Prediction of persistent acute kidney injury in postoperative intensive care unit patients using integrated machine learning: a retrospective cohort study
Abstract Acute kidney injury (AKI) often occurs in patients in the intensive care unit (ICU). AKI duration is closely related to the prognosis of critically ill patients. Identifying the disease course length in AKI is critical for developing effective individualised treatment. To predict persistent...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-21428-5 |
| _version_ | 1798029848099160064 |
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| author | Xuandong Jiang Yongxia Hu Shan Guo Chaojian Du Xuping Cheng |
| author_facet | Xuandong Jiang Yongxia Hu Shan Guo Chaojian Du Xuping Cheng |
| author_sort | Xuandong Jiang |
| collection | DOAJ |
| description | Abstract Acute kidney injury (AKI) often occurs in patients in the intensive care unit (ICU). AKI duration is closely related to the prognosis of critically ill patients. Identifying the disease course length in AKI is critical for developing effective individualised treatment. To predict persistent AKI at an early stage based on a machine learning algorithm and integrated models. Overall, 955 patients admitted to the ICU after surgery complicated by AKI were retrospectively evaluated. The occurrence of persistent AKI was predicted using three machine learning methods: a support vector machine (SVM), decision tree, and extreme gradient boosting and with an integrated model. External validation was also performed. The incidence of persistent AKI was 39.4–45.1%. In the internal validation, SVM exhibited the highest area under the receiver operating characteristic curve (AUC) value, followed by the integrated model. In the external validation, the AUC values of the SVM and integrated models were 0.69 and 0.68, respectively, and the model calibration chart revealed that all models had good performance. Critically ill patients with AKI after surgery had high incidence of persistent AKI. Our machine learning model could effectively predict the occurrence of persistent AKI at an early stage. |
| first_indexed | 2024-04-11T19:31:56Z |
| format | Article |
| id | doaj.art-7ed9ff83448d4cba89737e37cbbf6b39 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T19:31:56Z |
| publishDate | 2022-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-7ed9ff83448d4cba89737e37cbbf6b392022-12-22T04:06:58ZengNature PortfolioScientific Reports2045-23222022-10-0112111110.1038/s41598-022-21428-5Prediction of persistent acute kidney injury in postoperative intensive care unit patients using integrated machine learning: a retrospective cohort studyXuandong Jiang0Yongxia Hu1Shan Guo2Chaojian Du3Xuping Cheng4Intensive Care Unit, Affiliated Dongyang Hospital of Wenzhou Medical UniversityIntensive Care Unit, Affiliated Dongyang Hospital of Wenzhou Medical UniversityIntensive Care Unit, Affiliated Dongyang Hospital of Wenzhou Medical UniversityIntensive Care Unit, Affiliated Dongyang Hospital of Wenzhou Medical UniversityIntensive Care Unit, Affiliated Dongyang Hospital of Wenzhou Medical UniversityAbstract Acute kidney injury (AKI) often occurs in patients in the intensive care unit (ICU). AKI duration is closely related to the prognosis of critically ill patients. Identifying the disease course length in AKI is critical for developing effective individualised treatment. To predict persistent AKI at an early stage based on a machine learning algorithm and integrated models. Overall, 955 patients admitted to the ICU after surgery complicated by AKI were retrospectively evaluated. The occurrence of persistent AKI was predicted using three machine learning methods: a support vector machine (SVM), decision tree, and extreme gradient boosting and with an integrated model. External validation was also performed. The incidence of persistent AKI was 39.4–45.1%. In the internal validation, SVM exhibited the highest area under the receiver operating characteristic curve (AUC) value, followed by the integrated model. In the external validation, the AUC values of the SVM and integrated models were 0.69 and 0.68, respectively, and the model calibration chart revealed that all models had good performance. Critically ill patients with AKI after surgery had high incidence of persistent AKI. Our machine learning model could effectively predict the occurrence of persistent AKI at an early stage.https://doi.org/10.1038/s41598-022-21428-5 |
| spellingShingle | Xuandong Jiang Yongxia Hu Shan Guo Chaojian Du Xuping Cheng Prediction of persistent acute kidney injury in postoperative intensive care unit patients using integrated machine learning: a retrospective cohort study Scientific Reports |
| title | Prediction of persistent acute kidney injury in postoperative intensive care unit patients using integrated machine learning: a retrospective cohort study |
| title_full | Prediction of persistent acute kidney injury in postoperative intensive care unit patients using integrated machine learning: a retrospective cohort study |
| title_fullStr | Prediction of persistent acute kidney injury in postoperative intensive care unit patients using integrated machine learning: a retrospective cohort study |
| title_full_unstemmed | Prediction of persistent acute kidney injury in postoperative intensive care unit patients using integrated machine learning: a retrospective cohort study |
| title_short | Prediction of persistent acute kidney injury in postoperative intensive care unit patients using integrated machine learning: a retrospective cohort study |
| title_sort | prediction of persistent acute kidney injury in postoperative intensive care unit patients using integrated machine learning a retrospective cohort study |
| url | https://doi.org/10.1038/s41598-022-21428-5 |
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