Congenital hydrocephalus: a review of recent advances in genetic etiology and molecular mechanisms.

The global prevalence rate for congenital hydrocephalus (CH) is approximately one out of every five hundred births with multifaceted predisposing factors at play. Genetic influences stand as a major contributor to CH pathogenesis, and epidemiological evidence suggests their involvement in up to 40% of all cases observed globally. Knowledge about an individual's genetic susceptibility can significantly improve prognostic precision while aiding clinical decision-making processes. However, the precise genetic etiology has only been pinpointed in fewer than 5% of human instances. More occurrences of CH cases are required for comprehensive gene sequencing aimed at uncovering additional potential genetic loci. A deeper comprehension of its underlying genetics may offer invaluable insights into the molecular and cellular basis of this brain disorder. This review provides a summary of pertinent genes identified through gene sequencing technologies in humans, in addition to the 4 genes currently associated with CH (two X-linked genes L1CAM and AP1S2, two autosomal recessive MPDZ and CCDC88C). Others predominantly participate in aqueduct abnormalities, ciliary movement, and nervous system development. The prospective CH-related genes revealed through animal model gene-editing techniques are further outlined, focusing mainly on 4 pathways, namely cilia synthesis and movement, ion channels and transportation, Reissner's fiber (RF) synthesis, cell apoptosis, and neurogenesis. Notably, the proper functioning of motile cilia provides significant impulsion for cerebrospinal fluid (CSF) circulation within the brain ventricles while mutations in cilia-related genes constitute a primary cause underlying this condition. So far, only a limited number of CH-associated genes have been identified in humans. The integration of genotype and phenotype for disease diagnosis represents a new trend in the medical field. Animal models provide insights into the pathogenesis of CH and contribute to our understanding of its association with related complications, such as renal cysts, scoliosis, and cardiomyopathy, as these genes may also play a role in the development of these diseases. Genes discovered in animals present potential targets for new treatments but require further validation through future human studies.

Intracranial pressure monitoring in adult patients with traumatic brain injury: challenges and innovations.

Intracranial pressure monitoring enables the detection and treatment of intracranial hypertension, a potentially lethal insult after traumatic brain injury. Despite its widespread use, robust evidence supporting intracranial pressure monitoring and treatment remains sparse. International studies have shown large variations between centres regarding the indications for intracranial pressure monitoring and treatment of intracranial hypertension. Experts have reviewed these two aspects and, by consensus, provided practical approaches for monitoring and treatment. Advances have occurred in methods for non-invasive estimation of intracranial pressure although, for now, a reliable way to non-invasively and continuously measure intracranial pressure remains aspirational. Analysis of the intracranial pressure signal can provide information on brain compliance (ie, the ability of the cranium to tolerate volume changes) and on cerebral autoregulation (ie, the ability of cerebral blood vessels to react to changes in blood pressure). The information derived from the intracranial pressure signal might allow for more individualised patient management. Machine learning and artificial intelligence approaches are being increasingly applied to intracranial pressure monitoring, but many obstacles need to be overcome before their use in clinical practice could be attempted. Robust clinical trials are needed to support indications for intracranial pressure monitoring and treatment. Progress in non-invasive assessment of intracranial pressure and in signal analysis (for targeted treatment) will also be crucial.

Craniectomy versus craniotomy: What can we do for acute subdural hematoma?

Compared with hematoma evacuation craniotomy, decompressive craniectomy has a higher incidence of intracranial complications and no outcome benefit over craniotomy, which gives surgeons a safer decision-making options during surgery.

Random forest-based prediction of intracranial hypertension in patients with traumatic brain injury.

BACKGROUND: Treatment and prevention of intracranial hypertension (IH) to minimize secondary brain injury are central to the neurocritical care management of traumatic brain injury (TBI). Predicting the onset of IH in advance allows for a more aggressive prophylactic treatment. This study aimed to develop random forest (RF) models for predicting IH events in TBI patients. METHODS: We analyzed prospectively collected data from patients admitted to the intensive care unit with invasive intracranial pressure (ICP) monitoring. Patients with persistent ICP > 22 mmHg in the early postoperative period (first 6 h) were excluded to focus on IH events that had not yet occurred. ICP-related data from the initial 6 h were used to extract linear (ICP, cerebral perfusion pressure, pressure reactivity index, and cerebrospinal fluid compensatory reserve index) and nonlinear features (complexity of ICP and cerebral perfusion pressure). IH was defined as ICP > 22 mmHg for > 5 min, and severe IH (SIH) as ICP > 22 mmHg for > 1 h during the subsequent ICP monitoring period. RF models were then developed using baseline characteristics (age, sex, and initial Glasgow Coma Scale score) along with linear and nonlinear features. Fivefold cross-validation was performed to avoid overfitting. RESULTS: The study included 69 patients. Forty-three patients (62.3%) experienced an IH event, of whom 30 (43%) progressed to SIH. The median time to IH events was 9.83 h, and to SIH events, it was 11.22 h. The RF model showed acceptable performance in predicting IH with an area under the curve (AUC) of 0.76 and excellent performance in predicting SIH (AUC = 0.84). Cross-validation analysis confirmed the stability of the results. CONCLUSIONS: The presented RF model can forecast subsequent IH events, particularly severe ones, in TBI patients using ICP data from the early postoperative period. It provides researchers and clinicians with a potentially predictive pathway and framework that could help triage patients requiring more intensive neurological treatment at an early stage.

Effects of an Early Intensive Blood Pressure-lowering Strategy Using Remifentanil and Dexmedetomidine in Patients with Spontaneous Intracerebral Hemorrhage: A Multicenter, Prospective, Superiority, Randomized Controlled Trial.

BACKGROUND: Although it has been established that elevated blood pressure and its variability worsen outcomes in spontaneous intracerebral hemorrhage, antihypertensives use during the acute phase still lacks robust evidence. A blood pressure-lowering regimen using remifentanil and dexmedetomidine might be a reasonable therapeutic option given their analgesic and antisympathetic effects. The objective of this superiority trial was to validate the efficacy and safety of this blood pressure-lowering strategy that uses remifentanil and dexmedetomidine in patients with acute intracerebral hemorrhage. METHODS: In this multicenter, prospective, single-blinded, superiority randomized controlled trial, patients with intracerebral hemorrhage and systolic blood pressure (SBP) 150 mmHg or greater were randomly allocated to the intervention group (a preset protocol with a standard guideline management using remifentanil and dexmedetomidine) or the control group (standard guideline-based management) to receive blood pressure-lowering treatment. The primary outcome was the SBP control rate (less than 140 mmHg) at 1 h posttreatment initiation. Secondary outcomes included blood pressure variability, neurologic function, and clinical outcomes. RESULTS: A total of 338 patients were allocated to the intervention (n = 167) or control group (n = 171). The SBP control rate at 1 h posttreatment initiation in the intervention group was higher than that in controls (101 of 161, 62.7% vs. 66 of 166, 39.8%; difference, 23.2%; 95% CI, 12.4 to 34.1%; P < 0.001). Analysis of secondary outcomes indicated that patients in the intervention group could effectively reduce agitation while achieving lighter sedation, but no improvement in clinical outcomes was observed. Regarding safety, the incidence of bradycardia and respiratory depression was higher in the intervention group. CONCLUSIONS: Among intracerebral hemorrhage patients with a SBP 150 mmHg or greater, a preset protocol using a remifentanil and dexmedetomidine-based standard guideline management significantly increased the SBP control rate at 1 h posttreatment compared with the standard guideline-based management.

Predicting the Severity and Discharge Prognosis of Traumatic Brain Injury Based on Intracranial Pressure Data Using Machine Learning Algorithms.

OBJECTIVE: This study aimed to explore the potential of employing machine learning algorithms based on intracranial pressure (ICP), ICP-derived parameters, and their complexity to predict the severity and short-term prognosis of traumatic brain injury (TBI). METHODS: A single-center prospectively collected cohort of neurosurgical intensive care unit admissions was analyzed. We extracted ICP-related data within the first 6 hours and processed them using complex algorithms. To indicate TBI severity and short-term prognosis, Glasgow Coma Scale score on the first postoperative day and Glasgow Outcome Scale-Extended score at discharge were used as binary outcome variables. A univariate logistic regression model was developed to predict TBI severity using only mean ICP values. Subsequently, 3 multivariate Random Forest (RF) models were constructed using different combinations of mean and complexity metrics of ICP-related data. To avoid overfitting, five-fold cross-validations were performed. Finally, the best-performing multivariate RF model was used to predict patients' discharge Glasgow Outcome Scale-Extended score. RESULTS: The logistic regression model exhibited limited predictive ability with an area under the curve (AUC) of 0.558. Among multivariate models, the RF model, combining the mean and complexity metrics of ICP-related data, achieved the most robust ability with an AUC of 0.815. Finally, in terms of predicting discharge Glasgow Outcome Scale-Extended score, this model had a consistent performance with an AUC of 0.822. Cross-validation analysis confirmed the performance. CONCLUSIONS: This study demonstrates the clinical utility of the RF model, which integrates the mean and complexity metrics of ICP data, in accurately predicting the TBI severity and short-term prognosis.

An independently validated nomogram for individualised estimation of short-term mortality risk among patients with severe traumatic brain injury: a modelling analysis of the CENTER-TBI China Registry Study.

Background: Severe traumatic brain injury (sTBI) is extremely disabling and associated with high mortality. Early detection of patients at risk of short-term (≤14 days after injury) death and provision of timely treatment is critical. This study aimed to establish and independently validate a nomogram to estimate individualised short-term mortality for sTBI based on large-scale data from China. Methods: The data were from the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) China registry (between Dec 22, 2014, and Aug 1, 2017; registered at ClinicalTrials.gov, NCT02210221). This analysis included information of eligible patients with diagnosed sTBI from 52 centres (2631 cases). 1808 cases from 36 centres were enrolled in the training group (used to construct the nomogram) and 823 cases from 16 centres were enrolled in the validation group. Multivariate logistic regression was used to identify independent predictors of short-term mortality and establish the nomogram. The discrimination of the nomogram was evaluated using area under the receiver operating characteristic curves (AUC) and concordance indexes (C-index), the calibration was evaluated using calibration curves and Hosmer-Lemeshow tests (H-L tests). Decision curve analysis (DCA) was used to evaluate the net benefit of the model for patients. Findings: In the training group, multivariate logistic regression demonstrated that age (odds ratio [OR] 1.013, 95% confidence interval [CI] 1.003-1.022), Glasgow Coma Scale score (OR 33.997, 95% CI 14.657-78.856), Injury Severity Score (OR 1.020, 95% CI 1.009-1.032), abnormal pupil status (OR 1.738, 95% CI 1.178-2.565), midline shift (OR 2.266, 95% CI 1.378-3.727), and pre-hospital intubation (OR 2.059, 95% CI 1.472-2.879) were independent predictors for short-term death in patients with sTBI. A nomogram was built using the logistic regression prediction model. The AUC and C-index were 0.859 (95% CI 0.837-0.880). The calibration curve of the nomogram was close to the ideal reference line, and the H-L test p value was 0.504. DCA curve demonstrated significantly better net benefit with the model. Application of the nomogram in external validation group still showed good discrimination (AUC and C-index were 0.856, 95% CI 0.827-0.886), calibration, and clinical usefulness. Interpretation: A nomogram was developed for predicting the occurrence of short-term (≤14 days after injury) death in patients with sTBI. This can provide clinicians with an effective and accurate tool for the early prediction and timely management of sTBI, as well as support clinical decision-making around the withdrawal of life-sustaining therapy. This nomogram is based on Chinese large-scale data and is especially relevant to low- and middle-income countries. Funding: Shanghai Academic Research Leader (21XD1422400), Shanghai Medical and Health Development Foundation (20224Z0012).

Acute traumatic coma awakening by right median nerve electrical stimulation: a randomised controlled trial.

PURPOSE: Severe traumatic brain injury (TBI) leads to acute coma and may result in prolonged disorder of consciousness (pDOC). We aimed to determine whether right median nerve electrical stimulation is a safe and effective treatment for accelerating emergence from coma after TBI. METHODS: This randomised controlled trial was performed in 22 centres in China. Participants with acute coma at 7-14 days after TBI were randomly assigned (1:1) to either routine therapy and right median nerve electrical stimulation (RMNS group) or routine treatment (control group). The RMNS group received 20 mA, 300 µs, 40 Hz stimulation pulses, lasting 20 s per minutes, 8 h per day, for 2 weeks. The primary outcome was the proportion of patients who regained consciousness 6 months post-injury. The secondary endpoints were Glasgow Coma Scale (GCS), Full Outline of Unresponsiveness scale (FOUR), Coma Recovery Scale-Revised (CRS-R), Disability Rating Scale (DRS) and Glasgow Outcome Scale Extended (GOSE) scores reported as medians on day 28, 3 months and 6 months after injury, and GCS and FOUR scores on day 1 and day 7 during stimulation. Primary analyses were based on the intention-to-treat set. RESULTS: Between March 26, 2016, and October 18, 2020, 329 participants were recruited, of whom 167 were randomised to the RMNS group and 162 to the control group. At 6 months post-injury, a higher proportion of patients in the RMNS group regained consciousness compared with the control group (72.5%, n = 121, 95% confidence interval (CI) 65.2-78.7% vs. 56.8%, n = 92, 95% CI 49.1-64.2%, p = 0.004). GOSE at 3 months and 6 months (5 [interquartile range (IQR) 3-7] vs. 4 [IQR 2-6], p = 0.002; 6 [IQR 3-7] vs. 4 [IQR 2-7], p = 0.0005) and FOUR at 28 days (15 [IQR 13-16] vs. 13 [interquartile range (IQR) 11-16], p = 0.002) were significantly increased in the RMNS group compared with the control group. Trajectory analysis showed that significantly more patients in the RMNS group had faster GCS, CRS-R and DRS improvement (p = 0.01, 0.004 and 0.04, respectively). Adverse events were similar in both groups. No serious adverse events were associated with the stimulation device. CONCLUSION: Right median nerve electrical stimulation is a possible effective treatment for patients with acute traumatic coma, that will require validation in a confirmatory trial.

Perceived Utility of Intracranial Pressure Monitoring in Traumatic Brain Injury: A Seattle International Brain Injury Consensus Conference Consensus-Based Analysis and Recommendations.

BACKGROUND: Intracranial pressure (ICP) monitoring is widely practiced, but the indications are incompletely developed, and guidelines are poorly followed. OBJECTIVE: To study the monitoring practices of an established expert panel (the clinical working group from the Seattle International Brain Injury Consensus Conference effort) to examine the match between monitoring guidelines and their clinical decision-making and offer guidance for clinicians considering monitor insertion. METHODS: We polled the 42 Seattle International Brain Injury Consensus Conference panel members' ICP monitoring decisions for virtual patients, using matrices of presenting signs (Glasgow Coma Scale [GCS] total or GCS motor, pupillary examination, and computed tomography diagnosis). Monitor insertion decisions were yes, no, or unsure (traffic light approach). We analyzed their responses for weighting of the presenting signs in decision-making using univariate regression. RESULTS: Heatmaps constructed from the choices of 41 panel members revealed wider ICP monitor use than predicted by guidelines. Clinical examination (GCS) was by far the most important characteristic and differed from guidelines in being nonlinear. The modified Marshall computed tomography classification was second and pupils third. We constructed a heatmap and listed the main clinical determinants representing 80% ICP monitor insertion consensus for our recommendations. CONCLUSION: Candidacy for ICP monitoring exceeds published indicators for monitor insertion, suggesting the clinical perception that the value of ICP data is greater than simply detecting and monitoring severe intracranial hypertension. Monitor insertion heatmaps are offered as potential guidance for ICP monitor insertion and to stimulate research into what actually drives monitor insertion in unconstrained, real-world conditions.

Mortality Prediction in Severe Traumatic Brain Injury Using Traditional and Machine Learning Algorithms.

Abstract Prognostic prediction of traumatic brain injury (TBI) in patients is crucial in clinical decision and health care policy making. This study aimed to develop and validate prediction models for in-hospital mortality after severe traumatic brain injury (sTBI). We developed and validated logistic regression (LR), LASSO regression, and machine learning (ML) algorithms including support vector machines (SVM) and XGBoost models. Fifty-four candidate predictors were included. Model performance was expressed in terms of discrimination (C-statistic) and calibration (intercept and slope). For model development, 2804 patients with sTBI in the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) China Registry study were included. External validation was performed in 1113 patients with sTBI in the CENTER-TBI European Registry study. XGBoost achieved high discrimination in mortality prediction, and it outperformed logistic and LASSO regression. The XGBoost model established in this study also outperformed prediction models currently available, including the International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) core and International Mission for Prognosis and Analysis of Clinical Trials (CRASH) basic models. When including 54 variables, XGBoost and SVM reached C-statistics of 0.87 (95% confidence interval [CI]: 0.81-0.92) and 0.85 (95% CI: 0.79-0.90) at internal validation, and 0.88 (95% CI: 0.87-0.88) and 0.86 (95% CI: 0.85-0.87) at external validation, respectively. A simplified version of XGBoost and SVM using 26 variables selected by recursive feature elimination (RFE) reached C-statistics of 0.87 (95% CI: 0.82-0.92) and 0.86 (95% CI: 0.80-0.91) at internal validation, and 0.87 (95% CI: 0.87-0.88) and 0.87 (95% CI: 0.86-0.87) at external validation, respectively. However, when the number of variables included decreased, the difference between ML and LR diminished. All the prediction models can be accessed via a web-based calculator. Glasgow Coma Scale (GCS) score, age, pupillary light reflex, Injury Severity Score (ISS) for brain region, and the presence of acute subdural hematoma were the five strongest predictors for mortality prediction. The study showed that ML techniques such as XGBoost may capture information hidden in demographic and clinical predictors of patients with sTBI and yield more precise predictions compared with LR approaches.

Prognostication and Goals of Care Decisions in Severe Traumatic Brain Injury: A Survey of The Seattle International Severe Traumatic Brain Injury Consensus Conference Working Group.

Abstract Best practice guidelines have advanced severe traumatic brain injury (TBI) care; however, there is little that currently informs goals of care decisions and processes despite their importance and frequency. Panelists from the Seattle International severe traumatic Brain Injury Consensus Conference (SIBICC) participated in a survey consisting of 24 questions. Questions queried use of prognostic calculators, variability in and responsibility for goals of care decisions, and acceptability of neurological outcomes, as well as putative means of improving decisions that might limit care. A total of 97.6% of the 42 SIBICC panelists completed the survey. Responses to most questions were highly variable. Overall, panelists reported infrequent use of prognostic calculators, and observed variability in patient prognostication and goals of care decisions. They felt that it would be beneficial for physicians to improve consensus on what constitutes an acceptable neurological outcome as well as what chance of achieving that outcome is acceptable. Panelists felt that the public should help to define what constitutes a good outcome and expressed some support for a "nihilism guard." More than 50% of panelists felt that if it was certain to be permanent, a vegetative state or lower severe disability would justify a withdrawal of care decision, whereas 15% felt that upper severe disability justified such a decision. Whether conceptualizing an ideal or existing prognostic calculator to predict death or an unacceptable outcome, on average a 64-69% chance of a poor outcome was felt to justify treatment withdrawal. These results demonstrate important variability in goals of care decision making and a desire to reduce this variability. Our panel of recognized TBI experts opined on the neurological outcomes and chances of those outcomes that might prompt consideration of care withdrawal; however, imprecision of prognostication and existing prognostication tools is a significant impediment to standardizing the approach to care-limiting decisions.

Non-Invasive Assessment of Intracranial Hypertension in Patients with Traumatic Brain Injury Using Computed Tomography Radiomic Features: A Pilot Study.

This study aimed to assess intracranial hypertension in patients with traumatic brain injury non-invasively using computed tomography (CT) radiomic features. Fifty patients from the primary cohort were enrolled in this study. The clinical data, pre-operative cranial CT images, and initial intracranial pressure readings were collected and used to develop a prediction model. Data of 20 patients from another hospital were used to validate the model. Clinical features including age, sex, midline shift, basilar cistern status, and ventriculocranial ratio were measured. Radiomic features-i.e., 18 first-order and 40 second-order features- were extracted from the CT images. LASSO method was used for features filtration. Multi-variate logistic regression was used to develop three prediction models with clinical (CF model), first-order (FO model), and second-order features (SO model). The SO model achieved the most robust ability to predict intracranial hypertension. Internal validation showed that the C-statistic of the model was 0.811 (95% confidence interval [CI]: 0.691-0.931) with the bootstrapping method. The Hosmer Lemeshow test and calibration curve also showed that the SO model had excellent performance. The external validation results showed a good discrimination with an area under the curve of 0.725 (95% CI: 0.500-0.951). Although the FO model was inferior to the SO model, it had better prediction ability than the CF model. The study shows that the radiomic features analysis, especially second-order features, can be used to evaluate intracranial hypertension non-invasively compared with conventional clinical features, given its potential for clinical practice and further research.

The application value of CT radiomics features in predicting pressure amplitude correlation index in patients with severe traumatic brain injury.

Purpose: To explore the application value of a machine learning model based on CT radiomics features in predicting the pressure amplitude correlation index (RAP) in patients with severe traumatic brain injury (sTBI). Methods: Retrospectively analyzed the clinical and imaging data in 36 patients with sTBI. All patients underwent surgical treatment, continuous ICP monitoring, and invasive arterial pressure monitoring. The pressure amplitude correlation index (RAP) was collected within 1 h after surgery. Three volume of interest (VOI) was selected from the craniocerebral CT images of patients 1 h after surgery, and a total of 93 radiomics features were extracted from each VOI. Three models were established to be used to evaluate the patients' RAP levels. The accuracy, precision, recall rate, F1 score, receiver operating characteristic (ROC) curve, and area under the curve (AUC) were used to evaluate the predictive performance of each model. Results: The optimal number of features for three predicting models of RAP was five, respectively. The accuracy of predicting the model of the hippocampus was 77.78%, precision was 88.24%, recall rate was 60%, the F1 score was 0.6, and AUC was 0.88. The accuracy of predicting the model of the brainstem was 63.64%, precision was 58.33%, the recall rate was 60%, the F1 score was 0.54, and AUC was 0.82. The accuracy of predicting the model of the thalamus was 81.82%, precision was 88.89%, recall rate was 75%, the F1 score was 0.77, and AUC was 0.96. Conclusions: CT radiomics can predict RAP levels in patients with sTBI, which has the potential to establish a method of non-invasive intracranial pressure (NI-ICP) monitoring.

Chinese guideline on the application of anti-seizure medications in the perioperative period of supratentorial craniocerebral surgery.

Seizures are a common symptom of craniocerebral diseases, and epilepsy is one of the comorbidities of craniocerebral diseases. However, how to rationally use anti-seizure medications (ASMs) in the perioperative period of craniocerebral surgery to control or avoid seizures and reduce their associated harm is a problem. The China Association Against Epilepsy (CAAE) united with the Trauma Group of the Chinese Neurosurgery Society, Glioma Professional Committee of the Chinese Anti-Cancer Association, Neuro-Oncology Branch of the Chinese Neuroscience Society, and Neurotraumatic Group of Chinese Trauma Society, and selected experts for consultancy regarding outcomes from evidence-based medicine in domestic and foreign literature. These experts referred to the existing research evidence, drug characteristics, Chinese FDA-approved indications, and expert experience, and finished the current guideline on the application of ASMs during the perioperative period of craniocerebral surgery, aiming to guide relevant clinical practice. This guideline consists of six sections: application scope of guideline, concepts of craniocerebral surgery-related seizures and epilepsy, postoperative application of ASMs in patients without seizures before surgery, application of ASMs in patients with seizures associated with lesions before surgery, emergency treatment of postoperative seizures, and 16 recommendations.

The Value of the Correlation Coefficient Between the ICP Wave Amplitude and the Mean ICP Level (RAP) Combined With the Resistance to CSF Outflow (Rout) for Early Prediction of the Outcome Before Shunting in Posttraumatic Hydrocephalus.

Objective: To evaluate the value of the correlation coefficient between the ICP wave amplitude and the mean ICP level (RAP) and the resistance to CSF outflow (Rout) in predicting the outcome of patients with post-traumatic hydrocephalus (PTH) selected for shunting. Materials and Methods: As a training set, a total of 191 patients with PTH treated with VP shunting were retrospectively analyzed to evaluate the potential predictive value of Rout, collected from pre-therapeutic CSF infusion test, for a desirable recovery level (dRL), standing for the modified rankin scale (mRS) of 0-2. Eventually, there were 70 patients with PTH prospectively included as a validation set to evaluate the value of Rout-combined RAP as a predictor of dRL. We calculated Rout from a CSF infusion test and collected RAP during continuous external lumbar drainage (ELD). Maximum RAP (RAPmax) and its changes relative to the baseline (ΔRAPmax%) served as specific parameters of evaluation. Results: In the training set, Rout was proved to be a significant predictor of dRL to shunting, with the area under the curve (AUC) of 0.686 (p < 0.001) in receiver-operating characteristic (ROC) analysis. In the validation set, Rout alone did not present a significant value in the prediction of desirable recovery level (dRL). ΔRAPmax% after 1st or 2nd day of ELD both showed significance in predicting of dRL to shunting with the AUC of 0.773 (p < 0.001) and 0.786 (p < 0.001), respectively. Significantly, Rout increased the value of ΔRAPmax% in the prediction of dRL with the AUC of 0.879 (p < 0.001), combining with ΔRAPmax% after the 1st and 2nd days of ELD. RAPmax after the 1st and 2nd days of ELD showed a remarkable predictive value for non-dRL (Levels 3-6 in Modified Rankin Scale) with the AUC of 0.891 (p < 0.001) and 0.746 (p < 0.001). Conclusion: Both RAP and Rout can predict desirable recovery level (dRL) to shunting in patients with PTH in the early phases of treatment. A RAP-combined Rout is a better dRL predictor for a good outcome to shunting. These findings help the neurosurgeon predict the probability of dRL and facilitate the optimization of the individual treatment plan in the event of ineffective or unessential shunting.

Evaluation of Intracranial Hypertension in Patients With Hypertensive Intracerebral Hemorrhage Using Texture Analysis.

Purpose: Texture analysis based on clinical images had been widely used in neurological diseases. This study aimed to achieve depth information of computed tomography (CT) images by texture analysis and to establish a model for noninvasive evaluation of intracranial pressure (ICP) in patients with hypertensive intracerebral hemorrhage (HICH). Methods: Forty-seven patients with HICH were selected. Related CT images and ICP value were collected. The morphological features of hematoma volume, midline shift, and ventriculocranial ratio were measured. Forty textural features were extracted from regions of interest. Four models were established to predict intracranial hypertension with morphological features, textural features of anterior horn, textural features of temporal lobe, and textural features of posterior horn. Results: Model of posterior horn had the highest ability to predict intracranial hypertension (AUC = 0.90, F1 score = 0.72), followed by model of anterior horn (AUC = 0.70, F1 score = 0.53) and model of temporal lobe (AUC = 0.70, F1 score = 0.58), and model of morphological features displayed the worst performance (AUC = 0.42, F1 score = 0.38). Conclusion: Texture analysis can realize interpretation of CT images in depth, which has great potential in noninvasive evaluation of intracranial hypertension.