scholarly journals High incidence of glucocorticoid-induced hyperglycaemia in inflammatory bowel disease; metabolic and clinical predictors identified by machine learning

2020 ◽  
Author(s):  
Martin McDonnell ◽  
Richard J Harris ◽  
Florina Borca ◽  
Tilly Mills ◽  
Louise Downey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inflammatory Bowel Disease ◽  
Blood Glucose ◽  
Random Forest ◽  
Bowel Disease ◽  
Capillary Blood ◽  
Routine Clinical Practice ◽  
Capillary Blood Glucose ◽  
Machine Learning Methods ◽  
Inflammatory Bowel

ABSTRACTBackgroundGlucocorticosteroids (GC) are long-established, widely used agents for induction of remission in inflammatory bowel disease (IBD). Hyperglycaemia is a known complication of GC treatment with implications for morbidity and mortality. Published data on prevalence and risk factors for GC-induced hyperglycaemia in the IBD population are limited. We prospectively characterise this complication in our cohort, employing machine-learning methods to identify key predictors of risk.MethodsWe conducted a prospective observational study of IBD patients receiving intravenous hydrocortisone (IVH). Electronically triggered three times daily capillary blood glucose (CBG) monitoring was recorded alongside diabetes mellitus (DM) history, IBD biomarkers, nutritional and IBD clinical activity scores. Hyperglycaemia was defined as CBG ≥11·1mmol/L and undiagnosed DM as HbA1c ≥48 mmol/mol. Random Forest regression models were used to extract predictor-patterns present within the dataset.Findings94 consecutive IBD patients treated with IVH were included. 60% (56/94) of the cohort recorded an episode of hyperglycaemia, including 57% (50/88) of those with no prior history of DM, of which 19% (17/88) and 5% (4/88) recorded a CBG ≥14mmol/L and ≥20mmol/L, respectively. The Random Forest models identified increased CRP followed by a longer IBD duration as leading risk predictors for significant hyperglycaemia.InterpretationHyperglycaemia is common in IBD patients treated with intravenous GC, therefore CBG monitoring should be included in routine clinical practice. Machine learning methods can identify key risk factors for clinical complications. Physicians should consider steroid-sparing strategies in high-risk patients such as those with high admission CRP or a longer IBD duration. There is an emergent case for research to explore steroid-free treatment regimens for hospitalised patients with severe IBD flares.Evidence before this studyGlucocorticosteroids (GC) are long-established induction agents in the management of inflammatory bowel disease (IBD). They are recommended first-line therapy in consensus guidelines and prescribing remains widespread, with an estimated 30% of IBD patients exposed annually. Hyperglycaemia is a known complication of GC and has been linked to increased length of hospital stay, morbidity and mortality. Small case series of GC treated medical patients suggest a higher risk of hyperglycaemia in the hospitalised population but have suffered from a lack of systematic blood glucose monitoring.Added value of this studyThis is the first study utilising prospective, systematic monitoring of capillary blood glucose (CBG) to determine the frequency of hyperglycaemia in a GC-treated hospitalised IBD population. We report that more than half of IBD patients without prior diabetes mellitus treated with intravenous hydrocortisone (IVH), will develop hyperglycaemia (CBG ≥11·1mmol/L). Random Forest regressors pinpointed CRP and IBD duration as the strongest predictor of this adverse outcome.Implications of all the available evidenceHyperglycaemia is a common complication of IVH therapy in hospitalised IBD patients, particularly in those with high inflammatory burden. The monitoring and management of this complication, which has potential implications for the morbidity, mortality and subsequent risk of diabetes diagnosis should become part of routine clinical practice.

scholarly journals Investigation of gut microbiome association with inflammatory bowel disease and depression: a machine learning approach

F1000Research ◽  
2019 ◽  
Vol 7 ◽  
pp. 702
Author(s):  
Pedro Morell Miranda ◽  
Francesca Bertolini ◽  
Haja N. Kadarmideen
Keyword(s):  
Machine Learning ◽  
Inflammatory Bowel Disease ◽  
Random Forest ◽  
Bowel Disease ◽  
Gut Microbiome ◽  
Predictive Power ◽  
Learning Approach ◽  
Important Species ◽  
Machine Learning Approach ◽  
Inflammatory Bowel

Background: Inflammatory bowel disease (IBD) is a group of chronic diseases related to inflammatory processes in the digestive tract generally associated with an immune response to an altered gut microbiome in genetically predisposed subjects. For years, both researchers and clinicians have been reporting increased rates of anxiety and depression disorders in IBD, and these disorders have also been linked to an altered microbiome. However, the underlying pathophysiological mechanisms of comorbidity are poorly understood at the gut microbiome level. Methods: Metagenomic and metatranscriptomic data were retrieved from the Inflammatory Bowel Disease Multi-Omics Database. Samples from 70 individuals that had answered to a self-reported depression and anxiety questionnaire were selected and classified by their IBD diagnosis and their questionnaire results, creating six different groups. The cross-validation random forest algorithm was used in 90% of the individuals (training set) to retain the most important species involved in discriminating the samples without losing predictive power. The validation set that represented the remaining 10% of the samples equally distributed across the six groups was used to train a random forest using only the species selected in order to evaluate their predictive power. Results: A total of 24 species were identified as the most informative in discriminating the 6 groups. Several of these species were frequently described in dysbiosis cases, such as species from the genus Bacteroides and Faecalibacterium prausnitzii. Despite the different compositions among the groups, no common patterns were found between samples classified as depressed. However, distinct taxonomic profiles within patients of IBD depending on their depression status were detected. Conclusions: The machine learning approach is a promising approach for investigating the role of microbiome in IBD and depression. Abundance and functional changes in these species suggest that depression should be considered as a factor in future research on IBD.

scholarly journals High incidence of glucocorticoid-induced hyperglycaemia in inflammatory bowel disease: metabolic and clinical predictors identified by machine learning

2020 ◽  
Vol 7 (1) ◽  
pp. e000532
Author(s):  
Martin McDonnell ◽  
Richard J Harris ◽  
Florina Borca ◽  
Tilly Mills ◽  
Louise Downey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Clinical Activity ◽  
Published Data ◽  
Clinical Predictors ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Inflammatory Bowel

BackgroundGlucocorticosteroids (GC) are long-established, widely used agents for induction of remission in inflammatory bowel disease (IBD). Hyperglycaemia is a known complication of GC treatment with implications for morbidity and mortality. Published data on prevalence and risk factors for GC-induced hyperglycaemia in the IBD population are limited. We prospectively characterise this complication in our cohort, employing machine-learning methods to identify key predictors of risk.MethodsWe conducted a prospective observational study of IBD patients receiving intravenous hydrocortisone (IVH). Electronically triggered three times daily capillary blood glucose (CBG) monitoring was recorded alongside diabetes mellitus (DM) history, IBD biomarkers, nutritional and IBD clinical activity scores. Hyperglycaemia was defined as CBG ≥11.1 mmol/L and undiagnosed DM as glycated haemoglobin ≥48 mmol/mol. Random forest (RF) regression models were used to extract predictor-patterns present within the dataset.Results94 consecutive IBD patients treated with IVH were included. 60% (56/94) of the cohort recorded an episode of hyperglycaemia, including 57% (50/88) of those with no history of DM, of which 19% (17/88) and 5% (4/88) recorded a CBG ≥14 mmol/L and ≥20 mmol/L, respectively. The RF models identified increased C-reactive protein (CRP) followed by a longer IBD duration as leading risk predictors for significant hyperglycaemia.ConclusionHyperglycaemia is common in IBD patients treated with intravenous GC. Therefore, CBG monitoring should be included in routine clinical practice. Machine learning methods can identify key risk factors for clinical complications. Steroid-sparing treatment strategies may be considered for those IBD patients with higher admission CRP and greater disease duration, who appear to be at the greatest risk of hyperglycaemia.

scholarly journals Investigation of gut microbiome association with inflammatory bowel disease and depression: a machine learning approach

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 702 ◽  
Author(s):  
Pedro Morell Miranda ◽  
Francesca Bertolini ◽  
Haja N. Kadarmideen
Keyword(s):  
Machine Learning ◽  
Inflammatory Bowel Disease ◽  
Random Forest ◽  
Bowel Disease ◽  
Gut Microbiome ◽  
Predictive Power ◽  
Learning Approach ◽  
Important Species ◽  
Machine Learning Approach ◽  
Inflammatory Bowel

Background: Inflammatory bowel disease (IBD) is a group of chronic diseases related to inflammatory processes in the digestive tract generally associated with an immune response to an altered gut microbiome in genetically predisposed subjects. For years, both researchers and clinicians have been reporting increased rates of anxiety and depression disorders in IBD, and these disorders have also been linked to an altered microbiome. However, the underlying pathophysiological mechanisms of comorbidity are poorly understood at the gut microbiome level. Methods: Metagenomic and metatranscriptomic data were retrieved from the Inflammatory Bowel Disease Multi-Omics Database. Samples from 70 individuals that had answered to a self-reported depression and anxiety questionnaire were selected and classified by their IBD diagnosis and their questionnaire results, creating six different groups. The cross-validation random forest algorithm was used in 90% of the individuals (training set) to retain the most important species involved in discriminating the samples without losing predictive power. The validation set that represented the remaining 10% of the samples equally distributed across the six groups was used to train a random forest using only the species selected in order to evaluate their predictive power. Results: A total of 24 species were identified as the most informative in discriminating the 6 groups. Several of these species were frequently described in dysbiosis cases, such as species from the genus Bacteroides and Faecalibacterium prausnitzii. Despite the different compositions among the groups, no common patterns were found between samples classified as depressed. However, distinct taxonomic profiles within patients of IBD depending on their depression status were detected. Conclusions: The machine learning approach is a promising approach for investigating the role of microbiome in IBD and depression. Abundance and functional changes in these species suggest that depression should be considered as a factor in future research on IBD.

scholarly journals Development of Machine Learning Model to Predict the 5-Year Risk of Starting Biologic Agents in Patients with Inflammatory Bowel Disease (IBD): K-CDM Network Study

2020 ◽  
Vol 9 (11) ◽  
pp. 3427 ◽  
Author(s):  
Youn I Choi ◽  
Sung Jin Park ◽  
Jun-Won Chung ◽  
Kyoung Oh Kim ◽  
Jae Hee Cho ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inflammatory Bowel Disease ◽  
Validation Study ◽  
Bowel Disease ◽  
Medical Center ◽  
External Validation ◽  
Biologic Agents ◽  
Risk Level ◽  
Common Data Model ◽  
Inflammatory Bowel

Background: The incidence and global burden of inflammatory bowel disease (IBD) have steadily increased in the past few decades. Improved methods to stratify risk and predict disease-related outcomes are required for IBD. Aim: The aim of this study was to develop and validate a machine learning (ML) model to predict the 5-year risk of starting biologic agents in IBD patients. Method: We applied an ML method to the database of the Korean common data model (K-CDM) network, a data sharing consortium of tertiary centers in Korea, to develop a model to predict the 5-year risk of starting biologic agents in IBD patients. The records analyzed were those of patients diagnosed with IBD between January 2006 and June 2017 at Gil Medical Center (GMC; n = 1299) or present in the K-CDM network (n = 3286). The ML algorithm was developed to predict 5- year risk of starting biologic agents in IBD patients using data from GMC and externally validated with the K-CDM network database. Result: The ML model for prediction of IBD-related outcomes at 5 years after diagnosis yielded an area under the curve (AUC) of 0.86 (95% CI: 0.82–0.92), in an internal validation study carried out at GMC. The model performed consistently across a range of other datasets, including that of the K-CDM network (AUC = 0.81; 95% CI: 0.80–0.85), in an external validation study. Conclusion: The ML-based prediction model can be used to identify IBD-related outcomes in patients at risk, enabling physicians to perform close follow-up based on the patient’s risk level, estimated through the ML algorithm.

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