Lung Ultrasound in COVID-19 and Post-COVID-19 Patients, an Evidence-Based Approach.

OBJECTIVES: Worldwide, lung ultrasound (LUS) was utilized to assess coronavirus disease 2019 (COVID-19) patients. Often, imaging protocols were however defined arbitrarily and not following an evidence-based approach. Moreover, extensive studies on LUS in post-COVID-19 patients are currently lacking. This study analyses the impact of different LUS imaging protocols on the evaluation of COVID-19 and post-COVID-19 LUS data. METHODS: LUS data from 220 patients were collected, 100 COVID-19 positive and 120 post-COVID-19. A validated and standardized imaging protocol based on 14 scanning areas and a 4-level scoring system was implemented. We utilized this dataset to compare the capability of 5 imaging protocols, respectively based on 4, 8, 10, 12, and 14 scanning areas, to intercept the most important LUS findings. This to evaluate the optimal trade-off between a time-efficient imaging protocol and an accurate LUS examination. We also performed a longitudinal study, aimed at investigating how to eventually simplify the protocol during follow-up. Additionally, we present results on the agreement between AI models and LUS experts with respect to LUS data evaluation. RESULTS: A 12-areas protocol emerges as the optimal trade-off, for both COVID-19 and post-COVID-19 patients. For what concerns follow-up studies, it appears not to be possible to reduce the number of scanning areas. Finally, COVID-19 and post-COVID-19 LUS data seem to show differences capable to confuse AI models that were not trained on post-COVID-19 data, supporting the hypothesis of the existence of LUS patterns specific to post-COVID-19 patients. CONCLUSIONS: A 12-areas acquisition protocol is recommended for both COVID-19 and post-COVID-19 patients, also during follow-up.

Mid-term Follow-Up chest CT findings in recovered COVID-19 patients with residual symptoms.

OBJECTIVES: More than a year has passed since the initial outbreak of SARS-CoV-2, which caused many hospitalizations worldwide due to COVID-19 pneumonia and its complications. However, there is still a lack of information detailing short- and long-term outcomes of previously hospitalized patients. The purpose of this study is to analyze the most frequent lung CT findings in recovered COVID-19 patients at mid-term follow-ups. METHODS: A total of 407 consecutive COVID-19 patients who were admitted to the XXXX and discharged between February 27, 2020, and June 26, 2020 were recruited into this study. Out of these patients, a subset of 108 patients who presented with residual asthenia and dyspnea at discharge, altered spirometric data, positive lung ultrasound and positive chest X-ray was subsequently selected, and was scheduled to undergo a mid-term chest computer tomography study, which was evaluated for specific lung alterations and morphological patterns. RESULTS: The most frequently observed lung CT alterations, in order of frequency, were ground glass opacities (81%), linear opacities (74%), bronchiolectases (64,81%), and reticular opacities (63,88%). The most common morphological pattern was the nonspecific interstitial pneumonia pattern (63,88%). Features consistent with pulmonary fibrosis were observed in 32 patients (29,62%). CONCLUSIONS: Our work showed that recovered COVID-19 patients that were hospitalized and that exhibited residual symptoms after discharge had a slow radiological recovery with persistent residual lung alterations. ADVANCES IN KNOWLEDGE: This slow recovery process should be kept in mind when determining the follow-up phases in order to improve the long-term management of patients affected by COVID-19.

A mid-term follow-up with a lung ultrasonographic score correlates with the severity of COVID-19 acute phase.

Lung ultrasound (LUS) has rapidly emerged in COVID-19 diagnosis and for the follow-up during the acute phase. LUS is not yet used routinely in lung damage follow-up after COVID-19 infection. We investigated the correlation between LUS score, and clinical and laboratory parameters of severity of SARS-COV-2 damage during hospitalization and at follow-up visit. Observational retrospective study including all the patients discharged from the COVID-19 wards, who attended the post-COVID outpatient clinic of the IRCCS Policlinico San Matteo in April-June 2020. 115 patients were enrolled. Follow-up visits with LUS score measurements were at a median of 38 days (IQR 28-48) after discharge. LUS scores were associated with the length of hospitalization (p < 0.001), patients' age (p = 0.036), use of non-invasive ventilation (CPAP p < 0.001 or HFNC p = 0.018), administration of corticosteroids therapy (p = 0.030), and laboratory parameters during the acute phase (WBC p < 0.001, LDH p < 0.001, CRP p < 0.001, D-dimer p = 0.008, IL-6 p = 0.045), and inversely correlated with lymphocyte count (p = 0.007). We found correlation between LUS score and both LDH (p = 0.001) and the antibody anti-SARS-CoV-2 titers (p value = 0.008). Most of these finding were confirmed by dichothomizing the LUS score (≤ 9 or > 9 points). We found a significantly higher LUS score at the follow-up in the patients with persistent dyspnea (7.00, IQR 3.00-11.00) when compared to eupnoeic patients (3.00, IQR 0-7.00 p < 0.001). LUS score at follow-up visit correlates with more severe lung disease. These findings support the hypothesis that ultrasound could be a valid tool in the follow-up medium-term COVID-19 lung damage.

Machine Learning-based Voice Assessment for the Detection of Positive and Recovered COVID-19 Patients.

Many virological tests have been implemented during the Coronavirus Disease 2019 (COVID-19) pandemic for diagnostic purposes, but they appear unsuitable for screening purposes. Furthermore, current screening strategies are not accurate enough to effectively curb the spread of the disease. Therefore, the present study was conducted within a controlled clinical environment to determine eventual detectable variations in the voice of COVID-19 patients, recovered and healthy subjects, and also to determine whether machine learning-based voice assessment (MLVA) can accurately discriminate between them, thus potentially serving as a more effective mass-screening tool. Three different subpopulations were consecutively recruited: positive COVID-19 patients, recovered COVID-19 patients and healthy individuals as controls. Positive patients were recruited within 10 days from nasal swab positivity. Recovery from COVID-19 was established clinically, virologically and radiologically. Healthy individuals reported no COVID-19 symptoms and yielded negative results at serological testing. All study participants provided three trials for multiple vocal tasks (sustained vowel phonation, speech, cough). All recordings were initially divided into three different binary classifications with a feature selection, ranking and cross-validated RBF-SVM pipeline. This brough a mean accuracy of 90.24%, a mean sensitivity of 91.15%, a mean specificity of 89.13% and a mean AUC of 0.94 across all tasks and all comparisons, and outlined the sustained vowel as the most effective vocal task for COVID discrimination. Moreover, a three-way classification was carried out on an external test set comprised of 30 subjects, 10 per class, with a mean accuracy of 80% and an accuracy of 100% for the detection of positive subjects. Within this assessment, recovered individuals proved to be the most difficult class to identify, and all the misclassified subjects were declared positive; this might be related to mid and short-term vocal traces of COVID-19, even after the clinical resolution of the infection. In conclusion, MLVA may accurately discriminate between positive COVID-19 patients, recovered COVID-19 patients and healthy individuals. Further studies should test MLVA among larger populations and asymptomatic positive COVID-19 patients to validate this novel screening technology and test its potential application as a potentially more effective surveillance strategy for COVID-19.

Depletion of circulating IgM memory B cells predicts unfavourable outcome in COVID-19.

Impaired immune responses have been hypothesised to be a possible trigger of unfavourable outcomes in coronavirus disease 2019 (COVID-19). We aimed to characterise IgM memory B cells in patients with COVID-19 admitted to an internal medicine ward in Northern Italy. Overall, 66 COVID-19 patients (mean age 74 ± 16.6 years; 29 females) were enrolled. Three patients (4.5%; 1 female) had been splenectomised and were excluded from further analyses. Fifty-five patients (87.3%) had IgM memory B cell depletion, and 18 (28.6%) died during hospitalisation (cumulative incidence rate 9.26/100 person-week; 5.8-14.7 95% CI). All patients who died had IgM memory B cell depletion. A superimposed infection was found in 6 patients (9.5%), all of them having IgM memory B cell depletion (cumulative incidence rate 3.08/100 person-week; 1.3-6.8 95% CI). At bivariable analyses, older age, sex, number of comorbidities, and peripheral blood lymphocyte count < 1500/µl were not correlated with IgM memory B cell depletion. A discrete-to-marked reduction of the B-cell compartment was also noticed in autoptic spleen specimens of two COVID-19 patients. We conclude that IgM memory B cells are commonly depleted in COVID-19 patients and this correlates with increased mortality and superimposed infections.

A New Lung Ultrasound Protocol Able to Predict Worsening in Patients Affected by Severe Acute Respiratory Syndrome Coronavirus 2 Pneumonia.

OBJECTIVES: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can generate severe pneumonia associated with high mortality. A bedside lung ultrasound (LUS) examination has been shown to have a potential role in this setting. The purpose of this study was to evaluate the potential prognostic value of a new LUS protocol (evaluation of 14 anatomic landmarks, with graded scores of 0-3) in patients with SARS-CoV-2 pneumonia and the association of LUS patterns with clinical or laboratory findings. METHODS: A cohort of 52 consecutive patients with laboratory-confirmed SARS-CoV-2 underwent LUS examinations on admission in an internal medicine ward and before their discharge. A total LUS score as the sum of the scores at each explored area was computed. We investigated the association between the LUS score and clinical worsening, defined as a combination of high-flow oxygen support, intensive care unit admission, or 30-day mortality as the primary end point. RESULTS: Twenty (39%) patients showed a worse outcome during the observation period; the mean LUS scores ± SDs were 20.4 ± 8.5 and 29.2 ± 7.3 in patients without and with worsening, respectively (P < .001). In a multivariable analysis, adjusted for comorbidities (>2), age (>65 years), sex (male), and body mass index (≥25 kg/m2 ), the association between the LUS score and worsening (odds ratio, 1.17; 95% confidence interval, 1.05 to 1.29; P = .003) was confirmed, with good discrimination of the model (area under the receiver operating characteristic curve, 0.82). A median LUS score higher than 24 was associated with an almost 6-fold increase in the odds of worsening (odds ratio, 5.67; 95% confidence interval, 1.29 to 24.8; P = .021). CONCLUSIONS: Lung ultrasound can represent an effective tool for monitoring and stratifying the prognosis of patients with SARS-CoV-2 pulmonary involvement.