COVID-19 Chest CT Quantification: Triage and Prognostic Value in Different Ages.

Objective: We evaluated the triage and prognostic performance of seven proposed computed tomography (CT)-severity score (CTSS) systems in two different age groups.Design: Retrospective study.Setting: COVID-19 pandemic.Participants: Admitted COVID-19, PCR-positive patients were included, excluding patients with heart failure and significant pre-existing pulmonary disease.Methods: Patients were divided into two age groups: ≥65 years and ≤64 years. Clinical data indicating disease severity at presentation and at peak disease severity were recorded. Initial CT images were scored by two radiologists according to seven CTSSs (CTSS1-CTSS7). Receiver operating characteristic (ROC) analysis for the performance of each CTSS in diagnosing severe/critical disease on admission (triage performance) and at peak disease severity (prognostic performance) was done for the whole cohort and each age group separately.Results: Included were 96 patients. Intraclass correlation coefficient (ICC) between the two radiologists scoring the CT scan images were good for all the CTSSs (ICC=0.764-0.837). In the whole cohort, all CTSSs showed an unsatisfactory area under the curve (AUC) in the ROC curve for triage, excluding CTSS2 (AUC=0.700), and all CTSSs showed acceptable AUCs for prognostic usage (0.759-0.781). In the older group (≥65 years; n=55), all CTSSs excluding CTSS6 showed excellent AUCs for triage (0.804-0.830), and CTSS6 was acceptable (AUC=0.796); all CTSSs showed excellent or outstanding AUCs for prognostication (0.859-0.919). In the younger group (≤64 years; n=41), all CTSSs showed unsatisfactory AUCs for triage (AUC=0.487-0.565) and prognostic usage (AUC=0.668-0.694), excluding CTSS6, showing marginally acceptable AUC for prognostic performance (0.700).Conclusion: Those CTSSs requiring more numerous segmentations, namely CTSS2, CTSS7, and CTSS5 showed the best ICCs; therefore, they are the best when comparison between two separate scores is needed. Irrespective of patients' age, CTSSs show minimal value in triage and acceptable prognostic value in COVID-19 patients. CTSS performance is highly variable in different age groups. It is excellent in those aged ≥65 years, but has little if any value in younger patients. Multicenter studies with larger sample size to evaluate results of this study should be conducted.

Combined Cloud-Based Inference System for the Classification of COVID-19 in CT-Scan and X-Ray Images.

In the past few years, most of the work has been done around the classification of covid-19 using different images like CT-scan, X-ray, and ultrasound. But none of that is capable enough to deal with each of these image types on a single common platform and can identify the possibility that a person is suffering from COVID or not. Thus, we realized there should be a platform to identify COVID-19 in CT-scan and X-ray images on the fly. So, to fulfill this need, we proposed an AI model to identify CT-scan and X-ray images from each other and then use this inference to classify them of COVID positive or negative. The proposed model uses the inception architecture under the hood and trains on the open-source extended covid-19 dataset. The dataset consists of plenty of images for both image types and is of size 4 GB. We achieved an accuracy of 100%, average macro-Precision of 100%, average macro-Recall of 100%, average macro f1-score of 100%, and AUC score of 99.6%. Furthermore, in this work, cloud-based architecture is proposed to massively scale and load balance as the Number of user requests rises. As a result, it will deliver a service with minimal latency to all users.

Randomized placebo-controlled trial of oral tannin supplementation on COVID-19 symptoms, gut dysbiosis and cytokine response.

The clinical study aim was to investigate whether a tannin-based dietary supplementation could improve the efficacy of standard-of-care treatment of hospitalized COVID-19 patients by restoring gut microbiota function. Adverse events and immunomodulation post-tannin supplementation were also investigated. A total of 124 patients receiving standard-of-care treatment were randomized to oral tannin-based supplement or placebo for a total of 14 days. Longitudinal blood and stool samples were collected for cytokine and 16S rDNA microbiome profiling, and results were compared with 53 healthy controls. Although oral tannin supplementation did not result in clinical improvement or significant gut microbiome shifts after 14-days, a reduction in the inflammatory state was evident and significantly correlated with microbiota modulation. Among cytokines measured, MIP-1α was significantly decreased with tannin treatment (p=0.03) where it correlated positively with IL-1ß and TNF- α, and negatively with stool Bifidobacterium abundance.

Clinical Trial Subgroup Analyses to Investigate Clinical and Immunological Outcomes of Convalescent Plasma Therapy in Severe COVID-19.

Objective: To assess the clinical and immunological benefits of passive immunization using convalescent plasma therapy (CPT). Materials and Methods: A series of subclass analyses were performed on the previously published outcome data and accompanying clinical metadata from a completed randomized controlled trial (RCT) (Clinical Trial Registry of India, number CTRI/2020/05/025209). The subclass analyses were performed on the outcome data and accompanying clinical metadata from a completed RCT (patient recruitment between May 15, 2020 and October 31, 2020). Data on the plasma abundance of a large panel of cytokines from the same cohort of patients were also used to characterize the heterogeneity of the putative anti-inflammatory function of convalescent plasma (CP) in addition to passively providing neutralizing antibodies. Results: Although the primary clinical outcomes were not significantly different in the RCT across all age groups, significant immediate mitigation of hypoxia, reduction in hospital stay, and significant survival benefit were registered in younger (<67 years in our cohort) patients with severe coronavirus disease 2019 and acute respiratory distress syndrome on receiving CPT. In addition to neutralizing the antibody content of CP, its anti-inflammatory proteome, by attenuation of the systemic cytokine deluge, significantly contributed to the clinical benefits of CPT. Conclusion: Subgroup analyses revealed that clinical benefits of CPT in severe coronavirus disease 2019 are linked to the anti-inflammatory protein content of CP apart from the anti-severe acute respiratory syndrome coronavirus 2 neutralizing antibody content.

Association of Prognostic Nutritional Index with Severity and Mortality of Hospitalized Patients with COVID-19: A Systematic Review and Meta-Analysis.

The associations of prognostic nutritional index (PNI) with disease severity and mortality in patients with coronavirus disease 2019 (COVID-19) remain unclear. Electronic databases, including MEDLINE, EMBASE, Google scholar, and Cochrane Library, were searched from inception to 10 May 2022. The associations of PNI with risk of mortality (primary outcome) and disease severity (secondary outcome) were investigated. Merged results from meta-analysis of 13 retrospective studies (4204 patients) published between 2020 and 2022 revealed a lower PNI among patients in the mortality group [mean difference (MD): -8.65, p < 0.001] or severity group (MD: -5.19, p < 0.001) compared to those in the non-mortality or non-severity groups. A per-point increase in PNI was associated with a reduced risk of mortality [odds ratio (OR) = 0.84, 95% CI: 0.79 to 0.9, p < 0.001, I2 = 67.3%, seven studies] and disease severity (OR = 0.84, 95% CI: 0.77 to 0.92, p < 0.001, I2 = 83%, five studies). The pooled diagnostic analysis of mortality yielded a sensitivity of 0.76, specificity of 0.71, and area under curve (AUC) of 0.79. Regarding the prediction of disease severity, the sensitivity, specificity, and AUC were 0.8, 0.61, and 0.65, respectively. In conclusion, this study demonstrated a negative association between PNI and prognosis of COVID-19. Further large-scale trials are warranted to support our findings.

CT-severity score in COVID-19 patients: for whom is it applicable best?

Background: lung involvement in COVID-19 can be quantified by chest CT scan. We evaluated the triage and prognostication performance of seven proposed CT-severity score (CTSS) systems in two age groups of ≥65 and <65 years old. Methods: Confirmed COVID-19 patients by reverse transcriptase polymerase chain reaction (RT-PCR) admitted from February 20th, 2020 to July 22nd were included in a retrospective single center study. Clinical disease severity at presentation and at peak disease severity were recorded. CT images were scored according to seven different scoring systems (CTSS1-CTSS7). The cohort was divided into two age groups of ≥65 and <65 years old. Receiver operator characteristic (ROC) curves for each age group for diagnosis of severe/critical disease on admission (for triage) were plotted. Such curves were also plotted for predicting severe/critical disease at peak disease severity (for prognostication), and critical disease at peak severity (for prognostication). Areas under the curve (AUCs), best thresholds, and corresponding sensitivities (Sens.) and specificities (Spec.) were calculated. Results: 96 patients were included with a mean age of 63.6±17.4 years. All CTSSs in 65-year-old or more group (N=55) showed excellent performance (AUC=0.80-0.83, Sens.+Spec.= 155-162%) in triage and excellent or outstanding performance (AUC=0.81-0.92, Sens.+Spec.= 153-177%) in prognostication. In the younger group (N=44), all CTSSs were unsatisfactory for triage (AUC=0.49-0.57) and predicting severe/critical disease (AUC=0.67-0.70), but were acceptable for predicting critical disease (AUC=0.70-0.73, Sens.+Spec.= 132-151%). Conclusion: CTSS is an excellent tool in triage and prognostication in patients with COVID-19 ≥65 years old, but is of limited value in younger patients.

Real-time data of COVID-19 detection with IoT sensor tracking using artificial neural network.

The coronavirus pandemic has affected people all over the world and posed a great challenge to international health systems. To aid early detection of coronavirus disease-2019 (COVID-19), this study proposes a real-time detection system based on the Internet of Things framework. The system collects real-time data from users to determine potential coronavirus cases, analyses treatment responses for people who have been treated, and accurately collects and analyses the datasets. Artificial intelligence-based algorithms are an alternative decision-making solution to extract valuable information from clinical data. This study develops a deep learning optimisation system that can work with imbalanced datasets to improve the classification of patients. A synthetic minority oversampling technique is applied to solve the problem of imbalance, and a recursive feature elimination algorithm is used to determine the most effective features. After data balance and extraction of features, the data are split into training and testing sets for validating all models. The experimental predictive results indicate good stability and compatibility of the models with the data, providing maximum accuracy of 98% and precision of 97%. Finally, the developed models are demonstrated to handle data bias and achieve high classification accuracy for patients with COVID-19. The findings of this study may be useful for healthcare organisations to properly prioritise assets.

Improving Thermal Camera Performance in Fever Detection during COVID-19 Protocol with Random Forest Classification

The AMG8833 sensor can be utilized for a low-cost thermal camera-based body temperature measurement during COVID-19 protocol enforcement. However, the sensor is not accurate enough for body temperature measurement, so fever detection performance becomes poor. The aim of this study is to apply Random Forest as a classifier in a thermal camera body temperature measurement that uses the AMG8833 sensor and evaluate its performance in detecting fever. In addition to the AMG8833, the thermal camera made also uses a webcam for face detection, and a Raspberry Pi as a minicomputer and a place to apply the Random Forest model. That way, the Thermal camera undergoes three processes, namely face detection from the image captured from the webcam, then temperature and fever detection from AMG8833. From the receiver operating curve (ROC) test conducted, Random Forest area under curve (AUC) value is superior compared to the Logistic Regression and Decision Tree methods with a value of 0.977. Furthermore, the sensitivity and specificity values of Random Forest in detecting fever are 88.5% and 99.5%, respectively. This value is higher than a detection system that does not use Random Forest classification for fever detection. © 2021 IEEE.

Receiver operating characteristic curve: overview and practical use for clinicians.

Using diagnostic testing to determine the presence or absence of a disease is essential in clinical practice. In many cases, test results are obtained as continuous values and require a process of conversion and interpretation and into a dichotomous form to determine the presence of a disease. The primary method used for this process is the receiver operating characteristic (ROC) curve. The ROC curve is used to assess the overall diagnostic performance of a test and to compare the performance of two or more diagnostic tests. It is also used to select an optimal cut-off value for determining the presence or absence of a disease. Although clinicians who do not have expertise in statistics do not need to understand both the complex mathematical equation and the analytic process of ROC curves, understanding the core concepts of the ROC curve analysis is a prerequisite for the proper use and interpretation of the ROC curve. This review describes the basic concepts for the correct use and interpretation of the ROC curve, including parametric/nonparametric ROC curves, the meaning of the area under the ROC curve (AUC), the partial AUC, methods for selecting the best cut-off value, and the statistical software to use for ROC curve analyses.

COVID-19 disease identification from chest CT images using empirical wavelet transformation and transfer learning.

In the current scenario, novel coronavirus disease (COVID-19) spread is increasing day-by-day. It is very important to control and cure this disease. Reverse transcription-polymerase chain reaction (RT-PCR), chest computerized tomography (CT) imaging options are available as a significantly useful and more truthful tool to classify COVID-19 within the epidemic region. Most of the hospitals have CT imaging machines. It will be fruitful to utilize the chest CT images for early diagnosis and classification of COVID-19 patients. This requires a radiology expert and a good amount of time to classify the chest CT-based COVID-19 images especially when the disease is spreading at a rapid rate. During this pandemic COVID-19, there is a need for an efficient automated way to check for infection. CT is one of the best ways to detect infection inpatients. This paper introduces a new method for preprocessing and classifying COVID-19 positive and negative from CT scan images. The method which is being proposed uses the concept of empirical wavelet transformation for preprocessing, selecting the best components of the red, green, and blue channels of the image are trained on the proposed network. With the proposed methodology, the classification accuracy of 85.5%, F1 score of 85.28%, and AUC of 96.6% are achieved.

Distribution of Transmission Potential During Nonsevere COVID-19 Illness.

BACKGROUND: Patients recovering from coronavirus disease 2019 (COVID-19) often continue to test positive for the causative virus by polymerase chain reaction (PCR) even after clinical recovery, thereby complicating return-to-work plans. The purpose of this study was to evaluate transmission potential of COVID-19 by examining viral load with respect to time. METHODS: Health care personnel (HCP) at Cleveland Clinic diagnosed with COVID-19, who recovered without needing hospitalization, were identified. Threshold cycles (Ct) for positive PCR tests were obtained and viral loads calculated. The association of viral load with days since symptom onset was examined in a multivariable regression model, which was reduced by stepwise backward selection to only keep variables significant at a level of .05. Viral loads by day since symptom onset were predicted using the model and transmission potential evaluated by examination of a viral load-time curve. RESULTS: Over 6 weeks, 230 HCP had 528 tests performed. Viral loads declined by orders of magnitude within a few days of symptom onset. The only variable significantly associated with viral load was time since onset of symptoms. Of the area under the curve (AUC) spanning symptom onset to 30 days, 96.9% lay within the first 7 days, and 99.7% within 10 days. Findings were very similar when validated using split-sample and 10-fold cross-validation. CONCLUSIONS: Among patients with nonsevere COVID-19, viral loads in upper respiratory specimens peak by 2 or 3 days from symptom onset and decrease rapidly thereafter. The vast majority of the viral load-time AUC lies within 10 days of symptom onset.