COVID-19 Positive Group Research Articles

The Clinical Supervision of COVID-19 Infection: The Italian Experience of Home Care Unit in Lombardy Region

From March to June 2020 the Home Care Unit of Paxme Group evaluated and monitored 245 subjects COVID-19 positive or clinical suspected living in Lombardy Region. There were 101 males, 144 females, with mean age 59.1 years. The females had lower mean age than males (57.4 vs 61.9). In 67 cases (27%) the subjects were discharged from hospitals, the other 178 cases (73%) stay at home with no clinical history of hospitalization. The COVID-19 positive group had 35 males (52%) and 32 females (48%), in the clinical suspected 67 males (38%) and 111 females (62%). There were 2 deaths (1%) and 11 hospitalizations (5%). The 11 hospitalized subjects had a higher mean age (72 years), a higher presence of clinical suspected (72%) and a higher presence of females (72%). The 2 deaths were only in clinical suspected with mean age 87 ys. Older subjects (>65 years) had a higher presence of hospitalization and death. Our study suggests that the age and gender are important in the clinical evolution of COVID-19 infection. The Home Care supervision was an adequate answer to the public health needs, but for the future home healthcare should increase.

Higher frequency of hepatic steatosis at CT among COVID-19-positive patients.

PurposeRecent studies have demonstrated that obesity is significantly associated with increased disease severity, hospitalizations and mortality in COVID-19, with a potential role in the pathogenesis and prevalence in the new pandemic. The association with hepatic steatosis, however, a condition closely related to obesity within the spectrum of systemic metabolic dysfunctions, remains to be elucidated. We aimed to evaluate the frequency of hepatic steatosis as incidentally detected in chest CT examinations of COVID-19 positive patients in comparison to non-infected controls.MethodsA retrospective study was performed with 316 patients (204 RT-PCR positive; 112 RT-PCR negative and chest CT negative). Steatosis was measured with placement of a single ROI in the right lobe of the liver (segments VI-VII) and defined as a liver attenuation value ≤ 40 HU.ResultsThe frequency of hepatic steatosis was higher in the RT-PCR positive group in comparison to controls (31.9% vs. 7.1%, p < 0.001). Logistic linear regression analysis showed a 4.7 times odds of steatosis in the COVID-19 positive group as compared to controls after adjusting for age and sex (OR 4.698; 95% IC 2.12–10.41, p < 0.001).ConclusionA significantly higher prevalence of steatosis was found among COVID-19 positive individuals. These findings are in accordance with other recent studies linking obesity and COVID-19 infection, as there is an intricate relationship between liver steatosis, metabolic syndrome and obesity. Further studies are required to confirm if such association remains after accounting for multiple variables, as well as possible relationships with disease severity and worst clinical outcomes.

Stepwise Diagnosis and Dynamic Change&nbsp;Prediction AI System for COVID-19

Background: As the COVID-19 pandemic continues to spread worldwide, there is still no accurate rapid diagnostic test for COVID-19, or monitoring tool for patient’s clinical course. An artificial-intelligence system (CoviDet) was therefore developed and applied on chest computed tomography (CT), to evaluate its application for rapid diagnosis and potential monitoring of COVID-19 patients. Methods: 1,201,074 CT slices from 2527 patients were grouped into 3 main groups: COVID-19 positive group, non-COVID-19 viral pneumonia group and control group. They were used to train and validate CoviDet novel stepwise diagnostic algorithm to diagnose COVID-19, with or without clinical data. A subset of COVID-19 patients with more than 3 consecutive CT images were selected for training and validation of the auto-segmentation and monitoring algorithm. Findings: CoviDet outperforms radiologists, and can diagnose COVID-19 based on CT alone with sensitivity of 0.93, specificity of 0.95 and AUC of 0.98 (95%CI 0.97-0.99; P<0.001). Its auto-segmentation analyses co-related well with those by radiologists, with a Dice's coefficient of 0.77. Its can generate a predictive curve of patient’s clinical course if serial CT assessments are available. Interpretation: CoviDet is a useful aid for radiologist to diagnose COVID-19 rapidly and accurately based on CT, with or without clinical data. It can produce an objective predictive curve of patient’s clinical course for easy visualisation. In principle, CoviDet could be used on a web-based platform with built auto masking of privacy data, whereby clinicians can upload anonymised CT for COVID-19 analysis without sensitive clinical information, from anywhere in the world. Funding Statement: This study is supported by High-level university construction project of Guangzhou medical university (Grant No. 20182737, 201721007, 201715907, 2017160107); the Guangdong high level hospital construction reaching peak plan. Declaration of Interests: Qiang Du is the Founder and CEO of Beijing XiaoBaiShiJi Network Technical Co. Ltd. All other authors declare no competing interests. Ethics Approval Statement: This study is approved by the ethics committee of the First Affiliated Hospital of Guangzhou Medical University.