Prediction of Intracranial Infection in Patients under External Ventricular Drainage and Neurological Intensive Care: A Multicenter Retrospective Cohort Study
<i>Objective</i>: To generate an optimal prediction model along with identifying major contributors to intracranial infection among patients under external ventricular drainage and neurological intensive care. <i>Methods</i>: A retrospective cohort study was conducted among p...
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| Format: | Article |
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MDPI AG
2022-07-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/11/14/3973 |
| _version_ | 1797406064321232896 |
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| author | Pengfei Fu Yi Zhang Jun Zhang Jin Hu Yirui Sun |
| author_facet | Pengfei Fu Yi Zhang Jun Zhang Jin Hu Yirui Sun |
| author_sort | Pengfei Fu |
| collection | DOAJ |
| description | <i>Objective</i>: To generate an optimal prediction model along with identifying major contributors to intracranial infection among patients under external ventricular drainage and neurological intensive care. <i>Methods</i>: A retrospective cohort study was conducted among patients admitted into neurointensive care units between 1 January 2015 and 31 December 2020 who underwent external ventricular drainage due to traumatic brain injury, hydrocephalus, and nonaneurysmal spontaneous intracranial hemorrhage. Multivariate logistic regression in combination with the least absolute shrinkage and selection operator regression was applied to derive prediction models and optimize variable selections. Other machine-learning algorithms, including the support vector machine and K-nearest neighbor, were also applied to derive alternative prediction models. Five-fold cross-validation was used to train and validate each model. Model performance was assessed by calibration plots, receiver operating characteristic curves, and decision curves. A nomogram analysis was developed to explicate the weights of selected features for the optimal model. <i>Results</i>: Multivariate logistic regression showed the best performance among the three tested models with an area under curve of 0.846 ± 0.006. Six variables, including hemoglobin, albumin, length of operation time, American Society of Anesthesiologists grades, presence of traumatic subarachnoid hemorrhage, and a history of diabetes, were selected from 37 variable candidates as the top-weighted prediction features. The decision curve analysis showed that the nomogram could be applied clinically when the risk threshold is between 20% and 100%. <i>Conclusions</i>: The occurrence of external ventricular-drainage-associated intracranial infections could be predicted using optimal models and feature-selection approaches, which would be helpful for the prevention and treatment of this complication in neurointensive care units. |
| first_indexed | 2024-03-09T03:19:19Z |
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| id | doaj.art-d530ddc3544a45188e0b77257af13c70 |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-09T03:19:19Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-d530ddc3544a45188e0b77257af13c702023-12-03T15:12:34ZengMDPI AGJournal of Clinical Medicine2077-03832022-07-011114397310.3390/jcm11143973Prediction of Intracranial Infection in Patients under External Ventricular Drainage and Neurological Intensive Care: A Multicenter Retrospective Cohort StudyPengfei Fu0Yi Zhang1Jun Zhang2Jin Hu3Yirui Sun4Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, ChinaEngineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation Ministry of Education, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, ChinaDepartment of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, ChinaDepartment of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, ChinaDepartment of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China<i>Objective</i>: To generate an optimal prediction model along with identifying major contributors to intracranial infection among patients under external ventricular drainage and neurological intensive care. <i>Methods</i>: A retrospective cohort study was conducted among patients admitted into neurointensive care units between 1 January 2015 and 31 December 2020 who underwent external ventricular drainage due to traumatic brain injury, hydrocephalus, and nonaneurysmal spontaneous intracranial hemorrhage. Multivariate logistic regression in combination with the least absolute shrinkage and selection operator regression was applied to derive prediction models and optimize variable selections. Other machine-learning algorithms, including the support vector machine and K-nearest neighbor, were also applied to derive alternative prediction models. Five-fold cross-validation was used to train and validate each model. Model performance was assessed by calibration plots, receiver operating characteristic curves, and decision curves. A nomogram analysis was developed to explicate the weights of selected features for the optimal model. <i>Results</i>: Multivariate logistic regression showed the best performance among the three tested models with an area under curve of 0.846 ± 0.006. Six variables, including hemoglobin, albumin, length of operation time, American Society of Anesthesiologists grades, presence of traumatic subarachnoid hemorrhage, and a history of diabetes, were selected from 37 variable candidates as the top-weighted prediction features. The decision curve analysis showed that the nomogram could be applied clinically when the risk threshold is between 20% and 100%. <i>Conclusions</i>: The occurrence of external ventricular-drainage-associated intracranial infections could be predicted using optimal models and feature-selection approaches, which would be helpful for the prevention and treatment of this complication in neurointensive care units.https://www.mdpi.com/2077-0383/11/14/3973external ventricular drainageintracranial infectionlasso regressionlogistic regressionnomogrammachine learning |
| spellingShingle | Pengfei Fu Yi Zhang Jun Zhang Jin Hu Yirui Sun Prediction of Intracranial Infection in Patients under External Ventricular Drainage and Neurological Intensive Care: A Multicenter Retrospective Cohort Study Journal of Clinical Medicine external ventricular drainage intracranial infection lasso regression logistic regression nomogram machine learning |
| title | Prediction of Intracranial Infection in Patients under External Ventricular Drainage and Neurological Intensive Care: A Multicenter Retrospective Cohort Study |
| title_full | Prediction of Intracranial Infection in Patients under External Ventricular Drainage and Neurological Intensive Care: A Multicenter Retrospective Cohort Study |
| title_fullStr | Prediction of Intracranial Infection in Patients under External Ventricular Drainage and Neurological Intensive Care: A Multicenter Retrospective Cohort Study |
| title_full_unstemmed | Prediction of Intracranial Infection in Patients under External Ventricular Drainage and Neurological Intensive Care: A Multicenter Retrospective Cohort Study |
| title_short | Prediction of Intracranial Infection in Patients under External Ventricular Drainage and Neurological Intensive Care: A Multicenter Retrospective Cohort Study |
| title_sort | prediction of intracranial infection in patients under external ventricular drainage and neurological intensive care a multicenter retrospective cohort study |
| topic | external ventricular drainage intracranial infection lasso regression logistic regression nomogram machine learning |
| url | https://www.mdpi.com/2077-0383/11/14/3973 |
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