Sequential lateral positioning as a new lung recruitment maneuver: an exploratory study in early mechanically ventilated Covid-19 ARDS patients.

BACKGROUND: A sequential change in body position from supine-to-both lateral positions under constant ventilatory settings could be used as a postural recruitment maneuver in case of acute respiratory distress syndrome (ARDS), provided that sufficient positive end-expiratory pressure (PEEP) prevents derecruitment. This study aims to evaluate the feasibility and physiological effects of a sequential postural recruitment maneuver in early mechanically ventilated COVID-19 ARDS patients. METHODS: A cohort of 15 patients receiving lung-protective mechanical ventilation in volume-controlled with PEEP based on recruitability were prospectively enrolled and evaluated in five sequentially applied positions for 30 min each: Supine-baseline; Lateral-1st side; 2nd Supine; Lateral-2nd side; Supine-final. PEEP level was selected using the recruitment-to-inflation ratio (R/I ratio) based on which patients received PEEP 12 cmH2O for R/I ratio ≤ 0.5 or PEEP 15 cmH2O for R/I ratio > 0.5. At the end of each period, we measured respiratory mechanics, arterial blood gases, lung ultrasound aeration, end-expiratory lung impedance (EELI), and regional distribution of ventilation and perfusion using electric impedance tomography (EIT). RESULTS: Comparing supine baseline and final, respiratory compliance (29 ± 9 vs 32 ± 8 mL/cmH2O; p < 0.01) and PaO2/FIO2 ratio (138 ± 36 vs 164 ± 46 mmHg; p < 0.01) increased, while driving pressure (13 ± 2 vs 11 ± 2 cmH2O; p < 0.01) and lung ultrasound consolidation score decreased [5 (4-5) vs 2 (1-4); p < 0.01]. EELI decreased ventrally (218 ± 205 mL; p < 0.01) and increased dorsally (192 ± 475 mL; p = 0.02), while regional compliance increased in both ventral (11.5 ± 0.7 vs 12.9 ± 0.8 mL/cmH2O; p < 0.01) and dorsal regions (17.1 ± 1.8 vs 18.8 ± 1.8 mL/cmH2O; p < 0.01). Dorsal distribution of perfusion increased (64.8 ± 7.3% vs 66.3 ± 7.2%; p = 0.01). CONCLUSIONS: Without increasing airway pressure, a sequential postural recruitment maneuver improves global and regional respiratory mechanics and gas exchange along with a redistribution of EELI from ventral to dorsal lung areas and less consolidation. Trial registration ClinicalTrials.gov, NCT04475068. Registered 17 July 2020, https://clinicaltrials.gov/ct2/show/NCT04475068.

Lung ultrasound as a diagnostic tool for radiographically-confirmed pneumonia in low resource settings.

BACKGROUND: Pneumonia is a leading cause of morbidity and mortality in children worldwide; however, its diagnosis can be challenging, especially in settings where skilled clinicians or standard imaging are unavailable. We sought to determine the diagnostic accuracy of lung ultrasound when compared to radiographically-confirmed clinical pediatric pneumonia. METHODS: Between January 2012 and September 2013, we consecutively enrolled children aged 2-59 months with primary respiratory complaints at the outpatient clinics, emergency department, and inpatient wards of the Instituto Nacional de Salud del Niño in Lima, Peru. All participants underwent clinical evaluation by a pediatrician and lung ultrasonography by one of three general practitioners. We also consecutively enrolled children without respiratory symptoms. Children with respiratory symptoms had a chest radiograph. We obtained ancillary laboratory testing in a subset. RESULTS: Final clinical diagnoses included 453 children with pneumonia, 133 with asthma, 103 with bronchiolitis, and 143 with upper respiratory infections. In total, CXR confirmed the diagnosis in 191 (42%) of 453 children with clinical pneumonia. A consolidation on lung ultrasound, which is our primary endpoint for pneumonia, had a sensitivity of 88.5%, specificity of 100%, and an area under-the-curve of 0.94 (95% CI 0.92-0.97) when compared to radiographically-confirmed clinical pneumonia. When any abnormality on lung ultrasound was compared to radiographically-confirmed clinical pneumonia the sensitivity increased to 92.2% and the specificity decreased to 95.2%, with an area under-the-curve of 0.94 (95% CI 0.91-0.96). CONCLUSIONS: Lung ultrasound had high diagnostic accuracy for the diagnosis of radiographically-confirmed pneumonia. Added benefits of lung ultrasound include rapid testing and high inter-rater agreement. Lung ultrasound may serve as an alternative tool for the diagnosis of pediatric pneumonia.

Agreement Between the World Health Organization Algorithm and Lung Consolidation Identified Using Point-of-Care Ultrasound for the Diagnosis of Childhood Pneumonia by General Practitioners.

PURPOSE: The World Health Organization (WHO) case management algorithm for acute lower respiratory infections has moderate sensitivity and poor specificity for the diagnosis of pneumonia. We sought to determine the feasibility of using point-of-care ultrasound in resource-limited settings to identify pneumonia by general health practitioners and to determine agreement between the WHO algorithm and lung consolidations identified by point-of-care ultrasound. METHODS: An expert radiologist taught two general practitioners how to perform point-of-care ultrasound over a seven-day period. We then conducted a prospective study of children aged 2 months to 3 years in Peru and Nepal with and without respiratory symptoms, which were evaluated by point-of-care ultrasound to identify lung consolidation. RESULTS: We enrolled 378 children: 127 were controls without respiratory symptoms, 82 had respiratory symptoms without clinical pneumonia, and 169 had clinical pneumonia by WHO criteria. Point-of-care ultrasound was performed in the community (n = 180), in outpatient offices (n = 95), in hospital wards (n = 19), and in Emergency Departments (n = 84). Average time to perform point-of-care ultrasound was 6.4 ± 2.2 min. Inter-observer agreement for point-of-care ultrasound interpretation between general practitioners was high (κ = 0.79, 95 % CI 0.73-0.81). The diagnosis of pneumonia using the WHO algorithm yielded a sensitivity of 69.6 % (95 % CI 55.7-80.8 %), specificity of 59.6 % (95 % CI 54.0-65.0 %), and positive and negative likelihood ratios of 1.73 (95 % CI 1.39-2.15) and 0.51 (95 % CI 0.30-0.76) when lung consolidation on point-of-care ultrasound was used as the reference. CONCLUSIONS: The WHO algorithm disagreed with point-of-care ultrasound findings in more than one-third of children and had an overall low performance when compared with point-of-care ultrasound to identify lung consolidation. A paired approach with point-of-care ultrasound may improve case management in resource-limited settings.

Computerised lung sound analysis to improve the specificity of paediatric pneumonia diagnosis in resource-poor settings: protocol and methods for an observational study.

INTRODUCTION: WHO case management algorithm for paediatric pneumonia relies solely on symptoms of shortness of breath or cough and tachypnoea for treatment and has poor diagnostic specificity, tends to increase antibiotic resistance. Alternatives, including oxygen saturation measurement, chest ultrasound and chest auscultation, exist but with potential disadvantages. Electronic auscultation has potential for improved detection of paediatric pneumonia but has yet to be standardised. The authors aim to investigate the use of electronic auscultation to improve the specificity of the current WHO algorithm in developing countries. METHODS: This study is designed to test the hypothesis that pulmonary pathology can be differentiated from normal using computerised lung sound analysis (CLSA). The authors will record lung sounds from 600 children aged ≤5 years, 100 each with consolidative pneumonia, diffuse interstitial pneumonia, asthma, bronchiolitis, upper respiratory infections and normal lungs at a children's hospital in Lima, Peru. The authors will compare CLSA with the WHO algorithm and other detection approaches, including physical exam findings, chest ultrasound and microbiologic testing to construct an improved algorithm for pneumonia diagnosis. DISCUSSION: This study will develop standardised methods for electronic auscultation and chest ultrasound and compare their utility for detection of pneumonia to standard approaches. Utilising signal processing techniques, the authors aim to characterise lung sounds and through machine learning, develop a classification system to distinguish pathologic sounds. Data will allow a better understanding of the benefits and limitations of novel diagnostic techniques in paediatric pneumonia.