Background: Our purpose was to establish a noninvasive quantitative method for assessing intracranial pressure (ICP) levels in patients with traumatic brain injury (TBI) through investigating the Hounsfield unit (HU) features of computed tomography (CT) images. Methods: In this retrospective study, 47 patients with a closed TBI were recruited. Hounsfield unit features from the last cranial CT and the initial ICP value were collected. Three models were established to predict intracranial hypertension with Hounsfield unit (HU model), midline shift (MLS model), and clinical expertise (CE model) features. Results: The HU model had the highest ability to predict intracranial hypertension. In 34 patients with unilateral injury, the HU model displayed the highest performance. In three classifications of intracranial hypertension (ICP ≤ 22, 23–29, and ≥30 mmHg), the HU model achieved the highest F1 score. Conclusions: This radiological feature-based noninvasive quantitative approach showed better performance compared with conventional methods, such as the degree of midline shift and clinical expertise. The results show its potential in clinical practice and further research.
Intra-abdominal pressure (IAP) is increasingly being recognized as an indispensable and significant physiological parameter in intensive care units (ICU). IAP has been measured in a variety of ways with the development of many techniques in recent years. The level of intra-abdominal pressure under normal conditions is generally equal to or less than 12 mmHg. Accordingly, abdominal hypertension (IAH) is defined as two consecutive IAP measurements higher than 12 mmHg within 4-6 h. When IAH deteriorates further with IAP higher than 20 mmHg along with organ dysfunction and/ or failure, this clinical manifestation can be diagnosed as abdominal compartment syndrome (ACS). IAH and ACS are associated with gastrointestinal ischemia, acute renal failure, and lung injury, leading to severe morbidity and mortality. Elevated IAP and IAH may affect the cerebral venous return and outflow of the cerebrospinal fluid by increasing the intrathoracic pressure (ITP), ultimately leading to increased intracranial pressure (ICP). Therefore, it is essential to monitor IAP in critically ill patients. The reproducibility and accuracy of intra-bladder pressure (IBP) measurements in previous studies need to be further improved, although the indirect measurement of IAP is now a widely used technique.To address these limitations, we recently used a set of IAP monitoring systems with advantages of convenience, continuous monitoring, digital visualization, and long-term IAP recording and data storage in critically ill patients. This IAP monitoring system can detect intra-abdominal hypertension and potentially analyze clinical status in real time. The recorded IAP data and other physiological indicators, such as intracranial pressure, can be further used for correlation analysis to guide treatment and predict a patient's possible prognosis.
PurposeTo 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).MethodsRetrospectively 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.ResultsThe 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.ConclusionsCT 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.
Objective: Intracranial pressure (ICP) monitoring is an integral part of the multimodality monitoring system in the neural intensive care unit. The present study aimed to describe the morphology of the spindle wave (a shuttle shape with wide middle and narrow ends) during ICP signal monitoring in TBI patients and to investigate its clinical significance.Methods: Sixty patients who received ICP sensor placement and admitted to the neurosurgical intensive care unit between January 2021 and September 2021 were prospectively enrolled. The patient’s Glasgow Coma Scale (GCS) score on admission and at discharge and length of stay in hospital were recorded. ICP monitoring data were monitored continuously. The primary endpoint was 6-month Glasgow Outcome Scale-Extended (GOSE) score. Patients with ICP spindle waves were assigned to the spindle wave group and those without were assigned to the control group. The correlation between the spindle wave and 6-month GOSE was analyzed. Meanwhile, the mean ICP and two ICP waveform-derived indices, ICP pulse amplitude (AMP) and correlation coefficient between AMP and ICP (RAP) were comparatively analyzed.Results: There were no statistically significant differences between groups in terms of age (p = 0.89), gender composition (p = 0.62), and GCS score on admission (p = 0.73). Patients with spindle waves tended to have a higher GCS score at discharge (12.75 vs. 10.90, p = 0.01), a higher increment in GCS score during hospitalization (ΔGCS, the difference between discharge GCS score and admission GCS score) (4.95 vs. 2.80, p = 0.01), and a better 6-month GOSE score (4.90 vs. 3.68, p = 0.04) compared with the control group. And the total duration of the spindle wave was positively correlated with 6-month GOSE (r = 0.62, p = 0.004). Furthermore, the parameters evaluated during spindle waves, including mean ICP, AMP, and RAP, demonstrated significant decreases compared with the parameters before the occurrence of the spindle wave (all p < 0.025).Conclusion: The ICP spindle wave was associated with a better prognosis in TBI patients. Physiological parameters such as ICP, AMP, and RAP were significantly improved when spindle waves occurred, which may explain the enhancement of clinical outcomes. Further studies are needed to investigate the pathophysiological mechanisms behind this wave.
Background: Current intracranial pressure (ICP) related parameters monitoring is invasive and tends to cause complications, which limited their use to predict patients’ intracranial status and prognosis. Objective: To utilize postoperative computed tomography (CT) images radiomic features techniques to predict abnormal ICP related parameters levels consisting of an index of cerebrospinal compensatory reserve(RAP) and a pressure reactivity index (PRx)in traumatic brain injury (TBI) patients noninvasively. Methods: 48 patients were enrolled and randomized to training (n=34) and test (n=14) sets. A total of 107 radiomic features were extracted from each patients’ CT image. Their clinical and imaging data was collected and analyzed to establish prediction models of RAP and PRx respectively. Pearson correlation and univariate regression analysis were used for feature selection, multivariate logistic regression was used to develop the predicting models. The performance of models was assessed with their discrimination, calibration and clinical use. Results: The RAP model showed a good discrimination with the area under receiver operating characteristic curve (AUC) of training and test set were 0.771 and 0.727, and a good calibration; The performance of PRx model was inferior to the RAP model, but still have a significant discrimination with the AUCs of training and test were 0.713 and 0.667. Decision curve analysis indicated the prediction model have the potential clinical utility. Conclusion: The study illustrated that CT radiomic features as a clinical aid may have ability to predict ICP related parameters to reflect the intracranial condition of TBI patients noninvasively, given its potential for clinical treatment guidance and prognosis indication.
PurposeTexture 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).MethodsForty-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.ResultsModel 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).ConclusionTexture analysis can realize interpretation of CT images in depth, which has great potential in noninvasive evaluation of intracranial hypertension.
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