دورية أكاديمية
Optimize individualized energy delivery for septic patients using predictive deep learning models.
| العنوان: | Optimize individualized energy delivery for septic patients using predictive deep learning models. |
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| المؤلفون: | Wang L; Institute for Emergency and Disaster Medicine, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.; Sichuan Provincial Research Center for Emergency Medicine and Critical Illness, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China., Chang L; Department of Emergency Intensive Care Unit, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China., Zhang R; Institute for Emergency and Disaster Medicine, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.; Sichuan Provincial Research Center for Emergency Medicine and Critical Illness, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China., Li K; Department of Emergency Surgery, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China., Wang Y; Department of Clinical Nutrition, Department of Health Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China., Chen W; Department of Clinical Nutrition, Department of Health Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China., Feng X; Department of Emergency Intensive Care Unit, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China., Sun M; Institute for Emergency and Disaster Medicine, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.; Sichuan Provincial Research Center for Emergency Medicine and Critical Illness, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China., Wang Q; Department of Mathematics, University of South Carolina, Columbia, SC, USA., Lu CD; Sichuan Provincial Research Center for Emergency Medicine and Critical Illness, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China., Zeng J; Institute for Emergency and Disaster Medicine, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.; Sichuan Provincial Research Center for Emergency Medicine and Critical Illness, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China. Email: zengjun@med.uestc.edu.cn., Jiang H; Institute for Emergency and Disaster Medicine, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China. Email: jianghua@uestc.edu.cn.; Sichuan Provincial Research Center for Emergency Medicine and Critical Illness, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.; Department of Emergency Intensive Care Unit, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.; Department of Emergency Surgery, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China. |
| المصدر: | Asia Pacific journal of clinical nutrition [Asia Pac J Clin Nutr] 2024 Sep; Vol. 33 (3), pp. 348-361. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Qingdao University Country of Publication: China NLM ID: 9440304 Publication Model: Print Cited Medium: Internet ISSN: 1440-6047 (Electronic) Linking ISSN: 09647058 NLM ISO Abbreviation: Asia Pac J Clin Nutr Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: 2023- : Qingdao : Qingdao University Original Publication: London, UK : Published for the Asia Pacific Clinical Nutrition Society by Smith-Gordon, |
| مواضيع طبية MeSH: | Sepsis* , Deep Learning* , Energy Intake*, Humans ; Female ; Male ; Middle Aged ; Aged ; Intensive Care Units |
| مستخلص: | Background and Objectives: We aim to establish deep learning models to optimize the individualized energy delivery for septic patients. Methods and Study Design: We conducted a study of adult septic patients in ICU, collecting 47 indicators for 14 days. We filtered out nutrition-related features and divided the data into datasets according to the three metabolic phases proposed by ESPEN: acute early, acute late, and rehabilitation. We then established optimal energy target models for each phase using deep learning and conducted external validation. Results: A total of 179 patients in training dataset and 98 patients in external validation dataset were included in this study, and total data size was 3115 elements. The age, weight and BMI of the patients were 63.05 (95%CI 60.42-65.68), 61.31(95%CI 59.62-63.00) and 22.70 (95%CI 22.21-23.19), respectively. And 26.0% (72) of the patients were female. The models indicated that the optimal energy targets in the three phases were 900kcal/d, 2300kcal/d, and 2000kcal/d, respectively. Excessive energy intake increased mortality rapidly in the early period of the acute phase. Insufficient energy in the late period of the acute phase significantly raised the mortality as well. For the rehabilitation phase, too much or too little energy delivery were both associated with elevated death risk. Conclusions: Our study established time-series prediction models for septic patients to optimize energy delivery in the ICU. We recommended permissive underfeeding only in the early acute phase. Later, increased energy intake may improve survival and settle energy debts caused by underfeeding. Competing Interests: The authors declare that they have no competing interests |
| معلومات مُعتمدة: | No. 2021YFH0109 Sichuan Science and Technology Program; No. 2021YFS0378 Sichuan Science and Technology Program; No. 72074222 National Natural Science Foundation of China |
| فهرسة مساهمة: | Keywords: deep learning; energy delivery; machine learning; nutrition support; sepsis |
| تواريخ الأحداث: | Date Created: 20240705 Date Completed: 20240705 Latest Revision: 20240705 |
| رمز التحديث: | 20240705 |
| DOI: | 10.6133/apjcn.202409_33(3).0005 |
| PMID: | 38965722 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1440-6047 |
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| DOI: | 10.6133/apjcn.202409_33(3).0005 |