were recently developed with the same intention. Noninvasive monitoring for recurrence using a method with high negative predictive value has the potential to simplify management algorithms and only perform ileocolonoscopy in a subset of patients. Fecal calprotectin, intestinal ultrasound, and magnetic resonance enterography are all being evaluated for this purpose. The use of infliximab for the prevention of postoperative recurrence is well supported by data, but management decisions are fraught with uncertainty for patients with previous exposure to biologics. Data on the use of ustekinumab and vedolizumab for postoperative CD are emerging, but controlled studies are lacking.

Artificial Intelligence for Disease Assessment in Inflammatory Bowel Disease: How Will it Change Our Practice?

Artificial intelligence (AI) has arrived and it will directly impact how we assess, monitor, and manage inflammatory bowel disease (IBD). Advances in the machine learning methodologies that power AI have produced astounding results for replicating expert judgment and predicting clinical outcomes, particularly in the analysis of imaging. This review will cover general concepts for AI in IBD, with descriptions of common machine learning methods, including decision trees and neural networks. Applications of AI in IBD will cover recent achievements in endoscopic image interpretation and scoring, new capabilities for cross-sectional image analysis, natural language processing for automated understanding of clinical text, and progress in AI-powered clinical decision support tools. In addition to detailing current evidence supporting the capabilities of AI for replicating expert clinical judgment, speculative commentary on how AI may advance concepts of disease activity assessment, care pathways, and pathophysiologic mechanisms of IBD will be addressed.

Measuring the human immune response to surgery: multiomics for the prediction of postoperative outcomes

PURPOSE OF REVIEW

Postoperative complications including infections, cognitive impairment, and protracted recovery occur in one-third of the 300 million surgeries performed annually worldwide. Complications cause personal suffering along with a significant economic burden on our healthcare system. However, the accurate prediction of postoperative complications and patient-targeted interventions for their prevention remain as major clinical challenges.

RECENT FINDINGS

Although multifactorial in origin, the dysregulation of immunological mechanisms that occur in response to surgical trauma is a key determinant of postoperative complications. Prior research, primarily focusing on inflammatory plasma markers, has provided important clues regarding their pathogenesis. However, the recent advent of high-content, single-cell transcriptomic, and proteomic technologies has considerably improved our ability to characterize the immune response to surgery, thereby providing new means to understand the immunological basis of postoperative complications and to identify prognostic biological signatures.

SUMMARY

The comprehensive and single-cell characterization of the human immune response to surgery has significantly advanced our ability to predict the risk of postoperative complications. Multiomic modeling of patients' immune states holds promise for the discovery of preoperative predictive biomarkers, ultimately providing patients and surgeons with actionable information to improve surgical outcomes. Although recent studies have generated a wealth of knowledge, laying the foundation for a single-cell atlas of the human immune response to surgery, larger-scale multiomic studies are required to derive robust, scalable, and sufficiently powerful models to accurately predict the risk of postoperative complications in individual patients.

Machine Learning Modeling from Omics Data as Prospective Tool for Improvement of Inflammatory Bowel Disease Diagnosis and Clinical Classifications

Research of inflammatory bowel disease (IBD) has identified numerous molecular players involved in the disease development. Even so, the understanding of IBD is incomplete, while disease treatment is still far from the precision medicine. Reliable diagnostic and prognostic biomarkers in IBD are limited which may reduce efficient therapeutic outcomes. High-throughput technologies and artificial intelligence emerged as powerful tools in search of unrevealed molecular patterns that could give important insights into IBD pathogenesis and help to address unmet clinical needs. Machine learning, a subtype of artificial intelligence, uses complex mathematical algorithms to learn from existing data in order to predict future outcomes. The scientific community has been increasingly employing machine learning for the prediction of IBD outcomes from comprehensive patient data-clinical records, genomic, transcriptomic, proteomic, metagenomic, and other IBD relevant omics data. This review aims to present fundamental principles behind machine learning modeling and its current application in IBD research with the focus on studies that explored genomic and transcriptomic data. We described different strategies used for dealing with omics data and outlined the best-performing methods. Before being translated into clinical settings, the developed machine learning models should be tested in independent prospective studies as well as randomized controlled trials.

Machine Learning Predictive Outcomes Modeling in Inflammatory Bowel Diseases

There is a rising interest in use of big data approaches to personalize treatment of inflammatory bowel diseases (IBDs) and to predict and prevent outcomes such as disease flares and therapeutic nonresponse. Machine learning (ML) provides an avenue to identify and quantify features across vast quantities of data to produce novel insights in disease management. In this review, we cover current approaches in ML-driven predictive outcomes modeling for IBD and relate how advances in other fields of medicine may be applied to improve future IBD predictive models. Numerous studies have incorporated clinical, laboratory, or omics data to predict significant outcomes in IBD, including hospitalizations, outpatient corticosteroid use, biologic response, and refractory disease after colectomy, among others, with considerable health care dollars saved as a result. Encouraging results in other fields of medicine support efforts to use ML image analysis-including analysis of histopathology, endoscopy, and radiology-to further advance outcome predictions in IBD. Though obstacles to clinical implementation include technical barriers, bias within data sets, and incongruence between limited data sets preventing model validation in larger cohorts, ML-predictive analytics have the potential to transform the clinical management of IBD. Future directions include the development of models that synthesize all aforementioned approaches to produce more robust predictive metrics.

Artificial Intelligence Enhances Studies on Inflammatory Bowel Disease

Inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn’s disease (CD), is an idiopathic condition related to a dysregulated immune response to commensal intestinal microflora in a genetically susceptible host. As a global disease, the morbidity of IBD reached a rate of 84.3 per 100,000 persons and reflected a continued gradual upward trajectory. The medical cost of IBD is also notably extremely high. For example, in Europe, it has €3,500 in CD and €2,000 in UC per patient per year, respectively. In addition, taking into account the work productivity loss and the reduced quality of life, the indirect costs are incalculable. In modern times, the diagnosis of IBD is still a subjective judgment based on laboratory tests and medical images. Its early diagnosis and intervention is therefore a challenging goal and also the key to control its progression. Artificial intelligence (AI)-assisted diagnosis and prognosis prediction has proven effective in many fields including gastroenterology. In this study, support vector machines were utilized to distinguish the significant features in IBD. As a result, the reliability of IBD diagnosis due to its impressive performance in classifying and addressing region problems was improved. Convolutional neural networks are advanced image processing algorithms that are currently in existence. Digestive endoscopic images can therefore be better understood by automatically detecting and classifying lesions. This study aims to summarize AI application in the area of IBD, objectively evaluate the performance of these methods, and ultimately understand the algorithm–dataset combination in the studies.

Implications of artificial intelligence in inflammatory bowel disease: Diagnosis, prognosis and treatment follow up

Driven by the tremendous availability of data, artificial intelligence (AI) using deep learning has emerged as a breakthrough computer technology in the last few decades and has recently been acknowledged by the Task Force on AI as a golden opportunity for research. With its ability to understand, learn from and build on non-linear relationships, AI aims to individualize medical care in an attempt to save time, cost, effort and improve patient’s safety. AI has been applied in multiple medical fields with substantial progress made in gastroenterology mainly to facilitate accurate detection of pathology in different disease processes, among which inflammatory bowel disease (IBD) seems to drag significant attention, specifically by interpreting imaging studies, endoscopic images and videos and -to a lesser extent- disease genomics. Moreover, models have been built to predict IBD occurrence, flare ups, persistence of histological inflammation, disease-related structural abnormalities as well as disease remission. In this article, we will review the applications of AI in IBD in the present medical literature at multiple points of IBD timeline, starting from disease prediction via genomic assessment, diagnostic phase via interpretation of radiological studies and AI-assisted endoscopy, and the role of AI in the evaluation of therapy response and prognosis of IBD patients.

Clinical applications of artificial intelligence and machine learning-based methods in inflammatory bowel disease

Our objective was to review and exemplify how selected applications of artificial intelligence (AI) might facilitate and improve inflammatory bowel disease (IBD) care and to identify gaps for future work in this field. IBD is highly complex and associated with significant variation in care and outcomes. The application of AI to IBD has the potential to reduce variation in healthcare delivery and improve quality of care. AI refers to the ability of machines to mimic human intelligence. The range of AI's ability to perform tasks that would normally require human intelligence varies from prediction to complex decision-making that more closely resembles human thought. Clinical applications of AI have been applied to study pathogenesis, diagnosis, and patient prognosis in IBD. Despite these advancements, AI in IBD is in its early development and has tremendous potential to transform future care.

Big data in IBD: big progress for clinical practice

IBD is a complex multifactorial inflammatory disease of the gut driven by extrinsic and intrinsic factors, including host genetics, the immune system, environmental factors and the gut microbiome. Technological advancements such as next-generation sequencing, high-throughput omics data generation and molecular networks have catalysed IBD research. The advent of artificial intelligence, in particular, machine learning, and systems biology has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically translatable knowledge. In this narrative review, we discuss how big data integration and machine learning have been applied to translational IBD research. Approaches such as machine learning may enable patient stratification, prediction of disease progression and therapy responses for fine-tuning treatment options with positive impacts on cost, health and safety. We also outline the challenges and opportunities presented by machine learning and big data in clinical IBD research.

Development of a prognostic model for one-year surgery risk in Crohn’s disease patients: A retrospective study

BACKGROUND

Accelerated therapeutic treatment should be considered in patients with progressive Crohn’s disease (CD) to prevent complications as well as surgery. Therefore, screening for risk factors and predicting the need for early surgery are of great importance in clinical practice.

AIM

To establish a model to predict CD-related early surgery.

METHODS

This was a retrospective study collecting data from CD patients diagnosed at our inflammatory bowel disease center from January 1, 2012 to December 31, 2016. All data were randomly stratified into a training set and a testing set at a ratio of 8:2. Multivariable logistic regression analysis was conducted with receiver operating characteristic curves constructed and areas under the curve calculated. This model was further validated with calibration and discrimination estimated. A nomogram was finally developed.

RESULTS

A total of 1002 eligible patients were enrolled with a mean follow-up period of 53.54 ± 13.10 mo. In total, 24.25% of patients received intestinal surgery within 1 year after diagnosis due to complications or disease relapse. Disease behavior (B2: OR [odds ratio] = 6.693, P < 0.001; B3: OR = 14.405, P < 0.001), smoking (OR = 4.135, P < 0.001), body mass index (OR = 0.873, P < 0.001) and C-reactive protein (OR = 1.022, P = 0.001) at diagnosis, previous perianal (OR = 9.483, P < 0.001) or intestinal surgery (OR = 8.887, P < 0.001), maximum bowel wall thickness (OR = 1.965, P < 0.001), use of biologics (OR = 0.264, P < 0.001), and exclusive enteral nutrition (OR = 0.089, P < 0.001) were identified as independent significant factors associated with early intestinal surgery. A prognostic model was established and further validated. The receiver operating characteristic curves and calculated areas under the curves (94.7%) confirmed an ideal predictive ability of this model with a sensitivity of 75.92% and specificity of 95.81%. A nomogram was developed to simplify the use of the predictive model in clinical practice.

CONCLUSION

This prognostic model can effectively predict 1-year risk of CD-related intestinal surgery, which will assist in screening progressive CD patients and tailoring therapeutic management.

Prediction of treatment outcome and relapse in inflammatory bowel disease

Prediction of treatment outcome and clinical relapse in patients with inflammatory bowel disease (IBD), either ulcerative colitis (UC) or Crohn's disease (CD), is particularly important because therapeutics for IBD are not always effective and patients in remission could frequently relapse. Because undergoing endoscopy for the purpose is sometimes invasive and burdensome to patients, the performance of surrogate biomarkers has been investigated. Areas covered: We particularly featured the performance of patient symptoms, blood markers including C-reactive protein (CRP), fecal markers including fecal calprotectin (Fcal) and fecal immunochemical test (FIT) for prediction of endoscopic mucosal healing (MH) and prediction of relapse. Studies of other modalities and therapeutic drug monitoring (TDM) have also been explored. Expert opinion: Meticulous evaluation of patient symptoms could be predictive for MH in UC. CRP and Fcal may be accurate in prediction of MH of CD when MH is evaluated throughout the entire intestine including the small bowel. Repeated measurements of fecal markers including Fcal and FIT in patients with clinical remission would raise predictability of relapse. Prediction of treatment outcome by monitoring with blood markers including CRP, fecal markers including Fcal, and TDM has frequently been performed in recent clinical trials and shown to be effective.