Performance of lung ultrasound in the diagnosis of pediatric pneumonia in Mozambique and Pakistan

Ginsburg, Amy Sarah; Lenahan, Jennifer L.; Jehan, Fyezah; Bila, Rubao; Lamorte, Alessandro; Hwang, Jun Seong; Madrid, Lola; Vitorino, Pio; Kanth, Neel; Balcells, Reyes; Baloch, Benazir; May, Susanne; Valente, Marta; Varo, Rosauro; Nadeem, Naila; Bassat, Quique; Volpicelli, Giovanni

doi:10.1002/ppul.25176

Cited by 8 publications

(4 citation statements)

References 32 publications

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“…Among children with suspected pneumonia confirmed by chest computed tomography, LUS has demonstrated a sensitivity and accuracy of 0.906 and 0.661, respectively in comparison to CXR which had a sensitivity and accuracy of 0.79 and 0.559, respectively [ 35 ]. Furthermore, LUS expert interpreters have achieved substantially higher interrater reliability compared to CXR expert interpreters [ 43 ]. Without ionizing radiation as a limitation, LUS may be useful to monitor treatment changes and possibly differentiate features of viral versus bacterial pneumonia [ 44 – 46 ].…”

Section: Discussionmentioning

confidence: 99%

Chest radiography in children aged 2–59 months enrolled in the Innovative Treatments in Pneumonia (ITIP) project in Lilongwe Malawi: a secondary analysis

et al. 2022

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Background Pneumonia is the leading infectious cause of death in children aged under 5 years in low- and middle-income countries (LMICs). World Health Organization (WHO) pneumonia diagnosis guidelines rely on non-specific clinical features. We explore chest radiography (CXR) findings among select children in the Innovative Treatments in Pneumonia (ITIP) project in Malawi in relation to clinical outcomes. Methods When clinically indicated, CXRs were obtained from ITIP-enrolled children aged 2 to 59 months with community-acquired pneumonia hospitalized with treatment failure or relapse. ITIP1 (fast-breathing pneumonia) and ITIP2 (chest-indrawing pneumonia) trials enrolled children with non-severe pneumonia while ITIP3 enrolled children excluded from ITIP1 and ITIP2 with severe pneumonia and/or selected comorbidities. A panel of trained pediatricians classified the CXRs using the standardized WHO CXR research methodology. We analyzed the relationship between CXR classifications, enrollee characteristics, and outcomes. Results Between March 2016 and June 2018, of 114 CXRs obtained, 83 met analysis criteria with 62.7% (52/83) classified as having significant pathology per WHO standardized interpretation. ITIP3 (92.3%; 12/13) children had a higher proportion of CXRs with significant pathology compared to ITIP1 (57.1%, 12/21) and ITIP2 (57.1%, 28/49) (p-value = 0.008). The predominant pathological CXR reading was “other infiltrates only” in ITIP1 (83.3%, 10/12) and ITIP2 (71.4%, 20/28), while in ITIP3 it was “primary endpoint pneumonia”(66.7%, 8/12,; p-value = 0.008). The percent of CXRs with significant pathology among children clinically cured (60.6%, 40/66) vs those not clinically cured (70.6%, 12/17) at Day 14 was not significantly different (p-value = 0.58). Conclusions In this secondary analysis we observed that ITIP3 children with severe pneumonia and/or selected comorbidities had a higher frequency of CXRs with significant pathology, although these radiographic findings had limited relationship to Day 14 outcomes. The proportion of CXRs with “primary endpoint pneumonia” was low. These findings add to existing data that additional diagnostics and prognostics are important for improving the care of children with pneumonia in LMICs. Trial registration ITIP1, ITIP2, and ITIP3 were registered with ClinicalTrials.gov (NCT02760420, NCT02678195, and NCT02960919, respectively).

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Section: Discussionmentioning

confidence: 99%

Chest radiography in children aged 2–59 months enrolled in the Innovative Treatments in Pneumonia (ITIP) project in Lilongwe Malawi: a secondary analysis

et al. 2022

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“…Methods and primary results of this prospective, observational, facility-based cohort study were described previously [ 6 , 7 ]. The primary objective was to provide evidence assessing whether the addition of LUS to current pneumonia care pathways improves identification of pneumonia in children presenting to district hospitals in Manhiça, Mozambique and Karachi, Pakistan.…”

Section: Methodsmentioning

confidence: 99%

“…Key to evaluating the use case of LUS in pneumonia diagnosis in resource-constrained settings is a better understanding of the LUS patterns associated with paediatric pneumonia. We conducted a pilot study in Mozambique and Pakistan to investigate use of LUS for diagnosis and found that among children with World Health Organization (WHO) Integrated Management of Childhood Illness (IMCI) chest-indrawing pneumonia, expert LUS interpreters may achieve substantially higher interrater reliability (IRR) for LUS compared to CXR [ 6 ]. We chose to focus on non-severe chest-indrawing pneumonia rather than more severe pneumonia in this study, since we believed it unlikely that LUS imaging would change initial management of severe pneumonia, which is typically treated with intravenous antibiotics.…”

Section: Introductionmentioning

confidence: 99%

Lung ultrasound patterns in paediatric pneumonia in Mozambique and Pakistan

et al. 2020

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ObjectiveImproved pneumonia diagnostics are needed, particularly in resource-constrained settings. Lung ultrasound (LUS) is a promising point-of-care imaging technology for diagnosing pneumonia. The objective was to explore LUS patterns associated with pediatric pneumonia.MethodsWe conducted a prospective, observational study among children aged 2 through 23 months with World Health Organization Integrated Management of Childhood Illness chest-indrawing pneumonia and among children without fast breathing, chest indrawing or fever (no pneumonia cohort) at two district hospitals in Mozambique and Pakistan. We assessed LUS and chest radiograph (CXR) examinations, and viral and bacterial nasopharyngeal carriage, and performed a secondary analysis of LUS patterns.ResultsLUS demonstrated a range of distinctive patterns that differed between children with and without pneumonia and between children in Mozambique versus Pakistan. The presence of LUS consolidation or interstitial patterns was more common in children with chest-indrawing pneumonia than in those without pneumonia. Consolidations were also more common among those with only bacterial but no viral carriage detected (50.0%) than among those with both (13.0%) and those with only virus detected (8.3%;p-value 0.03). LUS showed high interrater reliability among expert LUS interpreters for overall determination of pneumonia (κ=0.915), consolidation (κ=0.915), and interstitial patterns (κ=0.901), but interrater reliability between LUS and CXR for detecting consolidations was poor (κ=0.159, Pakistan) to fair (κ=0.453, Mozambique).DiscussionPattern recognition was discordant between LUS and CXR imaging modalities. Further research is needed to define and standardise LUS patterns associated with pediatric pneumonia and to evaluate the potential value of LUS as a reference standard.

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“…Lung ultrasound (LUS) is a well described, portable, inexpensive, and accurate point of care technique to assess respiratory disease at the bedside [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ], with potential deployment in a wide variety of environments [ 8 , 9 ]. In comparison to the traditional methods used to image the lungs, such as a CT scan or chest X-ray, LUS displays comparable or improved diagnostic accuracy at a reduced cost [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Annotation Efficiency with Machine Learning: Automated Partitioning of a Lung Ultrasound Dataset by View

et al. 2022

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Background: Annotating large medical imaging datasets is an arduous and expensive task, especially when the datasets in question are not organized according to deep learning goals. Here, we propose a method that exploits the hierarchical organization of annotating tasks to optimize efficiency. Methods: We trained a machine learning model to accurately distinguish between one of two classes of lung ultrasound (LUS) views using 2908 clips from a larger dataset. Partitioning the remaining dataset by view would reduce downstream labelling efforts by enabling annotators to focus on annotating pathological features specific to each view. Results: In a sample view-specific annotation task, we found that automatically partitioning a 780-clip dataset by view saved 42 min of manual annotation time and resulted in 55±6 additional relevant labels per hour. Conclusions: Automatic partitioning of a LUS dataset by view significantly increases annotator efficiency, resulting in higher throughput relevant to the annotating task at hand. The strategy described in this work can be applied to other hierarchical annotation schemes.

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Performance of lung ultrasound in the diagnosis of pediatric pneumonia in Mozambique and Pakistan

Cited by 8 publications

References 32 publications

Chest radiography in children aged 2–59 months enrolled in the Innovative Treatments in Pneumonia (ITIP) project in Lilongwe Malawi: a secondary analysis

Chest radiography in children aged 2–59 months enrolled in the Innovative Treatments in Pneumonia (ITIP) project in Lilongwe Malawi: a secondary analysis

Lung ultrasound patterns in paediatric pneumonia in Mozambique and Pakistan

Enhancing Annotation Efficiency with Machine Learning: Automated Partitioning of a Lung Ultrasound Dataset by View

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