Optimize Individualized Energy Delivery for Septic Patients Using Predictive Deep Learning Models: A Real World Study
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911770774339584 |
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| author | Wang, Lu Chang, Li Zhang, Ruipeng Li, Kexun Wang, Yu Chen, Wei Feng, Xuanlin Sun, Mingwei Wang, Qi Lu, Charles Damien Zeng, Jun Jiang, Hua |
| author_facet | Wang, Lu Chang, Li Zhang, Ruipeng Li, Kexun Wang, Yu Chen, Wei Feng, Xuanlin Sun, Mingwei Wang, Qi Lu, Charles Damien Zeng, Jun Jiang, Hua |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02201 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimize Individualized Energy Delivery for Septic Patients Using Predictive Deep Learning Models: A Real World Study Wang, Lu Chang, Li Zhang, Ruipeng Li, Kexun Wang, Yu Chen, Wei Feng, Xuanlin Sun, Mingwei Wang, Qi Lu, Charles Damien Zeng, Jun Jiang, Hua Other Quantitative Biology 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. |
| title | Optimize Individualized Energy Delivery for Septic Patients Using Predictive Deep Learning Models: A Real World Study |
| topic | Other Quantitative Biology |
| url | https://arxiv.org/abs/2402.02201 |