Acute and Critical Care

Original Articles

Neurosurgery
Deep learning-based model for detection of intracranial waveforms with poor brain compliance in southern Thailand: Thara Tunthanathip, Avika Trakulpanitkit; Acute Crit Care. 2025;40(3):473-481. Published online August 29, 2025; DOI: https://doi.org/10.4266/acc.001425

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Background
Intracranial pressure (ICP) waveform analysis provides critical insights into brain compliance and can aid in the early detection of neurological deterioration. Deep learning (DL) has recently emerged as an effective approach for analyzing complex medical signals and imaging data. The aim of the present research was to develop a DL-based model for detecting ICP waveforms indicative of poor brain compliance. Methods: A retrospective cohort study was conducted using ICP wave images collected from postoperative hydrocephalus (HCP) patients who underwent ventriculostomy. The images were categorized into normal and poor compliance waveforms. Precision, recall, mean average precision at the 0.5 intersection over union (mAP_0.5), and the area under the receiver operating characteristic curve (AUC) were used to test. Results: The dataset consisted of 2,744 ICP wave images from 21 HCP patients. The best-performing model achieved a precision of 0.97, a recall of 0.96, and a mAP_0.5 of 0.989. The confusion matrix for poor brain compliance waveform detection using the test dataset also demonstrated a high classification accuracy, with true positive and true negative rates of 48.5% and 47.8%, respectively. Additionally, the model demonstrated high accuracy, achieving a mAP_0.5 of 0.994, sensitivity of 0.956, specificity of 0.970, and an AUC of 0.96 in the detection of poor compliance waveforms. Conclusions: The DL-based model successfully detected pathological ICP waveforms, thereby enhancing clinical decision-making. As DL advances, its significance in neurocritical care will help to pave the way for more individualized and data-driven approaches to brain monitoring and management

Citations

Citations to this article as recorded by

Perioperative Anesthetic Strategies in Emergent Neurosurgery During Severe Traumatic Brain Injury
Denise Baloi, Clayton Rawson, Deondra Montgomery, Michael Karsy, Mehrdad Pahlevani
Trauma Care.2026; 6(1): 5. CrossRef

Neurosurgery
Cost-effectiveness of intracranial pressure monitoring in severe traumatic brain injury in Southern Thailand: Jidapa Jitchanvichai, Thara Tunthanathip; Acute Crit Care. 2025;40(1):69-78. Published online February 21, 2025; DOI: https://doi.org/10.4266/acc.004080

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Abstract PDF Supplementary Material

Background
Traumatic brain injury (TBI) is a leading cause of fatalities and disabilities in the public health domain, particularly in Thailand. Guidelines for TBI patients advise intracranial pressure monitoring (ICPm) for intensive care. However, information about the cost-effectiveness (CE) of ICPm in cases of severe TBI is lacking. This study assessed the CE of ICPm in severe TBI.
Methods
This was a retrospective cohort economic evaluation study from the perspective of the healthcare system. Direct costs were sourced from electronic medical records, and quality-adjusted life years (QALY) for each individual were computed using multiple linear regression with standardization. Incremental costs, incremental QALY, and the incremental CE ratio (ICER) were estimated, and the bootstrap method with 1,000 iterations was used in uncertainty analysis.
Results
The analysis included 821 individuals, with 4.1% undergoing intraparenchymal ICPm. The average cost of hospitalization was United States dollar ($)8,697.13 (±6,271.26) in both groups. The incremental cost and incremental QALY of the ICPm group compared with the non-ICPm group were $3,322.88 and –0.070, with the base-case ICER of $–47,504.08 per additional QALY. Results demonstrated that 0.007% of bootstrapped ICERs were below the willingness-to-pay (WTP) threshold of Thailand.
Conclusions
ICPm for severe TBI was not cost-effective compared with the WTP threshold of Thailand. Resource allocation for TBI prognosis requires further development of cost-effective treatment guidelines.

Citations

Citations to this article as recorded by

Impact of Preoperative Hair Removal on Self-Esteem after Brain Tumor Surgery
Thara Tunthanathip, Natthanee Pisitthaworakul
Asian Journal of Neurosurgery.2026; 21(01): 147. CrossRef
Deep learning-based model for detection of intracranial waveforms with poor brain compliance in southern Thailand
Thara Tunthanathip, Avika Trakulpanitkit
Acute and Critical Care.2025; 40(3): 473. CrossRef
Feasibility comparison of deep learning image regressions to estimate intracranial pressure from cranial computed tomography in hydrocephalus
Thara Tunthanathip, Rakkrit Duangsoithong, Sakchai Sae-Heng
Journal of Neurosciences in Rural Practice.2025; 16: 606. CrossRef
Risk factors and dose-response relationship of catheter-associated urinary tract infection in neurosurgical patients
Thara Tunthanathip, Natthanee Pisitthaworakul
International Journal of Nutrition, Pharmacology, Neurological Diseases.2025; 15(4): 451. CrossRef
Prognosis of subarachnoid hemorrhage determined by intracranial pressure thresholds
Thara Tunthanathip, Rakkrit Duangsoithong, Sakchai Sae-Heng
Journal of Cerebrovascular and Endovascular Neurosurgery.2025; 27(4): 309. CrossRef

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

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80 Download
10 Web of Science
10 Crossref

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.

Citations

Citations to this article as recorded by

Impact of Preoperative Hair Removal on Self-Esteem after Brain Tumor Surgery
Thara Tunthanathip, Natthanee Pisitthaworakul
Asian Journal of Neurosurgery.2026; 21(01): 147. CrossRef
Cost-effectiveness of intracranial pressure monitoring in severe traumatic brain injury in Southern Thailand
Jidapa Jitchanvichai, Thara Tunthanathip
Acute and Critical Care.2025; 40(1): 69. CrossRef
Imaging biomarkers for detection and longitudinal monitoring of ventricular abnormalities from birth to childhood
Antonio Navarro-Ballester, Rosa Álvaro-Ballester, Miguel Á Lara-Martínez
World Journal of Radiology.2025;[Epub] CrossRef
Deep learning-based model for detection of intracranial waveforms with poor brain compliance in southern Thailand
Thara Tunthanathip, Avika Trakulpanitkit
Acute and Critical Care.2025; 40(3): 473. CrossRef
A nomogram for the prediction of traumatic intracranial abnormalities in the elderly: Development and validation
Apisorn Jongjit, Thara Tunthanathip
Chinese Journal of Traumatology.2025;[Epub] CrossRef
Feasibility comparison of deep learning image regressions to estimate intracranial pressure from cranial computed tomography in hydrocephalus
Thara Tunthanathip, Rakkrit Duangsoithong, Sakchai Sae-Heng
Journal of Neurosciences in Rural Practice.2025; 16: 606. CrossRef
Risk factors and dose-response relationship of catheter-associated urinary tract infection in neurosurgical patients
Thara Tunthanathip, Natthanee Pisitthaworakul
International Journal of Nutrition, Pharmacology, Neurological Diseases.2025; 15(4): 451. CrossRef
Prognosis of subarachnoid hemorrhage determined by intracranial pressure thresholds
Thara Tunthanathip, Rakkrit Duangsoithong, Sakchai Sae-Heng
Journal of Cerebrovascular and Endovascular Neurosurgery.2025; 27(4): 309. CrossRef
Assessing interpretability of data‐driven fuzzy models: Application in industrial regression problems
Jorge S. S. Júnior, Carlos Gaspar, Jérôme Mendes, Cristiano Premebida
Expert Systems.2024;[Epub] CrossRef
Progressive Optic Neuropathy in Hydrocephalic Ccdc13 Mutant Mice Caused by Impaired Axoplasmic Transport at the Optic Nerve Head
Mingjuan Wu, Xinyi Zhao, Shanzhen Peng, Xiaoyu Zhang, Jiali Ru, Lijing Xie, Tao Wen, Yingchun Su, Shujuan Xu, Dianlei Guo, Jianmin Hu, Haotian Lin, Tiansen Li, Chunqiao Liu
Investigative Ophthalmology & Visual Science.2024; 65(13): 5. CrossRef

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

5,762 View
229 Download
12 Web of Science
14 Crossref

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

Impact of Preoperative Hair Removal on Self-Esteem after Brain Tumor Surgery
Thara Tunthanathip, Natthanee Pisitthaworakul
Asian Journal of Neurosurgery.2026; 21(01): 147. CrossRef
The Prognostic Value of Immunonutritional Indexes in Pineal Region Tumor
Suchada Supbumrung, Anukoon Kaewborisutsakul, Thara Tunthanathip
Journal of Health and Allied Sciences NU.2025; 15(01): 109. CrossRef
Dynamic nomogram for predicting long-term survival in patients with brain abscess
Thara Tunthanathip, Rakkrit Duangsoithong, Waranyu Kittirojkasem, Akira Pongweat, Rattiyaphon Khongthep, Benchamat Sutchai, Assama Tohyunuh
Chinese Neurosurgical Journal.2025;[Epub] CrossRef
A nomogram for the prediction of traumatic intracranial abnormalities in the elderly: Development and validation
Apisorn Jongjit, Thara Tunthanathip
Chinese Journal of Traumatology.2025;[Epub] CrossRef
Deep learning-based model for detection of intracranial waveforms with poor brain compliance in southern Thailand
Thara Tunthanathip, Avika Trakulpanitkit
Acute and Critical Care.2025; 40(3): 473. CrossRef
Prognostic value of CT scoring systems and a simplified prediction model in pediatric moderate-to-severe traumatic brain injury
Yangyang Diao, Ping Liang
Chinese Journal of Traumatology.2025;[Epub] CrossRef
Feasibility comparison of deep learning image regressions to estimate intracranial pressure from cranial computed tomography in hydrocephalus
Thara Tunthanathip, Rakkrit Duangsoithong, Sakchai Sae-Heng
Journal of Neurosciences in Rural Practice.2025; 16: 606. CrossRef
Risk factors and dose-response relationship of catheter-associated urinary tract infection in neurosurgical patients
Thara Tunthanathip, Natthanee Pisitthaworakul
International Journal of Nutrition, Pharmacology, Neurological Diseases.2025; 15(4): 451. CrossRef
Prognosis of subarachnoid hemorrhage determined by intracranial pressure thresholds
Thara Tunthanathip, Rakkrit Duangsoithong, Sakchai Sae-Heng
Journal of Cerebrovascular and Endovascular Neurosurgery.2025; 27(4): 309. CrossRef
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; 14(04): 516. 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

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