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Lung ultrasound score as a predictor of failure to wean COVID-19 patients off mechanical ventilation: a prospective observational study (preprint)
researchsquare; 2023.
Preprint in English | PREPRINT-RESEARCHSQUARE | ID: ppzbmed-10.21203.rs.3.rs-3155874.v1
ABSTRACT
Background The lung ultrasound score was developed for rapidly assessing the extent of lung ventilation, and it can predict failure to wean various types of patients off mechanical ventilation. Whether it is also effective for COVID-19 patients is unclear.Methods This single-center, prospective, observational study was conducted to assess the ability of the 12-region lung ultrasound score to predict failure to wean COVID-19 patients off ventilation. In parallel, we assessed whether right hemidiaphragmatic excursion or previously published predictors of weaning failure can apply to these patients. Predictive ability was assessed in terms of the area under the receiver operating characteristic curve (AUC).Results Among the 35 patients in the study, 12 patients (37%) could not be weaned off mechanical ventilation. The lung ultrasound score predicted these failures with an AUC of 0.885 (95% CI 0.770–0.999, p < 0.001), and a threshold score of 10 provided specificity of 72.7% and sensitivity of 92.3%. AUCs were lower for previously published predictors of weaning failure, and right hemidiaphragmatic excursion did not differ significantly between the two groups.Conclusions The lung ultrasound score can accurately predict failure to wean critically ill COVID-19 patients off mechanical ventilation, whereas assessment of right hemidiaphragmatic excursion does not appear helpful in this regard.Trial registration https//clinicaltrials.gov/ct2/show/NCT05706441
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Full text: Available Collection: Preprints Database: PREPRINT-RESEARCHSQUARE Main subject: COVID-19 / Heart Failure Language: English Year: 2023 Document Type: Preprint

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Full text: Available Collection: Preprints Database: PREPRINT-RESEARCHSQUARE Main subject: COVID-19 / Heart Failure Language: English Year: 2023 Document Type: Preprint