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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.
Assuntos
Anti-Hipertensivos , Pressão Sanguínea , Hemorragia Cerebral , Dexmedetomidina , Remifentanil , Humanos , Dexmedetomidina/uso terapêutico , Dexmedetomidina/administração & dosagem , Remifentanil/administração & dosagem , Remifentanil/uso terapêutico , Masculino , Feminino , Estudos Prospectivos , Hemorragia Cerebral/tratamento farmacológico , Idoso , Pessoa de Meia-Idade , Método Simples-Cego , Pressão Sanguínea/efeitos dos fármacos , Anti-Hipertensivos/uso terapêutico , Anti-Hipertensivos/administração & dosagem , Resultado do Tratamento , Hipnóticos e Sedativos/uso terapêuticoRESUMO
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.
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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.
Assuntos
Lesões Encefálicas Traumáticas , Pressão Intracraniana , Aprendizado de Máquina , Humanos , Lesões Encefálicas Traumáticas/fisiopatologia , Lesões Encefálicas Traumáticas/diagnóstico , Pressão Intracraniana/fisiologia , Prognóstico , Masculino , Feminino , Pessoa de Meia-Idade , Adulto , Escala de Resultado de Glasgow , Escala de Coma de Glasgow , Alta do Paciente , Algoritmos , Estudos Prospectivos , Idoso , Estudos de CoortesRESUMO
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.
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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.
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Lesões Encefálicas Traumáticas , Hipertensão Intracraniana , Pressão Intracraniana , Humanos , Lesões Encefálicas Traumáticas/fisiopatologia , Lesões Encefálicas Traumáticas/diagnóstico , Lesões Encefálicas Traumáticas/terapia , Pressão Intracraniana/fisiologia , Hipertensão Intracraniana/diagnóstico , Hipertensão Intracraniana/fisiopatologia , Hipertensão Intracraniana/etiologia , Monitorização Fisiológica/métodos , Adulto , Monitorização Neurofisiológica/métodosRESUMO
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.