Predicting ICU readmission risks in intracerebral hemorrhage patients : Insights from machine learning models using MIMIC databases
Copyright © 2023 Elsevier B.V. All rights reserved..
BACKGROUND: Intracerebral hemorrhage (ICH) is a stroke subtype characterized by high mortality and complex post-event complications. Research has extensively covered the acute phase of ICH; however, ICU readmission determinants remain less explored. Utilizing the MIMIC-III and MIMIC-IV databases, this investigation develops machine learning (ML) models to anticipate ICU readmissions in ICH patients.
METHODS: Retrospective data from 2242 ICH patients were evaluated using ICD-9 codes. Recursive feature elimination with cross-validation (RFECV) discerned significant predictors of ICU readmissions. Four ML models-AdaBoost, RandomForest, LightGBM, and XGBoost-underwent development and rigorous validation. SHapley Additive exPlanations (SHAP) elucidated the effect of distinct features on model outcomes.
RESULTS: ICU readmission rates were 9.6% for MIMIC-III and 10.6% for MIMIC-IV. The LightGBM model, with an AUC of 0.736 (95% CI: 0.668-0.801), surpassed other models in validation datasets. SHAP analysis revealed hydrocephalus, sex, neutrophils, Glasgow Coma Scale (GCS), specific oxygen saturation (SpO2) levels, and creatinine as significant predictors of readmission.
CONCLUSION: The LightGBM model demonstrates considerable potential in predicting ICU readmissions for ICH patients, highlighting the importance of certain clinical predictors. This research contributes to optimizing patient care and ICU resource management. Further prospective studies are warranted to corroborate and enhance these predictive insights for clinical utilization.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:456 |
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| Enthalten in: |
Journal of the neurological sciences - 456(2024) vom: 15. Jan., Seite 122849 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Miao, Jinfeng [VerfasserIn] |
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| Links: |
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| Themen: |
ICU readmission |
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| Anmerkungen: |
Date Completed 15.01.2024 Date Revised 15.01.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.jns.2023.122849 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM366384589 |
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| 520 | |a Copyright © 2023 Elsevier B.V. All rights reserved. | ||
| 520 | |a BACKGROUND: Intracerebral hemorrhage (ICH) is a stroke subtype characterized by high mortality and complex post-event complications. Research has extensively covered the acute phase of ICH; however, ICU readmission determinants remain less explored. Utilizing the MIMIC-III and MIMIC-IV databases, this investigation develops machine learning (ML) models to anticipate ICU readmissions in ICH patients | ||
| 520 | |a METHODS: Retrospective data from 2242 ICH patients were evaluated using ICD-9 codes. Recursive feature elimination with cross-validation (RFECV) discerned significant predictors of ICU readmissions. Four ML models-AdaBoost, RandomForest, LightGBM, and XGBoost-underwent development and rigorous validation. SHapley Additive exPlanations (SHAP) elucidated the effect of distinct features on model outcomes | ||
| 520 | |a RESULTS: ICU readmission rates were 9.6% for MIMIC-III and 10.6% for MIMIC-IV. The LightGBM model, with an AUC of 0.736 (95% CI: 0.668-0.801), surpassed other models in validation datasets. SHAP analysis revealed hydrocephalus, sex, neutrophils, Glasgow Coma Scale (GCS), specific oxygen saturation (SpO2) levels, and creatinine as significant predictors of readmission | ||
| 520 | |a CONCLUSION: The LightGBM model demonstrates considerable potential in predicting ICU readmissions for ICH patients, highlighting the importance of certain clinical predictors. This research contributes to optimizing patient care and ICU resource management. Further prospective studies are warranted to corroborate and enhance these predictive insights for clinical utilization | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a ICU readmission | |
| 650 | 4 | |a Intracerebral hemorrhage | |
| 650 | 4 | |a MIMIC databases | |
| 650 | 4 | |a Machine learning | |
| 700 | 1 | |a Zuo, Chengchao |e verfasserin |4 aut | |
| 700 | 1 | |a Cao, Huan |e verfasserin |4 aut | |
| 700 | 1 | |a Gu, Zhongya |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Yaqi |e verfasserin |4 aut | |
| 700 | 1 | |a Song, Yu |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Furong |e verfasserin |4 aut | |
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