Monitoring of pulmonary involvement in critically ill COVID-19 patients - should lung ultrasound be preferred over CT?

BACKGROUND: It is unclear if relevant changes in pulmonary involvement in critically ill COVID-19 patients can be reliably detected by the CT severity score (CTSS) and lung ultrasound score (LUSS), or if these changes have prognostic implications. In addition, it has been argued that adding pleural abnormalities to the LUSS could improve its prognostic value. The objective of this study was to compare LUSS and CTSS for the monitoring of COVID-19 pulmonary involvement through: first, establishing the correlation of LUSS (± pleural abnormalities) and CTSS throughout admission; second, assessing agreement and measurement error between raters for LUSS, pleural abnormalities, and CTSS; third, evaluating the association of the LUSS (± pleural abnormalities) and CTSS with mortality at different timepoints. METHODS: This is a prospective, observational study, conducted during the second COVID-19 wave at the AmsterdamUMC, location VUmc. Adult COVID-19 ICU patients were prospectively included when a CT or a 12-zone LUS was performed at admission or at weekly intervals according to local protocol. Patients were followed 90 days or until death. We calculated the: (1) Correlation of the LUSS (± pleural abnormalities) and CTSS throughout admission with mixed models; (2) Intra-class correlation coefficients (ICCs) and smallest detectable changes (SDCs) between raters; (3) Association between the LUSS (± pleural abnormalities) and CTSS with mixed models. RESULTS: 82 consecutive patients were included. Correlation between LUSS and CTSS was 0.45 (95% CI 0.31-0.59). ICCs for LUSS, pleural abnormalities, and CTSS were 0.88 (95% CI 0.73-0.95), 0.94 (95% CI 0.90-0.96), and 0.84 (95% CI 0.65-0.93), with SDCs of 4.8, 1.4, and 3.9. The LUSS was associated with mortality in week 2, with a score difference between patients who survived or died greater than its SDC. Addition of pleural abnormalities was not beneficial. The CTSS was associated with mortality only in week 1, but with a score difference less than its SDC. CONCLUSIONS: LUSS correlated with CTSS throughout ICU admission but performed similar or better at agreement between raters and mortality prognostication. Given the benefits of LUS over CT, it should be preferred as initial monitoring tool.

Optimal use of procalcitonin to rule out bacteremia in patients with possible viral infections.

Objective: During the winter, many patients present with suspected infection that could be a viral or a bacterial (co)infection. The aim of this study is to investigate whether the optimal use of procalcitonin (PCT) is different in patients with and without proven viral infections for the purpose of excluding bacteremia. We hypothesize that when a viral infection is confirmed, this lowers the probability of bacteremia and, therefore, influences the appropriate cutoff of procalcitonin. Methods: This study was conducted in the emergency department of an academic medical center in The Netherlands in the winter seasons of 2019 and 2020. Adults (>18 years) with suspected infection, in whom a blood culture and a rapid polymerase chain reaction test for influenza was performed were included. Results: A total of 546 patients were included of whom 47 (8.6%) had a positive blood culture. PCT had an area under the curve of 0.85, 95% confidence interval (95% CI) 0.80-0.91, for prediction of bacteremia. In patients with a proven viral infection (N = 212) PCT < 0.5 µg/L had a sensitivity of 100% (95% CI 63.1-100) and specificity of 81.2% (95% CI 75.1-86.3) to exclude bacteremia. In patients without a viral infection, the procalcitonin cutoff point of < 0.25 µg/L showed a sensitivity of 87.2% (95% CI 72.6-95.7) and specificity of 64.1 % (95% CI 58.3-69.6). Conclusion: In patients with a viral infection, our findings suggest that a PCT concentration of <0.50 µg/L makes bacteremia unlikely. However, this finding needs to be confirmed in a larger population of patients with viral infections, especially because the rate of coinfection in our cohort was low.

The host response in different aetiologies of community-acquired pneumonia.

BACKGROUND: Community-acquired pneumonia (CAP) can be caused by a variety of pathogens, of which Streptococcus pneumoniae, Influenza and currently SARS-CoV-2 are the most common. We sought to identify shared and pathogen-specific host response features by directly comparing different aetiologies of CAP. METHODS: We measured 72 plasma biomarkers in a cohort of 265 patients hospitalized for CAP, all sampled within 48 hours of admission, and 28 age-and sex matched non-infectious controls. We stratified the biomarkers into several pathophysiological domains- antiviral response, vascular response and function, coagulation, systemic inflammation, and immune checkpoint markers. We directly compared CAP caused by SARS-CoV-2 (COVID-19, n=39), Streptococcus pneumoniae (CAP-strep, n=27), Influenza (CAP-flu, n=22) and other or unknown pathogens (CAP-other, n=177). We adjusted the comparisons for age, sex and disease severity scores. FINDINGS: Biomarkers reflective of a stronger cell-mediated antiviral response clearly separated COVID-19 from other CAPs (most notably granzyme B). Biomarkers reflecting activation and function of the vasculature showed endothelial barrier integrity was least affected in COVID-19, while glycocalyx degradation and angiogenesis were enhanced relative to other CAPs. Notably, markers of coagulation activation, including D-dimer, were not different between the CAP groups. Ferritin was most increased in COVID-19, while other systemic inflammation biomarkers such as IL-6 and procalcitonin were highest in CAP-strep. Immune checkpoint markers showed distinctive patterns in viral and non-viral CAP, with highly elevated levels of Galectin-9 in COVID-19. INTERPRETATION: Our investigation provides insight into shared and distinct pathophysiological mechanisms in different aetiologies of CAP, which may help guide new pathogen-specific therapeutic strategies. FUNDING: This study was financially supported by the Dutch Research Council, the European Commission and the Netherlands Organization for Health Research and Development.

Comparing lung ultrasound: extensive versus short in COVID-19 (CLUES): a multicentre, observational study at the emergency department

BackgroundBedside lung ultrasound (LUS) is an affordable diagnostic tool that could contribute to identifying COVID-19 pneumonia. Different LUS protocols are currently used at the emergency department (ED) and there is a need to know their diagnostic accuracy.DesignA multicentre, prospective, observational study, to compare the diagnostic accuracy of three commonly used LUS protocols in identifying COVID-19 pneumonia at the ED.Setting/patientsAdult patients with suspected COVID-19 at the ED, in whom we prospectively performed 12-zone LUS and SARS-CoV-2 reverse transcription PCR.MeasurementsWe assessed diagnostic accuracy for three different ultrasound protocols using both PCR and final diagnosis as a reference standard.ResultsBetween 19 March 2020 and 4 May 2020, 202 patients were included. Sensitivity, specificity and negative predictive value compared with PCR for 12-zone LUS were 91.4% (95% CI 84.4 to 96.0), 83.5% (95% CI 74.6 to 90.3) and 90.0% (95% CI 82.7 to 94.4). For 8-zone and 6-zone protocols, these results were 79.7 (95% CI 69.9 to 87.6), 69.0% (95% CI 59.6 to 77.4) and 81.3% (95% CI 73.8 to 87.0) versus 89.9% (95% CI 81.7 to 95.3), 57.5% (95% CI 47.9 to 66.8) and 87.8% (95% CI 79.2 to 93.2). Negative likelihood ratios for 12, 8 and 6 zones were 0.1, 0.3 and 0.2, respectively. Compared with the final diagnosis specificity increased to 83.5% (95% CI 74.6 to 90.3), 78.4% (95% CI 68.8 to 86.1) and 65.0% (95% CI 54.6 to 74.4), respectively, while the negative likelihood ratios were 0.1, 0.2 and 0.16.ConclusionIdentifying COVID-19 pneumonia at the ED can be aided by bedside LUS. The more efficient 6-zone protocol is an excellent screening tool, while the 12-zone protocol is more specific and gives a general impression on lung involvement.Trial registration numberNL8497.

Hospitalized COVID-19 Patients Were Five Times More Likely to Suffer From Total Sleep Deprivation Compared to Non-COVID-19 Patients; an Observational Comparative Study.

Objectives: Sleeping disorders are a common complaint in patients who suffer from an acute COVID-19 infection. Nonetheless, little is known about the severity of sleep disturbances in hospitalized COVID-19 patients, and whether these are caused by disease related symptoms, hospitalization, or the SARS-CoV-2 virus itself. Therefore, the aim of this study was to compare the quality and quantity of sleep in hospitalized patients with and without COVID-19, and to determine the main reasons for sleep disruption. Methods: This was an observational comparative study conducted between October 1, 2020 and February 1, 2021 at the pulmonary ward of an academic hospital in the Netherlands. This ward contained both COVID-19-positive and -negative tested patients. The sleep quality was assessed using the PROMIS-Sleep Disturbance Short Form and sleep quantity using the Consensus Sleep Diary. Patient-reported sleep disturbing factors were summarized. Results: A total of 79 COVID-19 patients (mean age 63.0, male 59.5%) and 50 non-COVID-19 patients (mean age 59.5, male 54.0%) participated in this study. A significantly larger proportion of patients with COVID-19 reported not to have slept at all (19% vs. 4% of non-COVID-19 patients, p = 0.011). The Sleep quality (PROMIS total score) and quantity (Total Sleep Time) did not significantly differ between both groups ((median PROMIS total score COVID-19; 26 [IQR 17-35], non-COVID-19; 23 [IQR 18-29], p = 0.104), (Mean Total Sleep Time COVID-19; 5 h 5 min, non-COVID-19 mean; 5 h 32 min, p = 0.405)). The most frequently reported disturbing factors by COVID-19 patients were; 'dyspnea', 'concerns about the disease', 'anxiety' and 'noises of other patients, medical staff and medical devices'. Conclusion: This study showed that both patients with and without an acute COVID-19 infection experienced poor quality and quantity of sleep at the hospital. Although the mean scores did not significantly differ between groups, total sleep deprivation was reported five times more often by COVID-19 patients. With one in five COVID-19 patients reporting a complete absence of night sleep, poor sleep seems to be a serious problem. Sleep improving interventions should focus on physical and psychological comfort and noise reduction in the hospital environment.

Assessing COVID-19 pneumonia-Clinical extension and risk with point-of-care ultrasound: A multicenter, prospective, observational study.

BACKGROUND: Assessing the extent of lung involvement is important for the triage and care of COVID-19 pneumonia. We sought to determine the utility of point-of-care ultrasound (POCUS) for characterizing lung involvement and, thereby, clinical risk determination in COVID-19 pneumonia. METHODS: This multicenter, prospective, observational study included patients with COVID-19 who received 12-zone lung ultrasound and chest computed tomography (CT) scanning in the emergency department (ED). We defined lung disease severity using the lung ultrasound score (LUS) and chest CT severity score (CTSS). We assessed the association between the LUS and poor outcome (ICU admission or 30-day all-cause mortality). We also assessed the association between the LUS and hospital length of stay. We examined the ability of the LUS to differentiate between disease severity groups. Lastly, we estimated the correlation between the LUS and CTSS and the interrater agreement for the LUS. We handled missing data by multiple imputation with chained equations and predictive mean matching. RESULTS: We included 114 patients treated between March 19, 2020, and May 4, 2020. An LUS ≥12 was associated with a poor outcome within 30 days (hazard ratio [HR], 5.59; 95% confidence interval [CI], 1.26-24.80; P = 0.02). Admission duration was shorter in patients with an LUS <12 (adjusted HR, 2.24; 95% CI, 1.47-3.40; P < 0.001). Mean LUS differed between disease severity groups: no admission, 6.3 (standard deviation [SD], 4.4); hospital/ward, 13.1 (SD, 6.4); and ICU, 18.0 (SD, 5.0). The LUS was able to discriminate between ED discharge and hospital admission excellently, with an area under the curve of 0.83 (95% CI, 0.75-0.91). Interrater agreement for the LUS was strong: κ = 0.88 (95% CI, 0.77-0.95). Correlation between the LUS and CTSS was strong: κ = 0.60 (95% CI, 0.48-0.71). CONCLUSIONS: We showed that baseline lung ultrasound - is associated with poor outcomes, admission duration, and disease severity. The LUS also correlates well with CTSS. Point-of-care lung ultrasound may aid the risk stratification and triage of patients with COVID-19 at the ED.

Can vital signs recorded in patients' homes aid decision making in emergency care? A Scoping Review.

AIM: Use of tele-health programs and wearable sensors that allow patients to monitor their own vital signs have been expanded in response to COVID-19. We aimed to explore the utility of patient-held data during presentation as medical emergencies. METHODS: We undertook a systematic scoping review of two groups of studies: studies using non-invasive vital sign monitoring in patients with chronic diseases aimed at preventing unscheduled reviews in primary care, hospitalization or emergency department visits and studies using vital sign measurements from wearable sensors for decision making by clinicians on presentation of these patients as emergencies. Only studies that described a comparator or control group were included. Studies limited to inpatient use of devices were excluded. RESULTS: The initial search resulted in 896 references for screening, nine more studies were identified through searches of references. 26 studies fulfilled inclusion and exclusion criteria and were further analyzed. The majority of studies were from telehealth programs of patients with congestive heart failure or Chronic Obstructive Pulmonary Disease. There was limited evidence that patient held data is currently used to risk-stratify the admission or discharge process for medical emergencies. Studies that showed impact on mortality or hospital admission rates measured vital signs at least daily. We identified no interventional study using commercially available sensors in watches or smart phones. CONCLUSIONS: Further research is needed to determine utility of patient held monitoring devices to guide management of acute medical emergencies at the patients' home, on presentation to hospital and after discharge back to the community.

Chest CT in COVID-19 at the ED: Validation of the COVID-19 Reporting and Data System (CO-RADS) and CT Severity Score: A Prospective, Multicenter, Observational Study.

BACKGROUND: CT is thought to play a key role in coronavirus disease 2019 (COVID-19) diagnostic workup. The possibility of comparing data across different settings depends on the systematic and reproducible manner in which the scans are analyzed and reported. The COVID-19 Reporting and Data System (CO-RADS) and the corresponding CT severity score (CTSS) introduced by the Radiological Society of the Netherlands (NVvR) attempt to do so. However, this system has not been externally validated. RESEARCH QUESTION: We aimed to prospectively validate the CO-RADS as a COVID-19 diagnostic tool at the ED and to evaluate whether the CTSS is associated with prognosis. STUDY DESIGN AND METHODS: We conducted a prospective, observational study in two tertiary centers in The Netherlands, between March 19 and May 28, 2020. We consecutively included 741 adult patients at the ED with suspected COVID-19, who received a chest CT and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) PCR (PCR). Diagnostic accuracy measures were calculated for CO-RADS, using PCR as reference. Logistic regression was performed for CTSS in relation to hospital admission, ICU admission, and 30-day mortality. RESULTS: Seven hundred forty-one patients were included. We found an area under the curve (AUC) of 0.91 (CI, 0.89-0.94) for CO-RADS using PCR as reference. The optimal CO-RADS cutoff was 4, with a sensitivity of 89.4% (CI, 84.7-93.0) and specificity of 87.2% (CI, 83.9-89.9). We found a significant association between CTSS and hospital admission, ICU admission, and 30-day mortality; adjusted ORs per point increase in CTSS were 1.19 (CI, 1.09-1.28), 1.23 (1.15-1.32), 1.14 (1.07-1.22), respectively. Intraclass correlation coefficients for CO-RADS and CTSS were 0.94 (0.91-0.96) and 0.82 (0.70-0.90). INTERPRETATION: Our findings support the use of CO-RADS and CTSS in triage, diagnosis, and management decisions for patients presenting with possible COVID-19 at the ED.

Diagnosing COVID-19 pneumonia in a pandemic setting: Lung Ultrasound versus CT (LUVCT) - a multicentre, prospective, observational study.

BACKGROUND: In this coronavirus disease 2019 (COVID-19) pandemic, fast and accurate testing is needed to profile patients at the emergency department (ED) and efficiently allocate resources. Chest imaging has been considered in COVID-19 workup, but evidence on lung ultrasound (LUS) is sparse. We therefore aimed to assess and compare the diagnostic accuracy of LUS and computed tomography (CT) in suspected COVID-19 patients. METHODS: This multicentre, prospective, observational study included adult patients with suspected COVID-19 referred to internal medicine at the ED. We calculated diagnostic accuracy measures for LUS and CT using both PCR and multidisciplinary team (MDT) diagnosis as reference. We also assessed agreement between LUS and CT, and between sonographers. RESULTS: One hundred and eighty-seven patients were recruited between March 19 and May 4, 2020. Area under the receiver operating characteristic (AUROC) was 0.81 (95% CI 0.75-0.88) for LUS and 0.89 (95% CI 0.84-0.94) for CT. Sensitivity and specificity for LUS were 91.9% (95% CI 84.0-96.7) and 71.0% (95% CI 61.1-79.6), respectively, versus 88.4% (95% CI 79.7-94.3) and 82.0% (95% CI 73.1-89.0) for CT. Negative likelihood ratio was 0.1 (95% CI 0.06-0.24) for LUS and 0.14 (95% CI 0.08-0.3) for CT. No patient with a false negative LUS required supplemental oxygen or admission. LUS specificity increased to 80% (95% CI 69.9-87.9) compared to MDT diagnosis, with an AUROC of 0.85 (95% CI 0.79-0.91). Agreement between LUS and CT was 0.65. Interobserver agreement for LUS was good: 0.89 (95% CI 0.83-0.93). CONCLUSION: LUS and CT have comparable diagnostic accuracy for COVID-19 pneumonia. LUS can safely exclude clinically relevant COVID-19 pneumonia and may aid COVID-19 diagnosis in high prevalence situations.

Potential Immunotherapeutic Targets for Hypoxia Due to COVI-Flu.

The world is currently embroiled in a pandemic of coronavirus disease 2019 (COVID-19), a respiratory illness caused by the novel betacoronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The severity of COVID-19 disease ranges from asymptomatic to fatal acute respiratory distress syndrome. In few patients, the disease undergoes phenotypic differentiation between 7 and 14 days of acute illness, either resulting in full recovery or symptom escalation. However, the mechanism of such variation is not clear, but the facts suggest that patient's immune status, comorbidities, and the systemic effects of the viral infection (potentially depending on the SARS-CoV-2 strain involved) play a key role. Subsequently, patients with the most severe symptoms tend to have poor outcomes, manifest severe hypoxia, and possess elevated levels of pro-inflammatory cytokines (including IL-1ß, IL-6, IFN-γ, and TNF-α) along with elevated levels of the anti-inflammatory cytokine IL-10, marked lymphopenia, and elevated neutrophil-to-lymphocyte ratios. Based on the available evidence, we propose a mechanism wherein SARS-CoV-2 infection induces direct organ damage while also fueling an IL-6-mediated cytokine release syndrome (CRS) and hypoxia, resulting in escalating systemic inflammation, multi-organ damage, and end-organ failure. Elevated IL-6 and hypoxia together predisposes patients to pulmonary hypertension, and the presence of asymptomatic hypoxia in COVID-19 further compounds this problem. Due to the similar downstream mediators, we discuss the potential synergistic effects and systemic ramifications of SARS-CoV-2 and influenza virus during co-infection, a phenomenon we have termed "COVI-Flu." Additionally, the differences between CRS and cytokine storm are highlighted. Finally, novel management approaches, clinical trials, and therapeutic strategies toward both SARS-CoV-2 and COVI-Flu infection are discussed, highlighting host response optimization and systemic inflammation reduction.