Table 2.

Microbiome-based prediction models for PIBD in previous studies

Author and year	Task	Model type	Countries	Cohort description	Model input	Model output	Model performance
Kolho et al. 2015	Prediction of fecal calprotectin level	Linear mixed effect model	Finland	N = 94 - Control, n = 26 - CD, n = 36 - UC, n = 26 - IBD-U, n = 6	Fecal microbiome	Predicted fecal calprotectin level	It is possible to predict calprotectin levels using selected bacterial taxa (AUC = 0.85).
Kolho et al. 2015	Prediction of anti-TNF-α response	Linear mixed effect model	Finland	Subset of the cohort: anti-TNF-α receivers, n = 11 - Responders, n = 6 - Non-responders, n = 5	Fecal microbiome: -Two taxa abundance (Clostridium sphenoides and Haemophilus)	Predicted fecal calprotectin level	It is possible to predict the patient response to anti-TNF-α using the two selected bacterial taxa (AUC = 0.88).
Douglas et al. 2018	Diagnosis	Random forest	UK	N = 40 - Control, n = 20 - CD, n = 20	Intestinal tissue microbiome	Diagnosis of CD	Prediction accuracy was highest with genus-level 16S profiles (84.2%).a
Douglas et al. 2018	Prediction of response to induction treatment	Random forest	UK	CD, n = 20	Intestinal tissue microbiome	The probability the sample is from a responder	Prediction accuracy was highest with the model combining different feature types (94.4%).a
Hyams et al. 2019	Prediction of response to anti-TNF-α	Multiple imputation multivariate logistic regression with LASSO variable selection	USA and Canada	UC, N = 386 - With biologics, n = 177 - Responders, n = 150	Fecal and rectal tissue microbiome: Host side: - Clinical indices - Gene expressions in rectal tissue	The probability the sample is from those who achieve CS-free remission at week 52	Clinical data alone (e.g., week 4 remission, PUCAI, baseline hemoglobin) predicted week 52 remission with an AUC of 0.68. Adding host gene expression and microbial features improved accuracy to an AUC of 0.75.
Jones et al. 2020	Prediction of response to EEN	Random forest	Canada	CD, N = 19 - All received EEN - Responders, n = 13	Fecal microbiome	The probability the sample is from those who achieve sustained remission until week 24	Sustained remission can be predicted based on ASVs (AUC = 0.74) but not with other taxonomic levels or shotgun-based profiles. The predictions were improved by the addition of species richness (AUC = 0.83) and further improved by the addition of disease location and behavior (AUC = 0.9).
Wang et al. 2021	Diagnosis	Random forest	China	N = 93 - IBD, n = 66 - Controls, n = 27	Fecal microbiome	Diagnosis of IBD	In the training set, predictions based on 11 OTUs achieved an AUC of 0.88. The validation dataset including IBD (n = 14) and IBS (n = 48) achieved an AUC of 0.84.
Zuo et al. 2022	Diagnosis	Random forest	USA	N = 42 - UC, n = 19 - Control, n = 23	Fecal microbiome	Diagnosis of UC	The best prediction was made with pathway abundance (AUC = 0.95). - Genus composition (AUC = 0.91) - Species composition (AUC = 0.91) The addition of sex- and age-related variables did not improve the model’s performance.
Dhaliwal et al. 2023	Prediction of escalation to anti-TNF-α	Cox proportional hazards regression	Canada	UC, N = 96 - Anti-TNF-α due to non-response to CS, n = 54 - Anti-TNF-α among CS responders, n = 24 - Clinical remission, n = 62	Clinical variables, Fecal microbiome	Hazard ratio and significance value per input clinical variable	Hypoalbuminemia, greater PUCAI, older age, and male sex were significant predictors of escalation to anti-TNF-α. The baseline microbiome was not predictive of escalation to anti-TNF-α.
Ventin-Holmberg et al. 2022	Prediction of response to anti-TNF-α	Regression (PathModel function in R package mare)	Finland	IBD, n = 30 - CD, n = 25 - UC, n = 2 - IBD-U, n = 3 Final cohort used in the model - Anti-TNF-α responders, n = 5 - Anti-TNF-α non-responders, n = 13	Fecal microbiome	Probability the sample is from a responder	The Week 6 response to anti-TNF-α can be predicted by the baseline fecal calprotectin level and Ruminococcus count (AUC = 0.89) - The baseline Ruminococcus count alone gives a slightly less accurate prediction (AUC = 0.79).
Our study	Prediction of response to induction treatment	Deep neural network, logistic regression, support vector machine	Multinational cohorts: Korea, USA, Canada, and UK; external validation: Czech	IBD, N = 248 - Responders, n = 147 - Non-responders, n = 101	Fecal microbiome Host side: - Age, sex, calprotectin level, and disease severity - Usage of anti-TNF-α, 5-ASA, AZA, EEN, and steroids	Probability the sample is from a responder	The ensemble model performed well (AUC = 0.9), demonstrating that the presence or absence of commensal and pathogenic bacteria can influence future PIBD remission or relapse.

5-ASA, 5-aminosalicylic acid; ASV, amplicon sequence variant; AUC, area under the curve; AZA, azathioprine; CD, Crohn disease; CS, corticosteroids; DNN, deep neural network; EEN, exclusive enteral nutrition; IBD-U, inflammatory bowel disease–unclassified; IBS, irritable bowel syndrome; N/A, not available; OTUs, operational taxonomic units; PIBD, pediatric inflammatory bowel disease; PUCAI, Pediatric Ulcerative Colitis Activity Index; TNF-α, tumor necrosis factor alpha; UC, ulcerative colitis.

^aNote that this study did not include the ASV profiles in the performance comparison.