A prediction model for nonresponsive outcomes in critically ill patients with acute respiratory distress syndrome undergoing prone position ventilation: A retrospective cohort study.

Autor: Yan Y; Department of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China; School of Nursing, Southern Medical University, Guangzhou, China., Geng B; Department of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China., Liang J; Department of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China., Wen Y; Department of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China., Bao J; Department of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China; Department of Anesthesiology, Nanfang Hospital, Southern Medical University, Guangzhou, China., Zhong X; Department of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China; School of Nursing, Southern Medical University, Guangzhou, China., Chen M; School of Nursing, Southern Medical University, Guangzhou, China; Department of Respiratory and Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China., Liu L; Department of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China., Duan J; Department of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China., Zeng Z; Department of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China., An S; Department of Biostatistics, School of Public Health (Guangdong Provincial Key Laboratory of Tropical Disease Research), Southern Medical University, Guangzhou, China. Electronic address: ASL0418@126.com., Chen Z; Department of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China. Electronic address: zhongqingchen2008@163.com., Hu H; Department of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China. Electronic address: hobewoos@163.com.
Jazyk: angličtina
Zdroj: Intensive & critical care nursing [Intensive Crit Care Nurs] 2024 Aug 23; Vol. 86, pp. 103804. Date of Electronic Publication: 2024 Aug 23.
DOI: 10.1016/j.iccn.2024.103804
Abstrakt: Objective: This study aimed to develop a reliable and effective nomogram model to identify high-risk populations with non-response to prone position ventilation (PPV) in acute respiratory distress syndrome (ARDS) patients.
Methods: This retrospective cohort study included 175 patients with ARDS undergoing PPV. An improvement of ≥ 20 mmHg in the PaO 2 /FiO 2 after the first PPV was defined as a 'response'. For the construction of the model, all patients were randomly assigned to the train and validation cohort according to 2:1. Multivariate logistic regression was useed to develop the nomogram. The area under the receiver operating characteristic curve (AUC), decision curve and calibration curve were assessed to evaluate the efficiency, clinical utility and calibration of the model.
Results: The overall rate of non-response to PPV in ARDS patients was approximately 32.6 %. In the training cohort and validation cohort, the rate are 29.9 % and 34.5 % respectively. Murray score ≥ 2.5 (OR: 4.29), procalcitonin (PCT) ≥ 2 ng/mL (OR: 2.52), N-terminal pro-B-type natriuretic peptide (Nt-proBNP) ≥ 2000 pg/ml (OR: 2.44), and hemoglobin ≤ 90 g/L (OR: 2.39) were independently associated with the rate of non-response to PPV and combined in prediction model. The model demonstrated good predictive value with AUC of 0.817 and 0.828 in the train and validation cohort. Calibration curve showed good calibration and decision curve analysis indicated favorable clinical utility.
Conclusions: This study constructed a risk prediction model for non-response to PPV, which demonstrated good predictive value and clinical utility.
Implications for Clinical Practice: Early identification of prone position response in ARDS is essential for timely alternative treatments, improving patient prognosis and healthcare efficiency. The predictive model included representative indicators of patients with ARDS, encompassing parameters such as the acute lung injury (Murray score), cardiac function (Nt-proBNP), infectious status (PCT), and hemoglobin levels.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
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