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

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.

PURPOSE: Traumatic brain injury (TBI) generally causes mortality and disability, particularly in children. Machine learning (ML) is a computer algorithm, applied as a clinical prediction tool. The present study aims to assess the predictability of ML for the functional outcomes of pediatric TBI. METHODS: A retrospective cohort study was performed targeting children with TBI who were admitted to the trauma center of southern Thailand between January 2009 and July 2020. The patient was excluded if he/she (1) did not undergo a CT scan of the brain, (2) died within the first 24 h, (3) had unavailable complete medical records during admission, or (4) was unable to provide updated outcomes. Clinical and radiologic characteristics were collected such as vital signs, Glasgow coma scale score, and characteristics of intracranial injuries. The functional outcome was assessed using the King's Outcome Scale for Childhood Head Injury, which was thus dichotomized into favourable outcomes and unfavourable outcomes: good recovery and moderate disability were categorized as the former, whereas death, vegetative state, and severe disability were categorized as the latter. The prognostic factors were estimated using traditional binary logistic regression. By data splitting, 70% of data were used for training the ML models and the remaining 30% were used for testing the ML models. The supervised algorithms including support vector machines, neural networks, random forest, logistic regression, naive Bayes and k-nearest neighbor were performed for training of the ML models. Therefore, the ML models were tested for the predictive performances by the testing datasets. RESULTS: There were 828 patients in the cohort. The median age was 72 months (interquartile range 104.7 months, range 2-179 months). Road traffic accident was the most common mechanism of injury, accounting for 68.7%. At hospital discharge, favourable outcomes were achieved in 97.0% of patients, while the mortality rate was 2.2%. Glasgow coma scale score, hypotension, pupillary light reflex, and subarachnoid haemorrhage were associated with TBI outcomes following traditional binary logistic regression; hence, the 4 prognostic factors were used for building ML models and testing performance. The support vector machine model had the best performance for predicting pediatric TBI outcomes: sensitivity 0.95, specificity 0.60, positive predicted value 0.99, negative predictive value 1.0; accuracy 0.94, and area under the receiver operating characteristic curve 0.78. CONCLUSION The ML algorithms of the present study have a high sensitivity; therefore they have the potential to be screening tools for predicting functional outcomes and counselling prognosis in general practice of pediatric TBIs.
Bayes Theorem ; Brain Injuries, Traumatic/therapy* ; Child ; Female ; Glasgow Coma Scale ; Humans ; Machine Learning ; Prognosis ; Retrospective Studies