Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model
Abstract Objective Early identifying sepsis patients who had higher risk of poor prognosis was extremely important. The aim of this study was to develop an artificial neural networks (ANN) model for early predicting clinical outcomes in sepsis. Methods This study was a retrospective design. Sepsis p...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2022-12-01
|
| Series: | European Journal of Medical Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40001-022-00925-3 |
| _version_ | 1797980328309030912 |
|---|---|
| author | Yingjie Su Cuirong Guo Shifang Zhou Changluo Li Ning Ding |
| author_facet | Yingjie Su Cuirong Guo Shifang Zhou Changluo Li Ning Ding |
| author_sort | Yingjie Su |
| collection | DOAJ |
| description | Abstract Objective Early identifying sepsis patients who had higher risk of poor prognosis was extremely important. The aim of this study was to develop an artificial neural networks (ANN) model for early predicting clinical outcomes in sepsis. Methods This study was a retrospective design. Sepsis patients from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were enrolled. A predictive model for predicting 30-day morality in sepsis was performed based on the ANN approach. Results A total of 2874 patients with sepsis were included and 30-day mortality was 29.8%. The study population was categorized into the training set (n = 1698) and validation set (n = 1176) based on the ratio of 6:4. 11 variables which showed significant differences between survivor group and nonsurvivor group in training set were selected for constructing the ANN model. In training set, the predictive performance based on the area under the receiver-operating characteristic curve (AUC) were 0.873 for ANN model, 0.720 for logistic regression, 0.629 for APACHEII score and 0.619 for SOFA score. In validation set, the AUCs of ANN, logistic regression, APAHCEII score, and SOFA score were 0.811, 0.752, 0.607, and 0.628, respectively. Conclusion An ANN model for predicting 30-day mortality in sepsis was performed. Our predictive model can be beneficial for early detection of patients with higher risk of poor prognosis. |
| first_indexed | 2024-04-11T05:52:46Z |
| format | Article |
| id | doaj.art-ffbe5eb8f1c247078b108f1a752465dc |
| institution | Directory Open Access Journal |
| issn | 2047-783X |
| language | English |
| last_indexed | 2024-04-11T05:52:46Z |
| publishDate | 2022-12-01 |
| publisher | BMC |
| record_format | Article |
| series | European Journal of Medical Research |
| spelling | doaj.art-ffbe5eb8f1c247078b108f1a752465dc2022-12-22T04:42:01ZengBMCEuropean Journal of Medical Research2047-783X2022-12-0127111010.1186/s40001-022-00925-3Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks modelYingjie Su0Cuirong Guo1Shifang Zhou2Changluo Li3Ning Ding4Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South ChinaDepartment of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South ChinaDepartment of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South ChinaDepartment of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South ChinaDepartment of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South ChinaAbstract Objective Early identifying sepsis patients who had higher risk of poor prognosis was extremely important. The aim of this study was to develop an artificial neural networks (ANN) model for early predicting clinical outcomes in sepsis. Methods This study was a retrospective design. Sepsis patients from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were enrolled. A predictive model for predicting 30-day morality in sepsis was performed based on the ANN approach. Results A total of 2874 patients with sepsis were included and 30-day mortality was 29.8%. The study population was categorized into the training set (n = 1698) and validation set (n = 1176) based on the ratio of 6:4. 11 variables which showed significant differences between survivor group and nonsurvivor group in training set were selected for constructing the ANN model. In training set, the predictive performance based on the area under the receiver-operating characteristic curve (AUC) were 0.873 for ANN model, 0.720 for logistic regression, 0.629 for APACHEII score and 0.619 for SOFA score. In validation set, the AUCs of ANN, logistic regression, APAHCEII score, and SOFA score were 0.811, 0.752, 0.607, and 0.628, respectively. Conclusion An ANN model for predicting 30-day mortality in sepsis was performed. Our predictive model can be beneficial for early detection of patients with higher risk of poor prognosis.https://doi.org/10.1186/s40001-022-00925-3Artificial neural networksSepsisMortalityMIMIC-III |
| spellingShingle | Yingjie Su Cuirong Guo Shifang Zhou Changluo Li Ning Ding Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model European Journal of Medical Research Artificial neural networks Sepsis Mortality MIMIC-III |
| title | Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model |
| title_full | Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model |
| title_fullStr | Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model |
| title_full_unstemmed | Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model |
| title_short | Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model |
| title_sort | early predicting 30 day mortality in sepsis in mimic iii by an artificial neural networks model |
| topic | Artificial neural networks Sepsis Mortality MIMIC-III |
| url | https://doi.org/10.1186/s40001-022-00925-3 |
| work_keys_str_mv | AT yingjiesu earlypredicting30daymortalityinsepsisinmimiciiibyanartificialneuralnetworksmodel AT cuirongguo earlypredicting30daymortalityinsepsisinmimiciiibyanartificialneuralnetworksmodel AT shifangzhou earlypredicting30daymortalityinsepsisinmimiciiibyanartificialneuralnetworksmodel AT changluoli earlypredicting30daymortalityinsepsisinmimiciiibyanartificialneuralnetworksmodel AT ningding earlypredicting30daymortalityinsepsisinmimiciiibyanartificialneuralnetworksmodel |