Renin-angiotensin system inhibitor is associated with the reduced risk of all-cause mortality in COVID-19 among patients with/without hypertension / 医学前沿

Consecutively hospitalized patients with confirmed coronavirus disease 2019 (COVID-19) in Wuhan, China were retrospectively enrolled from January 2020 to March 2020 to investigate the association between the use of renin-angiotensin system inhibitor (RAS-I) and the outcome of this disease. Associations between the use of RAS-I (angiotensin-converting enzyme inhibitor (ACEI) or angiotensin receptor blocker (ARB)), ACEI, and ARB and in-hospital mortality were analyzed using multivariate Cox proportional hazards regression models in overall and subgroup of hypertension status. A total of 2771 patients with COVID-19 were included, with moderate and severe cases accounting for 45.0% and 36.5%, respectively. A total of 195 (7.0%) patients died. RAS-I (hazard ratio (HR)= 0.499, 95% confidence interval (CI) 0.325-0.767) and ARB (HR = 0.410, 95% CI 0.240-0.700) use was associated with a reduced risk of all-cause mortality among patients with COVID-19. For patients with hypertension, RAS-I and ARB applications were also associated with a reduced risk of mortality with HR of 0.352 (95% CI 0.162-0.764) and 0.279 (95% CI 0.115-0.677), respectively. RAS-I exhibited protective effects on the survival outcome of COVID-19. ARB use was associated with a reduced risk of all-cause mortality among patients with COVID-19.

Interpreting report of urinalysis based on intelligent data and machine learning / 中华检验医学杂志

Objective:To establish an interpretive reporting system for urinalysis based on artificial intelligence (AI).Methods:Urine tests were collected from the First Affiliated Hospital, College of Medicine, Zhejiang University from 2008 to 2018, including 2 899 917 patient tests and 710 971 physical check-up tests. Then we set up a large population distribution with the frequency of different results of each item and established a health index of each sample and an abnormal level of each item according to data distribution, importance and degree of abnormality. We collected data of seven diseases, such as diabetes mellitus, urinary tract infection, glomerulonephritis and nephrotic syndrome, and matched them with a same number of healthy control group by gender and age. An integrated learner based on the AdaBoost algorithm was used to establish a diagnostic model and assess its algorithm performance. JAVA was used to develop data presentation software. The accuracy of the AI model for disease judgment was assessed by manual verification using 199 abnormal urine tests.Results:Each report could be graded as four levels: normal, abnormal, ill and critical. Each item could be judged as normal, mild, moderate, severe or extreme and the population distribution was provided with big data. The training accuracy, true positive rate and area under the curve were ≥88.3%, ≥80.0%, and ≥0.954 respectively using the machine learning model based on AdaBoost. The developed JAVA software presented the above results and displayed medical records and results, historical results, personalized advice, patient education and position in large population data. By manual verification, the accuracy rate of the AI model for disease judgment was 82.41% (166/199).Conclusion:This study established an intelligent interpretive reporting system for urine test results. It can distinguish the abnormality of each report, predict the disease of patients, and make personalized clinical decisions.