Impact of direct-access IBD physician delivered endoscopy on clinical outcomes: a pre-implementation and post-implementation study.

Introduction: Patients with suspected inflammatory bowel disease (IBD) referred from primary care often face diagnostic and treatment delays. This study aimed to compare a novel direct-access IBD endoscopy pathway with the traditional care model. Method: Single centre real-world study analysing primary care referrals with suspected IBD. Group A: patients triaged to direct-access IBD endoscopy. Group B: patients undergoing traditional outpatient appointments before the availability of direct-access IBD endoscopy. Demographics, fecal calprotectin (FCP), C-reactive protein (CRP), disease activity score, endoscopy findings, treatment and follow-up were collected and statistically analysed. Ranked semantic analysis of IBD symptoms contained within referral letters was performed. Results: Referral letters did not differ significantly in Groups A and B. Demographic data, FCP and CRP values were similar. Referral to treatment time (RTT) at the time of IBD endoscopy was reduced from 177 days (Group B) to 24 days (Group A) (p<0.0001). Diagnostic yield of IBD was 35.6% (Group B) versus 62.0% (Group A) (p=0.0003). 89.2% of patients underwent colonoscopy in Group B versus 46.4% in Group A. DNA rates were similar in both groups. The direct to IBD endoscopy pathway saved 100% of initial IBD consultant clinics with a 2.5-fold increase in IBD nurse-led follow-up. Conclusion: Our novel pathway resulted in an 86% reduction in RTT with associated increased diagnostic yield while saving 100% of initial IBD consultant outpatient appointments. Replication in other trusts may improve patient experience and accelerate time to diagnosis/treatment while optimising the use of healthcare resources.

Conditional inference tree models to perceive depth of invasion in T1 colorectal cancer.

BACKGROUND AND AIM: Accurate diagnosis of invasion depth for T1 colorectal cancer is of critical importance as it decides optimal resection technique. Few reports have previously covered the effects of endoscopic morphology on depth assessment. We developed and validated a novel diagnostic algorithm that accurately predicts the depth of early colorectal cancer. METHODS: We examined large pathological and endoscopic databases compiled between Jan 2015 and Dec 2018. Training and validation data cohorts were derived and real-world diagnostic performance of two conditional interference tree algorithms (Models 1 and 2) was evaluated against that of the Japan NBI-Expert Team (JNET) classification used by both expert and non-expert endoscopists. RESULTS: Model 1 had higher sensitivity in deep submucosal invasion than that of JNET alone in both training (45.1% vs. 28.6%, p < 0.01) and validation sets (52.3% vs. 40.0%, p < 0.01). Model 2 demonstrated higher sensitivity than Model 1 (66.2% vs. 52.3%, p < 0.01) in excluding deeper invasion of suspected Tis/T1a lesions. CONCLUSION: We discovered that machine-learning classifiers, including JNET and macroscopic features, provide the best non-invasive screen to exclude deeper invasion for suspected Tis/T1 lesions. Adding this algorithm improves depth diagnosis of T1 colorectal lesions for both expert and non-expert endoscopists.