Efficacy and safety of Paxlovid in severe adult patients with SARS-Cov-2 infection: a multicenter randomized controlled study.

Background: Nirmatrelvir plus ritonavir (Paxlovid) reduced the risk of hospitalization or death by 89% in high-risk, ambulatory adults with COVID-19. We aimed at studying the efficacy and safety of Paxlovid in hospitalized adult patients with SARS-Cov-2 (Omicron BA.2.2 variant) infection and severe comorbidities. Methods: We conducted an open-label, multicenter, randomized controlled trial in which hospitalized adult patients with severe comorbidities were eligible and assigned in a 1:1 ratio to receive either 300 mg of nirmatrelvir plus 100 mg of ritonavir every 12 h for 5 days with standard treatment or only standard treatment. All-cause mortality on day 28, the duration of SARS-CoV-2 RNA clearance, and safety were evaluated. Findings: 264 patients (mean age, 70.35 years; 122 [46.21%] female) who met the criteria were enrolled at 5 sites in Shanghai from April 10 to May 19 in 2022. After randomization, a total of 132 patients were assigned to receive Paxlovid treatment plus standard treatment, and 132 patients were assigned to receive only standard treatment. The overall 28-day mortality was 4.92%, 8 patients died in the standard treatment group and 5 died in the Paxlovid plus standard treatment group. There was no significant difference in mortality from any cause at 28 days between the Paxlovid plus standard treatment group and the standard treatment group (absolute risk difference [ARD], 2.27; 95% CI -2.94 to 7.49, P = 0.39). There was no significant difference in the duration of SARS-CoV-2 RNA clearance among the two groups (mean days, 10 in Paxlovid plus standard treatment group and 10.50 in the standard treatment group; ARD, -0.62; 95% CI -2.29 to 1.05, P = 0.42). The incidence of adverse events that occurred during the treatment period was similar in the two groups (any adverse event, 10.61% with Paxlovid plus standard treatment vs. 7.58% with the standard, P = 0.39; serious adverse events, 4.55% vs. 3.788%, P = 0.76). Interpretation: Paxlovid showed no significant reduction in the risk of all-cause mortality on day 28 and the duration of SARS-CoV-2 RNA clearance in hospitalized adult COVID-19 patients with severe comorbidities. Funding: National Natural Science Foundation of China (grant number: 82172152, 81873944).

Efficacy and safety of Paxlovid in severe adult patients with SARS-Cov-2 infection: a multicenter randomized controlled study

Background Nirmatrelvir plus ritonavir (Paxlovid) reduced the risk of hospitalization or death by 89% in high-risk, ambulatory adults with COVID-19. We aimed at studying the efficacy and safety of Paxlovid in hospitalized adult patients with SARS-Cov-2 (Omicron BA.2.2 variant) infection and severe comorbidities. Methods We conducted an open-label, multicenter, randomized controlled trial in which hospitalized adult patients with severe comorbidities were eligible and assigned in a 1:1 ratio to receive either 300 mg of nirmatrelvir plus 100 mg of ritonavir every 12 h for 5 days with standard treatment or only standard treatment. All-cause mortality on day 28, the duration of SARS-CoV-2 RNA clearance, and safety were evaluated. Findings 264 patients (mean age, 70.35 years;122 [46.21%] female) who met the criteria were enrolled at 5 sites in Shanghai from April 10 to May 19 in 2022. After randomization, a total of 132 patients were assigned to receive Paxlovid treatment plus standard treatment, and 132 patients were assigned to receive only standard treatment. The overall 28-day mortality was 4.92%, 8 patients died in the standard treatment group and 5 died in the Paxlovid plus standard treatment group. There was no significant difference in mortality from any cause at 28 days between the Paxlovid plus standard treatment group and the standard treatment group (absolute risk difference [ARD], 2.27;95% CI −2.94 to 7.49, P = 0.39). There was no significant difference in the duration of SARS-CoV-2 RNA clearance among the two groups (mean days, 10 in Paxlovid plus standard treatment group and 10.50 in the standard treatment group;ARD, −0.62;95% CI −2.29 to 1.05, P = 0.42). The incidence of adverse events that occurred during the treatment period was similar in the two groups (any adverse event, 10.61% with Paxlovid plus standard treatment vs. 7.58% with the standard, P = 0.39;serious adverse events, 4.55% vs. 3.788%, P = 0.76). Interpretation Paxlovid showed no significant reduction in the risk of all-cause mortality on day 28 and the duration of SARS–CoV-2 RNA clearance in hospitalized adult COVID-19 patients with severe comorbidities. Funding 10.13039/501100001809National Natural Science Foundation of China (grant number: 82172152, 81873944).

VV116 versus Nirmatrelvir-Ritonavir for Oral Treatment of Covid-19.

BACKGROUND: Nirmatrelvir-ritonavir has been authorized for emergency use by many countries for the treatment of coronavirus disease 2019 (Covid-19). However, the supply falls short of the global demand, which creates a need for more options. VV116 is an oral antiviral agent with potent activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS: We conducted a phase 3, noninferiority, observer-blinded, randomized trial during the outbreak caused by the B.1.1.529 (omicron) variant of SARS-CoV-2. Symptomatic adults with mild-to-moderate Covid-19 with a high risk of progression were assigned to receive a 5-day course of either VV116 or nirmatrelvir-ritonavir. The primary end point was the time to sustained clinical recovery through day 28. Sustained clinical recovery was defined as the alleviation of all Covid-19-related target symptoms to a total score of 0 or 1 for the sum of each symptom (on a scale from 0 to 3, with higher scores indicating greater severity; total scores on the 11-item scale range from 0 to 33) for 2 consecutive days. A lower boundary of the two-sided 95% confidence interval for the hazard ratio of more than 0.8 was considered to indicate noninferiority (with a hazard ratio of >1 indicating a shorter time to sustained clinical recovery with VV116 than with nirmatrelvir-ritonavir). RESULTS: A total of 822 participants underwent randomization, and 771 received VV116 (384 participants) or nirmatrelvir-ritonavir (387 participants). The noninferiority of VV116 to nirmatrelvir-ritonavir with respect to the time to sustained clinical recovery was established in the primary analysis (hazard ratio, 1.17; 95% confidence interval [CI], 1.01 to 1.35) and was maintained in the final analysis (median, 4 days with VV116 and 5 days with nirmatrelvir-ritonavir; hazard ratio, 1.17; 95% CI, 1.02 to 1.36). In the final analysis, the time to sustained symptom resolution (score of 0 for each of the 11 Covid-19-related target symptoms for 2 consecutive days) and to a first negative SARS-CoV-2 test did not differ substantially between the two groups. No participants in either group had died or had had progression to severe Covid-19 by day 28. The incidence of adverse events was lower in the VV116 group than in the nirmatrelvir-ritonavir group (67.4% vs. 77.3%). CONCLUSIONS: Among adults with mild-to-moderate Covid-19 who were at risk for progression, VV116 was noninferior to nirmatrelvir-ritonavir with respect to the time to sustained clinical recovery, with fewer safety concerns. (Funded by Vigonvita Life Sciences and others; ClinicalTrials.gov number, NCT05341609; Chinese Clinical Trial Registry number, ChiCTR2200057856.).

Example-based Explanations with Adversarial Attacks for Respiratory Sound Analysis (preprint)

Respiratory sound classification is an important tool for remote screening of respiratory-related diseases such as pneumonia, asthma, and COVID-19. To facilitate the interpretability of classification results, especially ones based on deep learning, many explanation methods have been proposed using prototypes. However, existing explanation techniques often assume that the data is non-biased and the prediction results can be explained by a set of prototypical examples. In this work, we develop a unified example-based explanation method for selecting both representative data (prototypes) and outliers (criticisms). In particular, we propose a novel application of adversarial attacks to generate an explanation spectrum of data instances via an iterative fast gradient sign method. Such unified explanation can avoid over-generalisation and bias by allowing human experts to assess the model mistakes case by case. We performed a wide range of quantitative and qualitative evaluations to show that our approach generates effective and understandable explanation and is robust with many deep learning models

The Acoustic Dissection of Cough: Diving into Machine Listening-based COVID-19 Analysis and Detection (preprint)

Purpose The coronavirus disease 2019 (COVID-19) has caused a crisis worldwide. Amounts of efforts have been made to prevent and control COVID-19’s transmission, from early screenings to vaccinations and treatments. Recently, due to the spring up of many automatic disease recognition applications based on machine listening techniques, it would be fast and cheap to detect COVID-19 from recordings of cough, a key symptom of COVID-19. To date, knowledge on the acoustic characteristics of COVID-19 cough sounds is limited, but would be essential for structuring effective and robust machine learning models. The present study aims to explore acoustic features for distinguishing COVID-19 positive individuals from COVID-19 negative ones based on their cough sounds. Methods With the theory of computational paralinguistics, we analyse the acoustic correlates of COVID-19 cough sounds based on the COMPARE feature set, i. e., a standardised set of 6,373 acoustic higher-level features. Furthermore, we train automatic COVID-19 detection models with machine learning methods and explore the latent features by evaluating the contribution of all features to the COVID-19 status predictions. Results The experimental results demonstrate that a set of acoustic parameters of cough sounds, e. g., statistical functionals of the root mean square energy and Mel-frequency cepstral coefficients, are relevant for the differentiation between COVID-19 positive and COVID-19 negative cough samples. Our automatic COVID-19 detection model performs significantly above chance level, i. e., at an unweighted average recall (UAR) of 0.632, on a data set consisting of 1,411 cough samples (COVID-19 positive/negative: 210/1,201). Conclusions Based on the acoustic correlates analysis on the COMPARE feature set and the feature analysis in the effective COVID-19 detection model, we find that the machine learning method to a certain extent relies on acoustic features showing higher effects in conventional group difference testing.

The EIHW-GLAM Deep Attentive Multi-model Fusion System for Cough-based COVID-19 Recognition in the DiCOVA 2021 Challenge (preprint)

Aiming to automatically detect COVID-19 from cough sounds, we propose a deep attentive multi-model fusion system evaluated on the Track-1 dataset of the DiCOVA 2021 challenge. Three kinds of representations are extracted, including hand-crafted features, image-from-audio-based deep representations, and audio-based deep representations. Afterwards, the best models on the three types of features are fused at both the feature level and the decision level. The experimental results demonstrate that the proposed attention-based fusion at the feature level achieves the best performance (AUC: 77.96%) on the test set, resulting in an 8.05% improvement over the official baseline.

Elevated Extracellular Volume Fraction and Reduced Global Longitudinal Strains in Participants Recovered from COVID-19 without Clinical Cardiac Findings.

Background It is unknown if there are cardiac abnormalities in persons who have recovered from coronavirus disease 2019 (COVID-19) without cardiac symptoms or in those who have normal biomarkers and normal electrocardiograms. Purpose To evaluate cardiac involvement in participants who had recovered from COVID-19 without clinical evidence of cardiac involvement by using cardiac MRI. Materials and Methods This prospective observational cohort study included 40 participants who had recovered from COVID-19 with moderate (n = 24) or severe (n = 16) pneumonia and who had no cardiovascular medical history, were without cardiac symptoms, had normal electrocardiograms, had normal serologic cardiac enzyme levels, and had been discharged for more than 90 days between May and September 2020. Demographic characteristics were recorded, serum cardiac enzyme levels were measured, and cardiac MRI was performed. Cardiac function, native T1, extracellular volume fraction (ECV), and two-dimensional (2D) strain were quantitatively evaluated and compared with values in control subjects (n = 25). Comparisons among the three groups were performed by using one-way analysis of variance with Bonferroni-corrected post hoc comparisons (for normal distribution) or Kruskal-Wallis tests with post hoc pairwise comparisons (for nonnormal distribution). Results Forty participants (mean age, 54 years ± 12 [standard deviation]; 24 men) were enrolled; participants had a mean time between admission and cardiac MRI of 158 days ± 18 and between discharge and cardiac MRI examination of 124 days ± 17. There were no left or right ventricular size or functional differences between participants who had recovered from COVID-19 and healthy control subjects. Only one (3%) participant had positive late gadolinium enhancement located at the mid inferior wall. Global ECV values were elevated in participants who had recovered from COVID-19 with moderate or severe pneumonia compared with those in healthy control subjects (median ECV, 29.7% vs 31.4% vs 25.0%, respectively; interquartile range, 28.0%-32.9% vs 29.3%-34.0% vs 23.7%-26.0%, respectively; P < .001 for both). The 2D global left ventricular longitudinal strain was reduced in both groups of participants (moderate COVID-19 group, -12.5% [interquartile range, -15.5% to -10.7%]; severe COVID-19 group, -12.5% [interquartile range, -15.4% to -8.7%]) compared with the healthy control group (-15.4% [interquartile range, -17.6% to -14.6%]) (P = .002 and P = .001, respectively). Conclusion Cardiac MRI myocardial tissue and strain imaging parameters suggest that a proportion of participants who had recovered from COVID-19 had subclinical myocardial abnormalities detectable months after recovery. © RSNA, 2021 Online supplemental material is available for this article.

An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety (preprint)

The COVID-19 outbreak was announced as a global pandemic by the World Health Organisation in March 2020 and has affected a growing number of people in the past few weeks. In this context, advanced artificial intelligence techniques are brought to the fore in responding to fight against and reduce the impact of this global health crisis. In this study, we focus on developing some potential use-cases of intelligent speech analysis for COVID-19 diagnosed patients. In particular, by analysing speech recordings from these patients, we construct audio-only-based models to automatically categorise the health state of patients from four aspects, including the severity of illness, sleep quality, fatigue, and anxiety. For this purpose, two established acoustic feature sets and support vector machines are utilised. Our experiments show that an average accuracy of .69 obtained estimating the severity of illness, which is derived from the number of days in hospitalisation. We hope that this study can foster an extremely fast, low-cost, and convenient way to automatically detect the COVID-19 disease.