Monitoring Of COVID-19 Patients Research Articles

The Role of Internet of Things to Control the Outbreak of COVID-19 Pandemic.

Currently, COVID-19 pandemic is the major cause of disease burden globally. So, there is a need for an urgent solution to fight against this pandemic. Internet of Things (IoT) has the ability of data transmission without human interaction. This technology enables devices to connect in the hospitals and other planned locations to combat this situation. This article provides a road map by highlighting the IoT applications that can help to control it. This study also proposes a real-time identification and monitoring of COVID-19 patients. The proposed framework consists of four components using the cloud architecture: 1) data collection of disease symptoms (using IoT-based devices); 2) health center or quarantine center (data collected using IoT devices); 3) data warehouse (analysis using machine learning models); and 4) health professionals (provide treatment). To predict the severity level of COVID-19 patients on the basis of IoT-based real-time data, we experimented with five machine learning models. The results reveal that random forest outperformed among all other models. IoT applications will help management, health professionals, and patients to investigate the symptoms of contagious disease and manage COVID-19 +ve patients worldwide.

A NEW APPROACH TO MEDICAL DIAGNOSIS OF COVID-19 AND CONTROLLING THE EFFECTS OF COVID-19 ON ARTERIAL BLOOD GAS WORKING

Aim: The purpose of this study is to provide information about the effect of the defense mechanism formed by the lung mucosal surface against Covid-19 and the effect of Covid-19's lung involvement on Arterial Blood Gas Values, whose number of cases and mortality rate increases day by day in our country. In addition, by sharing a new method in the diagnosis of Covid-19, important issues in the monitoring of Covid-19 patients with a high symptom burden and high risk of complications associated with lung damage were highlighted. Method: The blood values of 4000 patients who read the consent form and gave consent were collected in the SPSS 26 program in our study conducted in various public hospitals within the city of Istanbul with the permission of Istanbul Provincial Health Directorate with the number 15916306-604.02 and dated 08.09.2020. With our quantitative research, it is possible to interpret the arterial blood gas values of patients who have been diagnosed with covid-19 positive, to determine the common output and to determine the treatment methods, drugs, etc. It is aimed to be a resource for the studies. Common values were searched in the formed control groups and analyzed in accordance with the Henderson-Hasselbalch equation and in accordance with the literature. Arterial and vein blood from the patients participating in the study were collected in anaerobic environment with sterile syringes containing 1.0 or 5.0 mL lyophilized heparin. During blood collection, necessary measures have been taken to prevent or minimize blood contact with air. Since arterial blood gas collection requires expertise, no results that could be interpreted as suspicious were included in the study. Analyzes were performed with pH, pO2, pCO2, HCO3, LDH and Hgb values of 4000 participants. Results: Our study includes 4000 people between the ages of 19-88, who have been diagnosed with Covid-19 and voluntarily agree to participate in our study. 58% of the patients who participated in the study by approving the consent form were male and 42% were female. In order for the blood values of the deceased patients to be used in our study, permission was also requested from their relatives. In this group, 68% of the participants are male and 32% are female participants. It was found that the pO2 and pCO2 ratio of 4000 patients participating in the study did not exceed 10% of the LDH and HCO3 ratio (1O X (LDH) / 100 X (HCO3)> pO2 / pCO2). If the equation is correct in the equation [(LDH) X (pCO2)] / [(HCO3) X (pO2)] ≤ (Hgb), the covid-19 diagnostic status is positive. Conclusion: : It has been determined that the pO2 / pCO2 ratio does not exceed 10% of the LDH / HCO3 ratio in the critical cases examined within the scope of the research. As this rate decreased, the rate of survival of patients also decreased. Therefore, in positive cases, a reference equation that can detect covid-19 positivity has been found by looking at whether there is an anion gap and metabolic acidosis is observed and LDH level. This formula; [(LDH) X (pCO2)] / [(HCO3) X (pO2)] ≤ (Hgb). When the formula was applied, it was determined that the ratio of [(LDH) X (pCO2)] / [(HCO3) X (pO2)] was equal or less than the value of hemoglobin (Hgb).

Role of biochemical markers in the monitoring of COVID-19 patients.

COVID-19 is an infectious disease caused by the SARSCoV-2 virus, which has given rise to a global sanitary emergency. The clinical characteristics of COVID-19 are varied and can range from an asymptomatic infection to a mild to severe pneumonia. Recent studies have shown that different laboratory parameters become altered in these patients, and as such are useful as biomarkers to assess the progression of the disease and categorize patients that may present a severe and/or fatal clinical condition. This review analyzes biochemical and immunological markers that become altered in COVID-19 patients and their impact on different organs at a hepatic, cardiac, renal and pancreatic level, as well as markers of inflammation, analyzing their implications in the evolution of the disease.

An efficient approach based on 3D reconstruction of CT scan to improve the management and monitoring of COVID-19 patients

PurposeTo reconstruct a 3D visualization from CT images of COVID-19 patients in order to improve understanding of the disease for better management and follow-up. Materials and methodsWe have retrieved CT images of 185 COVID-19 patients from the Cheikh Zaid International University Hospital in Rabat, Morocco. We then performed computer processing that allowed us to obtain a 3D visualization of these patients. ResultsIn this article, we have chosen to do 3D reconstruction of three specific cases among 185 patients:- Cases (A1, A2) which are negative RT-PCR patient with normal CT images.- Cases (B1, B2) which are positive RT-PCR patient with abnormal CT images.- Case (C) which is a negative RT-PCR patient with CT abnormalities.To improve our results and have a better quality of the 3D reconstruction, we used different algorithms and a specific row data processing. Conclusion3D reconstruction has a significant role in the diagnosis and management of COVID-19 patients. The quality and reliability of 3D reconstructions allow the clinician to make a quick and efficient diagnosis and avoid an eventual false negative (produced by the RT-PCR test). We suggest including chest 3D reconstruction in the patient management and prognosis evaluation.

Segmentation of COVID-19 pneumonia lesions: A deep learning approach.

Background: Lung CT scan has a pivotal role in diagnosis and monitoring of COVID-19 patients, and with growing number of affected individuals, the need for artificial intelligence (AI)-based systems for interpretation of CT images is emerging. In current investigation we introduce a new deep learning-based automatic segmentation model for localization of COVID-19 pulmonary lesions. Methods: A total of 2469 CT scan slices, containing 1402 manually segmented abnormal and 1067 normal slices form 55 COVID-19 patients and 41 healthy individuals, were used to train a deep convolutional neural network (CNN) model based on Detectron2, an open-source modular object detection library. A dataset, including 1224 CT slices of 18 COVID-19 patients and 9 healthy individuals, was used to test the model. Results: The accuracy, sensitivity, and specificity of the trained model in marking a single image slice with COVID-19 lesion were 0.954, 0.928, and 0.961, respectively. Considering a threshold of 0.4% for percentage of lung involvement, the model was capable of diagnosing the patients with COVID-19 pneumonia, with a sensitivity of 0.982% and a specificity of 88.5%. Furthermore, the mean Intersection over Union (IoU) index for the test dataset was 0.865. Conclusion: The deep learning-based automatic segmentation method provides an acceptable accuracy in delineation and localization of COVID-19 lesions, assisting the clinicians and researchers for quantification of abnormal findings in chest CT scans. Moreover, instance segmentation is capable of monitoring longitudinal changes of the lesions, which could be beneficial to patients’ follow-up.

Sensitive detection and quantification of SARS-CoV-2 by multiplex droplet digital RT-PCR

The purpose of this study is to develop a one-step droplet digital RT-PCR (RT-ddPCR) multiplex assay that allows for sensitive quantification of SARS-CoV-2 RNA with respect to human-derived RNA and could be used for screening and monitoring of Covid-19 patients. A one-step RT-ddPCR multiplex assay was developed for simultaneous detection of SARS-CoV-2 E, RdRp and N viral RNA, and human Rpp30 DNA and GUSB mRNA, for internal nucleic acid (NA) extraction and RT-PCR control. Dilution series of viral RNA transcripts were prepared in water and total NA extract of Covid-19-negative patients. As reference assay, an E-GUSB duplex RT-PCR was used. GUSB mRNA detection was used to set validity criteria to assure viral RNA and RT-PCR assay quality and to enable quantification of SARS-CoV-2 RNA. In a background of at least 100 GUSB mRNA copies, 5 copies of viral RNA are reliably detectable and 10 copies viral RNA copies are reliably quantifiable. It was found that assay sensitivity of the RT-ddPCR was not affected by the total NA background while assay sensitivity of the gold standard RT-PCR assay is drastically decreased when SARS-CoV-2 copies were detected in a background of total NA extract compared with water. The present study describes a robust and sensitive one-step ddRT-PCR multiplex assay for reliable quantification of SARS-CoV-2 RNA. By determining the fractional abundance of viral RNA with respect to a human housekeeping gene, viral loads from different samples can be compared, what could be used to investigate the infectiveness and to monitor Covid-19 patients.

Rapid detection of SARS-CoV-2 antibodies using electrochemical impedance-based detector

Coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was classified as a pandemic by the World Health Organization and has caused over 550,000 deaths worldwide as of July 2020. Accurate and scalable point-of-care devices would increase screening, diagnosis, and monitoring of COVID-19 patients. Here, we demonstrate rapid label-free electrochemical detection of SARS-CoV-2 antibodies using a commercially available impedance sensing platform. A 16-well plate containing sensing electrodes was pre-coated with receptor binding domain (RBD) of SARS-CoV-2 spike protein, and subsequently tested with samples of anti-SARS-CoV-2 monoclonal antibody CR3022 (0.1 μg/ml, 1.0 μg/ml, 10 μg/ml). Subsequent blinded testing was performed on six serum specimens taken from COVID-19 and non-COVID-19 patients (1:100 dilution factor). The platform was able to differentiate spikes in impedance measurements from a negative control (1% milk solution) for all CR3022 samples. Further, successful differentiation and detection of all positive clinical samples from negative control was achieved. Measured impedance values were consistent when compared to standard ELISA test results showing a strong correlation between them (R2=0.9). Detection occurs in less than five minutes and the well-based platform provides a simplified and familiar testing interface that can be readily adaptable for use in clinical settings.

IFCC Interim Guidelines on Biochemical/Hematological Monitoring of COVID-19 Patients.

Routine biochemical and hematological tests have been reported to be useful in the stratification and prognostication of pediatric and adult patients with diagnosed coronavirus disease (COVID-19), correlating with poor outcomes such as the need for mechanical ventilation or intensive care, progression to multisystem organ failure, and/or death. While these tests are already well established in most clinical laboratories, there is still debate regarding their clinical value in the management of COVID-19, particularly in pediatrics, as well as the value of composite clinical risk scores in COVID-19 prognostication. This document by the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Task Force on COVID-19 provides interim guidance on: (A)clinical indications for testing, (B)recommendations for test selection and interpretation, (C)considerations in test interpretation, and (D)current limitations of biochemical/hematological monitoring of COVID-19 patients. These evidence-based recommendations will provide practical guidance to clinical laboratories worldwide, underscoring the contribution of biochemical and hematological testing to our collective pandemic response.

Monitoring of COVID-19 patients via telemedicine with telemonitoring

AimTo asses if telemedicine with telemonitoring is a clinically useful and safe tool for monitoring patients with COVID-19. MethodsA prospective observational study of patients with COVID-19 diagnosed via a positive PCR test who were considered high-risk and who were monitored with telemedicine and telemonitoring in the Lugo Healthcare Area between March 17th and April 17th, 2020, was conducted. Two groups of patients were included: those in outpatient monitoring from the beginning and those in outpatient monitoring following hospital discharge. Every patient completed a clinical questionnaire with his or her temperature once per day and oxygen saturation levels three times per day. Proactive monitoring was done by getting in touch with every patient at least once a day. ResultsA total of 313 patients (52.4% female) with a mean age of 60.9 (SD 15.9) years were included. Two patients refused to participate in the program. Finally, 224 were monitored from the beginning and 89 patients were monitored after discharged. In the first group, 38 (16.90%) were referred to the Emergency department on 43 occasions; 18 (8.03%) were hospitalized, and two died. There were no deaths or lifethreatening at home. Including the patients monitored after hospitalization, monitoring was performed in 304 cases. One patient was readmited (0.32%) and another left the program (0.32%). The mean time of monitoring was 11.64 (SD 3.58) days and 224 (73.68%) patients were discharged during the 30 days the study lasted. ConclusionsOur data suggest that telemedicine with at-home telemonitoring, when used proactively, allows for clinically useful and safe monitoring of high-risk patients with COVID-19.