At present, the monitoring methods fwor intracranial pressure adopted in clinical practice are almost all invasive. The invasive monitoring methods for intracranial pressure were accurate, but they were harmful to the patient's body. Therefore, non-invasive methods for intracranial pressure monitoring must be developed. Since 1980, many non-invasive methods have been sprung out in succession, but they can not be used clinically. In this paper, research contents and progress of present non-invasive intracranial pressure monitoring are summarized. Advantages and disadvantages of various ways are analyzed. And finally, perspectives of development for intracranial pressure monitoring are presented.
ObjectiveTo observe the predictive value of Volume OXygeneration (VOX) index for early non-invasive positive pressure ventilation (NIPPV) treatment in patients with type I Respiratory failure. MethodsRetrospective analysis was made on the patients with type I Respiratory failure admitted to the intensive care medicine from September 2019 to September 2022, who received early NIPPV treatment. After screening according to the discharge standard, they were grouped according to the NIPPV 2-hour VOX index. The observation group was VOX Youden index >20.95 (n=69), and the control group was VOX index ≤20.95 (n=64). Collect patient baseline data and NIPPV 2-hour, 12-hour, and 24-hour arterial blood gas values, and calculate NIPPV outcomes, intubation status, NIPPV usage time, hospital stay, and mortality rate. ResultsThere was a statistically significant difference in respiratory rate (RR) between the baseline data onto the two groups of patients, but others not. After early NIPPV treatment, the 2-hour oxygenation index (P/F) [(182.5 ± 66.14) vs. (144.1 ± 63.6) mm Hg, P<0.05] of the observation group showed a more significant increase. The failure rate of NIPPV intubation within 12 hours was lower (4.35% vs. 32.81%, P<0.05), the success rate of NIPPV withdrawal from 24 hours was higher (40.58% vs. 0%, P<0.05), and the failure rate of NIPPV intubation was lower (4.35% vs. 46.88%, P<0.05). The comparison of treatment outcomes showed that the intubation rates in the observation group (4.35% vs. 67.19%, P<0.05) was lower. The threshold of NIPPV 2-hour VOX index 20.95 was used as a predictor of Tracheal intubation, with sensitivity of 74.7% and specificity of 93.5%. ConclusionIn the early NIPPV treatment of patients with type I Respiratory failure, the NIPPV 2-hour VOX index>20.91 is taken as the evaluation index, which can better to predict the improvement in hypoxia and the risk of NIPPV failure Tracheal intubation, and has clinical significance.
The prevalence of cardiovascular disease in our country is increasing, and it has been a big problem affecting the social and economic development. It has been demonstrated that early intervention of cardiovascular risk factors can effectively reduce cardiovascular disease-caused mortality. Therefore, extensive implementation of cardiovascular testing and risk factor screening in the general population is the key to the prevention and treatment of cardiovascular disease. However, the categories of devices available for quick cardiovascular testing are limited, and in particular, many existing devices suffer from various technical problems, such as complex operation, unclear working principle, or large inter-individual variability in measurement accuracy, which lead to an overall low popularity and reliability of cardiovascular testing. In this study, we introduce the non-invasive measurement mechanisms and relevant technical progresses for several typical cardiovascular indices (e.g., peripheral/central arterial blood pressure, and arterial stiffness), with emphasis on describing the applications of biomechanical modeling and simulation in mechanism verification, analysis of influential factors, and technical improvement/innovation.
By studying the relationship between fingertip temperature changes and arterial function during vascular reactivity test, we established a new non-invasive method for detecting vascular function, in order to provide an assistance for early diagnosis and prevention of cardiovascular diseases. We customized three modules respectively for blood occlusion, measurement of finger temperature and blood oxygen acquisition, and then we established the hardware of data acquisition system. And the software was programmed with Labview. Healthy subjects [group A, n=24, (44.6±9.0) years] and subjects with cardiovascular diseases [group B, n=33, (57.2±9.9) years)] were chosen for the study. Subject's finger temperature, blood oxygen and occlusion pressure of block side during and after unilateral arm brachial artery occlusion were recorded, as well as some other regular physiological indexes. By time-domain analysis, we extracted 12 parameters from fingertip temperature signal, including the initial temperature (Ti), temperature rebound (TR), the time of the temperature recovering to initial status (RIt) and other parameters from the finger temperature signal. We in the experiment also measured other regular physiological body mass index (BMI), systolic blood pressure (SBP), diastiolic blood pressure (DBP) and so on. Results showed that 8 parameters difference between the two group of data were significant. based on the statistical results. A discriminant function of vascular function status was established afterwards. We found in the study that the changes of finger temperature during unilateral arms brachial artery occlusion and open were closely related to vascular function. We hope that the method presented in this article could lay a foundation of early detection of vascular function.
The use of non-invasive blood glucose detection techniques can help diabetic patients to alleviate the pain of intrusive detection, reduce the cost of detection, and achieve real-time monitoring and effective control of blood glucose. Given the existing limitations of the minimally invasive or invasive blood glucose detection methods, such as low detection accuracy, high cost and complex operation, and the laser source's wavelength and cost, this paper, based on the non-invasive blood glucose detector developed by the research group, designs a non-invasive blood glucose detection method. It is founded on dual-wavelength near-infrared light diffuse reflection by using the 1 550 nm near-infrared light as measuring light to collect blood glucose information and the 1 310 nm near-infrared light as reference light to remove the effects of water molecules in the blood. Fourteen volunteers were recruited for in vivo experiments using the instrument to verify the effectiveness of the method. The results indicated that 90.27% of the measured values of non-invasive blood glucose were distributed in the region A of Clarke error grid and 9.73% in the region B of Clarke error grid, all meeting clinical requirements. It is also confirmed that the proposed non-invasive blood glucose detection method realizes relatively ideal measurement accuracy and stability.
Objective To explore the predictive value of serum procalcitonin (PCT), D-dimer (D-D) and decoy receptor 3 (DcR3) for prognosis of patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) and respiratory failure undergoing non-invasive ventilation (NIV). Methods A total of 95 patients with AECOPD and respiratory failure undergoing basic treatment and NIV in the hospital were retrospectively enrolled between September (n=65) 2017 and February 2021. According to prognosis after treatment, they were divided into a good prognosis group and a poor prognosis group (n=30). The general data of all patients were collected. The influencing factors of prognosis were analyzed by multivariate logistic regression model. The levels of DcR3, PCT and D-D were detected by enzyme-linked immunosorbent assay, colloidal gold colorimetry and immunoturbidimetry. The patients condition was assessed by scores of acute physiology chronic health evaluation scoring system Ⅱ (APACHEⅡ). The partial pressure of arterial oxygen (PaO2) and partial pressure of carbon dioxide (PaCO2) were recorded. And the above indexes between the two groups were compared. The relationship between DcR3, PCT, D-D and APACHEⅡ score, PaO2, PaCO2 was analyzed by Pearson correlation analysis. The prognostic value of DcR3, PCT and D-D was analyzed by receiver operating characteristic (ROC) curve. Results There was no significant difference in gender, GOLD grading or underlying diseases between the poor prognosis group and the good prognosis group (P>0.05), but there were significant differences in age, DcR3, PCT, D-D, APACHEⅡ score, PaO2 and PaCO2 after treatment (P<0.05). DcR3, PCT, D-D, APACHEⅡ score and PaCO2 in the poor prognosis group were higher than those in the good prognosis group, while PaO2 was lower than that in the good prognosis group (P<0.05). Logistic regression analysis showed that DcR3 ≥5.50 ng/mL (OR=21.889), PCT ≥ 5.00 μg/L (OR=3.782), D-D ≥3.00 μg/L (OR=4.162) and APACHEⅡ score ≥20 points (OR=2.540) were all influencing factors of prognosis (P<0.05). The results of Pearson correlation analysis showed that DcR3, PCT and D-D were positively correlated with APACHEⅡ score and PaCO2, while negatively correlated with PaO2 (P<0.05). The results of ROC curve analysis showed that area under ROC curve of DcR3, PCT and D-D for predicting the prognosis were 0.745 (95%CI 0.631 - 0.859), 0.691 (95%CI 0.579 - 0.803) and 0.796 (95%CI 0.696 - 0.895), respectively (P<0.05). Conclusion The serum DcR3, PCT and D-D levels are related to disease progression in patients with AECOPD and respiratory failure after NIV, which have good predictive efficiency for prognosis and can be applied as important biological indexes to evaluate prognosis and guide treatment.
The precise recognition of feature points of impedance cardiogram (ICG) is the precondition of calculating hemodynamic parameters based on thoracic bioimpedance. To improve the accuracy of detecting feature points of ICG signals, a new method was proposed to de-noise ICG signal based on the adaptive ensemble empirical mode decomposition and wavelet threshold firstly, and then on the basis of adaptive ensemble empirical mode decomposition, we combined difference and adaptive segmentation to detect the feature points, A, B, C and X, in ICG signal. We selected randomly 30 ICG signals in different forms from diverse cardiac patients to examine the accuracy of the proposed approach and the accuracy rate of the proposed algorithm is 99.72%. The improved accuracy rate of feature detection can help to get more accurate cardiac hemodynamic parameters on the basis of thoracic bioimpedance.
Existing near-infrared non-invasive blood glucose detection modelings mostly detect multi-spectral signals with different wavelength, which is not conducive to the popularization of non-invasive glucose meter at home and does not consider the physiological glucose dynamics of individuals. In order to solve these problems, this study presented a non-invasive blood glucose detection model combining particle swarm optimization (PSO) and artificial neural network (ANN) by using the 1 550 nm near-infrared absorbance as the independent variable and the concentration of blood glucose as the dependent variable, named as PSO-2ANN. The PSO-2ANN model was based on two sub-modules of neural networks with certain structures and arguments, and was built up after optimizing the weight coefficients of the two networks by particle swarm optimization. The results of 10 volunteers were predicted by PSO-2ANN. It was indicated that the relative error of 9 volunteers was less than 20%; 98.28% of the predictions of blood glucose by PSO-2ANN were distributed in the regions A and B of Clarke error grid, which confirmed that PSO-2ANN could offer higher prediction accuracy and better robustness by comparison with ANN. Additionally, even the physiological glucose dynamics of individuals may be different due to the influence of environment, temper, mental state and so on, PSO-2ANN can correct this difference only by adjusting one argument. The PSO-2ANN model provided us a new prospect to overcome individual differences in blood glucose prediction.
Non-invasive biomarkers, due to their non-invasive and safe characteristics, hold significant potential for the diagnosis and prognosis of epilepsy. This review summarizes the research progress and future directions of non-invasive biomarkers for epilepsy, encompassing electrophysiological, imaging, biochemical, and genetic markers, and discusses biomarkers for specific types of epilepsy, such as structural lesion-related epilepsy, infection and inflammation-related epilepsy, autoimmune epilepsy, endocrine hormone-related epilepsy, and metabolic epilepsy, to facilitate their clinical application.
As one of the important indexes for the diagnosis and treatment of cardiovascular diseases, cardiac output can reflect the state of cardiovascular system timely, and can play a guiding role in the treatment of related diseases. In recent years detection technology of cardiac output has caused great attention, especially minimally invasive and non-invasive methods. In this paper, the principle of non-invasive detection methods and their recent developments are described, and various detection methods are also analyzed.