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INTRODUCTION: Remdesivir (REM) and monoclonal antibodies (mAbs) could alleviate severe COVID-19 in at-risk outpatients. However, data on their use in hospitalized patients, particularly in elderly or immunocompromised hosts, are lacking. METHODS: All consecutive patients hospitalized with COVID-19 at our unit from 1 July 2021 to 15 March 2022 were retrospectively enrolled. The primary outcome was the progression to severe COVID-19 (P/F < 200). Descriptive statistics, a Cox univariate-multivariate model, and an inverse probability treatment-weighted (IPTW) analysis were performed. RESULTS: Overall, 331 subjects were included; their median (q1-q3) age was 71 (51-80) years, and they were males in 52% of the cases. Of them, 78 (23%) developed severe COVID-19. All-cause in-hospital mortality was 14%; it was higher in those with disease progression (36% vs. 7%, p < 0.001). REM and mAbs resulted in a 7% (95%CI = 3-11%) and 14% (95%CI = 3-25%) reduction in the risk of severe COVID-19, respectively, after adjusting the analysis with the IPTW. In addition, by evaluating only immunocompromised hosts, the combination of REM and mAbs was associated with a significantly lower incidence of severe COVID-19 (aHR = 0.06, 95%CI = 0.02-0.77) when compared with monotherapy. CONCLUSIONS: REM and mAbs may reduce the risk of COVID-19 progression in hospitalized patients. Importantly, in immunocompromised hosts, the combination of mAbs and REM may be beneficial.
Subject(s)
COVID-19 , Aged , Male , Humans , Aged, 80 and over , Female , Retrospective Studies , COVID-19 Drug Treatment , Antibodies, Monoclonal/therapeutic use , Antibodies, Neutralizing , Immunocompromised Host , Disease ProgressionABSTRACT
BACKGROUND: Reliable clinical and laboratory predictors of coronavirus disease 2019 (COVID-19) disease progression could help to identify the subset of patients who are susceptible to severe symptoms. This study sought to identify the predictors for disease progression in patients with COVID-19. METHODS: This study recruited consecutive patients from four hospitals between March 1, 2020, and July 31, 2021. Demographic characteristics, laboratory results, and clinical outcomes were collected. RESULTS: Among the 239 enrolled patients, 39.3% (94/239) experienced in-hospital disease progression. Multivariate logistic regression revealed that coronary arterial disease (CAD) (OR,4.15;95% C.I., 1.47-11.66), cerebrovascular attack (CVA) (OR,12.98; 95% C.I., 1.30-129.51), platelet count < median value (OR, 3.23; 95% C.I., 1.65-6.32), and C-reactive protein (CRP) levels > median value of (OR, 2.25; 95% C.I., 1.02-4.99) were independent factors associated with COVID-19 progression. Patients who underwent disease progression at days 1, 4, and 7 presented lower lymphocyte counts and higher CRP levels, compared to patients without disease progression. CONCLUSIONS: The study revealed that in hospitalized COVID-19 patients, comorbidity with CAD and CVA, low platelet count, and elevated CRP levels were independently associated with disease progression. Compared with patients without disease progression, those with disease progression presented persistently low lymphocyte counts and elevated CRP levels.
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Responding to the fast-spreading SARS-CoV-2 Omicron variant, to improve screening efficiency, rapid antigen tests (RATs) were first added as a supplementary detection method in China in mid-March, 2022. What and how big a role RATs should play need to be supported by clinical data. Here, RAT performance and relevant factors in comparison with nucleic acid amplification tests (NAATs) were assessed in Omicron-infected inpatients. From the NAAT results, nasopharyngeal swabs (NPs) performed better than oropharyngeal swabs (OPs). RATs tested on NAAT positive NPs performed better than those with OP-positive samples. The RAT positivity rate was strongly associated with high levels of N and OFR1ab genes, especially in NPs where patients also had significantly longer hospital stays and shorter days from symptom onset to RAT testing. Self-performed RATs had a detection accuracy that was comparable to professionally performed RATs when the subjects were well guided. The antigen negative rate of the studied patients was 100% at discharge. These findings suggest that, in addition to a supplementary detection role, RATs can be an important strategy for evaluating the disease progression of Omicron-infected inpatients. This study provides important clinical data to support better rules regarding RATs under China's COVID-19 prevention and control policy.
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BACKGROUND: Recent work has shown the potential of using audio data (eg, cough, breathing, and voice) in the screening for COVID-19. However, these approaches only focus on one-off detection and detect the infection, given the current audio sample, but do not monitor disease progression in COVID-19. Limited exploration has been put forward to continuously monitor COVID-19 progression, especially recovery, through longitudinal audio data. Tracking disease progression characteristics and patterns of recovery could bring insights and lead to more timely treatment or treatment adjustment, as well as better resource management in health care systems. OBJECTIVE: The primary objective of this study is to explore the potential of longitudinal audio samples over time for COVID-19 progression prediction and, especially, recovery trend prediction using sequential deep learning techniques. METHODS: Crowdsourced respiratory audio data, including breathing, cough, and voice samples, from 212 individuals over 5-385 days were analyzed, alongside their self-reported COVID-19 test results. We developed and validated a deep learning-enabled tracking tool using gated recurrent units (GRUs) to detect COVID-19 progression by exploring the audio dynamics of the individuals' historical audio biomarkers. The investigation comprised 2 parts: (1) COVID-19 detection in terms of positive and negative (healthy) tests using sequential audio signals, which was primarily assessed in terms of the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity, with 95% CIs, and (2) longitudinal disease progression prediction over time in terms of probability of positive tests, which was evaluated using the correlation between the predicted probability trajectory and self-reported labels. RESULTS: We first explored the benefits of capturing longitudinal dynamics of audio biomarkers for COVID-19 detection. The strong performance, yielding an AUROC of 0.79, a sensitivity of 0.75, and a specificity of 0.71 supported the effectiveness of the approach compared to methods that do not leverage longitudinal dynamics. We further examined the predicted disease progression trajectory, which displayed high consistency with longitudinal test results with a correlation of 0.75 in the test cohort and 0.86 in a subset of the test cohort with 12 (57.1%) of 21 COVID-19-positive participants who reported disease recovery. Our findings suggest that monitoring COVID-19 evolution via longitudinal audio data has potential in the tracking of individuals' disease progression and recovery. CONCLUSIONS: An audio-based COVID-19 progression monitoring system was developed using deep learning techniques, with strong performance showing high consistency between the predicted trajectory and the test results over time, especially for recovery trend predictions. This has good potential in the postpeak and postpandemic era that can help guide medical treatment and optimize hospital resource allocations. The changes in longitudinal audio samples, referred to as audio dynamics, are associated with COVID-19 progression; thus, modeling the audio dynamics can potentially capture the underlying disease progression process and further aid COVID-19 progression prediction. This framework provides a flexible, affordable, and timely tool for COVID-19 tracking, and more importantly, it also provides a proof of concept of how telemonitoring could be applicable to respiratory diseases monitoring, in general.
Subject(s)
COVID-19 , Deep Learning , Voice , Cough/diagnosis , Disease Progression , HumansABSTRACT
COVID-19 is a world disaster. In response to COVID-19 quarantine, stress, anxiety, and depression may easily develop which negatively affect immunity and decrease the patient's response against the COVID-19 virus. This study investigated the effect of an integrated intervention combining cognitive-behavioural stress management (CBSM) and progressive muscle relaxation (PMRs) on immune biomarkers and disease severity and progression in patients with COVID-19 and the period to which these changes last. Thirty patients with mild or moderate COVID-19 were randomly distributed into intervention and control groups. The intervention group performed an integrated intervention combining CBSM and PMRs. There were three outcome measures including blood immune markers, salivary immunoglobulin A, and Wisconsin scale (WIS). Two-week post-intervention, there were significant differences between groups in the WIS total score, Leucocytes, Lymphocytes, Interleukin-6, and Immunoglobulin-A. While there were non-significant differences between both groups in Interleukin-10 and TNF-α. The significant differences between groups in the WIS total score, Leucocytes, Lymphocytes, Interleukin-6, and Immunoglobulin-A significantly continued 1 week as a follow-up. This study concluded that performing an integrated intervention combining CBSM and PMRs for 2 weeks significantly increases immune biomarkers mainly Leucocytes, Lymphocytes, Interleukin-10, and Interleukin-6 along with S-IgA. Also, this protocol significantly decreases disease severity and associated stress, anxiety, and depression; and enhances the quality of life in patients with COVID-19. The study was retrospectively registered with NCT04998708.
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IMPACT STATEMENT: The impact of the COVID-19 pandemic has been worldwide, and clinicians and researchers around the world have been working to develop effective and efficient methods for early detection as well as monitoring of the disease progression. This minireview compiles the various agency and expert recommendations, along with results from studies published in numerous countries, in an effort to facilitate the research in imaging technology development to benefit the detection and monitoring of COVID-19. To the best of our knowledge, this is the first review paper on the topic, and it provides a brief, yet comprehensive analysis.