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Comparison of chest radiograph scoring to lung weight as a quantitative index of pulmonary edema in organ donors.

ABSTRACT: Quantification of the degree of pulmonary edema in organ donors is useful for assessing the clinical severity of pulmonary edema, determining response to therapy, and as an endpoint for therapeutic trials. Currently, there is no accurate non-invasive method for assessing the degree of pulmonary edema. We tested the performance of a four-quadrant chest radiographic scoring system compared to quantification of pulmonary edema by excised lung weight in 84 donors whose lungs were not used for transplantation. Chest radiographs were taken 3.6 ± 3.0 h prior to organ procurement and were scored by two of the authors. Lungs were excised without perfusion and individually weighed. The chest radiographic scoring system had good performance: correlation between total radiographic score and total lung weight of 0.61, p < 0.001. Performance of the scoring system was improved when chest radiographs with atelectasis were excluded (r = 0.79, p < 0.001). The area under the receiver operator characteristic curve for the detection of moderate pulmonary edema (total lung weight >1000 g) was 0.80. This chest radiographic scoring system may potentially be used to assess the clinical severity of pulmonary edema and may be useful as part of the evaluation of donors for suitability for lung transplantation.

SUBMITTER: Ware LB

PROVIDER: S-EPMC3642873 | biostudies-literature | 2012 Sep-Oct

REPOSITORIES: biostudies-literature

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Comparison of chest radiograph scoring to lung weight as a quantitative index of pulmonary edema in organ donors.

Ware Lorraine B LB Neyrinck Arne A O'Neal Hollis R HR Lee Jae Woo JW Landeck Megan M Johnson Elizabeth E Calfee Carolyn S CS Matthay Michael A MA

Clinical transplantation 20120210 5

Quantification of the degree of pulmonary edema in organ donors is useful for assessing the clinical severity of pulmonary edema, determining response to therapy, and as an endpoint for therapeutic trials. Currently, there is no accurate non-invasive method for assessing the degree of pulmonary edema. We tested the performance of a four-quadrant chest radiographic scoring system compared to quantification of pulmonary edema by excised lung weight in 84 donors whose lungs were not used for transp ...[more]

PMID: 22324977

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Project description:ImportanceMultimodal generative artificial intelligence (AI) methodologies have the potential to optimize emergency department care by producing draft radiology reports from input images.ObjectiveTo evaluate the accuracy and quality of AI-generated chest radiograph interpretations in the emergency department setting.Design, setting, and participantsThis was a retrospective diagnostic study of 500 randomly sampled emergency department encounters at a tertiary care institution including chest radiographs interpreted by both a teleradiology service and on-site attending radiologist from January 2022 to January 2023. An AI interpretation was generated for each radiograph. The 3 radiograph interpretations were each rated in duplicate by 6 emergency department physicians using a 5-point Likert scale.Main outcomes and measuresThe primary outcome was any difference in Likert scores between radiologist, AI, and teleradiology reports, using a cumulative link mixed model. Secondary analyses compared the probability of each report type containing no clinically significant discrepancy with further stratification by finding presence, using a logistic mixed-effects model. Physician comments on discrepancies were recorded.ResultsA total of 500 ED studies were included from 500 unique patients with a mean (SD) age of 53.3 (21.6) years; 282 patients (56.4%) were female. There was a significant association of report type with ratings, with post hoc tests revealing significantly greater scores for AI (mean [SE] score, 3.22 [0.34]; P < .001) and radiologist (mean [SE] score, 3.34 [0.34]; P < .001) reports compared with teleradiology (mean [SE] score, 2.74 [0.34]) reports. AI and radiologist reports were not significantly different. On secondary analysis, there was no difference in the probability of no clinically significant discrepancy between the 3 report types. Further stratification of reports by presence of cardiomegaly, pulmonary edema, pleural effusion, infiltrate, pneumothorax, and support devices also yielded no difference in the probability of containing no clinically significant discrepancy between the report types.Conclusions and relevanceIn a representative sample of emergency department chest radiographs, results suggest that the generative AI model produced reports of similar clinical accuracy and textual quality to radiologist reports while providing higher textual quality than teleradiologist reports. Implementation of the model in the clinical workflow could enable timely alerts to life-threatening pathology while aiding imaging interpretation and documentation.

Dataset Information

Comparison of chest radiograph scoring to lung weight as a quantitative index of pulmonary edema in organ donors.

Publications

Comparison of chest radiograph scoring to lung weight as a quantitative index of pulmonary edema in organ donors.

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