Lu Wang, Li Chang, Ruipeng Zhang, Kexun Li, Yu Wang, Wei Chen, Xuanlin Feng, Mingwei Sun, Qi Wang, Charles Damien Lu, Jun Zeng, Hua Jiang
{"title":"利用预测性深度学习模型优化败血症患者的个体化能量输送:真实世界研究","authors":"Lu Wang, Li Chang, Ruipeng Zhang, Kexun Li, Yu Wang, Wei Chen, Xuanlin Feng, Mingwei Sun, Qi Wang, Charles Damien Lu, Jun Zeng, Hua Jiang","doi":"arxiv-2402.02201","DOIUrl":null,"url":null,"abstract":"Background and Objectives: We aim to establish deep learning models to\noptimize the individualized energy delivery for septic patients. Methods and\nStudy Design: We conducted a study of adult septic patients in Intensive Care\nUnit (ICU), collecting 47 indicators for 14 days. After data cleaning and\npreprocessing, we used stats to explore energy delivery in deceased and\nsurviving patients. We filtered out nutrition-related features and divided the\ndata into three metabolic phases: acute early, acute late, and rehabilitation.\nModels were built using data before September 2020 and validated on the rest.\nWe then established optimal energy target models for each phase using deep\nlearning. Results: A total of 277 patients and 3115 data were included in this\nstudy. The models indicated that the optimal energy targets in the three phases\nwere 900kcal/d, 2300kcal/d, and 2000kcal/d, respectively. Excessive energy\nintake increased mortality rapidly in the early period of the acute phase.\nInsufficient energy in the late period of the acute phase significantly raised\nthe mortality of septic patients. For the rehabilitation phase, too much or too\nlittle energy delivery both associated with high mortality. Conclusion: Our\nstudy established time-series prediction models for septic patients to optimize\nenergy delivery in the ICU. This approach indicated the feasibility of\ndeveloping nutritional tools for critically ill patients. We recommended\npermissive underfeeding only in the early acute phase. Later, increased energy\nintake may improve survival and settle energy debts caused by underfeeding.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"2017 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimize Individualized Energy Delivery for Septic Patients Using Predictive Deep Learning Models: A Real World Study\",\"authors\":\"Lu Wang, Li Chang, Ruipeng Zhang, Kexun Li, Yu Wang, Wei Chen, Xuanlin Feng, Mingwei Sun, Qi Wang, Charles Damien Lu, Jun Zeng, Hua Jiang\",\"doi\":\"arxiv-2402.02201\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background and Objectives: We aim to establish deep learning models to\\noptimize the individualized energy delivery for septic patients. Methods and\\nStudy Design: We conducted a study of adult septic patients in Intensive Care\\nUnit (ICU), collecting 47 indicators for 14 days. After data cleaning and\\npreprocessing, we used stats to explore energy delivery in deceased and\\nsurviving patients. We filtered out nutrition-related features and divided the\\ndata into three metabolic phases: acute early, acute late, and rehabilitation.\\nModels were built using data before September 2020 and validated on the rest.\\nWe then established optimal energy target models for each phase using deep\\nlearning. Results: A total of 277 patients and 3115 data were included in this\\nstudy. The models indicated that the optimal energy targets in the three phases\\nwere 900kcal/d, 2300kcal/d, and 2000kcal/d, respectively. Excessive energy\\nintake increased mortality rapidly in the early period of the acute phase.\\nInsufficient energy in the late period of the acute phase significantly raised\\nthe mortality of septic patients. For the rehabilitation phase, too much or too\\nlittle energy delivery both associated with high mortality. Conclusion: Our\\nstudy established time-series prediction models for septic patients to optimize\\nenergy delivery in the ICU. This approach indicated the feasibility of\\ndeveloping nutritional tools for critically ill patients. We recommended\\npermissive underfeeding only in the early acute phase. Later, increased energy\\nintake may improve survival and settle energy debts caused by underfeeding.\",\"PeriodicalId\":501219,\"journal\":{\"name\":\"arXiv - QuanBio - Other Quantitative Biology\",\"volume\":\"2017 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-02-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - QuanBio - Other Quantitative Biology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2402.02201\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Other Quantitative Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2402.02201","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimize Individualized Energy Delivery for Septic Patients Using Predictive Deep Learning Models: A Real World Study
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 Intensive Care
Unit (ICU), collecting 47 indicators for 14 days. After data cleaning and
preprocessing, we used stats to explore energy delivery in deceased and
surviving patients. We filtered out nutrition-related features and divided the
data into three metabolic phases: acute early, acute late, and rehabilitation.
Models were built using data before September 2020 and validated on the rest.
We then established optimal energy target models for each phase using deep
learning. Results: A total of 277 patients and 3115 data were included in this
study. 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 of septic patients. For the rehabilitation phase, too much or too
little energy delivery both associated with high mortality. Conclusion: Our
study established time-series prediction models for septic patients to optimize
energy delivery in the ICU. This approach indicated the feasibility of
developing nutritional tools for critically ill patients. We recommended
permissive underfeeding only in the early acute phase. Later, increased energy
intake may improve survival and settle energy debts caused by underfeeding.