Linhui Hu1, Zhiqiang Nie2, Yichen Zhang3, Yanlin Zhang4, Heng Ye5, Ruibin Chi6, Bei Hu7, Bo Lv8, Lifang Chen9, Xiunong Zhang10, Huajun Wang11, Chunbo Chen12. 1. Department of Intensive Care Unit of Cardiovascular Surgery, Guangdong Cardiovascular Institute, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 96 Dongchuan Road, Guangzhou 510080, Guangdong, China. Electronic address: hulinhui@live.cn. 2. Department of Epidemiology, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 ZhongshanEr Road, Guangzhou 510080, Guangdong, China. Electronic address: 304818029@qq.com. 3. Department of Intensive Care Unit, Guangzhou Red Cross Hospital, Medical College, Jinan University, 396 Tongfuzhong Road, Guangzhou 510220, Guangdong, China. Electronic address: zychen86@126.com. 4. Department of Critical Care Medicine, Xinjiang Kashgar Region's First People's Hospital, 66 Airport Road, Kashgar Region 844099, Xinjiang, China. Electronic address: zylks163@163.com. 5. Department of Critical Care Medicine, Guangzhou Nansha Central Hospital, 105 Fengzhedong Road, Guangzhou 511457, Guangdong, China. Electronic address: yeheng@139.com. 6. Department of Critical Care Medicine, Xiaolan People's Hospital of Zhongshan, 65 Jucheng Road, Zhongshan 528415, Guangdong, China. Electronic address: crb77970922@163.com. 7. Department of Critical Care Medicine, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou 510080, Guangdong Province, China. Electronic address: qhubei@hotmail.com. 8. Department of Critical Care Medicine, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou 510080, Guangdong Province, China. Electronic address: gdlvbo@163.com. 9. Department of Critical Care Medicine, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou 510080, Guangdong Province, China. Electronic address: 13610013473@139.com. 10. Department of Critical Care Medicine, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou 510080, Guangdong Province, China. Electronic address: zhangxiunong@126.com. 11. Department of Critical Care Medicine, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou 510080, Guangdong Province, China. Electronic address: 420484093@qq.com. 12. Department of Intensive Care Unit of Cardiovascular Surgery, Guangdong Cardiovascular Institute, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 96 Dongchuan Road, Guangzhou 510080, Guangdong, China. Electronic address: gghccm@163.com.
Abstract
BACKGROUND & AIMS: Equipment-aided or experience-dependent methods for postpyloric nasoenteric tube placement are not so readily accessible in the critically ill setting. Self-propelled postpyloric placement of a spiral nasoenteric tube can serve as an alternative approach. However, the success rate of this method is relatively low despite using prokinetics. This study aims to develop a user-friendly nomogram incorporating clinical markers to individually predict the probability of successful postpyloric nasoenteric tube placement and facilitate intensivists with improved decision-making before tube insertion. METHODS: Patients consecutively recruited in the stage between May 2012 through December 2016 constituted the development cohort for retrospective analysis to internally test the nomogram, and patients in the stage between January 2017 through March 2018 constituted the validation cohort for prospective analysis to external validate the nomogram. A multivariate logistic regression analysis was firstly performed in the development cohort by a backward stepwise method to identify the best-fit model, from which a nomogram was obtained. The nomogram was validated in the independent external validation cohort concerning discrimination, calibration. A decision curve analysis was also performed to evaluate the net benefit of insertion decision with the nomogram. RESULTS: A total of 364 and 119 patients, 52.7% and 55.5% with successful postpyloric placement, were included in the development and validation cohort, respectively. Predictors contained in the prediction nomogram included primary diagnosis, APACHE II score, AGI grade. The derived model showed good discrimination, with an area under the receiver operating characteristic curve (AUROC) of 0.809 (95%CI, 0.765-0.853) and good calibration. Application of the nomogram in the validation cohort also gave good discrimination with an AUROC of 0.776 (95%CI, 0.694-0.859) and good calibration. The decision curve analysis of the nomogram provided better net benefit than the alternate options (insert-all or insert-none). CONCLUSIONS: A prediction nomogram that incorporates primary diagnosis, together with APACHE II score and AGI grade can be conveniently used to facilitate the pre-insertion individualized prediction of postpyloric nasoenteric tube placement in critically ill patients.
BACKGROUND & AIMS: Equipment-aided or experience-dependent methods for postpyloric nasoenteric tube placement are not so readily accessible in the critically ill setting. Self-propelled postpyloric placement of a spiral nasoenteric tube can serve as an alternative approach. However, the success rate of this method is relatively low despite using prokinetics. This study aims to develop a user-friendly nomogram incorporating clinical markers to individually predict the probability of successful postpyloric nasoenteric tube placement and facilitate intensivists with improved decision-making before tube insertion. METHODS:Patients consecutively recruited in the stage between May 2012 through December 2016 constituted the development cohort for retrospective analysis to internally test the nomogram, and patients in the stage between January 2017 through March 2018 constituted the validation cohort for prospective analysis to external validate the nomogram. A multivariate logistic regression analysis was firstly performed in the development cohort by a backward stepwise method to identify the best-fit model, from which a nomogram was obtained. The nomogram was validated in the independent external validation cohort concerning discrimination, calibration. A decision curve analysis was also performed to evaluate the net benefit of insertion decision with the nomogram. RESULTS: A total of 364 and 119 patients, 52.7% and 55.5% with successful postpyloric placement, were included in the development and validation cohort, respectively. Predictors contained in the prediction nomogram included primary diagnosis, APACHE II score, AGI grade. The derived model showed good discrimination, with an area under the receiver operating characteristic curve (AUROC) of 0.809 (95%CI, 0.765-0.853) and good calibration. Application of the nomogram in the validation cohort also gave good discrimination with an AUROC of 0.776 (95%CI, 0.694-0.859) and good calibration. The decision curve analysis of the nomogram provided better net benefit than the alternate options (insert-all or insert-none). CONCLUSIONS: A prediction nomogram that incorporates primary diagnosis, together with APACHE II score and AGI grade can be conveniently used to facilitate the pre-insertion individualized prediction of postpyloric nasoenteric tube placement in critically illpatients.