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Original Articles
- Neurosurgery
- Comparison of intracranial pressure prediction in hydrocephalus patients among linear, non-linear, and machine learning regression models in Thailand
- Avika Trakulpanitkit, Thara Tunthanathip
- Acute Crit Care. 2023;38(3):362-370. Published online August 18, 2023
- DOI: https://doi.org/10.4266/acc.2023.00094
- 863 View
- 45 Download
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Abstract
PDF - Background
Hydrocephalus (HCP) is one of the most significant concerns in neurosurgical patients because it can cause increased intracranial pressure (ICP), resulting in mortality and morbidity. To date, machine learning (ML) has been helpful in predicting continuous outcomes. The primary objective of the present study was to identify the factors correlated with ICP, while the secondary objective was to compare the predictive performances among linear, non-linear, and ML regression models for ICP prediction.
Methods
A total of 412 patients with various types of HCP who had undergone ventriculostomy was retrospectively included in the present study, and intraoperative ICP was recorded following ventricular catheter insertion. Several clinical factors and imaging parameters were analyzed for the relationship with ICP by linear correlation. The predictive performance of ICP was compared among linear, non-linear, and ML regression models.
Results
Optic nerve sheath diameter (ONSD) had a moderately positive correlation with ICP (r=0.530, P<0.001), while several ventricular indexes were not statistically significant in correlation with ICP. For prediction of ICP, random forest (RF) and extreme gradient boosting (XGBoost) algorithms had low mean absolute error and root mean square error values and high R2 values compared to linear and non-linear regression when the predictive model included ONSD and ventricular indexes.
Conclusions
The XGBoost and RF algorithms are advantageous for predicting preoperative ICP and establishing prognoses for HCP patients. Furthermore, ML-based prediction could be used as a non-invasive method.
- Neurosurgery
- Development and internal validation of a nomogram for predicting outcomes in children with traumatic subdural hematoma
- Anukoon Kaewborisutsakul, Thara Tunthanathip
- Acute Crit Care. 2022;37(3):429-437. Published online June 23, 2022
- DOI: https://doi.org/10.4266/acc.2021.01795
- 2,375 View
- 208 Download
- 4 Web of Science
- 5 Crossref
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Abstract
PDF
- Background
A subdural hematoma (SDH) following a traumatic brain injury (TBI) in children can lead to unexpected death or disability. The nomogram is a clinical prediction tool used by physicians to provide prognosis advice to parents for making decisions regarding treatment. In the present study, a nomogram for predicting outcomes was developed and validated. In addition, the predictors associated with outcomes in children with traumatic SDH were determined.
Methods
In this retrospective study, 103 children with SDH after TBI were evaluated. According to the King’s Outcome Scale for Childhood Head Injury classification, the functional outcomes were assessed at hospital discharge and categorized into favorable and unfavorable. The predictors associated with the unfavorable outcomes were analyzed using binary logistic regression. Subsequently, a two-dimensional nomogram was developed for presentation of the predictive model.
Results
The predictive model with the lowest level of Akaike information criterion consisted of hypotension (odds ratio [OR], 9.4; 95% confidence interval [CI], 2.0–42.9), Glasgow coma scale scores of 3–8 (OR, 8.2; 95% CI, 1.7–38.9), fixed pupil in one eye (OR, 4.8; 95% CI, 2.6–8.8), and fixed pupils in both eyes (OR, 3.5; 95% CI, 1.6–7.1). A midline shift ≥5 mm (OR, 1.1; 95% CI, 0.62–10.73) and co-existing intraventricular hemorrhage (OR, 6.5; 95% CI, 0.003–26.1) were also included.
Conclusions
SDH in pediatric TBI can lead to mortality and disability. The predictability level of the nomogram in the present study was excellent, and external validation should be conducted to confirm the performance of the clinical prediction tool. -
Citations
Citations to this article as recorded by
- Prognostic factors and clinical nomogram for in-hospital mortality in traumatic brain injury
Thara Tunthanathip, Nakornchai Phuenpathom, Apisorn Jongjit
The American Journal of Emergency Medicine.2024; 77: 194. CrossRef - Development of a Clinical Nomogram for Predicting Shunt-Dependent Hydrocephalus
Avika Trakulpanitkit, Thara Tunthanathip
Journal of Health and Allied Sciences NU.2024;[Epub] CrossRef - Prediction performance of the machine learning model in predicting mortality risk in patients with traumatic brain injuries: a systematic review and meta-analysis
Jue Wang, Ming Jing Yin, Han Chun Wen
BMC Medical Informatics and Decision Making.2023;[Epub] CrossRef - Development and internal validation of a nomogram to predict massive blood transfusions in neurosurgical operations
Kanisorn Sungkaro, Chin Taweesomboonyat, Anukoon Kaewborisutsakul
Journal of Neurosciences in Rural Practice.2022; 13: 711. CrossRef - Prediction of massive transfusions in neurosurgical operations using machine learning
Chin Taweesomboonyat, Anukoon Kaewborisutsakul, Kanisorn Sungkaro
Asian Journal of Transfusion Science.2022;[Epub] CrossRef
- Prognostic factors and clinical nomogram for in-hospital mortality in traumatic brain injury
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