Predicting Hospitalization and Outpatient Corticosteroid Use in Inflammatory Bowel Disease Patients Using Machine Learning

Culturally Sensitive and Inclusive IBD Care

As the prevalence of inflammatory bowel disease (IBD) increases within historically disadvantaged communities, it is imperative to better understand how intersectionality-defined as the complex, cumulative way in which the effects of multiple forms of discrimination (such as racism, sexism, and classism)-intersects and social determinants of health influence the patient's experiences within the medical system when navigating their disease. Culturally sensitive care is characterized by the ability to deliver patient-centered care that recognizes how the intersectionality of an individual's identities impacts their disease journey. An intentional consideration and sensitivity to this impact play important roles in providing an inclusive and welcoming space for historically disadvantaged individuals living with IBD and will help address health inequity in IBD. Cultural competence implies mastery of care that understands and respects values and beliefs across cultures, while cultural humility involves recognizing the complexity of cultural identity and engaging in an ongoing learning process from individual patient experiences. Heightening our patient care goals from cultural competence to cultural sensitivity allows healthcare professionals and the systems in which they practice to lead with cultural humility as they adopt a more inclusive and humble perspective when caring for patient groups with a diverse array of identities and cultures and to avoid maintaining the status quo of implicit and explicit biases that impede the delivery of quality IBD care. In this article, we review the literature on IBD care in historically disadvantaged communities, address culturally sensitive care, and propose a framework to incorporating cultural humility in IBD practices and research.

Assessment of Physician Needs and Access to Inflammatory Bowel Disease Specialty Care Resources in a National Integrated Health System

BACKGROUND

The barriers to providing high-quality inflammatory bowel disease (IBD) care go beyond educational needs alone to include access to IBD-related resources such as medications, laboratory testing, and multidisciplinary teams. We assessed the needs and resource constraints of physicians caring for Veterans with IBD to inform efforts to improve access to high-quality care.

METHODS

We conducted a national observational survey study in July 2021 of gastroenterologists (GIs) and primary care providers (PCPs) caring for Veterans with IBD within the Veterans Health Administration with the intent of including physicians from all 18 Veterans Integrated Service Networks (VISN). We reported descriptive statistics and compared responses between gastroenterologists (GIs) and primary care providers (PCPs), practice locations, and years of experience using χ2 tests.

RESULTS

Overall, 173 of 2241 eligible physicians completed the survey, representing an individual physician response rate of 7.7% and VISN response rate of 18 out of 18 (100%). We identified several areas of IBD care where GIs and PCPs reported discomfort including medication prescribing, treatment strategies, and special populations. Further, variability in access to IBD services and awareness of the availability of IBD-targeted medications and laboratory tests was common. This survey also highlights the frequency with which PCPs were identified among the highest volume IBD providers in their facility.

CONCLUSIONS

Variation in GIs' and PCPs' comfort with IBD treatment and access to IBD resources is common and needs to be considered in leveraging virtual care and educational programs and managing the expansion of IBD support and resources within VA.

A Machine Learning Method for Differentiation Crohn's Disease and Intestinal Tuberculosis

Background

Whether machine learning (ML) can assist in the diagnosis of Crohn's disease (CD) and intestinal tuberculosis (ITB) remains to be explored.

Methods

We collected clinical data from 241 patients, and 51 parameters were included. Six ML methods were tested, including logistic regression, decision tree, k-nearest neighbor, multinomial NB, multilayer perceptron, and XGBoost. SHAP and LIME were subsequently introduced as interpretability methods. The ML model was tested in a real-world clinical practice and compared with a multidisciplinary team (MDT) meeting.

Results

XGBoost displays the best performance among the six ML models. The diagnostic AUROC and the accuracy of XGBoost were 0.946 and 0.884, respectively. The top three clinical features affecting our ML model's result prediction were T-spot, pulmonary tuberculosis, and onset age. The ML model's accuracy, sensitivity, and specificity in clinical practice were 0.860, 0.833, and 0.871, respectively. The agreement rate and kappa coefficient of the ML and MDT methods were 90.7% and 0.780, respectively (P<0.001).

Conclusion

We developed an ML model based on XGBoost. The ML model could provide effective and efficient differential diagnoses of ITB and CD with diagnostic bases. The ML model performs well in real-world clinical practice, and the agreement between the ML model and MDT is strong.

Inflammatory Bowel Disease Hospitalizations Are Similar for Patients Receiving Televisit-Delivered Outpatient Care and Those Receiving Traditional In-Person Care

INTRODUCTION

The coronavirus disease 2019 pandemic resulted in widespread expansion of telehealth. However, there are concerns that telehealth-delivered outpatient care may limit opportunities for managing complications and preventing hospitalizations for patients with inflammatory bowel disease (IBD). We aimed to assess the association between outpatient IBD care delivered through televisit (video or phone) and IBD-related hospitalizations.

METHODS

We conducted a case-control study of patients with IBD who had an IBD-related index hospitalization between April 2021 and July 2022 and received their care in the Veterans Health Administration. We matched these hospitalized patients to controls who were not hospitalized based on age, sex, race, Charlson comorbidity index, IBD type, IBD-related emergency department use, IBD-related hospitalizations, and outpatient gastroenterology visits in the preceding year. The variable of interest was the percentage of total clinic visits delivered through televisit in the year before the index hospitalization. We compared the risk of IBD-related hospitalization by exposure to televisit-delivered care using conditional logistic regression.

RESULTS

We identified 534 patients with an IBD-related hospitalization and 534 matched controls without an IBD-related hospitalization during the study period. Patients with IBD with a higher percentage of televisit-delivered (vs in-person) outpatient care were less likely to be hospitalized during the study period (for every 10% increase in televisit use, odds ratio 0.97, 95% confidence interval 0.94-1.00; P = 0.03).

DISCUSSION

Televisit-delivered outpatient IBD care is not associated with higher risk of IBD-related hospitalization. These findings may reassure clinicians that televisit-delivered outpatient care is appropriate for patients with complex chronic diseases such as IBD.

How Artificial Intelligence Will Transform Clinical Care, Research, and Trials for Inflammatory Bowel Disease

Artificial intelligence (AI) refers to computer-based methodologies that use data to teach a computer to solve pre-defined tasks; these methods can be applied to identify patterns in large multi-modal data sources. AI applications in inflammatory bowel disease (IBD) includes predicting response to therapy, disease activity scoring of endoscopy, drug discovery, and identifying bowel damage in images. As a complex disease with entangled relationships between genomics, metabolomics, microbiome, and the environment, IBD stands to benefit greatly from methodologies that can handle this complexity. We describe current applications, critical challenges, and propose future directions of AI in IBD.

Clinical and health care utilization variables can predict 90-day hospital re-admission in adults with Crohn's disease for point of care risk evaluation

BACKGROUND

Hospital re-admission for persons with Crohn's disease (CD) is a significant contributor to morbidity and healthcare costs. We derived prediction models of risk of 90-day re-hospitalization among persons with CD that could be applied at hospital discharge to target outpatient interventions mitigating this risk.

METHODS

We performed a retrospective study in persons with CD admitted between 2009 and 2016 for an acute CD-related indication. Demographic, clinical, and health services predictor variables were ascertained through chart review and linkage to administrative health databases. We derived and internally validated a multivariable logistic regression model of 90-day CD-related re-hospitalization. We selected the optimal probability cut-point to maximize Youden's index.

RESULTS

There were 524 CD hospitalizations and 57 (10.9%) CD re-hospitalizations within 90 days of discharge. Our final model included hospitalization within the prior year (adjusted odds ratio [aOR] 3.27, 95% confidence interval [CI] 1.76-6.08), gastroenterologist consultation within the prior year (aOR 0.185, 95% CI 0.0950-0.360), intra-abdominal surgery during index hospitalization (aOR 0.216, 95% CI 0.0500-0.934), and new diagnosis of CD during index hospitalization (aOR 0.327, 95% CI 0.0950-1.13). The model demonstrated good discrimination (optimism-corrected c-statistic value 0.726) and excellent calibration (Hosmer-Lemeshow goodness-of-fit p-value 0.990). The optimal model probability cut point allowed for a sensitivity of 71.9% and specificity of 70.9% for identifying 90-day re-hospitalization, at a false positivity rate of 29.1% and false negativity rate of 28.1%.

CONCLUSIONS

Demographic, clinical, and health services variables can help discriminate persons with CD at risk of early re-hospitalization, which could permit targeted post-discharge intervention.

Machine Learning-based Characterization of Longitudinal Health Care Utilization Among Patients With Inflammatory Bowel Diseases

BACKGROUND

Inflammatory bowel disease (IBD) is associated with increased health care utilization. Forecasting of high resource utilizers could improve resource allocation. In this study, we aimed to develop machine learning models (1) to cluster patients according to clinical utilization patterns and (2) to predict longitudinal utilization patterns based on readily available baseline clinical characteristics.

METHODS

We conducted a retrospective study of adults with IBD at 2 academic centers between 2015 and 2021. Outcomes included different clinical encounters, new prescriptions of corticosteroids, and initiation of biologic therapy. Machine learning models were developed to characterize health care utilization. Poisson regression compared frequencies of clinical encounters.

RESULTS

A total of 1174 IBD patients were followed for more than 5673 12-month observational windows. The clustering method separated patients according to low, medium, and high resource utilizers. In Poisson regression models, compared with low resource utilizers, moderate and high resource utilizers had significantly higher rates of each encounter type. Comparing moderate and high resource utilizers, the latter had greater utilization of each encounter type, except for telephone encounters and biologic therapy initiation. Machine learning models predicted longitudinal health care utilization with 81% to 85% accuracy (area under the receiver operating characteristic curve 0.84-0.90); these were superior to ordinal regression and random choice methods.

CONCLUSION

Machine learning models were able to cluster individuals according to relative health care resource utilization and to accurately predict longitudinal resource utilization using baseline clinical factors. Integration of such models into the electronic medical records could provide a powerful semiautomated tool to guide patient risk assessment, targeted care coordination, and more efficient resource allocation.

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.

Personalized Treatment for Crohn's Disease: Current Approaches and Future Directions

Crohn's disease is a complex, relapsing and remitting inflammatory disorder of the gastrointestinal tract with a variable disease course. While the treatment options for Crohn's disease have dramatically increased over the past two decades, predicting individual patient response to treatment remains a challenge. As a result, patients often cycle through multiple different therapies before finding an effective treatment which can lead to disease complications, increased costs, and decreased quality of life. Recently, there has been increased emphasis on personalized medicine in Crohn's disease to identify individual patients who require early advanced therapy to prevent complications of their disease. In this review, we summarize our current approach to management of Crohn's disease by identifying risk factors for severe or disabling disease and tailoring individual treatments to patient-specific goals. Lastly, we outline our knowledge gaps in implementing personalized Crohn's disease treatment and describe the future directions in precision medicine.

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.

A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data

With the advances in technology and data science, machine learning (ML) is being rapidly adopted by the health care sector. However, there is a lack of literature addressing the health conditions targeted by the ML prediction models within primary health care (PHC) to date. To fill this gap in knowledge, we conducted a systematic review following the PRISMA guidelines to identify health conditions targeted by ML in PHC. We searched the Cochrane Library, Web of Science, PubMed, Elsevier, BioRxiv, Association of Computing Machinery (ACM), and IEEE Xplore databases for studies published from January 1990 to January 2022. We included primary studies addressing ML diagnostic or prognostic predictive models that were supplied completely or partially by real-world PHC data. Studies selection, data extraction, and risk of bias assessment using the prediction model study risk of bias assessment tool were performed by two investigators. Health conditions were categorized according to international classification of diseases (ICD-10). Extracted data were analyzed quantitatively. We identified 106 studies investigating 42 health conditions. These studies included 207 ML prediction models supplied by the PHC data of 24.2 million participants from 19 countries. We found that 92.4% of the studies were retrospective and 77.3% of the studies reported diagnostic predictive ML models. A majority (76.4%) of all the studies were for models' development without conducting external validation. Risk of bias assessment revealed that 90.8% of the studies were of high or unclear risk of bias. The most frequently reported health conditions were diabetes mellitus (19.8%) and Alzheimer's disease (11.3%). Our study provides a summary on the presently available ML prediction models within PHC. We draw the attention of digital health policy makers, ML models developer, and health care professionals for more future interdisciplinary research collaboration in this regard.

Biological, genetic and epigenetic markers in ulcerative colitis

In this review, we have summarized the existing knowledge of ulcerative colitis (UC) markers based on current literature, specifically, the roles of potential new biomarkers, such as circulating, fecal, genetic, and epigenetic alterations, in UC onset, disease activity, and in therapy response. UC is a complex multifactorial inflammatory disease. There are many invasive and non-invasive diagnostic methods in UC, including several laboratory markers which are employed in diagnosis and disease assessment; however, colonoscopy remains the most widely used method. Common laboratory abnormalities currently used in the clinical practice include inflammation-induced alterations, serum autoantibodies, and antibodies against bacterial antigens. Other new serum and fecal biomarkers are supportive in diagnosis and monitoring disease activity and therapy response; and potential salivary markers are currently being evaluated as well. Several UC-related genetic and epigenetic alterations are implied in its pathogenesis and therapeutic response. Moreover, the use of artificial intelligence in the integration of laboratory biomarkers and big data could potentially be useful in clinical translation and precision medicine in UC management.

Creation of an Inflammatory Bowel Disease Referral Pathway for Identifying Patients Who Would Benefit From Inflammatory Bowel Disease Specialist Consultation

BACKGROUND

Recommendations regarding signs and symptoms that should prompt referral of patients with inflammatory bowel disease (IBD) to an IBD specialist for a consultation could serve to improve the quality of care for these patients. Our aim was to develop a consult care pathway consisting of clinical features related to IBD that should prompt appropriate consultation.

METHODS

A scoping literature review was performed to identify clinical features that should prompt consultation with an IBD specialist. A panel of 11 experts was convened over 4 meetings to develop a consult care pathway using the RAND/UCLA Appropriateness Method. Items identified via scoping review were ranked and were divided into major and minor criteria. Additionally, a literature and panel review was conducted assessing potential barriers and facilitators to implementing the consult care pathway.

RESULTS

Of 43 features assessed, 13 were included in the care pathway as major criteria and 15 were included as minor criteria. Experts agreed that stratification into major criteria and minor criteria was appropriate and that 1 major or 2 or more minor criteria should be required to consider consultation. The greatest barrier to implementation was considered to be organizational resource allocation, while endorsements by national gastroenterology and general medicine societies were considered to be the strongest facilitator.

CONCLUSIONS

This novel referral care pathway identifies key criteria that could be used to triage patients with IBD who would benefit from IBD specialist consultation. Future research will be required to validate these findings and assess the impact of implementing this pathway in routine IBD-related care.

The impact of commercial health datasets on medical research and health-care algorithms

As the health-care industry emerges into a new era of digital health driven by cloud data storage, distributed computing, and machine learning, health-care data have become a premium commodity with value for private and public entities. Current frameworks of health data collection and distribution, whether from industry, academia, or government institutions, are imperfect and do not allow researchers to leverage the full potential of downstream analytical efforts. In this Health Policy paper, we review the current landscape of commercial health data vendors, with special emphasis on the sources of their data, challenges associated with data reproducibility and generalisability, and ethical considerations for data vending. We argue for sustainable approaches to curating open-source health data to enable global populations to be included in the biomedical research community. However, to fully implement these approaches, key stakeholders should come together to make health-care datasets increasingly accessible, inclusive, and representative, while balancing the privacy and rights of individuals whose data are being collected.

Risk Prediction for Complications in Inflammatory Bowel Disease Surgery: External Validation of the American College of Surgeons' National Surgical Quality Improvement Program Surgical Risk Calculator

BACKGROUND AND AIMS

Many patients with inflammatory bowel disease [IBD] require surgery during their disease course. Having individual risk predictions available prior to surgery could aid in better informed decision making for personalised treatment trajectories in IBD surgery. The American College of Surgeons National Surgical Quality Improvement Program [ACS NSQIP] has developed a surgical risk calculator that calculates risks for postoperative outcomes using 20 patient and surgical predictors. We aimed to validate the calculator for IBD surgery to determine its accuracy in this patient cohort.

METHODS

Predicted risks were calculated for patients operated for IBD between December 2017 and January 2022 at two tertiary centres and compared with actual outcomes within 30 postoperative days. Predictive performance was assessed for several postoperative complications, using metrics for discrimination and calibration.

RESULTS

Risks were calculated for 508 patient trajectories undergoing surgery for IBD. Incidence of any complication, serious complications, reoperation, and readmission were 32.1%, 21.1%, 15.2%, and 18.3%, respectively. Of 212 patients with an anastomosis, 19 experienced leakage [9.0%]. Discriminative performance and calibration were modest. Risk prediction for any complication, serious complication, reoperation, readmission, and anastomotic leakage had a c statistic of 0.605 (95% confidence interval [CI] 0.534-0.640), 0.623 [95% CI 0.558-0.688], 0.590 [95% CI 0.513-0.668], 0.621 [95% CI 0.557-0.685], and 0.574 [95% CI 0.396-0.751], respectively, and a Brier score of 0.240, 0.166, 0.138, 0.152, and 0.113, respectively.

CONCLUSIONS

The accuracy of risks calculated by the ACS NSQIP Surgical Risk Calculator was deemed insufficient for patients undergoing surgery for IBD, generally underestimating postoperative risks. Recalibration or additional variables could be necessary to predict risks in this cohort.

Automatically detecting Crohn's disease and Ulcerative Colitis from endoscopic imaging

BACKGROUND

The SI-CURA project (Soluzioni Innovative per la gestione del paziente e il follow up terapeutico della Colite UlceRosA) is an Italian initiative aimed at the development of artificial intelligence solutions to discriminate pathologies of different nature, including inflammatory bowel disease (IBD), namely Ulcerative Colitis (UC) and Crohn's disease (CD), based on endoscopic imaging of patients (P) and healthy controls (N).

METHODS

In this study we develop a deep learning (DL) prototype to identify disease patterns through three binary classification tasks, namely (1) discriminating positive (pathological) samples from negative (healthy) samples (P vs N); (2) discrimination between Ulcerative Colitis and Crohn's Disease samples (UC vs CD) and, (3) discrimination between Ulcerative Colitis and negative (healthy) samples (UC vs N).

RESULTS

The model derived from our approach achieves a high performance of Matthews correlation coefficient (MCC) > 0.9 on the test set for P versus N and UC versus N, and MCC > 0.6 on the test set for UC versus CD.

CONCLUSION

Our DL model effectively discriminates between pathological and negative samples, as well as between IBD subgroups, providing further evidence of its potential as a decision support tool for endoscopy-based diagnosis.

The Association Between Nonsteroidal Anti-Inflammatory Drug Use and Inflammatory Bowel Disease Exacerbations: A True Association or Residual Bias?

INTRODUCTION

Studies suggest that nonsteroidal anti-inflammatory drugs (NSAID) may contribute to inflammatory bowel disease (IBD) exacerbations. We examined whether variation in the likelihood of IBD exacerbations is attributable to NSAID.

METHODS

In a cohort of patients with IBD (2004-2015), we used 3 analytic methods to examine the likelihood of an exacerbation after an NSAID exposure. First, we matched patients by propensity for NSAID use and examined the association between NSAID exposure and IBD exacerbation using an adjusted Cox proportional hazards model. To assess for residual confounding, we estimated a previous event rate ratio and used a self-controlled case series analysis to further explore the relationship between NSAID and IBD exacerbations.

RESULTS

We identified 15,705 (44.8%) and 19,326 (55.2%) IBD patients with and without an NSAID exposure, respectively. Findings from the Cox proportional hazards model suggested an association between NSAID and IBD exacerbation (hazard ratio 1.24; 95% confidence interval 1.16-1.33). However, the likelihood of an IBD exacerbation in the NSAID-exposed arm preceding NSAID exposure was similar (hazard ratio 1.30; 95% confidence interval 1.21-1.39). A self-controlled case series analysis of 3,968 patients who had both an NSAID exposure and IBD exacerbation demonstrated similar exacerbation rates in the 1 year preceding exposure, 2-6 weeks postexposure, and 6 weeks to 6 months postexposure, but a higher incidence in 0-2 weeks postexposure, suggesting potential confounding by reverse causality.

DISCUSSION

While we see an association between NSAID and IBD exacerbations using traditional methods, further analysis suggests this may be secondary to residual bias. These findings may reassure patients and clinicians considering NSAID as a nonopioid pain management option.

A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation

BACKGROUND

Inflammatory bowel disease (IBD) is a gastrointestinal chronic disease with an unpredictable disease course. Computational methods such as machine learning (ML) have the potential to stratify IBD patients for the provision of individualized care. The use of ML methods for IBD was surveyed, with an additional focus on how the field has changed over time.

METHODS

On May 6, 2021, a systematic review was conducted through a search of MEDLINE and Embase databases, with the search structure ("machine learning" OR "artificial intelligence") AND ("Crohn* Disease" OR "Ulcerative Colitis" OR "Inflammatory Bowel Disease"). Exclusion criteria included studies not written in English, no human patient data, publication before 2001, studies that were not peer reviewed, nonautoimmune disease comorbidity research, and record types that were not primary research.

RESULTS

Seventy-eight (of 409) records met the inclusion criteria. Random forest methods were most prevalent, and there was an increase in neural networks, mainly applied to imaging data sets. The main applications of ML to clinical tasks were diagnosis (18 of 78), disease course (22 of 78), and disease severity (16 of 78). The median sample size was 263. Clinical and microbiome-related data sets were most popular. Five percent of studies used an external data set after training and testing for additional model validation.

DISCUSSION

Availability of longitudinal and deep phenotyping data could lead to better modeling. Machine learning pipelines that consider imbalanced data and that feature selection only on training data will generate more generalizable models. Machine learning models are increasingly being applied to more complex clinical tasks for specific phenotypes, indicating progress towards personalized medicine for IBD.

Noninvasive diagnosis of AIH/PBC overlap syndrome based on prediction models

Autoimmune liver diseases (AILDs) are life-threatening chronic liver diseases, mainly including autoimmune hepatitis (AIH), primary biliary cholangitis (PBC), and AIH-PBC overlap syndrome (OS), which are difficult to distinguish clinically at early stages. This study aimed to establish model to achieve the purpose of the diagnosis of AIH/PBC OS in a noninvasive way. A total of 201 AILDs patients were included in this retrospective study who underwent liver biopsy during January 2011 to December 2020. Serological factors significantly associated with OS were determined by the univariate analysis. Two multivariate models based on these factors were constructed to predict the diagnosis of AIH/PBC OS using logistic regression and random forest analysis. The results showed that immunoglobulins G and M had significant importance in both models. In logistic regression model, anti-Sp100, anti-Ro-52, anti-SSA, or antinuclear antibody positivity were risk factors for OS. In random forest model, activated partial thromboplastin time and ɑ-fetoprotein level were important. To distinguish PBC and OS, the sensitivity and specificity of logistic regression model were 0.889 and 0.727, respectively, and the sensitivity and specificity of random forest model were 0.944 and 0.818, respectively. In conclusion, we established two predictive models for the diagnosis of AIH/PBC OS in a noninvasive method and they showed better performance than Paris criteria for the definition of AIH/PBC OS.

Artificial intelligence in gastroenterology and hepatology: how to advance clinical practice while ensuring health equity

Artificial intelligence (AI) and machine learning (ML) systems are increasingly used in medicine to improve clinical decision-making and healthcare delivery. In gastroenterology and hepatology, studies have explored a myriad of opportunities for AI/ML applications which are already making the transition to bedside. Despite these advances, there is a risk that biases and health inequities can be introduced or exacerbated by these technologies. If unrecognised, these technologies could generate or worsen systematic racial, ethnic and sex disparities when deployed on a large scale. There are several mechanisms through which AI/ML could contribute to health inequities in gastroenterology and hepatology, including diagnosis of oesophageal cancer, management of inflammatory bowel disease (IBD), liver transplantation, colorectal cancer screening and many others. This review adapts a framework for ethical AI/ML development and application to gastroenterology and hepatology such that clinical practice is advanced while minimising bias and optimising health equity.

Simplified Machine Learning Models Can Accurately Identify High-Need High-Cost Patients With Inflammatory Bowel Disease

INTRODUCTION

Hospitalization is the primary driver of inflammatory bowel disease (IBD)-related healthcare costs and morbidity. Traditional prediction models have poor performance at identifying patients at highest risk of unplanned healthcare utilization. Identification of patients who are high-need and high-cost (HNHC) could reduce unplanned healthcare utilization and healthcare costs.

METHODS

We conducted a retrospective cohort study in adult patients hospitalized with IBD using the Nationwide Readmissions Database (model derivation in the 2013 Nationwide Readmission Database and validation in the 2017 Nationwide Readmission Database). We built 2 tree-based algorithms (decision tree classifier and decision tree using gradient boosting framework [XGBoost]) and compared traditional logistic regression to identify patients at risk for becoming HNHC (patients in the highest decile of total days spent in hospital in a calendar year).

RESULTS

Of 47,402 adult patients hospitalized with IBD, we identified 4,717 HNHC patients. The decision tree classifier model (length of stay, Charlson Comorbidity Index, procedure, Frailty Risk Score, and age) had a mean area under the receiver operating characteristic curve (AUC) of 0.78 ± 0.01 in the derivation data set and 0.78 ± 0.02 in the validation data set. XGBoost (length of stay, procedure, chronic pain, drug abuse, and diabetic complication) had a mean AUC of 0.79 ± 0.01 and 0.75 ± 0.02 in the derivation and validation data sets, respectively, compared with AUC 0.55 ± 0.01 and 0.56 ± 0.01 with traditional logistic regression (peptic ulcer disease, paresthesia, admission for osteomyelitis, renal failure, and lymphoma) in derivation and validation data sets, respectively.

DISCUSSION

In hospitalized patients with IBD, simplified tree-based machine learning algorithms using administrative claims data can accurately predict patients at risk of progressing to HNHC.

Patient-Reported Outcomes and Risk of Hospitalization and Readmission in Patients with Inflammatory Bowel Diseases

BACKGROUND AND AIMS

Patient-reported outcome measures (PROMs) provide a wholesome view of patient well-being. We conducted a retrospective cohort study to evaluate whether PROMs inform risk of unplanned healthcare utilization in patients with IBD.

METHODS

We identified adult patients with IBD who completed at least two surveys in a large Internet-based cohort within 1 year. We evaluated the association between baseline patient characteristics, disease activity indices, medication use, and PROMs, assessed using NIH Patient-Reported Outcome Measurement Information System (PROMIS) and subsequent risk of incident hospitalization (at time of first follow-up) within 1 year, and readmission within 1 year (in patients with hospitalization at first follow-up), using multivariable logistic regression.

RESULTS

Of 7902 patients with IBD (45.5 year, 72% females, 63% Crohn's disease), 1377 (17.4%) were hospitalized within 1 year. Among PROMs, pain interference (adjusted OR per 5-point increase in PROMIS, 1.09; 95% CI 1.05-1.14), but not depression, anxiety, fatigue or sleep disturbance, was predictive of higher risk of hospitalization. Prior surgery or hospitalization, symptomatic disease, biologic, and corticosteroid use were also associated with higher risk of hospitalization. Of 521 patients hospitalized with IBD, 133 (25.5%) were readmitted within 1 year. Anxiety and pain interference were predictive of higher risk of readmission, whereas depression was associated with lower risk of readmission.

CONCLUSIONS

In a large Internet-based cohort study, PROMs may have a modest effect on modifying risk of unplanned healthcare utilization in patients with IBD, with pain interference being most consistently associated with increased risk of hospitalization and readmission.

Artificial Intelligence and the Future of Gastroenterology and Hepatology

The integration of artificial intelligence (AI) into gastroenterology and hepatology (GI) will inevitably transform the practice of GI in the coming decade. While the application of AI in health care is not new, advancements are occurring rapidly, and the future landscape of AI is beginning to come into focus. From endoscopic assistance via computer vision technology to the predictive capabilities of the vast information contained in the electronic health records, AI promises to optimize and expedite clinical and procedural practice and research in GI. The extensive body of literature already available on AI applications in gastroenterology may seem daunting at first; however, this review aims to provide a breakdown of the key studies conducted thus far and demonstrate the many potential ways this technology may impact the field. This review will also take a look into the future and imagine how GI can be transformed over the coming years, as well as potential limitations and pitfalls that must be overcome to realize this future.

Innovation in Gastroenterology-Can We Do Better?

The health system can reap significant benefits by adopting and implementing innovative measures, as was recently demonstrated and emphasized during the COVID-19 pandemic. Herein, we present our bird's eye view of gastroenterology's innovative technologies via utilizing a text-mining technique. We analyzed five research fields that comply with innovation: artificial intelligence (AI), virtual reality (VR), telemedicine, the microbiome, and advanced endoscopy. According to gastroenterology literature, the two most innovative fields were the microbiome and advanced endoscopy. Though artificial intelligence (AI), virtual reality (VR), and telemedicine trailed behind, the number of AI publications in gastroenterology has shown an exponential trend in the last couple of years. While VR and telemedicine are neglected compared to other fields, their implementation could improve physician and patient training, patient access to care, cost reduction, and patient outcomes.

Innovation in Gastroenterology-Can We Do Better?

The health system can reap significant benefits by adopting and implementing innovative measures, as was recently demonstrated and emphasized during the COVID-19 pandemic. Herein, we present our bird's eye view of gastroenterology's innovative technologies via utilizing a text-mining technique. We analyzed five research fields that comply with innovation: artificial intelligence (AI), virtual reality (VR), telemedicine, the microbiome, and advanced endoscopy. According to gastroenterology literature, the two most innovative fields were the microbiome and advanced endoscopy. Though artificial intelligence (AI), virtual reality (VR), and telemedicine trailed behind, the number of AI publications in gastroenterology has shown an exponential trend in the last couple of years. While VR and telemedicine are neglected compared to other fields, their implementation could improve physician and patient training, patient access to care, cost reduction, and patient outcomes.

Benchmark of Data Processing Methods and Machine Learning Models for Gut Microbiome-Based Diagnosis of Inflammatory Bowel Disease

Patients with inflammatory bowel disease (IBD) wait months and undergo numerous invasive procedures between the initial appearance of symptoms and receiving a diagnosis. In order to reduce time until diagnosis and improve patient wellbeing, machine learning algorithms capable of diagnosing IBD from the gut microbiome's composition are currently being explored. To date, these models have had limited clinical application due to decreased performance when applied to a new cohort of patient samples. Various methods have been developed to analyze microbiome data which may improve the generalizability of machine learning IBD diagnostic tests. With an abundance of methods, there is a need to benchmark the performance and generalizability of various machine learning pipelines (from data processing to training a machine learning model) for microbiome-based IBD diagnostic tools. We collected fifteen 16S rRNA microbiome datasets (7,707 samples) from North America to benchmark combinations of gut microbiome features, data normalization and transformation methods, batch effect correction methods, and machine learning models. Pipeline generalizability to new cohorts of patients was evaluated with two binary classification metrics following leave-one-dataset-out cross (LODO) validation, where all samples from one study were left out of the training set and tested upon. We demonstrate that taxonomic features processed with a compositional transformation method and batch effect correction with the naive zero-centering method attain the best classification performance. In addition, machine learning models that identify non-linear decision boundaries between labels are more generalizable than those that are linearly constrained. Lastly, we illustrate the importance of generating a curated training dataset to ensure similar performance across patient demographics. These findings will help improve the generalizability of machine learning models as we move towards non-invasive diagnostic and disease management tools for patients with IBD.

Development and validation of novel models for the prediction of intravenous corticosteroid resistance in acute severe ulcerative colitis using logistic regression and machine learning

Background

The early prediction of intravenous corticosteroid (IVCS) resistance in acute severe ulcerative colitis (ASUC) patients remains an unresolved challenge. This study aims to construct and validate a model that accurately predicts IVCS resistance.

Methods

A retrospective cohort was established, with consecutive inclusion of patients who met the diagnosis criteria of ASUC and received IVCS during index hospitalization in Peking Union Medical College Hospital between March 2012 and January 2020. The primary outcome was IVCS resistance. Classification models, including logistic regression and machine learning-based models, were constructed. External validation was conducted in an independent cohort from Shengjing Hospital of China Medical University.

Results

A total of 129 patients were included in the derivation cohort. During index hospitalization, 102 (79.1%) patients responded to IVCS and 27 (20.9%) failed; 18 (14.0%) patients underwent colectomy in 3 months; 6 received cyclosporin as rescue therapy, and 2 eventually escalated to colectomy; 5 succeeded with infliximab as rescue therapy. The Ulcerative Colitis Endoscopic Index of Severity (UCEIS) and C-reactive protein (CRP) level at Day 3 are independent predictors of IVCS resistance. The areas under the receiver-operating characteristic curves (AUROCs) of the logistic regression, decision tree, random forest, and extreme-gradient boosting models were 0.873 (95% confidence interval [CI], 0.704–1.000), 0.648 (95% CI, 0.463–0.833), 0.650 (95% CI, 0.441–0.859), and 0.604 (95% CI, 0.416–0.792), respectively. The logistic regression model achieved the highest AUROC value of 0.703 (95% CI, 0.473–0.934) in the external validation.

Conclusions

In patients with ASUC, UCEIS and CRP levels at Day 3 of IVCS treatment appeared to allow the prompt prediction of likely IVCS resistance. We found no evidence of better performance of machine learning-based models in IVCS resistance prediction in ASUC. A nomogram based on the logistic regression model might aid in the management of ASUC patients.

Artificial Intelligence for Inflammatory Bowel Diseases (IBD); Accurately Predicting Adverse Outcomes Using Machine Learning

BACKGROUND

Inflammatory Bowel Diseases with its complexity and heterogeneity could benefit from the increased application of Artificial Intelligence in clinical management.

AIM

To accurately predict adverse outcomes in patients with IBD using advanced computational models in a nationally representative dataset for potential use in clinical practice.

METHODS

We built a training model cohort and validated our result in a separate cohort. We used LASSO and Ridge regressions, Support Vector Machines, Random Forests and Neural Networks to balance between complexity and interpretability and analyzed their relative performances and reported the strongest predictors to the respective models. The participants in our study were patients with IBD selected from The OptumLabs® Data Warehouse (OLDW), a longitudinal, real-world data asset with de-identified administrative claims and electronic health record (EHR) data.

RESULTS

We included 72,178 and 69,165 patients in the training and validation set, respectively. In total, 4.1% of patients in the validation set were hospitalized, 2.9% needed IBD-related surgeries, 17% used long-term steroids and 13% of patients were initiated with biological therapy. Of the AI models we tested, the Random Forest and LASSO resulted in high accuracies (AUCs 0.70-0.92). Our artificial neural network performed similarly well in most of the models (AUCs 0.61-0.90).

CONCLUSIONS

This study demonstrates feasibility of accurately predicting adverse outcomes using complex and novel AI models on large longitudinal data sets of patients with IBD. These models could be applied for risk stratification and implementation of preemptive measures to avoid adverse outcomes in a clinical setting.

Charting a path forward for clinical research in artificial intelligence and gastroenterology

Gastroenterology has been an early leader in bridging the gap between artificial intelligence (AI) model development and clinical trial validation, and in recent years we have seen the publication of several randomized clinical trials examining the role of AI in gastroenterology. As AI applications for clinical medicine advance rapidly, there is a clear need for guidance surrounding AI-specific study design, evaluation, comparison, analysis and reporting of results. Several initiatives are in the publication or pre-publication phase including AI-specific amendments to minimum reporting guidelines for clinical trials, society task force initiatives aimed at priority use cases and research priorities, and minimum reporting guidelines that guide the reporting of clinical prediction models. In this paper, we examine applications of AI in clinical trials and discuss elements of newly published AI-specific extensions to the Consolidated Standards of Reporting Trials and Standard Protocol Items: Recommendations for Interventional Trials statements that guide clinical trial reporting and development. We then review AI applications at the pre-trial level in both endoscopy and other subfields of gastroenterology and explore areas where further guidance is needed to supplement the current guidance available at the pre-trial level.

Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review

PURPOSE

The advent of clinically adapted machine learning algorithms can solve numerous problems ranging from disease diagnosis and prognosis to therapy recommendations. This systematic review examines the performance of machine learning (ML) algorithms and evaluates the progress made to date towards their implementation in clinical practice.

METHODS

Systematic searching of databases (PubMed, MEDLINE, Scopus, Google Scholar, Cochrane Library and WHO Covid-19 database) to identify original articles published between January 2011 and October 2021. Studies reporting ML techniques in clinical practice involving humans and ML algorithms with a performance metric were considered.

RESULTS

Of 873 unique articles identified, 36 studies were eligible for inclusion. The XGBoost (extreme gradient boosting) algorithm showed the highest potential for clinical applications (n = 7 studies); this was followed jointly by random forest algorithm, logistic regression, and the support vector machine, respectively (n = 5 studies). Prediction of outcomes (n = 33), in particular Inflammatory diseases (n = 7) received the most attention followed by cancer and neuropsychiatric disorders (n = 5 for each) and Covid-19 (n = 4). Thirty-three out of the thirty-six included studies passed more than 50% of the selected quality assessment criteria in the TRIPOD checklist. In contrast, none of the studies could achieve an ideal overall bias rating of 'low' based on the PROBAST checklist. In contrast, only three studies showed evidence of the deployment of ML algorithm(s) in clinical practice.

CONCLUSIONS

ML is potentially a reliable tool for clinical decision support. Although advocated widely in clinical practice, work is still in progress to validate clinically adapted ML algorithms. Improving quality standards, transparency, and interpretability of ML models will further lower the barriers to acceptability.

Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review

Adverse drug events (ADEs) represent one of the most prevalent types of health-care-related harm, and there is substantial room for improvement in the way that they are currently predicted and detected. We conducted a scoping review to identify key use cases in which artificial intelligence (AI) could be leveraged to reduce the frequency of ADEs. We focused on modern machine learning techniques and natural language processing. 78 articles were included in the scoping review. Studies were heterogeneous and applied various AI techniques covering a wide range of medications and ADEs. We identified several key use cases in which AI could contribute to reducing the frequency and consequences of ADEs, through prediction to prevent ADEs and early detection to mitigate the effects. Most studies (73 [94%] of 78) assessed technical algorithm performance, and few studies evaluated the use of AI in clinical settings. Most articles (58 [74%] of 78) were published within the past 5 years, highlighting an emerging area of study. Availability of new types of data, such as genetic information, and access to unstructured clinical notes might further advance the field.

Variation in Provider Connectedness Associates With Outcomes of Inflammatory Bowel Diseases in an Analysis of Data From a National Health System

BACKGROUND & AIMS

Inflammatory bowel diseases (IBD) often require multidisciplinary care with tight coordination among providers. Provider connectedness, a measure of the relationship among providers, is an important aspect of care coordination that has been linked to higher quality care. We aimed to assess variation in provider connectedness among medical centers, and to understand the association between this established measure of care coordination and outcomes of patients with IBD.

METHODS

We conducted a national cohort study of 32,949 IBD patients with IBD from 2005 to 2014. We used network analysis to examine provider connectedness, defined using network properties that measure the strength of the collaborative relationship, team cohesiveness, and between-facility collaborations. We used multilevel modeling to examine variations in provider connectedness and association with patient outcomes.

RESULTS

There was wide variation in provider connectedness among facilities in complexity, rural designation, and volume of patients with IBD. In a multivariable model, patients followed in a facility with team cohesiveness (odds ratio, 0.38; 95% CI, 0.16-0.88) and where providers often collaborated with providers outside their facility (odds ratio, 0.48; 95% CI, 0.31-0.75) were less likely to have clinically active disease, defined by a composite of outpatient flare, inpatient flare, and IBD-related surgery.

CONCLUSIONS

A national study found evidence for heterogeneity in patient-sharing among IBD care teams. Patients with IBD seen at health centers with higher provider connectedness appear to have better outcomes. Understanding provider connectedness is a step toward designing network-based interventions to improve coordination and quality of care.

Artificial intelligence in gastroenterology: A state-of-the-art review

The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.

Deep learning vs conventional learning algorithms for clinical prediction in Crohn's disease: A proof-of-concept study

BACKGROUND

Traditional methods of developing predictive models in inflammatory bowel diseases (IBD) rely on using statistical regression approaches to deriving clinical scores such as the Crohn's disease (CD) activity index. However, traditional approaches are unable to take advantage of more complex data structures such as repeated measurements. Deep learning methods have the potential ability to automatically find and learn complex, hidden relationships between predictive markers and outcomes, but their application to clinical prediction in CD and IBD has not been explored previously.

AIM

To determine and compare the utility of deep learning with conventional algorithms in predicting response to anti-tumor necrosis factor (anti-TNF) therapy in CD.

METHODS

This was a retrospective single-center cohort study of all CD patients who commenced anti-TNF therapy (either adalimumab or infliximab) from January 1, 2010 to December 31, 2015. Remission was defined as a C-reactive protein (CRP) < 5 mg/L at 12 mo after anti-TNF commencement. Three supervised learning algorithms were compared: (1) A conventional statistical learning algorithm using multivariable logistic regression on baseline data only; (2) A deep learning algorithm using a feed-forward artificial neural network on baseline data only; and (3) A deep learning algorithm using a recurrent neural network on repeated data. Predictive performance was assessed using area under the receiver operator characteristic curve (AUC) after 10× repeated 5-fold cross-validation.

RESULTS

A total of 146 patients were included (median age 36 years, 48% male). Concomitant therapy at anti-TNF commencement included thiopurines (68%), methotrexate (18%), corticosteroids (44%) and aminosalicylates (33%). After 12 mo, 64% had CRP < 5 mg/L. The conventional learning algorithm selected the following baseline variables for the predictive model: Complex disease behavior, albumin, monocytes, lymphocytes, mean corpuscular hemoglobin concentration and gamma-glutamyl transferase, and had a cross-validated AUC of 0.659, 95% confidence interval (CI): 0.562-0.756. A feed-forward artificial neural network using only baseline data demonstrated an AUC of 0.710 (95%CI: 0.622-0.799; P = 0.25 vs conventional). A recurrent neural network using repeated biomarker measurements demonstrated significantly higher AUC compared to the conventional algorithm (0.754, 95%CI: 0.674-0.834; P = 0.036).

CONCLUSION

Deep learning methods are feasible and have the potential for stronger predictive performance compared to conventional model building methods when applied to predicting remission after anti-TNF therapy in CD.

Adoption of New Technologies: Artificial Intelligence

Over the past decade, artificial intelligence (AI) has been broadly applied to many aspects of human life, with recent groundbreaking successes in facial recognition, natural language processing, autonomous driving, and medical imaging. Gastroenterology has applied AI to a vast array of clinical problems, and some of the earliest prospective trials examining AI in medicine have been in computer vision applied to endoscopy. Evidence is mounting for 2 broad areas of AI as applied to gastroenterology: computer-aided detection and computer-aided diagnosis.

Application of Artificial Intelligence to Clinical Practice in Inflammatory Bowel Disease - What the Clinician Needs to Know

Artificial intelligence [AI] techniques are quickly spreading across medicine as an analytical method to tackle challenging clinical questions. What were previously thought of as highly complex data sources, such as images or free text, are now becoming manageable. Novel analytical methods merge the latest developments in information technology infrastructure with advances in computer science. Once primarily associated with Silicon Valley, AI techniques are now making their way into medicine, including in the field of inflammatory bowel diseases [IBD]. Understanding potential applications and limitations of these techniques can be difficult, in particular for busy clinicians. In this article, we explain the basic terminologies and provide a particular focus on the foundations behind state-of-the-art AI methodologies in both imaging and text. We explore the growing applications of AI in medicine, with a specific focus on IBD to inform the practising gastroenterologist and IBD specialist. Finally, we outline possible future uses of these technologies in daily clinical practice.

Role of Digital Health and Artificial Intelligence in Inflammatory Bowel Disease: A Scoping Review

Inflammatory bowel diseases (IBD), subdivided into Crohn's disease (CD) and ulcerative colitis (UC), are chronic diseases that are characterized by relapsing and remitting periods of inflammation in the gastrointestinal tract. In recent years, the amount of research surrounding digital health (DH) and artificial intelligence (AI) has increased. The purpose of this scoping review is to explore this growing field of research to summarize the role of DH and AI in the diagnosis, treatment, monitoring and prognosis of IBD. A review of 21 articles revealed the impact of both AI algorithms and DH technologies; AI algorithms can improve diagnostic accuracy, assess disease activity, and predict treatment response based on data modalities such as endoscopic imaging and genetic data. In terms of DH, patients utilizing DH platforms experienced improvements in quality of life, disease literacy, treatment adherence, and medication management. In addition, DH methods can reduce the need for in-person appointments, decreasing the use of healthcare resources without compromising the standard of care. These articles demonstrate preliminary evidence of the potential of DH and AI for improving the management of IBD. However, the majority of these studies were performed in a regulated clinical environment. Therefore, further validation of these results in a real-world environment is required to assess the efficacy of these methods in the general IBD population.

Replicating prediction algorithms for hospitalization and corticosteroid use in patients with inflammatory bowel disease

Introduction

Previous work had shown that machine learning models can predict inflammatory bowel disease (IBD)-related hospitalizations and outpatient corticosteroid use based on patient demographic and laboratory data in a cohort of United States Veterans. This study aimed to replicate this modeling framework in a nationally representative cohort.

Methods

A retrospective cohort design using Optum Electronic Health Records (EHR) were used to identify IBD patients, with at least 12 months of follow-up between 2007 and 2018. IBD flare was defined as an inpatient/emergency visit with a diagnosis of IBD or an outpatient corticosteroid prescription for IBD. Predictors included demographic and laboratory data. Logistic regression and random forest (RF) models were used to predict IBD flare within 6 months of each visit. A 70% training and 30% validation approach was used.

Results

A total of 95,878 patients across 780,559 visits were identified. Of these, 22,245 (23.2%) patients had at least one IBD flare. Patients were predominantly White (87.7%) and female (57.1%), with a mean age of 48.0 years. The logistic regression model had an area under the receiver operating curve (AuROC) of 0.66 (95% CI: 0.65−0.66), sensitivity of 0.69 (95% CI: 0.68−0.70), and specificity of 0.74 (95% CI: 0.73−0.74) in the validation cohort. The RF model had an AuROC of 0.80 (95% CI: 0.80−0.81), sensitivity of 0.74 (95% CI: 0.73−0.74), and specificity of 0.72 (95% CI: 0.72−0.72) in the validation cohort. Important predictors of IBD flare in the RF model were the number of previous flares, age, potassium, and white blood cell count.

Conclusion

The machine learning modeling framework was replicated and results showed a similar predictive accuracy in a nationally representative cohort of IBD patients. This modeling framework could be embedded in routine practice as a tool to distinguish high-risk patients for disease activity.

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-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review

BACKGROUND AND AIMS

There is increasing interest in machine learning-based prediction models in inflammatory bowel diseases [IBD]. We synthesised and critically appraised studies comparing machine learning vs traditional statistical models, using routinely available clinical data for risk prediction in IBD.

METHODS

Through a systematic review till January 1, 2021, we identified cohort studies that derived and/or validated machine learning models, based on routinely collected clinical data in patients with IBD, to predict the risk of harbouring or developing adverse clinical outcomes, and reported its predictive performance against a traditional statistical model for the same outcome. We appraised the risk of bias in these studies using the Prediction model Risk of Bias ASsessment [PROBAST] tool.

RESULTS

We included 13 studies on machine learning-based prediction models in IBD, encompassing themes of predicting treatment response to biologics and thiopurines and predicting longitudinal disease activity and complications and outcomes in patients with acute severe ulcerative colitis. The most common machine learning models used were tree-based algorithms, which are classification approaches achieved through supervised learning. Machine learning models outperformed traditional statistical models in risk prediction. However, most models were at high risk of bias, and only one was externally validated.

CONCLUSIONS

Machine learning-based prediction models based on routinely collected data generally perform better than traditional statistical models in risk prediction in IBD, though frequently have high risk of bias. Future studies examining these approaches are warranted, with special focus on external validation and clinical applicability.

Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease

Assessment of endoscopic disease activity can be difficult in patients with inflammatory bowel disease (IBD) [comprises Crohn's disease (CD) and ulcerative colitis (UC)]. Endoscopic assessment is currently the foundation of disease evaluation and the grading is pivotal for the initiation of certain treatments. Yet, disharmony is found among experts; even when reassessed by the same expert. Some studies have demonstrated that the evaluation is no better than flipping a coin. In UC, the greatest achieved consensus between physicians when assessing endoscopic disease activity only reached a Kappa value of 0.77 (or 77% agreement adjustment for chance/accident). This is unsatisfactory when dealing with patients at risk of surgery or disease progression without proper care. Lately, across all medical specialities, computer assistance has become increasingly interesting. Especially after the emanation of machine learning – colloquially referred to as artificial intelligence (AI). Compared to other data analysis methods, the strengths of AI lie in its capability to derive complex models from a relatively small dataset and its ability to learn and optimise its predictions from new inputs. It is therefore evident that with such a model, one hopes to be able to remove inconsistency among humans and standardise the results across educational levels, nationalities and resources. This has manifested in a handful of studies where AI is mainly applied to capsule endoscopy in CD and colonoscopy in UC. However, due to its recent place in IBD, there is a great inconsistency between the results, as well as the reporting of the same. In this opinion review, we will explore and evaluate the method and results of the published studies utilising AI within IBD (with examples), and discuss the future possibilities AI can offer within IBD.

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.

The Use of Readily Available Longitudinal Data to Predict the Likelihood of Surgery in Crohn Disease

BACKGROUND

Although imaging, endoscopy, and inflammatory biomarkers are associated with future Crohn disease (CD) outcomes, common laboratory studies may also provide prognostic opportunities. We evaluated machine learning models incorporating routinely collected laboratory studies to predict surgical outcomes in U.S. Veterans with CD.

METHODS

Adults with CD from a Veterans Health Administration, Veterans Integrated Service Networks (VISN) 10 cohort examined between 2001 and 2015 were used for analysis. Patient demographics, medication use, and longitudinal laboratory values were used to model future surgical outcomes within 1 year. Specifically, data at the time of prediction combined with historical laboratory data characteristics, described as slope, distribution statistics, fluctuation, and linear trend of laboratory values, were considered and principal component analysis transformations were performed to reduce the dimensionality. Lasso regularized logistic regression was used to select features and construct prediction models, with performance assessed by area under the receiver operating characteristic using 10-fold cross-validation.

RESULTS

We included 4950 observations from 2809 unique patients, among whom 256 had surgery, for modeling. Our optimized model achieved a mean area under the receiver operating characteristic of 0.78 (SD, 0.002). Anti-tumor necrosis factor use was associated with a lower probability of surgery within 1 year and was the most influential predictor in the model, and corticosteroid use was associated with a higher probability of surgery. Among the laboratory variables, high platelet counts, high mean cell hemoglobin concentrations, low albumin levels, and low blood urea nitrogen values were identified as having an elevated influence and association with future surgery.

CONCLUSIONS

Using machine learning methods that incorporate current and historical data can predict the future risk of CD surgery.

Artificial intelligence and inflammatory bowel disease

With the development of artificial intelligence (AI) and its gradual application in the medical field, AI has brought new ideas to the medical development. The research and application of AI in inflammatory l bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn's disease (CD), are increasing. Selecting appropriate models and methods through machine learning can help diagnose, treat, and predict the prognosis of IBD. In recent years, AI combined with endoscopy has made an appearance in the diagnosis of IBD and achieved satisfactory results. At the same time, AI plays an important role in the process of disease prediction and treatment evaluation for patients with IBD. However, we should also be aware that there are still some problems with AI. This paper gives a brief review of the practical application value of AI in IBD.

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.

Current Strategies and Potential Prospects of Nanomedicine-Mediated Therapy in Inflammatory Bowel Disease

Inflammatory bowel diseases (IBD) such as Crohn's disease and ulcerative colitis are highly debilitating. IBDs are associated with the imbalance of inflammatory mediators within the inflamed bowel. Conventional drugs for IBD treatment include anti-inflammatory medications and immune suppressants. However, they suffer from a lack of bioavailability and high dose-induced systemic side effects. Nanoparticle (NP)-derived therapy improves therapeutic efficacy and increases targeting specificity. Recent studies have shown that nanomedicines, based on bowel disease's pathophysiology, are a fast-growing field. NPs can prolong the circulation period and reduce side effects by improving drug encapsulation and targeted delivery. Here, this review summarizes various IBD therapies with a focus on NP-derived applications, whereas their challenges and future perspectives have also been discussed.

Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions

Inflammatory bowel disease (IBD) is a complex and multifaceted disorder of the gastrointestinal tract that is increasing in incidence worldwide and associated with significant morbidity. The rapid accumulation of large datasets from electronic health records, high-definition multi-omics (including genomics, proteomics, transcriptomics, and metagenomics), and imaging modalities (endoscopy and endomicroscopy) have provided powerful tools to unravel novel mechanistic insights and help address unmet clinical needs in IBD. Although the application of artificial intelligence (AI) methods has facilitated the analysis, integration, and interpretation of large datasets in IBD, significant heterogeneity in AI methods, datasets, and clinical outcomes and the need for unbiased prospective validations studies are current barriers to incorporation of AI into clinical practice. The purpose of this review is to summarize the most recent advances in the application of AI and machine learning technologies in the diagnosis and risk prediction, assessment of disease severity, and prediction of clinical outcomes in patients with IBD.

Gut microbiome-based supervised machine learning for clinical diagnosis of inflammatory bowel diseases

Despite the availability of various diagnostic tests for inflammatory bowel diseases (IBD), misdiagnosis of IBD occurs frequently, and thus, there is a clinical need to further improve the diagnosis of IBD. As gut dysbiosis is reported in patients with IBD, we hypothesized that supervised machine learning (ML) could be used to analyze gut microbiome data for predictive diagnostics of IBD. To test our hypothesis, fecal 16S metagenomic data of 729 subjects with IBD and 700 subjects without IBD from the American Gut Project were analyzed using five different ML algorithms. Fifty differential bacterial taxa were identified [linear discriminant analysis effect size (LEfSe): linear discriminant analysis (LDA) score > 3] between the IBD and non-IBD groups, and ML classifications trained with these taxonomic features using random forest (RF) achieved a testing area under the receiver operating characteristic curves (AUC) of ∼0.80. Next, we tested if operational taxonomic units (OTUs), instead of bacterial taxa, could be used as ML features for diagnostic classification of IBD. Top 500 high-variance OTUs were used for ML training, and an improved testing AUC of ∼0.82 (RF) was achieved. Lastly, we tested if supervised ML could be used for differentiating Crohn's disease (CD) and ulcerative colitis (UC). Using 331 CD and 141 UC samples, 117 differential bacterial taxa (LEfSe: LDA score > 3) were identified, and the RF model trained with differential taxonomic features or high-variance OTU features achieved a testing AUC > 0.90. In summary, our study demonstrates the promising potential of artificial intelligence via supervised ML modeling for predictive diagnostics of IBD using gut microbiome data.NEW & NOTEWORTHY Our study demonstrates the promising potential of artificial intelligence via supervised machine learning modeling for predictive diagnostics of different types of inflammatory bowel diseases using fecal gut microbiome data.