Faculty Bibliography

BACKGROUND:Ischemic/anoxic brain injury is often assumed to occur within minutes of severe cerebral ischemia. However, emerging evidence suggests brain tissue may be more resilient, with important implications for resuscitation. We hypothesized that during prolonged cardiac arrest, cortical electrical activity may be restorable if cerebral oxygenation thresholds are met and may be associated with return of spontaneous circulation (ROSC). METHODS:) during cardiopulmonary resuscitation (CPR). RESULTS:≥16%, and alpha at >40%. Alpha activity was seen up to 35 min, and delta/theta up to 60 min into CPR. Suppression reverted to near-normal in 12% of transitions. Alpha activity was associated with ROSC (OR 5.4; 95% CI 1.08-29.20; p = 0.045), while suppression predicted lower ROSC odds (OR 0.12; 95% CI 0.02-0.53; p = 0.002). Survival analysis was limited by small sample size. CONCLUSION/CONCLUSIONS:Near-physiologic brain activity may be restored during prolonged CPR if oxygenation thresholds are met and is associated with ROSC. Further research is needed to evaluate survival outcomes.

INTRODUCTION: Colorectal cancer (CRC) remains a leading cause of cancer-related death in the United States. While colonoscopy based screening is the most effective of all CRC prevention strategies, it is operator dependent and can result in missed lesions, which may contribute to interval cancer. Incorporation of artificial intelligence (AI) to routine colonoscopy has been investigated in several small studies. In this meta-analysis, we attempt to collate evidence from recent randomized controlled trials (RCTs) to further define the role of AI in colonoscopy based CRC screening.
METHOD(S): A comprehensive search of MEDLINE, EMBASE, CENTRAL, and ClinicalTrials.gov from inception through May 2020 was completed. Pooled statistics using bivariate random-effects, odds ratio for binary outcomes, and standardized difference in means for continuous outcomes were used. Primary outcomes were Adenoma Detection Rate (ADR) and Polyp Detection Rate (PDR). Secondary outcomes were mean adenomas and polyps/procedure, withdrawal (WT) and cecal intubation times (CIT), and adequacy of bowel preparation. Post-hoc sensitivity analysis and subgroup analyses for adenomas and polyps was completed.
RESULT(S): 6 RCTs were included. Of 4996 patients, 2487 had AI-assisted, and 2509 had routine colonoscopies. Mean age was 51.99 +/- 4.43 years, 51% of males. AI had higher ADRs (P = 0.00) and PDRs (P = 0.00) [Figure 1]. Similar findings were noted for mean number of adenomas and polyps/procedure. Mean WTs favored AI when biopsy times were included. CIT and adequacy of bowel preparation were similar in both groups [Figure 2]. On subgroup analysis, AI systems had significantly better ADRs and PDRs in the transverse colon. AI had higher detection rates for adenomas < 5 mm (P = 0.00) and polyps < 10 mm (P = 0.00). However, routine colonoscopies outperformed AI in detecting pedunculated polyps (P = 0.00). PDRs had no differences based on shape. Sensitivity analysis remained unchanged for ADR, PDR, mean adenomas or polyps detected per procedure and WTs that included biopsies. Begg's funnel plots were relatively symmetrical for ADR (P - 0.25) and PDR (P - 0.20) [Figure 3].
CONCLUSION(S): The use of AI has the potential to improve the sensitivity of colonoscopy based screening. Colonoscopies using AI algorithms demonstrated significantly improved detection rates for adenomas and polyps. However, research and advancements are needed to refine the AI systems to detect pedunculated polyps and incorporate optical diagnosis into the algorithms