Development and testing of a deep learning algorithm to detect lung consolidation among children with pneumonia using hand-held ultrasound.

BACKGROUND AND OBJECTIVES: Severe pneumonia is the leading cause of death among young children worldwide, disproportionately impacting children who lack access to advanced diagnostic imaging. Here our objectives were to develop and test the accuracy of an artificial intelligence algorithm for detecting features of pulmonary consolidation on point-of-care lung ultrasounds among hospitalized children. METHODS: This was a prospective, multicenter center study conducted at academic Emergency Department and Pediatric inpatient or intensive care units between 2018-2020. Pediatric participants from 18 months to 17 years old with suspicion of lower respiratory tract infection were enrolled. Bedside lung ultrasounds were performed using a Philips handheld Lumify C5-2 transducer and standardized protocol to collect video loops from twelve lung zones, and lung features at both the video and frame levels annotated. Data from both affected and unaffected lung fields were split at the participant level into training, tuning, and holdout sets used to train, tune hyperparameters, and test an algorithm for detection of consolidation features. Data collected from adults with lower respiratory tract disease were added to enrich the training set. Algorithm performance at the video level to detect consolidation on lung ultrasound was determined using reference standard diagnosis of positive or negative pneumonia derived from clinical data. RESULTS: Data from 107 pediatric participants yielded 117 unique exams and contributed 604 positive and 589 negative videos for consolidation that were utilized for the algorithm development process. Overall accuracy for the model for identification and localization of consolidation was 88.5%, with sensitivity 88%, specificity 89%, positive predictive value 89%, and negative predictive value 87%. CONCLUSIONS: Our algorithm demonstrated high accuracy for identification of consolidation features on pediatric chest ultrasound in children with pneumonia. Automated diagnostic support on an ultraportable point-of-care device has important implications for global health, particularly in austere settings.

High-throughput Sequencing to Identify Bacteremia in Children with Cancer.

Current methods to diagnose bacteremia are limited. In this pilot study of children with cancer presenting with fever, we determined the concordance between a novel high-throughput sequencing platform called BacCapSeq and blood culture. High-throughput sequencing had modest concordance with blood culture. Discordant organisms included those with both unlikely or potential clinical relevance.

Lack of Association Between a Quantified Lung Ultrasound Score and Illness Severity in Pediatric Emergency Department Patients With Acute Lower Respiratory Infections.

OBJECTIVES: Lung ultrasound (LUS) may help determine illness severity in children with acute lower respiratory tract infections (LRTI) but limited pediatric studies exist. Our objective was to determine the association between LUS findings and illness severity in children with LRTI. METHODS: We conducted a prospective study of patients <20 years with LRTI. Trained investigators performed standardized LUS examinations of 12 regions. Blinded sonologists reviewed examinations for individual pathologic features and also calculated a Quantified Lung Ultrasound Score (QLUS). We defined focal severity as QLUS of ≥2 in ≥1 region, and diffuse severity as QLUS of ≥1 in ≥3 regions. The primary outcome was the Respiratory component of the Pediatric Early Warning Score (RPEWS), a 14-item scale measuring respiratory illness severity. Secondary outcomes included hospital admission, length of stay, supplemental oxygen, and antibiotic use. RESULTS: We enrolled 85 patients with LRTIs, 46 (54%) whom were hospitalized (5.4% intensive care). Median RPEWS was 1 (interquartile range 2). Neither individual features on ultrasound nor total QLUS were associated with RPEWS, hospitalization, length of stay, or oxygen use. Mean RPEWS was similar for participants regardless of focal (1.46 versus 1.26, P = .57) or diffuse (1.47 versus 1.21, P = .47) severity findings, but those with focal or diffuse severity, or isolated consolidation, had greater antibiotic administration (P < .001). CONCLUSIONS: In children with LRTI, neither individual features nor QLUS were associated with illness severity. Antibiotics were more likely in patients with either focal or diffuse severity or presence of consolidation on ultrasound.

Novel Coronavirus Infection in Febrile Infants Aged 60 Days and Younger.

In this case series, we describe the clinical course and outcomes of 7 febrile infants aged ≤60 days with confirmed severe acute respiratory syndrome coronavirus 2 infection. No infant had severe outcomes, including the need for mechanical ventilation or ICU level of care. Two infants had concurrent urinary tract infections, which were treated with antibiotics. Although a small sample, our data suggest that febrile infants with severe acute respiratory syndrome coronavirus 2 infection often have mild illness.