Validation of a new optical diagnosis training module to improve dysplasia characterization in inflammatory bowel disease: a multicenter international study.

BACKGROUND AND AIMS: Inflammatory bowel disease (IBD) increases risk of dysplasia and colorectal cancer. Advanced endoscopic techniques allow for the detection and characterization of IBD dysplastic lesions, but specialized training is not widely available. We aimed to develop and validate an online training platform to improve the detection and characterization of colonic lesions in IBD: OPtical diagnosis Training to Improve dysplasia Characterization in Inflammatory Bowel Disease (OPTIC-IBD). METHODS: We designed a web-based learning module that includes surveillance principles, optical diagnostic methods, approach to characterization, and classifications of colonic lesions using still images and videos. We invited gastroenterologists from Canada, Italy, and the United Kingdom with a wide range of experience. Participants reviewed 24 educational videos of IBD colonic lesions, predicted histology, and rated their confidence. The primary endpoint was to improve accuracy in detecting dysplastic lesions after training on the platform. Furthermore, participants were randomized 1:1 to get additional training or not, with a final assessment occurring after 60 days. Diagnostic performance for dysplasia and rater confidence were measured. RESULTS: A total of 117 participants completed the study and were assessed for the primary endpoint. Diagnostic accuracy improved from 70.8% to 75.0% (P = .002) after training, with the greatest improvements seen in less experienced endoscopists. Improvements in both accuracy and confidence were sustained after 2 months of assessment, although the group randomized to receive additional training did not improve further. Similarly, participants' confidence in characterizing lesions significantly improved between before and after the course (P < .001), and it was sustained after 2 months of assessment. CONCLUSIONS: The OPTIC-IBD training module demonstrated that an online platform could improve participants' accuracy and confidence in the optical diagnosis of dysplasia in patients with IBD. The training platform can be widely available and improve endoscopic care for people with IBD. (Clinical trial registration number: NCT04924543.).

CARRoT: R-package for predictive modelling by means of regression, adjusted for multiple regularisation methods.

We present an R-package for predictive modelling, CARRoT (Cross-validation, Accuracy, Regression, Rule of Ten). CARRoT is a tool for initial exploratory analysis of the data, which performs exhaustive search for a regression model yielding the best predictive power with heuristic 'rules of thumb' and expert knowledge as regularization parameters. It uses multiple hold-outs in order to internally validate the model. The package allows to take into account multiple factors such as collinearity of the predictors, event per variable rules (EPVs) and R-squared statistics during the model selection. In addition, other constraints, such as forcing specific terms and restricting complexity of the predictive models can be used. The package allows taking pairwise and three-way interactions between variables into account as well. These candidate models are then ranked by predictive power, which is assessed via multiple hold-out procedures and can be parallelised in order to reduce the computational time. Models which exhibited the highest average predictive power over all hold-outs are returned. This is quantified as absolute and relative error in case of continuous outcomes, accuracy and AUROC values in case of categorical outcomes. In this paper we briefly present statistical framework of the package and discuss the complexity of the underlying algorithm. Moreover, using CARRoT and a number of datasets available in R we provide comparison of different model selection techniques: based on EPVs alone, on EPVs and R-squared statistics, on lasso regression, on including only statistically significant predictors and on stepwise forward selection technique.

RNA contact prediction by data efficient deep learning.

On the path to full understanding of the structure-function relationship or even design of RNA, structure prediction would offer an intriguing complement to experimental efforts. Any deep learning on RNA structure, however, is hampered by the sparsity of labeled training data. Utilizing the limited data available, we here focus on predicting spatial adjacencies ("contact maps") as a proxy for 3D structure. Our model, BARNACLE, combines the utilization of unlabeled data through self-supervised pre-training and efficient use of the sparse labeled data through an XGBoost classifier. BARNACLE shows a considerable improvement over both the established classical baseline and a deep neural network. In order to demonstrate that our approach can be applied to tasks with similar data constraints, we show that our findings generalize to the related setting of accessible surface area prediction.

Artificial Intelligence Enabled Histological Prediction of Remission or Activity and Clinical Outcomes in Ulcerative Colitis.

BACKGROUND & AIMS: Microscopic inflammation has significant prognostic value in ulcerative colitis (UC); however, its assessment is complex with high interobserver variability. We aimed to develop and validate an artificial intelligence (AI) computer-aided diagnosis system to evaluate UC biopsies and predict prognosis. METHODS: A total of 535 digitalized biopsies (273 patients) were graded according to the PICaSSO Histologic Remission Index (PHRI), Robarts, and Nancy Histological Index. A convolutional neural network classifier was trained to distinguish remission from activity on a subset of 118 biopsies, calibrated on 42 and tested on 375. The model was additionally tested to predict the corresponding endoscopic assessment and occurrence of flares at 12 months. The system output was compared with human assessment. Diagnostic performance was reported as sensitivity, specificity, prognostic prediction through Kaplan-Meier, and hazard ratios of flares between active and remission groups. We externally validated the model in 154 biopsies (58 patients) with similar characteristics but more histologically active patients. RESULTS: The system distinguished histological activity/remission with sensitivity and specificity of 89% and 85% (PHRI), 94% and 76% (Robarts Histological Index), and 89% and 79% (Nancy Histological Index). The model predicted the corresponding endoscopic remission/activity with 79% and 82% accuracy for UC endoscopic index of severity and Paddington International virtual ChromoendoScopy ScOre, respectively. The hazard ratio for disease flare-up between histological activity/remission groups according to pathologist-assessed PHRI was 3.56, and 4.64 for AI-assessed PHRI. Both histology and outcome prediction were confirmed in the external validation cohort. CONCLUSION: We developed and validated an AI model that distinguishes histologic remission/activity in biopsies of UC and predicts flare-ups. This can expedite, standardize, and enhance histologic assessment in practice and trials.

A virtual chromoendoscopy artificial intelligence system to detect endoscopic and histologic activity/remission and predict clinical outcomes in ulcerative colitis.

BACKGROUND: Endoscopic and histological remission (ER, HR) are therapeutic targets in ulcerative colitis (UC). Virtual chromoendoscopy (VCE) improves endoscopic assessment and the prediction of histology; however, interobserver variability limits standardized endoscopic assessment. We aimed to develop an artificial intelligence (AI) tool to distinguish ER/activity, and predict histology and risk of flare from white-light endoscopy (WLE) and VCE videos. METHODS: 1090 endoscopic videos (67 280 frames) from 283 patients were used to develop a convolutional neural network (CNN). UC endoscopic activity was graded by experts using the Ulcerative Colitis Endoscopic Index of Severity (UCEIS) and Paddington International virtual ChromoendoScopy ScOre (PICaSSO). The CNN was trained to distinguish ER/activity on endoscopy videos, and retrained to predict HR/activity, defined according to multiple indices, and predict outcome; CNN and human agreement was measured. RESULTS: The AI system detected ER (UCEIS ≤ 1) in WLE videos with 72 % sensitivity, 87 % specificity, and an area under the receiver operating characteristic curve (AUROC) of 0.85; for detection of ER in VCE videos (PICaSSO ≤ 3), the sensitivity was 79 %, specificity 95 %, and the AUROC 0.94. The prediction of HR was similar between WLE and VCE videos (accuracies ranging from 80 % to 85 %). The model's stratification of risk of flare was similar to that of physician-assessed endoscopy scores. CONCLUSIONS: Our system accurately distinguished ER/activity and predicted HR and clinical outcome from colonoscopy videos. This is the first computer model developed to detect inflammation/healing on VCE using the PICaSSO and the first computer tool to provide endoscopic, histologic, and clinical assessment.

Constrained multiple instance learning for ulcerative colitis prediction using histological images.

BACKGROUND AND OBJECTIVE: Ulcerative colitis (UC) is an inflammatory bowel disease (IBD) affecting the colon and the rectum characterized by a remitting-relapsing course. To detect mucosal inflammation associated with UC, histology is considered the most stringent criteria. In turn, histologic remission (HR) correlates with improved clinical outcomes and has been recently recognized as a desirable treatment target. The leading biomarker for assessing histologic remission is the presence or absence of neutrophils. Therefore, the finding of this cell in specific colon structures indicates that the patient has UC activity. However, no previous studies based on deep learning have been developed to identify UC based on neutrophils detection using whole-slide images (WSI). METHODS: The methodological core of this work is a novel multiple instance learning (MIL) framework with location constraints able to determine the presence of UC activity using WSI. In particular, we put forward an effective way to introduce constraints about positive instances to effectively explore additional weakly supervised information that is easy to obtain and enjoy a significant boost to the learning process. In addition, we propose a new weighted embedding to enlarge the relevance of the positive instances. RESULTS: Extensive experiments on a multi-center dataset of colon and rectum WSIs, PICASSO-MIL, demonstrate that using the location information we can improve considerably the results at WSI-level. In comparison with prior MIL settings, our method allows for 10% improvements in bag-level accuracy. CONCLUSION: Our model, which introduces a new form of constraints, surpass the results achieved from current state-of-the-art methods that focus on the MIL paradigm. Our method can be applied to other histological concerns where the morphological features determining a positive WSI are tiny and similar to others in the image.

PICaSSO virtual electronic chromendoscopy accurately reflects combined endoscopic and histological assessment for prediction of clinical outcomes in ulcerative colitis.

BACKGROUND AND AIMS: A composite endoscopic-histologic remission is increasingly explored as an important endpoint in ulcerative colitis (UC). We investigated combined endoscopic-histologic remission for predicting clinical outcomes at 12 months compared with endoscopic remission alone using the high definition virtual chromoendoscopy (VCE) Paddington International virtual ChromoendoScopy ScOre (PICaSSO) and histology scores. METHODS: Ulcerative colitis patients, prospectively enrolled from 11 international centres, underwent VCE with targeted biopsies and followed up for 12 months. Endoscopic activity was assessed by Mayo Endoscopic Score (MES), Ulcerative Colitis Endoscopic Index Severity (UCEIS) followed by VCE-PICaSSO. Robarts Histopathological Index|Robarts Histological index≤3 without neutrophils in mucosa, and Nancy Histological index (NHI)≤ 1 were used to define histologic remission. Combined endoscopic-histologic remission was compared with endoscopic remission alone by Cox proportional hazards model and by two- and three-proportion analysis using pre-specified clinical outcomes. RESULTS: 307 patients were recruited and 302 analysed. There was no difference in survival without specified clinical outcomes between PICaSSO defined endoscopic remission alone and endoscopic plus histologic remission in the rectum (HR 0.42, 95%CI 0.16-1.11 and HR 1.03, 95%CI 0.42-2.52 for Robarts Histological index and NHI respectively) at 12 months. There was however a significant survival advantage without specified clinical outcome events for UCEIS combined with histology compared with UCEIS alone (HR 0.30, 95%CI 0.12-0.75, p = 0.02) at 12 months (but not combined with NHI). For MES there was no advantage for predicting specified clinical outcomes at 12 months for endoscopy alone versus endoscopy plus histology, but there were differences in two and three proportion analysis at 6 months. CONCLUSION: Endoscopic remission by VCE-PICaSSO alone was similar to combined endoscopic and histologic remission for predicting specified clinical outcomes at 12 months. Larger studies with specific therapeutic interventions are required to further confirm the findings.

Reproducibility of the electronic chromoendoscopy PICaSSO score (Paddington International Virtual ChromoendoScopy ScOre) in ulcerative colitis using multiple endoscopic platforms: a prospective multicenter international study (with video).

BACKGROUND AND AIMS: Endoscopic and histologic remission (HR) are key therapeutic targets in the management of ulcerative colitis (UC). The aim of this study was to evaluate the reproducibility of the Paddington International virtual ChromoendoScopy ScOre (PICaSSO), a virtual chromoendoscopy score originally validated by use of the iSCAN platform, with the narrow-band imaging (NBI), linked-color imaging (LCI), and blue-laser imaging (BLI) platforms. METHODS: We evaluated endoscopic activity using the Mayo Endoscopic Score (MES), the Ulcerative Colitis Endoscopic Index of Severity (UCEIS), and PICaSSO in 159 UC patients (78 NBI and 81 BLI/LCI) who underwent colonoscopy in 2 tertiary referral centers. HR was defined by the Robarts Histopathology Index (RHI) and the Nancy Histologic Index (NHI). Receiver operating characteristic curves were plotted to evaluate endoscopic scores for the prediction of HR. Intraclass correlation coefficients (ICC) between endoscopists were evaluated. RESULTS: PICaSSO had an ICC of 0.825 when the NBI and BLI/LCI cohorts were combined, higher than MES and UCEIS. The correlation between PICaSSO and RHI and NHI was 0.83 and 0.79 in the NBI cohort and between 0.63 and 0.65 in LCI/BLI. In the NBI cohort, the accuracy of MES, UCEIS, and PICaSSO was 0.936, 0.897, and 0.808 for HR measured by RHI and 0.897, 0.885, and 0.821 by NHI, respectively. In the BLI/LCI cohort, the accuracy of MES, UCEIS, LCI PICaSSO and BLI PICaSSO was 0.765, 0.778, 0.827, and 0.79 to predict HR with RHI and NHI, respectively. CONCLUSIONS: The PICaSSO score can be consistently and accurately reproduced with NBI and LCI/BLI and therefore can be applied to all virtual electronic chromoendoscopy platforms.

PICaSSO Histologic Remission Index (PHRI) in ulcerative colitis: development of a novel simplified histological score for monitoring mucosal healing and predicting clinical outcomes and its applicability in an artificial intelligence system.

Histological remission is evolving as an important treatment target in UC. We aimed to develop a simple histological index, aligned to endoscopy, correlated with clinical outcomes, and suited to apply to an artificial intelligence (AI) system to evaluate inflammatory activity. METHODS: Using a set of 614 biopsies from 307 patients with UC enrolled into a prospective multicentre study, we developed the Paddington International virtual ChromoendoScopy ScOre (PICaSSO) Histologic Remission Index (PHRI). Agreement with multiple other histological indices and validation for inter-reader reproducibility were assessed. Finally, to implement PHRI into a computer-aided diagnosis system, we trained and tested a novel deep learning strategy based on a CNN architecture to detect neutrophils, calculate PHRI and identify active from quiescent UC using a subset of 138 biopsies. RESULTS: PHRI is strongly correlated with endoscopic scores (Mayo Endoscopic Score and UC Endoscopic Index of Severity and PICaSSO) and with clinical outcomes (hospitalisation, colectomy and initiation or changes in medical therapy due to UC flare-up). A PHRI score of 1 could accurately stratify patients' risk of adverse outcomes (hospitalisation, colectomy and treatment optimisation due to flare-up) within 12 months. Our inter-reader agreement was high (intraclass correlation 0.84). Our preliminary AI algorithm differentiated active from quiescent UC with 78% sensitivity, 91.7% specificity and 86% accuracy. CONCLUSIONS: PHRI is a simple histological index in UC, and it exhibits the highest correlation with endoscopic activity and clinical outcomes. A PHRI-based AI system was accurate in predicting histological remission.

Ultra-high Magnification Endocytoscopy and Molecular Markers for Defining Endoscopic and Histologic Remission in Ulcerative Colitis-An Exploratory Study to Define Deep Remission.

BACKGROUND: Endoscopic and histological remission are both important treatment goals in patients with ulcerative colitis (UC). We aimed to define cellular architecture, expression of molecular markers, and their correlation with endoscopic scores assessed by ultra-high magnification endocytoscopy (ECS) and histological scores. METHODS: Patients with UC (n = 29) were prospectively recruited. The correlation among ECS score (ECSS), Mayo endoscopic score (MES), and histological scores were determined. Area under curve were plotted to determine the best thresholds for ECSS that predicted histological remission by Robarts (RHI) and Nancy Histological Index (NHI).Soluble analytes relevant to inflammation were measured in serum and mucosal culture supernatants using ProcartaPlex Luminex assays and studied by partial least square discriminant analysis and logistic model. Mucosal RNA sequencing and bioinformatics analysis were performed to define differentially expressed genes/pathways. RESULTS: Endocytoscope scoring system correlated strongly with RHI (r = 0.89; 95% CI, 0.51-0.98) and NHI (r = 0.86; 95% CI, 0.42-0.98) but correlated poorly with MES (r = 0.28; 95% CI, 0.27-0.70). We identified soluble brain-derived neurotrophic factors (BDNF), macrophage inflammatory proteins (MIP-1 α) and soluble vascular cell adhesion molecule 1 (sVCAM-1) predicted histological remission. Mucosal biopsy cultures also identified sVCAM-1 associated with healed mucosa. RNA-seq analysis identified gene expressions shared between ECSS, RHI, or NHI defined healing. A number of gene expressions and pathways were identified including inflammation and metabolic and tumor suppressors that discriminated healed from nonhealed mucosa. CONCLUSIONS: Endocytoscopy represents an interesting tool that may sit between endoscopy and histology-but closer to the latter-identifying gene expression markers and pathways that are also identified by histology.

An International Multicenter Real-Life Prospective Study of Electronic Chromoendoscopy Score PICaSSO in Ulcerative Colitis.

BACKGROUND & AIMS: Endoscopic and histologic remission are important goals in the treatment of ulcerative colitis (UC). We investigated the correlation of the recently developed Paddington International Virtual ChromoendoScopy ScOre (PICaSSO) and other established endoscopic scores against multiple histological indices and prospectively assessed outcomes. METHODS: In this prospective multicenter international study, inflammatory activity was assessed with high-definition and virtual chromoendoscopy in the rectum and sigmoid using the Mayo Endoscopic Score (MES), UC Endoscopic Index of Severity (UCEIS), and PICaSSO. Targeted biopsies were taken for assessment using Robarts Histological Index (RHI), Nancy Histological index (NHI), ECAP (Extent, Chronicity, Activity, Plus score), Geboes, and Villanacci. Follow-up data were obtained at 6 and 12 months after colonoscopy. RESULTS: A total of 307 patients were recruited. There was strong correlation between PICaSSO and histology scores, significantly superior to correlation coefficients of MES and UCEIS with histology scores. A PICaSSO score of ≤3 detected histologic remission by RHI (≤3 + absence of neutrophils) with area under the receiver operating characteristic curve (AUROC) 0.90 (95% confidence interval [CI] 0.86-0.94) and NHI (≤1) AUROC 0.82 (95% CI 0.77-0.87). The interobserver agreement for PICaSSO was 0.88 (95% CI 0.83-0.92). At 6- and 12-months follow-up, PICaSSO score ≤3 predicted better outcomes than PICaSSO >3 (hazard ratio [HR] 0.19 [0.11-0.33] and 0.22 [0.13-0.34], respectively),} as well as PICaSSO 4-8 (HR 0.25 [0.12-0.53] and 0.22 (0.12-0.39), respectively) and similar to histologic remission. CONCLUSION: In this first real-life multicenter study, the PICaSSO score correlated strongly with multiple histological indices. Furthermore, PICaSSO score predicted specified clinical outcomes at 6 and 12 months, similar to histology. Thus, PICaSSO can be a useful endoscopic tool in the therapeutic management of UC.

Integration of Metabolomic and Clinical Data Improves the Prediction of Intensive Care Unit Length of Stay Following Major Traumatic Injury.

Recent advances in emergency medicine and the co-ordinated delivery of trauma care mean more critically-injured patients now reach the hospital alive and survive life-saving operations. Indeed, between 2008 and 2017, the odds of surviving a major traumatic injury in the UK increased by nineteen percent. However, the improved survival rates of severely-injured patients have placed an increased burden on the healthcare system, with major trauma a common cause of intensive care unit (ICU) admissions that last ≥10 days. Improved understanding of the factors influencing patient outcomes is now urgently needed. We investigated the serum metabolomic profile of fifty-five major trauma patients across three post-injury phases: acute (days 0-4), intermediate (days 5-14) and late (days 15-112). Using ICU length of stay (LOS) as a clinical outcome, we aimed to determine whether the serum metabolome measured at days 0-4 post-injury for patients with an extended (≥10 days) ICU LOS differed from that of patients with a short (<10 days) ICU LOS. In addition, we investigated whether combining metabolomic profiles with clinical scoring systems would generate a variable that would identify patients with an extended ICU LOS with a greater degree of accuracy than models built on either variable alone. The number of metabolites unique to and shared across each time segment varied across acute, intermediate and late segments. A one-way ANOVA revealed the most variation in metabolite levels across the different time-points was for the metabolites lactate, glucose, anserine and 3-hydroxybutyrate. A total of eleven features were selected to differentiate between <10 days ICU LOS vs. >10 days ICU LOS. New Injury Severity Score (NISS), testosterone, and the metabolites cadaverine, urea, isoleucine, acetoacetate, dimethyl sulfone, syringate, creatinine, xylitol, and acetone form the integrated biomarker set. Using metabolic enrichment analysis, we found valine, leucine and isoleucine biosynthesis, glutathione metabolism, and glycine, serine and threonine metabolism were the top three pathways differentiating ICU LOS with a p < 0.05. A combined model of NISS and testosterone and all nine selected metabolites achieved an AUROC of 0.824. Differences exist in the serum metabolome of major trauma patients who subsequently experience a short or prolonged ICU LOS in the acute post-injury setting. Combining metabolomic data with anatomical scoring systems allowed us to discriminate between these two groups with a greater degree of accuracy than that of either variable alone.

Fecal Calprotectin Thresholds to Predict Endoscopic Remission Using Advanced Optical Enhancement Techniques and Histological Remission in IBD Patients.

BACKGROUND: Fecal calprotectin (FC) is a common surrogate marker of mucosal healing (MH) in patients with ulcerative colitis (UC) and Crohn's disease (CD). We investigated the optimum FC thresholds for defining endoscopic remission (ER) and histological remission (HR) using advanced endoscopic techniques. PATIENTS AND METHODS: In this cross-sectional study, we collected clinical, endoscopic, histological data, and FC from 76 UC and 41 CD patients. Receiver operating characteristic curves were created to evaluate the optimum cut-off of FC to predict ER evaluated by Mayo Endoscopic Score (MES), Ulcerative Colitis Endoscopic Index of Severity (UCEIS), and modified PICaSSO (Paddington International Virtual Chromoendoscopy Score) for UC patients and Simple Endoscopic Score (SES-CD) in CD patients; and HR was scored by the Robarts Histology Index (RHI) and Nancy Index for UC and modified Riley for CD. RESULTS: In UC patients, the best thresholds of FC to identify ER calculated with MES, UCEIS, and modified PICaSSO were 112, 148, and 161 mcg/g with accuracy of 86.9% 86.8%, and 81.6%, respectively. The best value of FC to predict HR was 112 mcg/g and 172 mcg/g with accuracy of 84.2% and 81.6% for RHI and Nancy Index, respectively.In CD patients, the best cut-off of FC to predict ER was 96 mcg/g with accuracy of 82.9%. The HR was best predicted by an FC value of 225 mcg/g with accuracy of 75.6%. CONCLUSIONS: The FC value threshold between 112 and 172 mcg/g could identify ER and HR in UC patients, whereas a value under 225 mcg/g should be considered for CD patients.

A predictable conserved DNA base composition signature defines human core DNA replication origins.

DNA replication initiates from multiple genomic locations called replication origins. In metazoa, DNA sequence elements involved in origin specification remain elusive. Here, we examine pluripotent, primary, differentiating, and immortalized human cells, and demonstrate that a class of origins, termed core origins, is shared by different cell types and host ~80% of all DNA replication initiation events in any cell population. We detect a shared G-rich DNA sequence signature that coincides with most core origins in both human and mouse genomes. Transcription and G-rich elements can independently associate with replication origin activity. Computational algorithms show that core origins can be predicted, based solely on DNA sequence patterns but not on consensus motifs. Our results demonstrate that, despite an attributed stochasticity, core origins are chosen from a limited pool of genomic regions. Immortalization through oncogenic gene expression, but not normal cellular differentiation, results in increased stochastic firing from heterochromatin and decreased origin density at TAD borders.

Assessment of Endoscopic Healing by Using Advanced Technologies Reflects Histological Healing in Ulcerative Colitis.

BACKGROUND: Several studies have reported that ulcerative colitis [UC] patients with endoscopic mucosal healing may still have histological inflammation. We investigated the relationship between mucosal healing defined by modified PICaSSO [Paddington International Virtual ChromoendoScopy ScOre], Mayo Endoscopic Score [MES] and probe-based confocal laser endomicroscopy [pCLE] with histological indices in UC. METHODS: A prospective study enrolling 82 UC patients [male 66%] was conducted. High-definition colonoscopy was performed to evaluate the activity of the disease with MES assessed with High-Definition MES [HD-MES] and modified PICaSSO and targeted biopsies were taken; pCLE was then performed. Receiver operating characteristic [ROC] curves were plotted to determine the best thresholds for modified PICaSSO and pCLE scores that predicted histological healing according to the Robarts Histopathology Index [RHI] and ECAP 'Extension, Chronicity, Activity, Plus' histology score. RESULTS: A modified PICaSSO of ≤ 4 predicted histological healing at RHI ≤ 3, with sensitivity, specificity, accuracy and area under the ROC curve [AUROC] of 89.8%, 95.7%, 91.5% and 95.9% respectively. The sensitivity, specificity, accuracy and AUROC of HD-MES to predict histological healing by RHI were 81.4%, 95.7%, 85.4% and 92.1%, respectively. A pCLE ≤ 10 predicted histological healing with sensitivity of 94.9%, specificity of 91.3%, accuracy of 93.9% and AUROC of 96.5%. An ECAP of ≤ 10 was predicted by modified PICaSSO ≤ 4 with accuracy of 91.5% and AUROC of 95.9%. CONCLUSION: Histological healing by RHI and ECAP is accurately predicted by HD-MES and modified virtual electronic chromoendoscopy PICaSSO, endoscopic score; and the use of pCLE did not improve the accuracy any further.

Clinical outcomes, predictors of prognosis and health economics consequences in IBD patients after discontinuation of the first biological therapy.

BACKGROUND: In real-world clinical practice, biologics in inflammatory bowel diseases (IBD) may be discontinued for a variety of reasons, including discontinuation initiated by gastroenterologists. The aims of the study are to report outcomes after discontinuation and predictors of prognosis after a minimum follow-up of 24 months; outcomes of gastroenterologist-initiated discontinuation with resulting direct cost implications on the health system were also studied. METHODS: IBD patients who discontinued their first-use biologics between January 2013 and December 2016 were identified at our tertiary centre. Reasons for discontinuation and pre-defined adverse outcomes (AO) were recorded. Data were analysed using univariable and multivariable logistic regressions within a machine learning technique to predict AO. Gastroenterologist-initiated discontinuations were analysed separately, and Kaplan-Meier survival analysis performed; direct costs of AO due to discontinuation were assessed. RESULTS: A total of 147 patients discontinued biologics (M = 74; median age 39 years; Crohn's Disease = 110) with median follow-up of 40 months (range 24-60 months). In the total cohort, there were fewer AO among gastroenterologist-initiated discontinuations compared with patient-initiated; 54% (of the total group) had AO within 6 months. Among 59 gastroenterologist-initiated discontinuations, 23 (40%) had IBD-related AO within 6 months and 53 (90%) patients had AO by end of follow-up. Some 44 (75%) patients needed to restart biologics during follow-up, and direct costs due to AO and restart of biologics were high. CONCLUSIONS: The proportion of patients who have AO following discontinuation of biologics is high; clinicians need to carefully consider predictors of poor prognosis and high relapse rates when discussing discontinuation. The direct costs of managing AO probably offset theoretical economic gains, especially in the era where cost of biologics is reducing. Biologics should probably be continued without interruptions in most patients who have achieved remission for the duration these remain effective and safe.

In Situ Metabolomics Expands the Spectrum of Renal Tumours Positive on 99mTc-sestamibi Single Photon Emission Computed Tomography/Computed Tomography Examination.

BACKGROUND: Definite noninvasive characterisation of renal tumours positive on 99mTc-sestamibi single photon emission computed tomography/computed tomography (SPECT/CT) examination including renal oncocytomas (ROs), hybrid oncocytic chromophobe tumours (HOCTs), and chromophobe renal cell carcinoma (chRCC) is currently not feasible. OBJECTIVE: To investigate whether combined 99mTc-sestamibi SPECT/CT and in situ metabolomic profiling can accurately characterise renal tumours exhibiting 99mTc-sestamibi uptake. DESIGN SETTING AND PARTICIPANTS: A tissue microarray analysis of 33 tumour samples from 28 patients was used to investigate whether their in situ metabolomic status correlates with their features on 99mTc-sestamibi SPECT/CT examination. In order to validate emerging data, an independent cohort comprising 117 tumours was subjected to matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI MSI). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: MALDI MSI data analysis and image generation were facilitated by FlexImaging v. 4.2, while k-means analysis by SCiLS Lab software followed by R-package CARRoT analysis was used for assessing the highest predictive power in the differential of RO versus chRCC. Heatmap-based clustering, sparse partial least-squares discriminant analysis, and volcano plots were created with MetaboAnalyst 3.0. RESULTS AND LIMITATIONS: We identified a discriminatory metabolomic signature for 99mTc-sestamibi SPECT/CT-positive Birt-Hogg-Dubè-associated HOCTs versus other renal oncocytic tumours. Metabolomic differences were also evident between 99mTc-sestamibi-positive and 99mTc-sestamibi-negative chRCCs, prompting additional expert review; two of three 99mTc-sestamibi-positive chRCCs were reclassified as low-grade oncocytic tumours (LOTs). Differences were identified between distal-derived tumours from those of proximal tubule origin, including differences between ROs and chRCCs. CONCLUSIONS: The current study expands the spectrum of 99mTc-sestamibi SPECT/CT-positive renal tumours, encompassing ROs, HOCTs, LOTs, and chRCCs, and supports the feasibility of in situ metabolomic profiling in the diagnostics and classification of renal tumours. PATIENT SUMMARY: For preoperative evaluation of solid renal tumours, 99mTc-sestamibi single photon emission computed tomography/computed tomography (SPECT/CT) is a novel examination method. To increase diagnostic accuracy, we propose that 99mTc-sestamibi-positive renal tumours should be biopsied and followed by a combined histometabolomic analysis.

A multicentre development and validation study of a novel lower gastrointestinal bleeding score-The Birmingham Score.

PURPOSE: Lower gastrointestinal bleeding (LGIB) is common and risk stratification scores can guide clinical decision-making. There is no robust risk stratification tool specific for LGIB, with existing tools not routinely adopted. We aimed to develop and validate a risk stratification tool for LGIB. METHODS: Retrospective review of LGIB admissions to three centres between 2010 and 2018 formed the derivation cohort. Using regressional analysis within a machine learning technique, risk factors for adverse outcomes were identified, forming a simple risk stratification score-The Birmingham Score. Retrospective review of an additional centre, not included in the derivation cohort, was performed to validate the score. RESULTS: Data from 469 patients were included in the derivation cohort and 180 in the validation cohort. Admission haemoglobin OR 1.07(95% CI 1.06-1.08) and male gender OR 2.29(95% CI 1.40-3.77) predicted adverse outcomes in the derivation cohort AUC 0.86(95% CI 0.82-0.90) which outperformed the Blatchford 0.81(95% CI 0.77-0.85), Rockall 0.60(95% CI 0.55-0.65) and AIM65 0.55(0.50-0.60) scores and in the validation cohort AUC 0.80(95% CI 0.73-0.87) which outperformed the Blatchford 0.77(95% CI 0.70-0.85), Rockall 0.67(95% CI 0.59-0.75) and AIM 65 scores 0.61(95% CI 0.53-0.69). The Birmingham Score also performs well at predicting adverse outcomes from diverticular bleeding AUC 0.87 (95% CI 0.75-0.98). A score of 7 predicts a 94% probability of adverse outcome. CONCLUSION: The Birmingham Score represents a simple risk stratification score that can be used promptly on patients admitted with LGIB.

Performance measures in inflammatory bowel disease surveillance colonoscopy: Implementing changes to practice improves performance.

BACKGROUND AND AIM: Dye-based chromoendoscopy (DCE) with targeted biopsies is recommended for inflammatory bowel disease (IBD) surveillance. However, DCE has not yet been widely adopted into clinical practice. We evaluated quality indicators in IBD surveillance following introduction of structured changes in service delivery. METHODS: In August 2016, we introduced a number of changes to IBD surveillance practice in our endoscopy unit. These included training using interactive videos/images in a structured module, DCE as standard by using a foot-pedal operated pump jet, allocation of 45-minute procedure timeslots, targeted biopsies (except in high-risk patients), scoring of endoscopic disease activity, and lesion detection/morphology characterization. All IBD surveillance colonoscopies were allocated to a small team of four DCE-trained endoscopists. We compared quality measures for surveillance procedures carried out pre- and post-August 2016. The two groups were compared using chi-squared statistics RESULTS: A total of 598 IBD surveillance procedures (277 pre-August 2016 and 321 post-August 2016) were done and included in the study. Use of DCE increased (54.2% vs 76.0% P < 0.0005) whereas random biopsy surveillance decreased (12.3% vs 3.1% P < 0.0005). Use of Paris classification (26.1% vs 57.0% P < 0.0005) and Kudo pit pattern increased (21.7% vs 59.0% P < 0.0005). There was also an increase in lesion detection rate (24.9% vs 33.1% P < 0.05). CONCLUSIONS: Implementation of extensive changes in practice of surveillance colonoscopy resulted in significant improvement in quality indicators within a short period of time. Training, education and audit may continue to facilitate the adoption of DCE and further improve quality of performance in IBD surveillance.