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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. [ABSTRACT FROM AUTHOR]

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., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

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.

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., (Copyright © 2020 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved.)