Yuhang Yan , Bingxuan Geng , Jingyi Liang , Yinghong Wen , Junying Bao , Xiangning Zhong , Meijia Chen , Li Liu , Jiaxin Duan , Zhenhua Zeng , Shengli An , Zhongqing Chen , HongBin Hu
{"title":"俯卧位通气的急性呼吸窘迫综合征重症患者无反应结局的预测模型：回顾性队列研究","authors":"Yuhang Yan , Bingxuan Geng , Jingyi Liang , Yinghong Wen , Junying Bao , Xiangning Zhong , Meijia Chen , Li Liu , Jiaxin Duan , Zhenhua Zeng , Shengli An , Zhongqing Chen , HongBin Hu","doi":"10.1016/j.iccn.2024.103804","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><p>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.</p></div><div><h3>Methods</h3><p>This retrospective cohort study included 175 patients with ARDS undergoing PPV. An improvement of ≥ 20 mmHg in the PaO<sub>2</sub>/FiO<sub>2</sub> 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.</p></div><div><h3>Results</h3><p>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.</p></div><div><h3>Conclusions</h3><p>This study constructed a risk prediction model for non-response to PPV, which demonstrated good predictive value and clinical utility.</p></div><div><h3>Implications for clinical practice</h3><p>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.</p></div>","PeriodicalId":51322,"journal":{"name":"Intensive and Critical Care Nursing","volume":"86 ","pages":"Article 103804"},"PeriodicalIF":4.9000,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0964339724001897/pdfft?md5=c55b692b82e8a36a6607f76544b13b7f&pid=1-s2.0-S0964339724001897-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A prediction model for nonresponsive outcomes in critically ill patients with acute respiratory distress syndrome undergoing prone position ventilation: A retrospective cohort study\",\"authors\":\"Yuhang Yan , Bingxuan Geng , Jingyi Liang , Yinghong Wen , Junying Bao , Xiangning Zhong , Meijia Chen , Li Liu , Jiaxin Duan , Zhenhua Zeng , Shengli An , Zhongqing Chen , HongBin Hu\",\"doi\":\"10.1016/j.iccn.2024.103804\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><p>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.</p></div><div><h3>Methods</h3><p>This retrospective cohort study included 175 patients with ARDS undergoing PPV. An improvement of ≥ 20 mmHg in the PaO<sub>2</sub>/FiO<sub>2</sub> 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.</p></div><div><h3>Results</h3><p>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.</p></div><div><h3>Conclusions</h3><p>This study constructed a risk prediction model for non-response to PPV, which demonstrated good predictive value and clinical utility.</p></div><div><h3>Implications for clinical practice</h3><p>Early identification of prone position response in ARDS is essential for timely alternative treatments, improving patient prognosis and healthcare efficiency. 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A prediction model for nonresponsive outcomes in critically ill patients with acute respiratory distress syndrome undergoing prone position ventilation: A retrospective cohort study
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 PaO2/FiO2 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.
期刊介绍：
The aims of Intensive and Critical Care Nursing are to promote excellence of care of critically ill patients by specialist nurses and their professional colleagues; to provide an international and interdisciplinary forum for the publication, dissemination and exchange of research findings, experience and ideas; to develop and enhance the knowledge, skills, attitudes and creative thinking essential to good critical care nursing practice. The journal publishes reviews, updates and feature articles in addition to original papers and significant preliminary communications. Articles may deal with any part of practice including relevant clinical, research, educational, psychological and technological aspects.
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