Evaluating the effect of an artificial intelligence system on the anesthesia quality control during gastrointestinal endoscopy with sedation: a randomized controlled trial.

BACKGROUND: Sedative gastrointestinal endoscopy is extensively used worldwide. An appropriate degree of sedation leads to more acceptability and satisfaction. Artificial intelligence has rapidly developed in the field of digestive endoscopy in recent years and we have constructed a mature computer-aided diagnosis (CAD) system. This system can identify the remaining parts to be examined in real-time endoscopic procedures, which may help anesthetists use anesthetics properly to keep patients in an appropriate degree of sedation. AIMS: This study aimed to evaluate the effects of the CAD system on anesthesia quality control during gastrointestinal endoscopy. METHODS: We recruited 154 consecutive patients at Renmin Hospital of Wuhan University, including 76 patients in the CAD group and 78 in the control group. Anesthetists in the CAD group were able to see the CAD system's indications, while anesthetists in the control group could not. The primary outcomes included emergence time (from examination completion to spontaneous eye opening when doctors called the patients' names), recovery time (from examination completion to achievement of the primary recovery endpoints) and patient satisfaction scores. The secondary outcomes included anesthesia induction time (from sedative administration to successful sedation), procedure time (from scope insertion to scope withdrawal), total dose of propofol, vital signs, etc. This trial was registered in the Primary Registries of the WHO Registry Network, with registration number ChiCTR2100042621. RESULTS: Emergence time in the CAD group was significantly shorter than that in the control group (p < 0.01). The recovery time was also significantly shorter in the CAD group (p < 0.01). Patients in the CAD group were significantly more satisfied with their sedation than those in control group (p < 0.01). Vital signs were stable during the examinations in both groups. Propofol doses during the examinations were comparable between the two groups. CONCLUSION: This CAD system possesses great potential for anesthesia quality control. It can improve patient satisfaction during endoscopic examinations with sedation. TRIAL REGISTRATION: ChiCTR2100042621.

Impact of Computer-Assisted System on the Learning Curve and Quality in Esophagogastroduodenoscopy: Randomized Controlled Trial.

Background and Aims: To investigate the impact of the computer-assisted system on esophagogastroduodenoscopy (EGD) training for novice trainees in a prospective randomized controlled trial. Methods: We have constructed a computer-aided system (CAD) using retrospective images based on deep learning which could automatically monitor the 26 anatomical landmarks of the upper digestive tract and document standard photos. Six novice trainees were allocated and grouped into the CAD group and control group. Each of them took the training course, pre and post-test, and EGD examination scored by two experts. The CAD group was trained with the assistance of the CAD system and the control group without. Results: Both groups achieved great improvements in EGD skills. The CAD group received a higher examination grading score in the EGD examination (72.83 ± 16.12 vs. 67.26 ± 15.64, p = 0.039), especially in the mucosa observation (26.40 ± 6.13 vs. 24.11 ± 6.21, p = 0.020) and quality of collected images (7.29 ± 1.09 vs. 6.70 ± 1.05). The CAD showed a lower blind spot rate (2.19 ± 2.28 vs. 3.92 ± 3.30, p = 0.008) compared with the control group. Conclusion: The artificial intelligence assistant system displayed assistant capacity on standard EGD training, and assisted trainees in achieving a learning curve with high operation quality, which has great potential for application. Clinical Trial Registration: This trial is registered at https:/clinicaltrials.gov/, number NCT04682821.

Effect of a deep learning-based system on the miss rate of gastric neoplasms during upper gastrointestinal endoscopy: a single-centre, tandem, randomised controlled trial.

BACKGROUND: White light endoscopy is a pivotal first-line tool for the detection of gastric neoplasms. However, gastric neoplasms can be missed during upper gastrointestinal endoscopy due to the subtle nature of these lesions and varying skill among endoscopists. Here, we aimed to evaluate the effect of an artificial intelligence (AI) system designed to detect focal lesions and diagnose gastric neoplasms on reducing the miss rate of gastric neoplasms in clinical practice. METHODS: This single-centre, randomised controlled, tandem trial was done at Renmin Hospital of Wuhan University, China. We recruited consecutive patients (≥18 years old) undergoing routine upper gastrointestinal endoscopy for screening, surveillance, or investigation of symptoms. Same-day tandem upper gastrointestinal endoscopy was done where patients first underwent either AI-assisted (AI-first) or routine (routine-first) white light endoscopy, followed immediately by the other procedure, with targeted biopsies for all detected lesions taken at the end of the second examination. Patients were randomly assigned (1:1) to the AI-first or routine-first group using a computer-generated random numerical series and block randomisation (block size of four). Endoscopists were not blinded to randomisation status, whereas patients and pathologists were. The primary endpoint was the miss rate of gastric neoplasms and the analysis was done per protocol. This trial is registered with the Chinese Clinical Trial Registry, ChiCTR2000034453, and has been completed. FINDINGS: Between July 6, 2020, and Dec 11, 2020, 907 patients were randomly assigned to the AI-first group and 905 to the routine-first group. The gastric neoplasm miss rate was significantly lower in the AI-first group than in the routine-first group (6·1%, 95% CI 1·6-17·9 [3/49] vs 27·3%, 15·5-43·0 [12/44]; relative risk 0·224, 95% CI 0·068-0·744; p=0·015). The only reported adverse event was bleeding from a target lesion after biopsy. INTERPRETATION: The use of an AI system during upper gastrointestinal endoscopy significantly reduced the gastric neoplasm miss rate. AI-assisted endoscopy has the potential to improve the yield of gastric neoplasms by endoscopists. FUNDING: The Project of Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision and the Hubei Province Major Science and Technology Innovation Project.

Clinical characteristics of COVID-19 complicated with pleural effusion.

BACKGROUND: Epidemiological and clinical features of patients with corona virus disease 2019 (COVID-19) were well delineated. However, no researches described the patients complicated with pleural effusion (PE). In the present study, we aimed to clinically characterize the COVID-19 patients complicated with PE and to create a predictive model on the basis of PE and other clinical features to identify COVID-19 patients who may progress to critical condition. METHODS: This retrospective study examined 476 COVID-19 inpatients, involving 153 patients with PE and 323 without PE. The data on patients' past history, clinical features, physical checkup findings, laboratory results and chest computed tomography (CT) findings were collected and analyzed. LASSO regression analysis was employed to identify risk factors associated with the severity of COVID-19. RESULTS: Laboratory findings showed that patients with PE had higher levels of white blood cells, neutrophils, lactic dehydrogenase, C-reactive protein and D-dimer, and lower levels of lymphocytes, platelets, hemoglobin, partial pressure of oxygen and oxygen saturation. Meanwhile, patients with PE had higher incidence of severe or critical illness and mortality rate, and longer hospital stay time compared to their counterparts without pleural effusion. Moreover, LASSO regression analysis exhibited that pleural effusion, lactic dehydrogenase (LDH), D-dimer and total bilirubin (TBIL) might be risk factors for critical COVID-19. CONCLUSIONS: Pleural effusion could serve as an indicator for severe inflammation and poor clinical outcomes, and might be a complementary risk factor for critical type of COVID-19.

Clinical sequelae of COVID-19 survivors in Wuhan, China: a single-centre longitudinal study.

OBJECTIVES: To describe the prevalence, nature and risk factors for the main clinical sequelae in coronavirus disease 2019 (COVID-19) survivors who have been discharged from the hospital for more than 3 months. METHODS: This longitudinal study was based on a telephone follow-up survey of COVID-19 patients hospitalized and discharged from Renmin Hospital of Wuhan University, Wuhan, China before 1 March 2020. Demographic and clinical characteristics and self-reported clinical sequelae of the survivors were described and analysed. A cohort of volunteers who were free of COVID-19 and lived in the urban area of Wuhan during the outbreak were also selected as the comparison group. RESULTS: Among 538 survivors (293, 54.5% female), the median (interquartile range) age was 52.0 (41.0-62.0) years, and the time from discharge from hospital to first follow-up was 97.0 (95.0-102.0) days. Clinical sequelae were common, including general symptoms (n = 267, 49.6%), respiratory symptoms (n = 210, 39%), cardiovascular-related symptoms (n = 70, 13%), psychosocial symptoms (n = 122, 22.7%) and alopecia (n = 154, 28.6%). We found that physical decline/fatigue (p < 0.01), postactivity polypnoea (p= 0.04) and alopecia (p < 0.01) were more common in female than in male subjects. Dyspnoea during hospitalization was associated with subsequent physical decline/fatigue, postactivity polypnoea and resting heart rate increases but not specifically with alopecia. A history of asthma during hospitalization was associated with subsequent postactivity polypnoea sequela. A history of pulse ≥90 bpm during hospitalization was associated with resting heart rate increase in convalescence. The duration of virus shedding after COVID-19 onset and hospital length of stay were longer in survivors with physical decline/fatigue or postactivity polypnoea than in those without. CONCLUSIONS: Clinical sequelae during early COVID-19 convalescence were common; some of these sequelae might be related to gender, age and clinical characteristics during hospitalization.

Prevalence and recovery time of olfactory and gustatory dysfunction in hospitalized patients with COVID­19 in Wuhan, China.

OBJECTIVE: To investigate olfactory and gustatory dysfunction in patients with coronavirus disease 2019 (COVID-19) in Wuhan using a telephone interview. METHODS: This retrospective telephone survey investigated 196 consecutive patients with COVID-19 at 3 months after discharge from two hospitals in Wuhan, China. The characteristics of the patient's disease course and time to recovery from olfactory and/or gustatory dysfunction (OD and/or GD) were collected by telephone interview. Demographic data were collected from the patient medical records. RESULTS: A total of 196 patients with COVID-19 completed the study. The most prevalent general symptoms were fever, cough, and fatigue. Overall, 19.9% of the patients reported OD and/or GD. In 87.2% of these cases, OD or GD appeared after the general symptoms. The time to recovery from OD and/or GD was more than 4 weeks in 51.4% of the patients. Patients with COVID-19 and OD and/or GD had significantly higher rates of cardiovascular disease than patients without OD and/or GD (p = 0.002). CONCLUSIONS: Recovery from chemosensory dysfunction (OD and/or GD) was slow, with over half of the patients taking more than 4 weeks to recover. Cardiovascular disease might be related to the development of olfactory or taste disorders in patients with COVID-19.

Circulating bilirubin level is determined by both erythrocyte amounts and the proportion of aged erythrocytes in ageing and cardiovascular diseases.

BACKGROUND: Bilirubin has been involved in the process of ageing and the pathology of ageing-related diseases. Circulating bilirubin is mainly derived from the clearance of disintegrated erythrocytes in the blood. However, the change of serum bilirubin level and its regulation during ageing and in ageing-related diseases remain to be elucidated. METHODS: A retrospective study was conducted by analyzing the blood cell test results and liver function results of 14,049 healthy research subjects at the Physical Examination Center and 2052 patients with various types of cardiovascular diseases (CVD) at the Department of Cardiology in Renmin Hospital of Wuhan University. Spearman correlation analysis and linear-regression analysis were used for correlation studies. Differences between male and female were investigated. RESULTS: Whereas the erythrocyte counts continuously decreased along with age, the proportion of aged erythrocytes was significantly increased in both male and female. The level of total circulating bilirubin was positively correlated with age and erythrocyte counts. The increase of bilirubin was associated with the increased morphological deviation of erythrocytes during ageing. Compared with health controls, the level of circulating bilirubin in CVD patients was significantly decreased consistent with the decline of erythrocyte counts and hemoglobin. CONCLUSIONS: Ageing may be accompanied by an increased ageing rate of erythrocytes, which contributes to the ageing-related decline of erythrocyte counts. Both erythrocyte counts and the proportion of aged erythrocytes coordinately might determine the circulating level of bilirubin during ageing. In CVD, the decline of circulating bilirubin may be largely attributed to concurrent anemia.

Bidirectional gut-brain-microbiota axis as a potential link between inflammatory bowel disease and ischemic stroke.

Emerging evidence suggests that gut-brain-microbiota axis (GBMAx) may play a pivotal role linking gastrointestinal and neuronal disease. In this review, we summarize the latest advances in studies of GBMAx in inflammatory bowel disease (IBD) and ischemic stroke. A more thorough understanding of the GBMAx could advance our knowledge about the pathophysiology of IBD and ischemic stroke and help to identify novel therapeutic targets via modulation of the GBMAx.