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Evaluation of the prognostic value of lncRNA UCA1 combined with extravascular lung water index and lung ultrasound score in patients with acute lung injury
INTRODUCTION: Acute lung injury (ALI) is a common and rapidly developing critical inflammatory lung disease in clinic. This study investigated the predictive value of lncRNA UCA1, extravascular lung water index (EVLWI), and lung ultrasound score (LUS) in predicting the overall outcome of patients wi...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10363805/ https://www.ncbi.nlm.nih.gov/pubmed/37321562 http://dx.doi.org/10.1111/crj.13651 |
Sumario: | INTRODUCTION: Acute lung injury (ALI) is a common and rapidly developing critical inflammatory lung disease in clinic. This study investigated the predictive value of lncRNA UCA1, extravascular lung water index (EVLWI), and lung ultrasound score (LUS) in predicting the overall outcome of patients with ALI. METHODS: Patients with ALI were recruited for detecting the content of UCA1, EVLWI, and LUS. All patients were cataloged into the survival group and death group according to the prognosis. The discrepancy of UCA1, EVLWI, and LUS was compared in the two groups. The prognostic significance of UCA1, EVLWI, LUS, and their combination was estimated by logistic regression and the receiver operating characteristic (ROC) curve. RESULTS: The levels of UCA1, LUS, and EVLWI were elevated in the death group compared with the survival group. The content of UCA1 was positively correlated with LUS scores and EVLWI scores. UCA1, LUS, and EVLWI were independent indicators of predicting the prognosis of patients with ALI. The ROC curve reflected that UCA1, LUS, and EVLWI could forecast the endpoint events of patients with ALI whereas their combined approach had the highest accuracy. CONCLUSION: Highly expressed UCA1 is a biomarker in forecasting the outcome of patients with ALI. It had high accuracy in predicting the endpoint of patients with ALI when combined with LUS and EVLWI. |
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