Sex and ethnic disparities in hepatitis B evaluation and treatment across the world.

BACKGROUND & AIMS: Oral antiviral therapy with nucleos(t)ide analogues (NAs) for chronic hepatitis B (CHB) is well-tolerated and lifesaving, but real-world data on utilization are limited. We examined rates of evaluation and treatment in patients from the REAL-B consortium. METHODS: This was a cross-sectional study nested within our retrospective multinational clinical consortium (2000-2021). We determined the proportions of patients receiving adequate evaluation, meeting AASLD treatment criteria, and initiating treatment at any time during the study period. We also identified factors associated with receiving adequate evaluation and treatment using multivariable logistic regression analyses. RESULTS: We analyzed 12,566 adult treatment-naïve patients with CHB from 25 centers in 9 countries (mean age 47.1 years, 41.7% female, 96.1% Asian, 49.6% Western region, 8.7% cirrhosis). Overall, 73.3% (9,206 patients) received adequate evaluation. Among the adequately evaluated, 32.6% (3,001 patients) were treatment eligible by AASLD criteria, 83.3% (2,500 patients) of whom were initiated on NAs, with consistent findings in analyses using EASL criteria. On multivariable logistic regression adjusting for age, sex, cirrhosis, and ethnicity plus region, female sex was associated with adequate evaluation (adjusted odds ratio [aOR] 1.13, p = 0.004), but female treatment-eligible patients were about 50% less likely to initiate NAs (aOR 0.54, p <0.001). Additionally, the lowest evaluation and treatment rates were among Asian patients from the West, but no difference was observed between non-Asian patients and Asian patients from the East. Asian patients from the West (vs. East) were about 40-50% less likely to undergo adequate evaluation (aOR 0.60) and initiate NAs (aOR 0.54) (both p <0.001). CONCLUSIONS: Evaluation and treatment rates were suboptimal for patients with CHB in both the East and West, with significant sex and ethnic disparities. Improved linkage to care with linguistically competent and culturally sensitive approaches is needed. IMPACT AND IMPLICATIONS: Significant sex and ethnic disparities exist in hepatitis B evaluation and treatment, with female treatment-eligible patients about 50% less likely to receive antiviral treatment and Asian patients from Western regions also about 50% less likely to receive adequate evaluation or treatment compared to Asians from the East (there was no significant difference between Asian patients from the East and non-Asian patients). Improved linkage to care with linguistically competent and culturally sensitive approaches is needed.

Artificial intelligence-assisted real-time monitoring of effective withdrawal time during colonoscopy: a novel quality marker of colonoscopy.

BACKGROUND AND AIMS: The importance of withdrawal time during colonoscopy cannot be overstated in mitigating the risk of missed lesions and postcolonoscopy colorectal cancer. We evaluated a novel colonoscopy quality metric called the effective withdrawal time (EWT), which is an artificial intelligence (AI)-derived quantitative measure of quality withdrawal time, and its association with various colonic lesion detection rates as compared with standard withdrawal time (SWT). METHODS: Three hundred fifty video recordings of colonoscopy withdrawal (from the cecum to the anus) were assessed by the new AI model. The primary outcome was adenoma detection rate (ADR) according to different quintiles of EWT. Multivariate logistic regression, adjusting for baseline covariates, was used to determine the adjusted odd ratios (ORs) for EWT on lesion detection rates, with the lowest quintile as reference. The area under the receiver-operating characteristic curve of EWT was compared with SWT. RESULTS: The crude ADR in different quintiles of EWT, from lowest to highest, was 10.0%, 31.4%, 33.3%, 53.5%, and 85.7%. The ORs of detecting adenomas and polyps were significantly higher in all top 4 quintiles when compared with the lowest quintile. Each minute increase in EWT was associated with a 49% increase in ADR (aOR, 1.49; 95% confidence interval [CI], 1.36-1.65). The area under the receiver-operating characteristic curve of EWT was also significantly higher than SWT on adenoma detection (.80 [95% CI, .75-.84] vs .70 [95% CI, .64-.74], P < .01). CONCLUSIONS: AI-derived monitoring of EWT is a promising novel quality indicator for colonoscopy, which is more associated with ADR than SWT.

Computer-assisted detection versus conventional colonoscopy for proximal colonic lesions: a multicenter, randomized, tandem-colonoscopy study.

BACKGROUND AND AIMS: Computer-assisted detection (CADe) is a promising technologic advance that enhances adenoma detection during colonoscopy. However, the role of CADe in reducing missed colonic lesions is uncertain. The aim of this study was to determine the miss rates of proximal colonic lesions by CADe and conventional colonoscopy. METHODS: This was a prospective, multicenter, randomized, tandem-colonoscopy study conducted in 3 Asian centers. Patients were randomized to receive CADe or conventional white-light colonoscopy during the first withdrawal of the proximal colon (cecum to splenic flexure), immediately followed by tandem examination of the proximal colon with white light in both groups. The primary outcome was adenoma/polyp miss rate, which was defined as any adenoma/polyp detected during the second examination. RESULTS: Of 223 patients (48.6% men; median age, 63 years) enrolled, 7 patients did not have tandem examination, leaving 108 patients in each group. There was no difference in the miss rate for proximal adenomas (CADe vs conventional: 20.0% vs 14.0%, P = .07) and polyps (26.7% vs 19.6%, P = .06). The CADe group, however, had significantly higher proximal polyp (58.0% vs 46.7%, P = .03) and adenoma (44.7% vs 34.6%, P = .04) detection rates than the conventional group. The mean number of proximal polyps and adenomas detected per patient during the first examination was also significantly higher in the CADe group (polyp: 1.20 vs .86, P = .03; adenoma, .91 vs .61, P = .03). Subgroup analysis showed that CADe enhanced proximal adenoma detection in patients with fair bowel preparation, shorter withdrawal time, and endoscopists with lower adenoma detection rate. CONCLUSIONS: This multicenter trial from Asia confirmed that CADe can further enhance proximal adenoma and polyp detection but may not be able to reduce the number of missed proximal colonic lesions. (Clinical trial registration number: NCT04294355.).

New insights on missed colonic lesions during colonoscopy through artificial intelligence-assisted real-time detection (with video).

BACKGROUND AND AIMS: Meta-analysis shows that up to 26% of adenomas could be missed during colonoscopy. We investigated whether the use of artificial intelligence (AI)-assisted real-time detection could provide new insights into mechanisms underlying missed lesions during colonoscopy. METHODS: A validated real-time deep-learning AI model for the detection of colonic polyps was first tested in videos of tandem colonoscopy of the proximal colon for missed lesions. The real-time AI model was then prospectively validated in a total colonoscopy in which the endoscopist was blinded to real-time AI findings. Segmental unblinding of the AI findings were provided, and the colonic segment was then re-examined when missed lesions were detected by AI but not the endoscopist. All polyps were removed for histologic examination as the criterion standard. RESULTS: Sixty-five videos of tandem examination of the proximal colon were reviewed by AI. AI detected 79.1% (19/24) of missed proximal adenomas in the video of the first-pass examination. In 52 prospective colonoscopies, real-time AI detection detected at least 1 missed adenoma in 14 patients (26.9%) and increased the total number of adenomas detected by 23.6%. Multivariable analysis showed that a missed adenoma(s) was more likely when there were multiple polyps (adjusted odds ratio, 1.05; 95% confidence interval, 1.02-1.09; P < .0001) or colonoscopy was performed by less-experienced endoscopists (adjusted odds ratio, 1.30; 95% confidence interval, 1.05-1.62; P = .02). CONCLUSIONS: Our findings provide new insights on the prominent role of human factors, including inexperience and distraction, on missed colonic lesions. With the use of real-time AI assistance, up to 80% of missed adenomas could be prevented. (Clinical trial registration number: NCT04227795.).

Linked color imaging versus narrow-band imaging for colorectal polyp detection: a prospective randomized tandem colonoscopy study.

BACKGROUND AND AIMS: Linked color imaging (LCI) is a newly available image-enhanced endoscopy (IEE) system that emphasizes the red mucosal color. No study has yet compared LCI with other available IEE systems. Our aim was to investigate polyp detection rates using LCI compared with narrow-band imaging (NBI). METHODS: This is a prospective randomized tandem colonoscopy study. Eligible patients who underwent colonoscopy for symptoms or screening/surveillance were randomized in a 1:1 ratio to receive tandem colonoscopy with both colonoscope withdrawals using LCI or NBI. The primary outcome was the polyp detection rate. RESULTS: Two hundred seventy-two patients were randomized (mean age, 62 years; 48.2% male; colonoscopy for symptoms, 72.8%) with 136 in each arm. During the first colonoscopy, the polyp detection rate (71.3% vs 55.9%; P = .008), serrated lesion detection rate (34.6% vs 22.1%; P = .02), and mean number of polyps detected (2.04 vs 1.35; P = .02) were significantly higher in the NBI group than in the LCI group. There was also a trend of higher adenoma detection rate in the NBI group compared with the LCI group (51.5% vs 39.7%, respectively; P = .05). Multivariable analysis confirmed that use of NBI (adjusted odds ratio, 1.99; 95% confidence interval, 1.09-3.68) and withdrawal time >8 minutes (adjusted odds ratio, 5.11; 95% confidence interval, 2.79-9.67) were associated with polyp detection. Overall, 20.5% of polyps and 18.1% of adenomas were missed by the first colonoscopy, but there was no significant difference in the miss rates between the 2 groups. CONCLUSION: NBI was significantly better than LCI for colorectal polyp detection. However, both LCI and NBI missed 20.5% of polyps. (Clinical trial registration number: NCT03336359.).

Endoscopic prediction of deeply submucosal invasive carcinoma with use of artificial intelligence.

Background and study aims We evaluated use of artificial intelligence (AI) assisted image classifier in determining the feasibility of curative endoscopic resection of large colonic lesion based on non-magnified endoscopic images Methods AI image classifier was trained by 8,000 endoscopic images of large (≥ 2 cm) colonic lesions. The independent validation set consisted of 567 endoscopic images from 76 colonic lesions. Histology of the resected specimens was used as gold standard. Curative endoscopic resection was defined as histology no more advanced than well-differentiated adenocarcinoma, ≤ 1 mm submucosal invasion and without lymphovascular invasion, whereas non-curative resection was defined as any lesion that could not meet the above requirements. Performance of the trained AI image classifier was compared with that of endoscopists. Results In predicting endoscopic curative resection, AI had an overall accuracy of 85.5 %. Images from narrow band imaging (NBI) had significantly higher accuracy (94.3 % vs 76.0 %; P  < 0.00001) and area under the ROC curve (AUROC) (0.934 vs 0.758; P  = 0.002) than images from white light imaging (WLI). AI was superior to two junior endoscopists in terms of accuracy (85.5 % vs 61.9 % or 82.0 %, P  < 0.05), AUROC (0.837 vs 0.638 or 0.717, P  < 0.05) and confidence level (90.1 % vs 83.7 % or 78.3 %, P  < 0.05). However, there was no statistical difference in accuracy and AUROC between AI and a senior endoscopist. Conclusions The trained AI image classifier based on non-magnified images can accurately predict probability of curative resection of large colonic lesions and is better than junior endoscopists. NBI images have better accuracy than WLI for AI prediction.