Lung Ultrasound in Predicting Outcomes in Patients with COVID-19 Treated with Extracorporeal Membrane Oxygenation

ESICM-ESPNIC international expert consensus on quantitative lung ultrasound in intensive care

PURPOSE

To provide an international expert consensus on technical aspects and clinical applications of quantitative lung ultrasound in adult, paediatric and neonatal intensive care.

METHODS

The European Society of Intensive Care (ESICM) and the European Society of Paediatric and Neonatal Intensive Care (ESPNIC) endorsed the project. We selected an international panel of 20 adult, paediatric and neonatal intensive care experts with clinical and research expertise in quantitative lung ultrasound, plus two non-voting methodologists. Fourteen clinical questions were proposed by the chairs to the panel, who voted for their priority (1-9 Likert-type scale) and proposed modifications/supplementing (two-round vote). All the questions achieved the predefined threshold (mean score > 5) and 14 groups of 3 mixed adult/paediatric experts were identified to develop the statements for each clinical question; predefined groups of experts in the fields of adult and paediatric/neonatal intensive care voted statements specific for these subgroups. An iterative approach was used to obtain the final consensus statements (two-round vote, 1-9 Likert-type scale); statements were classified as with agreement (range 7-9), uncertainty (4-6), disagreement (1-3) when the median score and ≥ 75% of votes laid within a specific range.

RESULTS

A total of 46 statements were produced (4 adults-only, 4 paediatric/neonatal-only, 38 interdisciplinary); all obtained agreement. This result was also achieved by acknowledging in the statements the current limitations of quantitative lung ultrasound.

CONCLUSION

This consensus guides the use of quantitative lung ultrasound in adult, paediatric and neonatal intensive care and helps identify the fields where further research will be needed in the future.

Use of Extracorporeal Membrane Oxygenation for Patients with Coronavirus Disease 2019 Infection

The coronavirus disease 2019 (COVID-19) pandemic was a cataclysmic event that infected over 772 million and killed over 6.9 million people worldwide. The pandemic pushed hospitals and society to their limits and resulted in incredibly severe respiratory disease in millions of people. This severe respiratory disease often necessitated maximum medical therapy, including the use of extracorporeal membrane oxygenation. While our understanding of COVID-19 and its treatment continue to evolve, we review the current evidence to guide the care of patients with severe COVID-19 infection.

Advanced Respiratory Monitoring during Extracorporeal Membrane Oxygenation

Advanced respiratory monitoring encompasses a diverse range of mini- or noninvasive tools used to evaluate various aspects of respiratory function in patients experiencing acute respiratory failure, including those requiring extracorporeal membrane oxygenation (ECMO) support. Among these techniques, key modalities include esophageal pressure measurement (including derived pressures), lung and respiratory muscle ultrasounds, electrical impedance tomography, the monitoring of diaphragm electrical activity, and assessment of flow index. These tools play a critical role in assessing essential parameters such as lung recruitment and overdistention, lung aeration and morphology, ventilation/perfusion distribution, inspiratory effort, respiratory drive, respiratory muscle contraction, and patient-ventilator synchrony. In contrast to conventional methods, advanced respiratory monitoring offers a deeper understanding of pathological changes in lung aeration caused by underlying diseases. Moreover, it allows for meticulous tracking of responses to therapeutic interventions, aiding in the development of personalized respiratory support strategies aimed at preserving lung function and respiratory muscle integrity. The integration of advanced respiratory monitoring represents a significant advancement in the clinical management of acute respiratory failure. It serves as a cornerstone in scenarios where treatment strategies rely on tailored approaches, empowering clinicians to make informed decisions about intervention selection and adjustment. By enabling real-time assessment and modification of respiratory support, advanced monitoring not only optimizes care for patients with acute respiratory distress syndrome but also contributes to improved outcomes and enhanced patient safety.

A Novel Automatic Algorithm to Support Lung Ultrasound Non-Expert Physicians in Interstitial Pneumonia Evaluation: A Single-Center Study

INTRODUCTION

Lung ultrasound (LUS) is widely used in clinical practice for identifying interstitial lung diseases (ILDs) and assessing their progression. Although high-resolution computed tomography (HRCT) remains the gold standard for evaluating the severity of ILDs, LUS can be performed as a screening method or as a follow-up tool post-HRCT. Minimum training is needed to better identify typical lesions, and the integration of innovative artificial intelligence (AI) automatic algorithms may enhance diagnostic efficiency.

AIM

This study aims to assess the effectiveness of a novel AI algorithm in automatic ILD recognition and scoring in comparison to an expert LUS sonographer. The "SensUS Lung" device, equipped with an automatic algorithm, was employed for the automatic recognition of the typical ILD patterns and to calculate an index grading of the interstitial involvement.

METHODS

We selected 33 Caucasian patients in follow-up for ILDs exhibiting typical HRCT patterns (honeycombing, ground glass, fibrosis). An expert physician evaluated all patients with LUS on twelve segments (six per side). Next, blinded to the previous evaluation, an untrained operator, a non-expert in LUS, performed the exam with the SensUS device equipped with the automatic algorithm ("SensUS Lung") using the same protocol. Pulmonary functional tests (PFT) and DLCO were conducted for all patients, categorizing them as having reduced or preserved DLCO. The SensUS device indicated different grades of interstitial involvement named Lung Staging that were scored from 0 (absent) to 4 (peak), which was compared to the Lung Ultrasound Score (LUS score) by dividing it by the number of segments evaluated. Statistical analyses were done with Wilcoxon tests for paired values or Mann-Whitney for unpaired samples, and correlations were performed using Spearman analysis; p < 0.05 was considered significant.

RESULTS

Lung Staging was non-inferior to LUS score in identifying the risk of ILDs (median SensUS 1 [0-2] vs. LUS 0.67 [0.25-1.54]; p = 0.84). Furthermore, the grade of interstitial pulmonary involvement detected with the SensUS device is directly related to the LUS score (r = 0.607, p = 0.002). Lung Staging values were inversely correlated with forced expiratory volume at first second (FEV1%, r = -0.40, p = 0.027), forced vital capacity (FVC%, r = -0.39, p = 0.03) and forced expiratory flow (FEF) at 25th percentile (FEF25%, r = -0.39, p = 0.02) while results directly correlated with FEF25-75% (r = 0.45, p = 0.04) and FEF75% (r = 0.43, p = 0.01). Finally, in patients with reduced DLCO, the Lung Staging was significantly higher, overlapping the LUS (reduced median 1 [1-2] vs. preserved 0 [0-1], p = 0.001), and overlapping the LUS (reduced median 18 [4-20] vs. preserved 5.5 [2-9], p = 0.035).

CONCLUSIONS

Our data suggest that the considered AI automatic algorithm may assist non-expert physicians in LUS, resulting in non-inferior-to-expert LUS despite a tendency to overestimate ILD lesions. Therefore, the AI algorithm has the potential to support physicians, particularly non-expert LUS sonographers, in daily clinical practice to monitor patients with ILDs. The adopted device is user-friendly, offering a fully automatic real-time analysis. However, it needs proper training in basic skills.