Lower mortality of COVID-19 by early recognition and intervention: experience from Jiangsu Province

Duration of the first prone positioning maneuver and its association with 90-day mortality in patients with acute respiratory failure due to COVID-19: A retrospective study of time terciles

OBJECTIVE

To investigate the association between the duration of the first prone positioning maneuver (PPM) and 90-day mortality in patients with C-ARDS.

DESIGN

Retrospective, observational, and analytical study.

SETTING

COVID-19 ICU of a tertiary hospital.

PATIENTS

Adults over 18 years old, with a confirmed diagnosis of SARS-CoV-2 disease requiring PPM.

INTERVENTIONS

Multivariable analysis of 90-day survival.

MAIN VARIABLES OF INTEREST

Duration of the first PPM, number of PPM sessions, 90-day mortality.

RESULTS

271 patients undergoing PPM were analyzed: first tertile (n = 111), second tertile (n = 95) and third tertile (n = 65). The results indicated that the median duration of PDP was 14 h (95% CI: 10-16 h) in the first tertile, 19 h (95% CI: 18-20 h) in the second tertile and 22 h (95% CI: 21-24 h) in the third tertile. Comparison of survival curves using the Logrank test did not reach statistical significance (p = 0.11). Cox Regression analysis showed an association between the number of pronation sessions (patients receiving between 2 and 5 sessions (HR = 2.19; 95% CI: 1.07-4.49); and those receiving more than 5 sessions (HR = 6.05; 95% CI: 2.78-13.16) and 90-day mortality.

CONCLUSIONS

while the duration of PDP does not appear to significantly influence 90-day mortality, the number of pronation sessions is identified as a significant factor associated with an increased risk of mortality.

Day-to-Day Variability in Measurements of Respiration Using Bioimpedance from a Non-Standard Location

Non-invasive monitoring of pulmonary health may be useful for tracking several conditions such as COVID-19 recovery and the progression of pulmonary edema. Some proposed methods use impedance-based technologies to non-invasively measure the thorax impedance as a function of respiration but face challenges that limit the feasibility, accuracy, and practicality of tracking daily changes. In our prior work, we demonstrated a novel approach to monitor respiration by measuring changes in impedance from the back of the thigh. We reported the concept of using thigh-thigh bioimpedance measurements for measuring the respiration rate and demonstrated a linear relationship between the thigh-thigh bioimpedance and lung tidal volume. Here, we investigate the variability in thigh-thigh impedance measurements to further understand the feasibility of the technique for detecting a change in the respiratory status due to disease onset or recovery if used for long-term in-home monitoring. Multiple within-session and day-to-day impedance measurements were collected at 80 kHz using dry electrodes (thigh) and wet electrodes (thorax) across the five healthy subjects, along with simultaneous gold standard spirometer measurements for three consecutive days. The peak-peak bioimpedance measurements were found to be highly correlated (0.94 ± 0.03 for dry electrodes across thigh; 0.92 ± 0.07 for wet electrodes across thorax) with the peak-peak spirometer tidal volume. The data across five subjects indicate that the day-to-day variability in the relationship between impedance and volume for thigh-thigh measurements is smaller (average of 14%) than for the thorax (40%). However, it is affected by food and water and might limit the accuracy of the respiratory tidal volume.

The effectiveness of community health worker training, equipping, and deployment in reducing COVID-19 infections and deaths in rural Western Kenya: A comparison of two counties

COVID-19 and other pandemics remain significant threats to population health, particularly in rural settings where health systems are disproportionately weak. There is a lack of evidence on whether trained, equipped, and deployed community health workers (CHWs) can lead to significant reductions in COVID-19 infections and deaths. Our objective was to measure the effectiveness of deploying trained and equipped CHWs in reducing COVID-19 infections and deaths by comparing outcomes in two counties in rural Western Kenya, a setting with limited critical care capacity and limited access to COVID-19 vaccines and oral COVID-19 antivirals. In Siaya, trained CHWs equipped with thermometers, pulse oximeters, and KN95 masks, visited households to convey health information about COVID-19 prevention. They screened, isolated, and referred COVID-19 cases to facilities with oxygen capacity. They measured and digitally recorded vital signs at the household level. In Kisii county, the standard Kenya national COVID-19 protocol was implemented. We performed a comparative analysis of differences in CHW skills, activity, and COVID-19 infections and deaths using district health information system (DHIS2) data. Trained Siaya CHWs were more skilled in using pulse oximeters and digitally reporting vital signs at the household level. The mean number of oxygen saturation measurements conducted in Siaya was 24.19 per COVID-19 infection; and the mean number of temperature measurements per COVID-19 infection was 17.08. Siaya CHWs conducted significantly more household visits than Kisii CHWs (the mean monthly CHW household visits in Siaya was 146,648.5, standard deviation 11,066.5 versus 42,644.5 in Kisii, standard deviation 899.5, p value = 0.01). Deploying trained and equipped CHWs in rural Western Kenya was associated with lower risk ratios for COVID-19 infections and deaths: 0.54, 95% CI [0.48-0.61] and 0.29, CI [0.13-0.65], respectively, consistent with a beneficial effect.

Quantitative Computed Tomography Lung COVID Scores with Laboratory Markers: Utilization to Predict Rapid Progression and Monitor Longitudinal Changes in Patients with Coronavirus 2019 (COVID-19) Pneumonia

Coronavirus disease 2019 (COVID-19), is an ongoing issue in certain populations, presenting rapidly worsening pneumonia and persistent symptoms. This study aimed to test the predictability of rapid progression using radiographic scores and laboratory markers and present longitudinal changes. This retrospective study included 218 COVID-19 pneumonia patients admitted at the Chungnam National University Hospital. Rapid progression was defined as respiratory failure requiring mechanical ventilation within one week of hospitalization. Quantitative COVID (QCOVID) scores were derived from high-resolution computed tomography (CT) analyses: (1) ground glass opacity (QGGO), (2) mixed diseases (QMD), and (3) consolidation (QCON), and the sum, quantitative total lung diseases (QTLD). Laboratory data, including inflammatory markers, were obtained from electronic medical records. Rapid progression was observed in 9.6% of patients. All QCOVID scores predicted rapid progression, with QMD showing the best predictability (AUC = 0.813). In multivariate analyses, the QMD score and interleukin(IL)-6 level were important predictors for rapid progression (AUC = 0.864). With >2 months follow-up CT, remained lung lesions were observed in 21 subjects, even after several weeks of negative reverse transcription polymerase chain reaction test. AI-driven quantitative CT scores in conjugation with laboratory markers can be useful in predicting the rapid progression and monitoring of COVID-19.

Prone positioning during CPAP therapy in SARS-CoV-2 pneumonia: a concise clinical review

During the COVID-19 pandemic, the number of patients with hypoxemic acute respiratory failure (ARF) due to SARS-CoV-2 pneumonia threatened to overwhelm intensive care units. To reduce the need for invasive mechanical ventilation (IMV), clinicians tried noninvasive strategies to manage ARF, including the use of awake prone positioning (PP) with continuous positive airway pressure (CPAP). In this article, we review the patho-physiologic rationale, clinical effectiveness and practical issues of the use of PP during CPAP in non-intubated, spontaneously breathing patients affected by SARS-CoV-2 pneumonia with ARF. Use of PP during CPAP appears to be safe and feasible and may have a lower rate of adverse events compared to IMV. A better response to PP is observed among patients in early phases of acute respiratory distress syndrome. While PP during CPAP may improve oxygenation, the impact on the need for intubation and mortality remains unclear. It is possible to speculate on the role of PP during CPAP in terms of improvement of ventilation mechanics and reduction of strain stress.

RNA Analysis Using Immunoassay Detection Format

Oligonucleotide probe tagging and reverse transcriptase polymerase-chain reaction (RT-PCR) are the most widely used techniques currently used for detecting and analyzing RNA. RNA detection using labeled oligonucleotide probe-based approaches is suitable for point-of-care (POC) applications but lacks assay sensitivity, whereas RT-PCR requires complex instrumentation. As an alternative, immunoassay detection formats coupled with isothermal RNA amplification techniques have been proposed for handheld assay development. In this chapter, we describe a robust technique comprising of: (a) target RNA tagging with a complementary oligonucleotide probe labeled with a hapten moiety to form a DNA/RNA duplex hybrid; (b) complexing the DNA/RNA duplex with a pre-coated antibody (Ab) directed at the hapten moiety; (c) sandwich complex formation with an Ab that selectively recognizes the DNA/RNA structural motif; and (d) detection of the sandwich complex using a secondary Ab enzyme conjugate targeting the anti-DNA/RNA Ab followed by standard enzyme-linked immunosorbent assay (ELISA) visualization.

Multivariable Risk Modelling and Survival Analysis with Machine Learning in SARS-CoV-2 Infection

AIM

To evaluate the performance of a machine learning model based on demographic variables, blood tests, pre-existing comorbidities, and computed tomography(CT)-based radiomic features to predict critical outcome in patients with acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

METHODS

We retrospectively enrolled 694 SARS-CoV-2-positive patients. Clinical and demographic data were extracted from clinical records. Radiomic data were extracted from CT. Patients were randomized to the training (80%, n = 556) or test (20%, n = 138) dataset. The training set was used to define the association between severity of disease and comorbidities, laboratory tests, demographic, and CT-based radiomic variables, and to implement a risk-prediction model. The model was evaluated using the C statistic and Brier scores. The test set was used to assess model prediction performance.

RESULTS

Patients who died (n = 157) were predominantly male (66%) over the age of 50 with median (range) C-reactive protein (CRP) = 5 [1, 37] mg/dL, lactate dehydrogenase (LDH) = 494 [141, 3631] U/I, and D-dimer = 6.006 [168, 152.015] ng/mL. Surviving patients (n = 537) had median (range) CRP = 3 [0, 27] mg/dL, LDH = 484 [78, 3.745] U/I, and D-dimer = 1.133 [96, 55.660] ng/mL. The strongest risk factors were D-dimer, age, and cardiovascular disease. The model implemented using the variables identified using the LASSO Cox regression analysis classified 90% of non-survivors as high-risk individuals in the testing dataset. In this sample, the estimated median survival in the high-risk group was 9 days (95% CI; 9-37), while the low-risk group did not reach the median survival of 50% (p < 0.001).

CONCLUSIONS

A machine learning model based on combined data available on the first days of hospitalization (demographics, CT-radiomics, comorbidities, and blood biomarkers), can identify SARS-CoV-2 patients at risk of serious illness and death.

Evaluation of an adjusted MEWS (Modified Early Warning Score) for COVID-19 patients to identify risk of ICU admission or death in the Kingdom of Bahrain

BACKGROUND

While most COVID-19 cases have uncomplicated infection, a small proportion has the potential to develop life-threatening disease, as such development of a prediction tool using patients baseline characteristics at the time of diagnosis should aid in early identification of high-risk groups and devise pertinent management. Hence, we set up this retrospective study to determine preadmission triaging tool to predict the development of severe COVID-19 in the Kingdom of Bahrain MATERIALS AND METHODS: A retrospective study was conducted from 1 September 2020 to 30 November 2020 with enrolment of all SARS-CoV-2 PCR-confirmed persons aged ≥ 14 years who attended Al-Shamil Field Hospital (SFH) in the Kingdom of Bahrain for triaging and assessment with recording of the following parameters: systolic blood pressure, heart rate, respiratory rate, temperature, the alert, verbal, pain, unresponsive neurological score, age, oxygen saturation, comorbidities, Body Mass Index (BMI), duration of symptoms and living with immunocompromised populations to develop our local adjusted MEWS as predictor for ICU admission & for consideration of suitable isolation at home. Follow up data of all patients was obtained from the electronic medical records system including CXR findings, treatments/medications received, need of oxygen supplements /intubation, needs of ICU care, and the outcome (death /discharged alive) IBM SPSS statistic version 21 program was used for data analysis.

RESULTS

Our study showed that using the locally developed adjusted MEWS score, there was an significant association between high value of this adjusted MEWS score and abnormal radiographic finding (49.7 % Vs. 17 % for patients with high score Vs. those with low score respectively). Out of the 181 patients with high scores on adjusted MEWS; 38.7 % required oxygen via nasal cannula, 14.4 % required face mask and 8.3 % non-rebreather mask; this proportion was significantly higher than their counterpart patients who score low on adjusted MEWS (20.9 %, 7.7 %, 4.8 %respectively) with statistically significance difference between the two groups (p value of 0.00, 0.00,.004 respectively) Requirement of ICU admission was significantly higher among patients with high score in comparison to those with low score (14.4 % vs. 3 %) with significant p value (0.00) But higher score value was not associated significantly with increase mortality rate among COVID patients.

CONCLUSION

Development of our new Adjusted MEWS score system by adding the additional elements of age, oxygen saturation, comorbidities, Body Mass Index (BMI) and duration of symptoms found to be very useful predictor tool for preadmission triaging of COVID patients based on their risk assessment to help clinician to decide on the appropriate placement to different level of isolation facilities.

From Emergence to Endemicity: A Comprehensive Review of COVID-19

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), later renamed coronavirus disease 2019 (COVID-19), was first identified in Wuhan, China, in early December 2019. Initially, the China office of the World Health Organization was informed of numerous cases of pneumonia of unidentified etiology in Wuhan, Hubei Province at the end of 2019. This would subsequently result in a global pandemic with millions of confirmed cases of COVID-19 and millions of deaths reported to the WHO. We have analyzed most of the data published since the beginning of the pandemic to compile this comprehensive review of SARS-CoV-2. We looked at the core ideas, such as the etiology, epidemiology, pathogenesis, clinical symptoms, diagnostics, histopathologic findings, consequences, therapies, and vaccines. We have also included the long-term effects and myths associated with some therapeutics of COVID-19. This study presents a comprehensive assessment of the SARS-CoV-2 virology, vaccines, medicines, and significant variants identified during the course of the pandemic. Our review article is intended to provide medical practitioners with a better understanding of the fundamental sciences, clinical treatment, and prevention of COVID-19. As of May 2023, this paper contains the most recent data made accessible.

Early referring saved lives in kidney transplant recipients with COVID-19: a beneficial role of telemedicine

Introduction

There is a strong impetus for the use of telemedicine for boosting early detection rates and enabling early treatment and remote monitoring of COVID-19 cases, particularly in chronically ill patients such as kidney transplant recipients (KTRs). However, data regarding the effectiveness of this practice are lacking.

Methods

In this retrospective, observational study with prospective data gathering we analyzed the outcomes of all confirmed COVID-19 cases (n = 955) in KTRs followed at our center between March 1, 2020, and April 30, 2022. Risk factors of COVID-19 related mortality were analyzed with focus on the role of early referral to the transplant center, which enabled early initiation of treatment and remote outpatient management. This proactive approach was dependent on the establishment and use of a telemedicine system, which facilitated patient-physician communication and expedited diagnostics and treatment. The main exposure evaluated was early referral of KTRs to the transplantation center after confirmed or suspected COVID-19 infection. The primary outcome was the association of early referral to the transplantation center with the risk of death within 30 days following a COVID-19 diagnosis, evaluated by logistic regression.

Results

We found that KTRs who referred their illness to the transplant center late had a higher 30-day mortality (4.5 vs. 13.6%, p < 0.001). Thirty days mortality after the diagnosis of COVID-19 was independently associated with late referral to the transplant center (OR 2.08, 95% CI 1.08-3.98, p = 0.027), higher age (OR 1.09, 95% CI 1.05-1.13, p < 0.001), higher body mass index (OR 1.06, 95% CI 1.01-1.12, p = 0.03), and lower eGFR (OR 0.96, 95% CI 0.94-0.98, p < 0.001) in multivariable logistic regression. Furthermore, KTRs who contacted the transplant center late were older, had longer time from transplantation, lived farther from the center and presented with higher Charlson comorbidity index.

Discussion

A well-organized telemedicine program can help to protect KTRs during an infectious disease outbreak by facilitating pro-active management and close surveillance. Furthermore, these results can be likely extrapolated to other vulnerable populations, such as patients with chronic kidney disease, diabetes or autoimmune diseases requiring the use of immunosuppression.

Predicting mortality among patients with severe COVID-19 pneumonia based on admission vital sign indices: a retrospective cohort study

BACKGROUND

Coronavirus disease 2019 (COVID-19) pneumonia remains a major public health concern. Vital sign indices-shock index (SI; heart rate [HR]/systolic blood pressure [SBP]), shock index age (SIA, SI × age), MinPulse (MP; maximum HR-HR), Pulse max index (PMI; HR/maximum HR), and blood pressure-age index (BPAI; SBP/age)-are better predictors of mortality in patients with trauma compared to traditional vital signs. We hypothesized that these vital sign indices may serve as predictors of mortality in patients with severe COVID-19 pneumonia. This study aimed to describe the association between vital sign indices at admission and COVID-19 pneumonia mortality and to modify the CURB-65 with the best performing vital sign index to establish a new mortality prediction tool.

METHODS

This retrospective study was conducted at a tertiary care center in southern Thailand. Adult patients diagnosed with COVID-19 pneumonia were enrolled in this study between January 2020 and July 2022. Patient demographic and clinical data on admission were collected from an electronic database. The area under the receiver operating characteristic (AUC) curve analysis was used to assess the predictive power of the resultant multivariable logistic regression model after univariate and multivariate analyses of variables with identified associations with in-hospital mortality.

RESULTS

In total, 251 patients with COVID-19 pneumonia were enrolled in this study. The in-hospital mortality rate was 27.9%. Non-survivors had significantly higher HR, respiratory rate, SIA, and PMI and lower MP and BPAI than survivors. A cutoff value of 51 for SIA (AUC, 0.663; specificity, 80%) was used to predict mortality. When SIA was introduced as a modifier for the CURB-65 score, the new score (the CURSIA score) showed a higher AUC than the Acute Physiology and Chronic Health Evaluation II and CURB-65 scores (AUCs: 0.785, 0.780, and 0.774, respectively) without statistical significance.

CONCLUSIONS

SIA and CURSIA scores were significantly associated with COVID-19 pneumonia mortality. These scores may contribute to better patient triage than traditional vital signs.

Application of clinical and CT imaging features in the evaluation of disease progression in patients with COVID-19

BACKGROUND

The Corona Virus Disease 2019(COVID-19) pandemic has strained healthcare systems worldwide, necessitating the early prediction of patients requiring critical care. This study aimed to analyze the laboratory examination indicators, CT features, and prognostic risk factors in COVID-19 patients.

METHODS

A retrospective study was conducted on 90 COVID-19 patients at the First Affiliated Hospital of Gannan Medical University between December 17, 2022, and March 17, 2023. Clinical data, laboratory examination results, and computed tomography (CT) imaging data were collected. Logistic multivariate regression analysis was performed to identify independent risk factors, and the predictive ability of each risk factor was assessed using the area under the receiver operating characteristic (ROC) curve.

RESULTS

Multivariate logistic regression analysis revealed that comorbid diabetes (odds ratio [OR] = 526.875, 95%CI = 1.384-1960.84, P = 0.053), lymphocyte count reduction (OR = 8.773, 95%CI = 1.432-53.584, P = 0.064), elevated D-dimer level (OR = 362.426, 95%CI = 1.228-984.995, P = 0.023), and involvement of five lung lobes (OR = 0.926, 95%CI = 0.026-0.686, P = 0.025) were risk factors for progression to severe COVID-19. ROC curve analysis showed the highest predictive value for 5 lung lobes (AUC = 0.782). Oxygen saturation was positively correlated with normally aerated lung volume and the proportion of normally aerated lung volume (P < 0.05).

CONCLUSIONS

The study demonstrated that comorbid diabetes, lymphocyte count reduction, elevated D-dimer levels, and involvement of the five lung lobes are significant risk factors for severe COVID-19. In CT lung volume quantification, normal aerated lung volume and the proportion of normal aerated lung volume correlated with blood oxygen saturation.

Effect of awake prone positioning on tracheal intubation rates in patients with COVID-19: A meta-analysis

Purpose

We investigated the effect of awake prone positioning on endotracheal intubation rates in spontaneously breathing patients with COVID-19 not undergoing endotracheal intubation.

Methods

We searched the CINAHL, Cochrane Library, PUBMED, MEDLINE, and Web of Science databases until December 31, 2022. Prospective randomized controlled, cohort, and case-control studies were included. A meta-analysis was performed on the primary outcome measure, tracheal intubation rates, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

Results

Ten studies with a total of 2641 patients were included. The tracheal intubation rate in the awake prone position was 34% (95%CI: 0.59-1.10; P = 0.18; I2 = 55%), showing a non-significant benefit. Mortality was lower in prone-positioned than in supine-positioned patients (odds ratio: 0.75; 95% CI: 0.61-0.93; P = 0.007; I2 = 46%), prone positioning significantly improved the PaO2/FiO2 ratio (mean difference -29.17; 95%CI: -50.91 to -7.43; P = 0.009; I2 = 44%).

Conclusions

Prone positioning can improve the PaO2/FIO2 ratio in patients with COVID-19 but we found no significant effect on tracheal intubation rates. Awake prone positioning seems to be associated with lower mortality, however, and may thus be a beneficial and effective intervention for patients with COVID-19. The optimal timing, duration, and target population need to be determined in future studies.

Days spent on non-invasive ventilation support: can it determine when to initiate VV- ECMO? Observational study in a cohort of Covid-19 patients

BACKGROUND

The study evaluates the impact of the time between commencing non-invasive ventilation (NIV) support and initiation of venovenous extracorporeal membrane oxygenation (VV-ECMO) in a cohort of critically ill patients with coronavirus disease 2019 (COVID-19) associated acute respiratory distress syndrome (ARDS).

METHODS

Prospective observational study design in an intensive Care Unit (ICU) of a tertiary hospital in Barcelona (Spain). All patients requiring VV-ECMO support due to COVID-19 associated ARDS between March 2020 and January 2022 were analysed. Survival outcome was determined at 90 days after VV-ECMO initiation. Demographic data, comorbidities at ICU admission, RESP (respiratory ECMO survival prediction) score, antiviral and immunomodulatory treatments received, inflammatory biomarkers, the need for vasopressors, the thromboprophylaxis regimen received, and respiratory parameters including the length of intubation previous to ECMO and the length of each NIV support (high-flow nasal cannula, continuous positive airway pressure and bi-level positive airway pressure), were also collated in order to assess risk factors for day-90 mortality. The effect of the time lapse between NIV support and VV-ECMO on survival was evaluated using logistic regression and adjusting the association with all factors that were significant in the univariate analysis.

RESULTS

Seventy-two patients finally received VV-ECMO support. At 90 days after commencing VV-ECMO 35 patients (48%) had died and 37 patients (52%) were alive. Multivariable analysis showed that at VV-ECMO initiation, age (p = 0.02), lactate (p = 0.001), and days from initiation of NIV support to starting VV-ECMO (p = 0.04) were all associated with day-90 mortality.

CONCLUSIONS

In our small cohort of VV-ECMO patients with COVID-19 associated ARDS, the time spent between initiation of NIV support and VV-ECMO (together with age and lactate) appeared to be a better predictor of mortality than the time between intubation and VV-ECMO.

Diagnostic of Patients with COVID-19 Pneumonia Using Passive Medical Microwave Radiometry (MWR)

BACKGROUND

Chest CT is widely regarded as a dependable imaging technique for detecting pneumonia in COVID-19 patients, but there is growing interest in microwave radiometry (MWR) of the lungs as a possible substitute for diagnosing lung involvement.

AIM

The aim of this study is to examine the utility of the MWR approach as a screening tool for diagnosing pneumonia with complications in patients with COVID-19.

METHODS

Our study involved two groups of participants. The control group consisted of 50 individuals (24 male and 26 female) between the ages of 20 and 70 years who underwent clinical evaluations and had no known medical conditions. The main group included 142 participants (67 men and 75 women) between the ages of 20 and 87 years who were diagnosed with COVID-19 complicated by pneumonia and were admitted to the emergency department between June 2020 to June 2021. Skin and lung temperatures were measured at 14 points, including 2 additional reference points, using a previously established method. Lung temperature data were obtained with the MWR2020 (MMWR LTD, Edinburgh, UK). All participants underwent clinical evaluations, laboratory tests, chest CT scans, MWR of the lungs, and reverse transcriptase polymerase chain reaction (RT-PCR) testing for SARS-CoV-2.

RESULTS

The MWR exhibits a high predictive capacity as demonstrated by its sensitivity of 97.6% and specificity of 92.7%.

CONCLUSIONS

MWR of the lungs can be a valuable substitute for chest CT in diagnosing pneumonia in patients with COVID-19, especially in situations where chest CT is unavailable or impractical.

Healthcare worker burnout during the first COVID-19 lockdown in Italy: experiences from an intensive neurological rehabilitation unit

OBJECTIVE

The study aim was to investigate the prevalence of behavioral symptoms and burnout in healthcare workers in an intensive neurological rehabilitation unit in Messina, Italy, during the first COVID-19 lockdown in Italy.

METHODS

Forty-seven healthcare workers (including neurologists, physiatrists, nurses and rehabilitation therapists) were enrolled in this cross-sectional study from February 2020 to June 2020. Participants were administered the following psychometric tests to investigate burnout and related symptoms: the Maslach Burnout Inventory, which measures emotional exhaustion, depersonalization and reduced personal accomplishment; the Zung Self-Rating Depression Scale (SDS); the Pre-Sleep Arousal Scale (PSAS); the Dyadic Adjustment Scale; and the Buss-Perry Aggression Questionnaire (BPAQ).

RESULTS

We found several correlations between test scores and burnout subdimensions. Emotional exhaustion was correlated with SDS (r = 0.67), PSAS-Cognitive (r = 0.67) and PSAS-Somatic (r = 0.70) scores, and moderately correlated with all BPAQ dimensions (r = 0.42). Depersonalization was moderately correlated with SDS (r = 0.54), PSAS-Cognitive (r = 0.53) and PSAS-Somatic (r = 0.50) scores.

CONCLUSION

During the first COVID-19 lockdown in Italy, healthcare workers were more exposed to physical and mental exhaustion and burnout. Research evaluating organizational and system-level interventions to promote psychological well-being at work for healthcare workers are needed.

Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study

BACKGROUND

An artificial intelligence (AI) model using chest radiography (CXR) may provide good performance in making prognoses for COVID-19.

OBJECTIVE

We aimed to develop and validate a prediction model using CXR based on an AI model and clinical variables to predict clinical outcomes in patients with COVID-19.

METHODS

This retrospective longitudinal study included patients hospitalized for COVID-19 at multiple COVID-19 medical centers between February 2020 and October 2020. Patients at Boramae Medical Center were randomly classified into training, validation, and internal testing sets (at a ratio of 8:1:1, respectively). An AI model using initial CXR images as input, a logistic regression model using clinical information, and a combined model using the output of the AI model (as CXR score) and clinical information were developed and trained to predict hospital length of stay (LOS) ≤2 weeks, need for oxygen supplementation, and acute respiratory distress syndrome (ARDS). The models were externally validated in the Korean Imaging Cohort of COVID-19 data set for discrimination and calibration.

RESULTS

The AI model using CXR and the logistic regression model using clinical variables were suboptimal to predict hospital LOS ≤2 weeks or the need for oxygen supplementation but performed acceptably in the prediction of ARDS (AI model area under the curve [AUC] 0.782, 95% CI 0.720-0.845; logistic regression model AUC 0.878, 95% CI 0.838-0.919). The combined model performed better in predicting the need for oxygen supplementation (AUC 0.704, 95% CI 0.646-0.762) and ARDS (AUC 0.890, 95% CI 0.853-0.928) compared to the CXR score alone. Both the AI and combined models showed good calibration for predicting ARDS (P=.079 and P=.859).

CONCLUSIONS

The combined prediction model, comprising the CXR score and clinical information, was externally validated as having acceptable performance in predicting severe illness and excellent performance in predicting ARDS in patients with COVID-19.

Deep Learning-Based Time-to-Death Prediction Model for COVID-19 Patients Using Clinical Data and Chest Radiographs

Accurate estimation of mortality and time to death at admission for COVID-19 patients is important and several deep learning models have been created for this task. However, there are currently no prognostic models which use end-to-end deep learning to predict time to event for admitted COVID-19 patients using chest radiographs and clinical data. We retrospectively implemented a new artificial intelligence model combining DeepSurv (a multiple-perceptron implementation of the Cox proportional hazards model) and a convolutional neural network (CNN) using 1356 COVID-19 inpatients. For comparison, we also prepared DeepSurv only with clinical data, DeepSurv only with images (CNNSurv), and Cox proportional hazards models. Clinical data and chest radiographs at admission were used to estimate patient outcome (death or discharge) and duration to the outcome. The Harrel's concordance index (c-index) of the DeepSurv with CNN model was 0.82 (0.75-0.88) and this was significantly higher than the DeepSurv only with clinical data model (c-index = 0.77 (0.69-0.84), p = 0.011), CNNSurv (c-index = 0.70 (0.63-0.79), p = 0.001), and the Cox proportional hazards model (c-index = 0.71 (0.63-0.79), p = 0.001). These results suggest that the time-to-event prognosis model became more accurate when chest radiographs and clinical data were used together.

The accuracy of the National Early Warning Score 2 in predicting early death in prehospital and emergency department settings: a systematic review and meta-analysis

Background

Many studies have explored the accuracy of the National Early Warning Score 2 (NEWS2) in predicting mortality in prehospital and emergency settings, but their findings are inconsistent. Whether NEWS2 is reliable for the pre-examination and triage of patients in prehospital settings and emergency departments remains debatable. Hence, this study aimed to evaluate the accuracy of NEWS2 in predicting mortality in prehospital settings and emergency departments.

Methods

We searched PubMed, Embase, Cochrane Library, Web of Science, CNKI, Wan Fang Data, Vip Database and SinoMed from the inception of each database to January 2023. The inclusion criteria: (I) patients in the prehospital settings or emergency departments; (II) the NEWS2 for predicting 2-day mortality, 30-day mortality, and in-hospital mortality; (III) sufficient data, such as sensitivity, specificity, overall survival, and deaths, were provided for the study; (IV) the type of study was accuracy prediction study. Two authors independently extracted data, including authors, year of publication, country of origin, study design, sample size, threshold cutoff values of NEWS2, and mortality. The PROBAST was used to assess the risk of bias in the included studies.

Results

Thirty studies with 185,835 participants were included. Among the 30 included studies, 13 have a high risk of bias, and 17 have a low risk of bias. The pooled sensitivity, specificity and AUC of 2-day mortality (early mortality), 30-day mortality and in-hospital mortality were 0.81 vs. 0.76 vs. 0.72 (95% CI: 0.61, 0.80), 0.81 vs. 0.69 vs. 0.78 (95% CI: 0.49, 0.93) and 0.88 vs. 0.80 vs. 0.78 (95% CI: 0.74, 0.82), respectively.

Conclusions

NEWS2 has excellent sensitivity and specificity in predicting early mortality in patients in the prehospitals setting and emergency departments. Nonetheless, it has poor performance in predicting in-hospital mortality and 30-day mortality. Our findings underpin the use of NEWS2 as a pre-examination and triage tool to predict early death in the prehospital settings and emergency departments. To improve the predictive accuracy, it should be used to monitor patients continuously rather than at a single point-in-time.

COVID-19 diagnostic approaches with an extensive focus on computed tomography in accurate diagnosis, prognosis, staging, and follow-up

Although a long time has passed since its outbreak, there is currently no specific treatment for COVID-19, and it seems that the most appropriate strategy to combat this pandemic is to identify and isolate infected individuals. Various clinical diagnosis methods such as molecular techniques, serologic assays, and imaging techniques have been developed to identify suspected patients. Although reverse transcription-quantitative PCR (RT-qPCR) has emerged as a reference standard method for diagnosis of SARS-CoV-2, the high rate of false-negative results and limited supplies to meet current demand are the main shortcoming of this technique. Based on a comprehensive literature review, imaging techniques, particularly computed tomography (CT), show an acceptable level of sensitivity in the diagnosis and follow-up of COVID-19. Indeed, because lung infection or pneumonia is a common complication of COVID-19, the chest CT scan can be an alternative testing method in the early diagnosis and treatment assessment of the disease. In this review, we summarize all the currently available frontline diagnostic tools for the detection of SARS-CoV-2-infected individuals and highlight the value of chest CT scan in the diagnosis, prognosis, staging, management, and follow-up of infected patients.

Impact of the seventh day nucleated red blood cell count on mortality in COVID-19 intensive care unit patients: A retrospective case-control study

Background

COVID-19 covers a broad clinical spectrum, threatening global health. Although several studies have investigated various prognostic biochemical and hematological parameters, they generally lack specificity and are insufficient for decision-making. Beyond the neonatal period, NRBCs (nucleated red blood cells) in peripheral blood is rare and often associated with malignant neoplasms, bone marrow diseases, and other severe disorders such as sepsis and hypoxia. Therefore, we investigated if NRBCs can predict mortality in hypoxic ICU (Intensive Care Unit) patients of COVID-19.

Methods

Seventy-one unvaccinated RT-PCR confirmed COVID-19 ICU patients was divided into those who survived (n=35, mean age=58) and died (n=36, mean age=75). Venous blood samples were collected in K3 EDTA tubes and analyzed on a Sysmex XN-1000 hematology analyzer with semiconductor laser flow cytometry and nucleic acid fluorescence staining method for NRBC analysis. NRBC numbers and percentages of the patients were compared on the first and seventh days of admission to the ICU. Results are reported as a proportion of NRBCs per 100 WBCs NRBCs/100 WBC (NRBC% and as absolute NRBC count (NRBC #, × 109/L).

Results

NRBC 7th-day count and % values were statistically higher in non-survival ones. The sensitivity for 7th day NRBC value <0.01 (negative) was 86.11%, the specificity was 48.57%, for <0.02; 75.00%, and 77.14%, for <0.03; 61.11%, and 94.60%.

Conclusions

In conclusion, our results indicate that NRBC elevation (>0.01) significantly predicts mortality in ICU hospitalized patients due to COVID-19. Worse, a high mortality rate is expected, especially with NRBC values of >0.03.

Adaptive immune system in severe COVID-19 patients in the first week of illness: A pilot study

Introduction

The presentation of the course of COVID-19-related T-cell responses in the first week of the disease may be a more specific period for adaptive immune response assessment. This study aimed to clarify the relationship between changes in peripheral blood lymphocyte counts and death in patients with COVID-19 pneumonia.

Methods

Thirty-three patients (14 females and 19 males) admitted for severe and desaturated COVID-19 pneumonia confirmed by polymerase chain reaction were included. Lymphocyte subsets and CD4+/CD8+ and CD16+/CD56+ rates were measured using flow cytometry from peripheral blood at admission and on the day of death or hospital discharge.

Results

Twenty-eight patients survived and five died. On the day of admission, the CD4+ cell count was significantly higher and the saturation of O2 was significantly lower in the deceased patients compared to the survivors (P < 0.05). The CD16+/CD56+ rate was significantly lower on the day of death in the deceased patients than in discharge day for the survivors (P = 0.013).

Conclusion

CD4+ lymphocyte percentages and O2 saturation in samples taken on the day of admission to the hospital and CD16+/CD56+ ratios taken at the time of discharge from the hospital were found to be associated with the mortality in patients with severe COVID-19.

Evaluation and limitations of different approaches among COVID-19 fatal cases using whole-exome sequencing data

BACKGROUND

COVID-19 caused by the SARS-CoV-2 infection may result in various disease symptoms and severity, ranging from asymptomatic, through mildly symptomatic, up to very severe and even fatal cases. Although environmental, clinical, and social factors play important roles in both susceptibility to the SARS-CoV-2 infection and progress of COVID-19 disease, it is becoming evident that both pathogen and host genetic factors are important too. In this study, we report findings from whole-exome sequencing (WES) of 27 individuals who died due to COVID-19, especially focusing on frequencies of DNA variants in genes previously associated with the SARS-CoV-2 infection and the severity of COVID-19.

RESULTS

We selected the risk DNA variants/alleles or target genes using four different approaches: 1) aggregated GWAS results from the GWAS Catalog; 2) selected publications from PubMed; 3) the aggregated results of the Host Genetics Initiative database; and 4) a commercial DNA variant annotation/interpretation tool providing its own knowledgebase. We divided these variants/genes into those reported to influence the susceptibility to the SARS-CoV-2 infection and those influencing the severity of COVID-19. Based on the above, we compared the frequencies of alleles found in the fatal COVID-19 cases to the frequencies identified in two population control datasets (non-Finnish European population from the gnomAD database and genomic frequencies specific for the Slovak population from our own database). When compared to both control population datasets, our analyses indicated a trend of higher frequencies of severe COVID-19 associated risk alleles among fatal COVID-19 cases. This trend reached statistical significance specifically when using the HGI-derived variant list. We also analysed other approaches to WES data evaluation, demonstrating its utility as well as limitations.

CONCLUSIONS

Although our results proved the likely involvement of host genetic factors pointed out by previous studies looking into severity of COVID-19 disease, careful considerations of the molecular-testing strategies and the evaluated genomic positions may have a strong impact on the utility of genomic testing.

A Retrospective Analysis of Ventilatory Strategy Comparing Non-invasive Ventilation (NIV) With Invasive Ventilation in Patients Admitted With Severe COVID-19 Pneumonia

Background The second wave of the COVID-19 pandemic in India saw a sudden upsurge of critically ill patients getting admitted to the ICU. The guidance for respiratory support was unclear in the early phase. But later reports showed lower mortality with non-invasive ventilation (NIV) than with intubation. The aim of this study was to assess the end result of initial methods of ventilation in COVID-19 patients. Methodology Patients admitted to ICU with COVID-19 were categorized as group 1 (IPPV-intubated within 24 hrs of admission), group 2 (NIV -NIV only), group 3 (NIV+ IPPV-intubated after 24 hrs), and group 4 (NRBM - Non-Rebreathing Mask only). All causes in the hospital or 30-day mortality, length of stay in ICU, and incidence of pneumothorax were compared between groups. Logistic regression analysis was done to determine the odds of mortality. Results The overall mortality rate among patients admitted to tertiary care centers was 15% and the rate among patients in ICU was 54.07%. Patients in group 1 and group 3 had significantly high mortality rates of 90.47% and 93.75%, respectively, as compared to 51.28% in group 2 patients. The odds of mortality were high in group 3 (OR 29.57, 95% CI 4.51 and 193.52) and group 1 (OR 8.01, 95% CI 1.35 and 47.48). Conclusion In a resource-limited setting, the use of NIV is associated with higher survival in COVID-19 patients. The prognosis of patients who are intubated early or after a trial of NIV is the same with increased odds of mortality.

Health insurance system and resilience to epidemics

We theoretically analyze the resilience (efficiency) of health insurance systems and diverse factors including trace and test technology, infection and contagion rates, and social distancing/lockdown policy, in coping with contagious diseases like COVID-19. Our findings can be summarized as follows. First, public insurance is more resilient than market insurance, as the former's investment in test technology is made at the social optimum, whereas the latter's investment is less. The decentralized behavior of competing insurers leads to a less resilient outcome. Second, resilience decreases as the market becomes more competitive because the externality effect becomes more severe. Third, a higher contagion rate, a more cost-efficient test technology or a higher initial infection rate unless it is not too high, leads to a higher test accuracy level. Fourth, the socially optimal social distancing/lockdown policy is determined by comparison between its relative costs and the benefit from contagion reduction.

Characterization of COVID-19 cases in the early phase (March to July 2020) of the pandemic in Kenya

Background

Kenya detected the first case of COVID-19 on March 13, 2020, and as of July 30, 2020, 17 975 cases with 285 deaths (case fatality rate (CFR) = 1.6%) had been reported. This study described the cases during the early phase of the pandemic to provide information for monitoring and response planning in the local context.

Methods

We reviewed COVID-19 case records from isolation centres while considering national representation and the WHO sampling guideline for clinical characterization of the COVID-19 pandemic within a country. Socio-demographic, clinical, and exposure data were summarized using median and mean for continuous variables and proportions for categorical variables. We assigned exposure variables to socio-demographics, exposure, and contact data, while the clinical spectrum was assigned outcome variables and their associations were assessed.

Results

A total of 2796 case records were reviewed including 2049 (73.3%) male, 852 (30.5%) aged 30-39 years, 2730 (97.6%) Kenyans, 636 (22.7%) transporters, and 743 (26.6%) residents of Nairobi City County. Up to 609 (21.8%) cases had underlying medical conditions, including hypertension (n = 285 (46.8%)), diabetes (n = 211 (34.6%)), and multiple conditions (n = 129 (21.2%)). Out of 1893 (67.7%) cases with likely sources of exposure, 601 (31.8%) were due to international travel. There were 2340 contacts listed for 577 (20.6%) cases, with 632 contacts (27.0%) being traced. The odds of developing COVID-19 symptoms were higher among case who were aged above 60 years (odds ratio (OR) = 1.99, P = 0.007) or had underlying conditions (OR = 2.73, P < 0.001) and lower among transport sector employees (OR = 0.31, P < 0.001). The odds of developing severe COVID-19 disease were higher among cases who had underlying medical conditions (OR = 1.56, P < 0.001) and lower among cases exposed through community gatherings (OR = 0.27, P < 0.001). The odds of survival of cases from COVID-19 disease were higher among transport sector employees (OR = 3.35, P = 0.004); but lower among cases who were aged ≥60 years (OR = 0.58, P = 0.034) and those with underlying conditions (OR = 0.58, P = 0.025).

Conclusion

The early phase of the COVID-19 pandemic demonstrated a need to target the elderly and comorbid cases with prevention and control strategies while closely monitoring asymptomatic cases.

Logistics of temporary testing centers for coronavirus disease

The ongoing COVID-19 pandemic has caused the death of millions of people, and PCR testing is widely used as the gold standard method to detect the infections to restrict the outbreak. Through the interviews conducted with people from the field in South Korea, the UK, and Turkey, we have found that there are numerous testing strategies worldwide. Those testing strategies include drive-through and home delivery testing capabilities, local test sites, and mobile test centers. Our primary motivation is to propose a generic model based on the best practices in the UK and South Korea. Also, we aim to present a case study on Turkey for the implementation of vital procedures and increase their availability. This paper represents a study on how to construct a temporary testing logistics system during the initial phases of pandemics to increase the availability of PCR testing with the primary objective of maximizing total sample collection. The design also considers minimizing the maximum walking distance to increase the convenience of sample collection for the people living in the neighborhoods. The proposed system consists of temporary testing centers and a central laboratory. Temporary testing centers perform direct tours to the potential areas to collect samples and bring the collected sample to the designated central laboratories located at central hospitals. Moreover, to represent the non-linear inheritance of the pandemic progress within a population, we consider diminishing sample potentials over time and coverage. This new problem is defined as an extension of the Selective Vehicle Routing Problem and Covering Tour Problem. We propose a mathematical model and four two-stage math-heuristic algorithms to determine the location and routing of the temporary testing centers and their lengths of stay at each visited location. The performances of the proposed solution methodologies are tested on two data sets. The first set is constructed by the confirmed cases of the districts of Seoul, Korea, and by the interview of health personnel of H+ Yangji Hospital COVID-19 semi-mobile booth application, and the second set is constructed by 99 hospital/health centers from distinct neighborhoods of 22 districts of Istanbul, Turkey. The Pareto set of optimum solutions is generated based on total sample collection and maximum walking distance. Finally, sensitivity analyses on some design parameters are conducted.

Neural-Symbolic Ensemble Learning for early-stage prediction of critical state of Covid-19 patients

Recently, Artificial Intelligence (AI) and Machine Learning (ML) have been successfully applied to many domains of interest including medical diagnosis. Due to the availability of a large quantity of data, it is possible to build reliable AI systems that assist humans in making decisions. The recent Covid-19 pandemic quickly spread over the world causing serious health problems and severe economic and social damage. Computer scientists are actively working together with doctors on different ML models to diagnose Covid-19 patients using Computed Tomography (CT) scans and clinical data. In this work, we propose a neural-symbolic system that predicts if a Covid-19 patient arriving at the hospital will end in a critical condition. The proposed system relies on Deep 3D Convolutional Neural Networks (3D-CNNs) for analyzing lung CT scans of Covid-19 patients, Decision Trees (DTs) for predicting if a Covid-19 patient will eventually pass away by analyzing its clinical data, and a neural system that integrates the previous ones using Hierarchical Probabilistic Logic Programs (HPLPs). Predicting if a Covid-19 patient will end in a critical condition is useful for managing the limited number of intensive care at the hospital. Moreover, knowing early that a Covid-19 patient could end in serious conditions allows doctors to gain early knowledge on patients and provide special treatment to those predicted to finish in critical conditions. The proposed system, entitled Neural HPLP, obtains good performance in terms of area under the receiver operating characteristic and precision curves with values of about 0.96 for both metrics. Therefore, with Neural HPLP, it is possible not only to efficiently predict if Covid-19 patients will end in severe conditions but also possible to provide an explanation of the prediction. This makes Neural HPLP explainable, interpretable, and reliable. Graphical abstract Representation of Neural HPLP. From top to bottom, the two different types of data collected from the same patient and used in this project are represented. This data feeds the two different machine learning systems and the integration of the two systems using Hierarchical Probabilistic Logic Program.

Automatic detection of Covid-19 from chest X-ray and lung computed tomography images using deep neural networks and transfer learning

The world has been undergoing the most ever unprecedented circumstances caused by the coronavirus pandemic, which is having a devastating global effect in different aspects of life. Since there are not effective antiviral treatments for Covid-19 yet, it is crucial to early detect and monitor the progression of the disease, thereby helping to reduce mortality. While different measures are being used to combat the virus, medical imaging techniques have been examined to support doctors in diagnosing the disease. In this paper, we present a practical solution for the detection of Covid-19 from chest X-ray (CXR) and lung computed tomography (LCT) images, exploiting cutting-edge Machine Learning techniques. As the main classification engine, we make use of EfficientNet and MixNet, two recently developed families of deep neural networks. Furthermore, to make the training more effective and efficient, we apply three transfer learning algorithms. The ultimate aim is to build a reliable expert system to detect Covid-19 from different sources of images, making it be a multi-purpose AI diagnosing system. We validated our proposed approach using four real-world datasets. The first two are CXR datasets consist of 15,000 and 17,905 images, respectively. The other two are LCT datasets with 2,482 and 411,528 images, respectively. The five-fold cross-validation methodology was used to evaluate the approach, where the dataset is split into five parts, and accordingly the evaluation is conducted in five rounds. By each evaluation, four parts are combined to form the training data, and the remaining one is used for testing. We obtained an encouraging prediction performance for all the considered datasets. In all the configurations, the obtained accuracy is always larger than 95.0%. Compared to various existing studies, our approach yields a substantial performance gain. Moreover, such an improvement is statistically significant.

Prevention is Key to Reducing the Spread of COVID-19 in Long-Term Care Facilities

The novel coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has greatly affected the older people who live in long-term care facilities (LTCFs). Older people and those with underlying chronic conditions in LTCFs have experienced disproportionately high morbidity and mortality. COVID-19 vaccines plus a booster shot provide strong protection against severe illness, hospitalizations, and deaths, but new COVID-19 variants, such as Omicron, have a remarkable ability to evade immunity from vaccines, past infection, or both. Prevention is key to reducing the spread of COVID-19 in LTCFs. This study aims to investigate a prevention approach for protecting residents and staff from COVID-19. This paper first presents a case study of massive coronavirus outbreaks at a big nursing home facility and demonstrates how the facility incorrectly responded to COVID-19. It further investigates prevention measures, such as improving vaccination, early detection, isolation and intervention to prevent the spread of COVID-19. It concludes by discussing the implications of the study and directions of future research.

The Prognostic Value of Lung Injury and Fibrosis Markers, KL-6, TGF-β1, FGF-2 in COVID-19 Patients

Background:

Biomarkers of lung injury and interstitial fibrosis give insight about the extent of involvement and prognosis in well-known interstitial lung diseases (ILD). Serum Krebs von den Lungen-6 (KL-6) reflects direct alveolar injury and, transforming growth factor-beta1 (TGF-β1) and fibroblast growth factor-2 (FGF-2) are principal mediators of fibrosis in ILD and in almost all fibrotic diseases. In this sense, we aimed to assess associations of these biomarkers with traditional inflammatory markers and clinical course of COVID-19.

Methods:

Patients with COVID-19 who had confirmed diagnosis with SARS-CoV-2 nucleic acid RT-PCR were enrolled and followed up prospectively with a standardized approach one month after diagnosis. Patients were divided into severe and non-severe groups according to National Institutes of Health criteria. Outcome was assessed for the requirement of intensive care unit (ICU) admission, long term respiratory support and death. Blood samples were collected at enrollment and serum levels of KL-6, TGF-β1, FGF-2 were determined by ELISA. Association between these markers with other prognostic markers and prognosis were analyzed.

Results:

Overall 31 severe and 28 non-severe COVID-19 patients were enrolled and were compared with healthy control subjects (n = 30). Serum KL-6 levels in COVID-19 patients were significantly higher (median [IQR]; 11.54 [4.86] vs 8.54 [3.98] ng/mL, P = .001] and FGF-2 levels were lower (median [IQR]; 76.84 [98.2] vs 101.62 [210.6] pg/mL) compared to healthy control group. A significant correlation was found between KL-6 values and CRP, fibrinogen, d-dimer and lymphocyte counts. However, we did not find an association between these markers and subsequent severity of COVID-19, mortality and long-term prognosis.

Conclusions:

Serum KL-6 levels were significantly elevated at the diagnosis of COVID-19 and correlated well with the other traditional prognostic inflammatory markers. Serum levels of principal fibrosis mediators, TGF-β1, FGF-2, were not elevated at diagnosis of COVID-19, therefore did not help to anticipate long term prognosis.

Reduced critical care demand with early CPAP and proning in COVID-19 at Bradford: A single-centre cohort

Background

Guidance in COVID-19 respiratory failure has favoured early intubation, with concerns over the use of CPAP. We adopted early CPAP and self-proning, and evaluated the safety and efficacy of this approach.

Methods

This retrospective observational study included all patients with a positive COVID-19 PCR, and others with high clinical suspicion. Our protocol advised early CPAP and self-proning for severe cases, aiming to prevent rather than respond to deterioration. CPAP was provided outside critical care by ward staff supported by physiotherapists and an intensive critical care outreach program. Data were analysed descriptively and compared against a large UK cohort (ISARIC).

Results

559 patients admitted before 1 May 2020 were included. 376 were discharged alive, and 183 died. 165 patients (29.5%) received CPAP, 40 (7.2%) were admitted to critical care and 28 (5.0%) were ventilated. Hospital mortality was 32.7%, and 50% for critical care. Following CPAP, 62% of patients with S:F or P:F ratios indicating moderate or severe ARDS, who were candidates for escalation, avoided intubation. Figures for critical care admission, intubation and hospital mortality are lower than ISARIC, whilst critical care mortality is similar. Following ISARIC proportions we would have admitted 92 patients to critical care and intubated 55. Using the described protocol, we intubated 28 patients from 40 admissions, and remained within our expanded critical care capacity.

Conclusion

Bradford's protocol produced good results despite our population having high levels of co-morbidity and ethnicities associated with poor outcomes. In particular we avoided overloading critical care capacity. We advocate this approach as both effective and safe.

Nurse-led implementation of awake prone positioning

This article discusses the pathophysiology of COVID-19 acute respiratory distress syndrome (ARDS), the evidence supporting the use of awake prone positioning (APP) for adult patients with COVID-19 ARDS cared for in acute care medical units, and a quality improvement initiative to support a standardized APP process on a COVID-19 medical unit.

Preventive measures significantly reduced the risk of nosocomial infection in elderly inpatients during the COVID-19 pandemic

In December 2019, there was an outbreak of pneumonia of unknown causes in Wuhan, China. The etiological pathogen was identified to be a novel coronavirus, named severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19). The number of infected patients has markedly increased since the 2019 outbreak and COVID-19 has also proven to be highly contagious. In particular, the elderly are among the group of patients who are the most susceptible to succumbing to COVID-19 within the general population. Cross-infection in the hospital is one important route of SARS-CoV-2 transmission, where elderly patients are more susceptible to nosocomial infections due to reduced immunity. Therefore, the present study was conducted to search for ways to improve the medical management workflow in geriatric departments to ultimately reduce the risk of nosocomial infection in elderly inpatients. The present observational retrospective cohort study analysed elderly patients who were hospitalised in the Geriatric Department of the First Affiliated Hospital with Nanjing Medical University (Nanjing, China). A total of 4,066 elderly patients, who were admitted between January and March in 2019 and 2020 and then hospitalised for >48 h were selected. Among them, 3,073 (75.58%) patients hospitalised from January 2019 to March 2019 were allocated into the non-intervention group, whereas the remaining 933 (24.42%) patients hospitalised from January 2020 to March 2020 after the COVID-19 outbreak were allocated into the intervention group. Following multivariate logistic regression analysis, the risk of nosocomial infections was found to be lower in the intervention group compared with that in the non-intervention group. After age stratification and adjustment for sex, chronic disease, presence of malignant tumour and trauma, both inverse probability treatment weighting and standardised mortality ratio revealed a lower risk of nosocomial infections in the intervention group compared with that in the non-intervention group. To rule out interference caused by changes in the community floating population and social environment during this 1-year study, 93 long-stay patients in stable condition were selected as a subgroup based on 4,066 patients. The so-called floating population refers to patients who have been in hospital for <2 years. Patients aged ≥65 years were included in the geriatrics program. The incidence of nosocomial infections during the epidemic prevention and control period (24 January 2020 to 24 March 2020) and the previous period of hospitalisation (24 January 2019 to 24 March 2019) was also analysed. In the subgroup analysis, a multivariate analysis was also performed on 93 elderly patients who experienced long-term hospitalisation. The risk of nosocomial and pulmonary infections was found to be lower in the intervention group compared with that in the non-intervention group. During the pandemic, the geriatric department took active preventative measures. However, whether these measures can be normalised to reduce the risk of nosocomial infections among elderly inpatients remain unclear. In addition, the present study found that the use of an indwelling gastric tube is an independent risk factor of nosocomial pulmonary infection in elderly inpatients. However, nutritional interventions are indispensable for the long-term wellbeing of patients, especially for those with dysphagia in whom an indwelling gastric tube is the most viable method of providing enteral nutrition. To conclude, the present retrospective analysis of the selected cases showed that enacting preventative and control measures resulted in the effective control of the incidence of nosocomial infections.

Fine-Grained Assessment of COVID-19 Severity Based on Clinico-Radiological Data Using Machine Learning

BACKGROUND

The severe and critical cases of COVID-19 had high mortality rates. Clinical features, laboratory data, and radiological features provided important references for the assessment of COVID-19 severity. The machine learning analysis of clinico-radiological features, especially the quantitative computed tomography (CT) image analysis results, may achieve early, accurate, and fine-grained assessment of COVID-19 severity, which is an urgent clinical need.

OBJECTIVE

To evaluate if machine learning algorithms using CT-based clinico-radiological features could achieve the accurate fine-grained assessment of COVID-19 severity.

METHODS

The clinico-radiological features were collected from 78 COVID-19 patients with different severities. A neural network was developed to automatically measure the lesion volume from CT images. The severity was clinically diagnosed using two-type (severe and non-severe) and fine-grained four-type (mild, regular, severe, critical) classifications, respectively. To investigate the key features of COVID-19 severity, statistical analyses were performed between patients' clinico-radiological features and severity. Four machine learning algorithms (decision tree, random forest, SVM, and XGBoost) were trained and applied in the assessment of COVID-19 severity using clinico-radiological features.

RESULTS

The CT imaging features (CTscore and lesion volume) were significantly related with COVID-19 severity (p < 0.05 in statistical analysis for both in two-type and fine-grained four-type classifications). The CT imaging features significantly improved the accuracy of machine learning algorithms in assessing COVID-19 severity in the fine-grained four-type classification. With CT analysis results added, the four-type classification achieved comparable performance to the two-type one.

CONCLUSIONS

CT-based clinico-radiological features can provide an important reference for the accurate fine-grained assessment of illness severity using machine learning to achieve the early triage of COVID-19 patients.

Facilitating Safe Discharge Through Predicting Disease Progression in Moderate Coronavirus Disease 2019 (COVID-19): A Prospective Cohort Study to Develop and Validate a Clinical Prediction Model in Resource-Limited Settings

BACKGROUND

In locations where few people have received coronavirus disease 2019 (COVID-19) vaccines, health systems remain vulnerable to surges in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Tools to identify patients suitable for community-based management are urgently needed.

METHODS

We prospectively recruited adults presenting to 2 hospitals in India with moderate symptoms of laboratory-confirmed COVID-19 to develop and validate a clinical prediction model to rule out progression to supplemental oxygen requirement. The primary outcome was defined as any of the following: SpO2 < 94%; respiratory rate > 30 BPM; SpO2/FiO2 < 400; or death. We specified a priori that each model would contain three clinical parameters (age, sex, and SpO2) and 1 of 7 shortlisted biochemical biomarkers measurable using commercially available rapid tests (C-reactive protein [CRP], D-dimer, interleukin 6 [IL-6], neutrophil-to-lymphocyte ratio [NLR], procalcitonin [PCT], soluble triggering receptor expressed on myeloid cell-1 [sTREM-1], or soluble urokinase plasminogen activator receptor [suPAR]), to ensure the models would be suitable for resource-limited settings. We evaluated discrimination, calibration, and clinical utility of the models in a held-out temporal external validation cohort.

RESULTS

In total, 426 participants were recruited, of whom 89 (21.0%) met the primary outcome; 257 participants comprised the development cohort, and 166 comprised the validation cohort. The 3 models containing NLR, suPAR, or IL-6 demonstrated promising discrimination (c-statistics: 0.72-0.74) and calibration (calibration slopes: 1.01-1.05) in the validation cohort and provided greater utility than a model containing the clinical parameters alone.

CONCLUSIONS

We present 3 clinical prediction models that could help clinicians identify patients with moderate COVID-19 suitable for community-based management. The models are readily implementable and of particular relevance for locations with limited resources.

Multiwalled Carbon Nanotube-Based On-Body Patch Antenna for Detecting COVID-19-Affected Lungs

A novel rectangular patch antenna based on multiwall carbon nanotubes has been designed and developed for assisting the initial detection of COVID-19-affected lungs. Due to their highly conductive nature, each nanotube echoes electromagnetic waves in a unique manner, influencing the increase in bandwidth. The proposed antenna operates at 6.63, 7.291, 7.29, and 7.22 GHz with a higher bandwidth classified as an ultrawide band and can be used on a human body phantom model because of its flexibility and decreased radiation qualities. Flame retardant 4 is chosen as a substrate with a uniform thickness of 1.62 mm due to its inexpensive cost and excellent electrical properties. The maximum specific absorption rate of the proposed antenna is obtained as 1.77 W/kg for 10 g of tissues. For testing purposes, a model including all the known features of COVID-19-affected lungs is developed. The designed antenna exhibits excellent performance in free space, normal lungs, and affected lung environments. It might be utilized as a first screening device for COVID-19 patients, especially in resource-constrained areas where traditional medical equipment such as X-ray and computerized tomography scans are scarce.

Comparison of demographic features and laboratory parameters between COVID-19 deceased patients and surviving severe and critically ill cases

BACKGROUND

Coronavirus disease 2019 (COVID-19) has been far more devastating than expected, showing no signs of slowing down at present. Heilongjiang Province is the most northeastern province of China, and has cold weather for nearly half a year and an annual temperature difference of more than 60ºC, which increases the underlying morbidity associated with pulmonary diseases, and thus leads to lung dysfunction. The demographic features and laboratory parameters of COVID-19 deceased patients in Heilongjiang Province, China with such climatic characteristics are still not clearly illustrated.

AIM

To illustrate the demographic features and laboratory parameters of COVID-19 deceased patients in Heilongjiang Province by comparing with those of surviving severe and critically ill cases.

METHODS

COVID-19 deceased patients from different hospitals in Heilongjiang Province were included in this retrospective study and compared their characteristics with those of surviving severe and critically ill cases in the COVID-19 treatment center of the First Affiliated Hospital of Harbin Medical University. The surviving patients were divided into severe group and critically ill group according to the Diagnosis and Treatment of New Coronavirus Pneumonia (the seventh edition). Demographic data were collected and recorded upon admission. Laboratory parameters were obtained from the medical records, and then compared among the groups.

RESULTS

Twelve COVID-19 deceased patients, 27 severe cases and 26 critically ill cases were enrolled in this retrospective study. No differences in age, gender, and number of comorbidities between groups were found. Neutrophil percentage (NEUT%), platelet (PLT), C-reactive protein (CRP), creatine kinase isoenzyme (CK-MB), serum troponin I (TNI) and brain natriuretic peptides (BNP) showed significant differences among the groups (P = 0.020, P = 0.001, P < 0.001, P = 0.001, P < 0.001, P < 0.001, respectively). The increase of CRP, D-dimer and NEUT% levels, as well as the decrease of lymphocyte count (LYMPH) and PLT counts, showed significant correlation with death of COVID-19 patients (P = 0.023, P = 0.008, P = 0.045, P = 0.020, P = 0.015, respectively).

CONCLUSION

Compared with surviving severe and critically ill cases, no special demographic features of COVID-19 deceased patients were observed, while some laboratory parameters including NEUT%, PLT, CRP, CK-MB, TNI and BNP showed significant differences. COVID-19 deceased patients had higher CRP, D-dimer and NEUT% levels and lower LYMPH and PLT counts.

Clinical update on COVID-19 for the emergency clinician: Airway and resuscitation

INTRODUCTION

Coronavirus disease of 2019 (COVID-19) has resulted in millions of cases worldwide. As the pandemic has progressed, the understanding of this disease has evolved.

OBJECTIVE

This narrative review provides emergency clinicians with a focused update of the resuscitation and airway management of COVID-19.

DISCUSSION

Patients with COVID-19 and septic shock should be resuscitated with buffered/balanced crystalloids. If hypotension is present despite intravenous fluids, vasopressors including norepinephrine should be initiated. Stress dose steroids are recommended for patients with severe or refractory septic shock. Airway management is the mainstay of initial resuscitation in patients with COVID-19. Patients with COVID-19 and ARDS should be managed similarly to those ARDS patients without COVID-19. Clinicians should not delay intubation if indicated. In patients who are more clinically stable, physicians can consider a step-wise approach as patients' oxygenation needs escalate. High-flow nasal cannula (HFNC) and non-invasive positive pressure ventilation (NIPPV) are recommended over elective intubation. Prone positioning, even in awake patients, has been shown to lower intubation rates and improve oxygenation. Strategies consistent with ARDSnet can be implemented in this patient population, with a goal tidal volume of 4-8 mL/kg of predicted body weight and targeted plateau pressures <30 cm H₂O. Limited data support the use of neuromuscular blocking agents (NBMA), recruitment maneuvers, inhaled pulmonary vasodilators, and extracorporeal membrane oxygenation (ECMO).

CONCLUSION

This review presents a concise update of the resuscitation strategies and airway management techniques in patients with COVID-19 for emergency medicine clinicians.

Effectiveness of prone position in spontaneously breathing patients with COVID-19: A prospective cohort study

We present a single centre study describing the effect of awake prone position (PP) on oxygenation and clinical outcomes in spontaneously breathing patients with novel coronavirus disease (COVID-19). Between 1st March and 30th April 2020, forty eight of 138 patients managed outside of the critical care unit with facemask oxygen, high flow nasal oxygen (HFNO) or continuous positive airway pressure (CPAP), underwent PP. Prone position was associated with significant improvement in oxygenation, lower ICU admission, tracheal intubation, and shorter ICU length of stay. Lack of response to PP may be an indicator of treatment failure, requiring early escalation.

Lessons from the frontline: The value of emergency care processes and data to pandemic responses across the Pacific region

Background

Emergency care (EC) addresses the needs of patients with acute illness and injury, and has fulfilled a critical function during the COVID-19 pandemic. 'Processes' (e.g. triage) and 'data' (e.g. surveillance) have been nominated as essential building blocks for EC systems. This qualitative research sought to explore the impact of the pandemic on EC clinicians across the Pacific region, including the contribution of EC building blocks to effective responses.

Methods

The study was conducted in three phases, with data obtained from online support forums, key informant interviews and focus group discussions. There were 116 participants from more than 14 Pacific Island Countries and Territories. A phenomenological approach was adopted, incorporating inductive and deductive methods. The deductive thematic analysis utilised previously identified building blocks for Pacific EC. This paper summarises findings for the building blocks of 'processes' and 'data'.

Findings

Establishing triage and screening capacity, aimed at assessing urgency and transmission risk respectively, were priorities for EC clinicians. Enablers included support from senior hospital leaders, previous disaster experience and consistent guidelines. The introduction of efficient patient flow processes, such as streaming, proved valuable to emergency departments, and checklists and simulation were useful implementation strategies. Some response measures impacted negatively on non-COVID patients, and proactive approaches were required to maintain 'business as usual'. The pandemic also highlighted the value of surveillance and performance data.

Interpretation

Developing effective processes for triage, screening and streaming, among other areas, was critical to an effective EC response. Beyond the pandemic, strengthening processes and data management capacity will build resilience in EC systems.

Funding

Phases 1 and 2A of this study were part of an Epidemic Ethics/World Health Organization (WHO) initiative, supported by Foreign, Commonwealth and Development Office/Wellcome Grant 214711/Z/18/Z. Co-funding for this research was received from the Australasian College for Emergency Medicine Foundation via an International Development Fund Grant.

Development and Validation of a Multimodal-Based Prognosis and Intervention Prediction Model for COVID-19 Patients in a Multicenter Cohort

The ability to accurately predict the prognosis and intervention requirements for treating highly infectious diseases, such as COVID-19, can greatly support the effective management of patients, especially in resource-limited settings. The aim of the study is to develop and validate a multimodal artificial intelligence (AI) system using clinical findings, laboratory data and AI-interpreted features of chest X-rays (CXRs), and to predict the prognosis and the required interventions for patients diagnosed with COVID-19, using multi-center data. In total, 2282 real-time reverse transcriptase polymerase chain reaction-confirmed COVID-19 patients’ initial clinical findings, laboratory data and CXRs were retrospectively collected from 13 medical centers in South Korea, between January 2020 and June 2021. The prognostic outcomes collected included intensive care unit (ICU) admission and in-hospital mortality. Intervention outcomes included the use of oxygen (O2) supplementation, mechanical ventilation and extracorporeal membrane oxygenation (ECMO). A deep learning algorithm detecting 10 common CXR abnormalities (DLAD-10) was used to infer the initial CXR taken. A random forest model with a quantile classifier was used to predict the prognostic and intervention outcomes, using multimodal data. The area under the receiver operating curve (AUROC) values for the single-modal model, using clinical findings, laboratory data and the outputs from DLAD-10, were 0.742 (95% confidence interval [CI], 0.696−0.788), 0.794 (0.745−0.843) and 0.770 (0.724−0.815), respectively. The AUROC of the combined model, using clinical findings, laboratory data and DLAD-10 outputs, was significantly higher at 0.854 (0.820−0.889) than that of all other models (p < 0.001, using DeLong’s test). In the order of importance, age, dyspnea, consolidation and fever were significant clinical variables for prediction. The most predictive DLAD-10 output was consolidation. We have shown that a multimodal AI model can improve the performance of predicting both the prognosis and intervention in COVID-19 patients, and this could assist in effective treatment and subsequent resource management. Further, image feature extraction using an established AI engine with well-defined clinical outputs, and combining them with different modes of clinical data, could be a useful way of creating an understandable multimodal prediction model.

A novel algorithm for detection of COVID-19 by analysis of chest CT images using Hopfield neural network

BACKGROUND

Widely spread of the COVID-19 virus has put the whole world in jeopardy. At this moment, using new techniques to detect and treat this novel disease is of significance or maybe the first priority of many scientists and researchers throughout the world.

PURPOSE

To present a new algorithm for detecting the novel coronavirus 2019 using chest CT images with high accuracy.

MATERIALS AND METHODS

In this study, we looked at the newly-presented data and detection methods of this disease using chest CT; then, a new neural network algorithm was presented to recognize the COVID-19 symptoms. A mathematical model is used to enhance the accuracy of masking, and a high accuracy Hopfield Neural Network (HNN) is used for finding symptoms. A dataset of CT scans, including 12 pattern images, was trained by this neural network, and 295CT images from three different datasets were tested via the model.

RESULTS

The sensitivity and specificity of the model for detecting COVID-19 in test data were 97.4% (149 of 153) and 98.6% (140 of 142) respectively. Also, the sensitivity and specificity of the model for detecting CAP (community-acquired pneumonia) in test data were 97.3% (106 of 109) and 99.5% (185 of 186) respectively, and, the sensitivity and specificity of the model for detecting non-pneumonia patients were 100% (33 of 33) and 98.5% (258 of 262) respectively.

CONCLUSION

This new algorithm can potentially help detect the novel Coronavirus patients using CT images.

Changes in laboratory value improvement and mortality rates over the course of the pandemic: an international retrospective cohort study of hospitalised patients infected with SARS-CoV-2

OBJECTIVE

To assess changes in international mortality rates and laboratory recovery rates during hospitalisation for patients hospitalised with SARS-CoV-2 between the first wave (1 March to 30 June 2020) and the second wave (1 July 2020 to 31 January 2021) of the COVID-19 pandemic.

DESIGN, SETTING AND PARTICIPANTS

This is a retrospective cohort study of 83 178 hospitalised patients admitted between 7 days before or 14 days after PCR-confirmed SARS-CoV-2 infection within the Consortium for Clinical Characterization of COVID-19 by Electronic Health Record, an international multihealthcare system collaborative of 288 hospitals in the USA and Europe. The laboratory recovery rates and mortality rates over time were compared between the two waves of the pandemic.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome was all-cause mortality rate within 28 days after hospitalisation stratified by predicted low, medium and high mortality risk at baseline. The secondary outcome was the average rate of change in laboratory values during the first week of hospitalisation.

RESULTS

Baseline Charlson Comorbidity Index and laboratory values at admission were not significantly different between the first and second waves. The improvement in laboratory values over time was faster in the second wave compared with the first. The average C reactive protein rate of change was -4.72 mg/dL vs -4.14 mg/dL per day (p=0.05). The mortality rates within each risk category significantly decreased over time, with the most substantial decrease in the high-risk group (42.3% in March-April 2020 vs 30.8% in November 2020 to January 2021, p<0.001) and a moderate decrease in the intermediate-risk group (21.5% in March-April 2020 vs 14.3% in November 2020 to January 2021, p<0.001).

CONCLUSIONS

Admission profiles of patients hospitalised with SARS-CoV-2 infection did not differ greatly between the first and second waves of the pandemic, but there were notable differences in laboratory improvement rates during hospitalisation. Mortality risks among patients with similar risk profiles decreased over the course of the pandemic. The improvement in laboratory values and mortality risk was consistent across multiple countries.

Estimation of respiratory rate in various environments using microphones embedded in face masks

Wearable health devices and respiratory rates (RRs) have drawn attention to the healthcare domain as it helps healthcare workers monitor patients' health status continuously and in a non-invasive manner. However, to monitor health status outside healthcare professional settings, the reliability of this wearable device needs to be evaluated in complex environments (i.e., public street and transportation). Therefore, this study proposes a method to estimate RR from breathing sounds recorded by a microphone placed inside three types of masks: surgical, a respirator mask (Korean Filter 94), and reusable masks. The Welch periodogram method was used to estimate the power spectral density of the breathing signals to measure the RR. We evaluated the proposed method by collecting data from 10 healthy participants in four different environments: indoor (office) and outdoor (public street, public bus, and subway). The results obtained errors as low as 0% for accuracy and repeatability in most cases. This research demonstrated that the acoustic-based method could be employed as a wearable device to monitor RR continuously, even outside the hospital environment.

Faster indicators of chikungunya incidence using Google searches

Chikungunya, a mosquito-borne disease, is a growing threat in Brazil, where over 640,000 cases have been reported since 2017. However, there are often long delays between diagnoses of chikungunya cases and their entry in the national monitoring system, leaving policymakers without the up-to-date case count statistics they need. In contrast, weekly data on Google searches for chikungunya is available with no delay. Here, we analyse whether Google search data can help improve rapid estimates of chikungunya case counts in Rio de Janeiro, Brazil. We build on a Bayesian approach suitable for data that is subject to long and varied delays, and find that including Google search data reduces both model error and uncertainty. These improvements are largest during epidemics, which are particularly important periods for policymakers. Including Google search data in chikungunya surveillance systems may therefore help policymakers respond to future epidemics more quickly.

To respond quickly to disease outbreaks, policymakers need rapid data on the number of new infections. However, for many diseases, such data is very delayed, due to the administrative work required to record each case in a disease surveillance system. This is a problem for data on chikungunya, a mosquito-borne disease which is a growing threat in Brazil. In Rio de Janeiro, delays in chikungunya cases being recorded average four weeks. These delays are sometimes longer and sometimes shorter. In stark contrast to chikungunya data, data on what people are searching for on Google is available almost immediately. People suffering from chikungunya might search on Google for information about the disease. Here, we investigate whether rapidly available Google data can help generate quick estimates of the number of chikungunya cases in Rio de Janeiro in the previous week. Our model uses a Bayesian methodology to help account for the varying delays in the chikungunya data. We show that including Google search data in the model reduces both the error and uncertainty of the chikungunya case count estimates, in particular during epidemics. Our method could be used to help policymakers to respond more quickly to future chikungunya epidemics.

Case Report: The Experience of Managing a Moderate ARDS Caused by SARS-CoV-2 Omicron BA.2 Variant in Chongqing, China: Can We Do Better?

Background

The severe coronavirus disease 2019 (COVID-19) pandemic is still raging worldwide, and the Omicron BA.2 variant has become the new circulating epidemic strain. However, our understanding of the Omicron BA.2 variant is still scarce. This report aims to present a case of a moderate acute respiratory distress syndrome (ARDS) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) Omicron BA.2 variant and to discuss some management strategies that may benefit this type of case.

Case Presentation

A 78-year-old man, who had four negative nucleic acid tests and a fifth positive, was admitted to our hospital. This patient was generally good upon admission and tested negative for anti-SARS-CoV-2 antibodies even after receiving two doses of the COVID-19 vaccine. On the 7th day of hospitalization, he developed a moderate ARDS. Improved inflammatory index and decreased oxygen index were primarily found in this patient, and a series of treatments, including anti-inflammation and oxygen therapies, were used. Then this patient's condition improved soon and reached two negative results of nucleic acid tests on the 18th day of hospitalization.

Conclusion

At-home COVID-19 rapid antigen test could be complementary to existing detection methods, and the third booster dose of COVID-19 vaccine may be advocated in the face of the omicron BA.2 variant. Anti-inflammatory and oxygen therapies are still essential treatments for ARDS patients infected with SARS-CoV-2 Omicron BA.2 variant.