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.
Artery stiffness is a main factor causing the various cardiovascular diseases in physiology and pathology. Therefore, the development of the non-invasive detection of arteriosclerosis is significant in preventing cardiovascular problems. In this study, the characterized parameters indicating the vascular stiffness were obtained by analyzing the electrocardiogram (ECG) and pulse wave signals, which can reflect the early change of vascular condition, and can predict the risk of cardiovascular diseases. Considering the coupling of ECG and pulse wave signals, and the association with atherosclerosis, we used the ECG signal characteristic parameters, including RR interval, QRS wave width and T wave amplitude, as well as the pulse wave signal characteristic parameters (the number of peaks, 20% main wave width, the main wave slope, pulse rate and the relative height of the three peaks), to evaluate the samples. We then built an assessment model of arteriosclerosis based on Adaptive Network-based Fuzzy Interference System (ANFIS) using the obtained forty sets samples data of ECG and pulse wave signals. The results showed that the model could noninvasively assess the arteriosclerosis by self-learning diagnosis based on expert experience, and the detection method could be further developed to a potential technique for evaluating the risk of cardiovascular diseases. The technique will facilitate the reduction of the morbidity and mortality of the cardiovascular diseases with the effective and prompt medical intervention.
Objective To explore a novel method for early lung cancer screening based on exhaled breath analysis. MethodsThis study enrolled patients with suspected pulmonary malignancies and healthy individuals undergoing physical examinations at Sir Run Run Shaw Hospital, Zhejiang University School of Medicine (Qingchun and Qiantang campuses) from September 2023 to June 2024. Enrolled subjects were categorized into a lung cancer group, a benign nodule/tumor group, and a healthy control group. Exhaled breath samples were collected using a sensor array constructed from multiple graphene composite materials to capture breath fingerprints. Based on the collected data, screening and diagnostic models for lung cancer were developed and their performance was evaluated. ResultsA total of 4 580 subjects were included. Among them, 3 195 were pathologically diagnosed with pulmonary malignancies, including 1 394 males and 1 801 females with a mean age of (58.93±12.37) years, 599 were diagnosed with benign nodules/tumors including 339 males and 260 females with a mean age of (57.10±11.06) years, and 786 were healthy controls with no pulmonary nodules detected on chest CT including 420 males and 366 females with a mean age of (29.75±9.32) years. The screening model for high-risk populations (distinguishing patients with lung cancer/high-risk pulmonary nodules from healthy individuals) demonstrated excellent performance, with an area under the receiver operating characteristic curve (AUC) of 0.926. At the optimal Youden’s index (cutoff threshold of 63.5%), the external test set achieved a specificity of 85.2%, a sensitivity of 88.4%, and an accuracy of 86.8%. The diagnostic model (distinguishing patients with lung cancer/premalignant lesions from those with benign pulmonary nodules/healthy individuals) achieved an AUC of 0.818. At its optimal Youden’s index (cutoff threshold of 47.0%), the external test set showed a specificity of 71.7%, a sensitivity of 77.3%, and an accuracy of 74.5%. ConclusionThe non-invasive breath analysis platform based on a sensor array, developed in this study, can achieve rapid and relatively accurate lung cancer screening by analyzing breath fingerprints. This confirms the feasibility of this technology for early lung cancer screening and holds promise for facilitating the early detection and intervention of lung cancer.
Objective To compare the sequential efficacy of high-flow nasal cannula oxygen therapy (HFNC) with non-invasive mechanical ventilation (NIV). Methods Randomized controlled trials comparing the efficacy of NIV sequential invasive mechanical ventilation with HFNC were included in the Chinese Journal Full-text Database, VIP Journal database, Wanfang Database, Chinese Biomedical Literature Database, PubMed, Cochrane Library and Embase. Meta-analysis was performed using RevMan5.4 software. Results A total of 2404 subjects were included in 19 studies. Meta-analysis results showed that compared with NIV, HFNC had a statistically significant difference in reducing patients' re-intubation rate in invasive mechanical ventilation sequence [relative risk (RR)=0.65, 95% confidence interval (CI) 0.50 - 0.86, Z=3.10, P=0.002]. HFNC showed statistically significant difference compared with NIV in reducing lung infection rate (RR=0.40, 95%CI 0.21 - 0.79, Z=2.67, P=0.008). HFNC was significantly different from NIV in terms of length of stay in Intensive Care Unit (ICU) (MD=–5.77, 95%CI –7.64 - –3.90, Z=6.05, P<0.00001). HFNC was significantly different from NIV in improving 24 h oxygenation index (MD=13.16, 95%CI 8.77 - 17.55, Z=5.87, P<0.00001). There was no significant difference in ICU mortality between HFNC and NIV (RR=0.70, 95%CI 0.45 - 1.08, Z=1.61, P=0.11). Conclusion Compared with NIV, sequential application of HFNC in invasive mechanical ventilation can improve the reintubation rate and pulmonary infection rate to a certain extent, reduce the length of ICU stay and improve the 24 h oxygenation index, while there is no difference in ICU mortality, which is worthy of clinical application.
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.
Acute respiratory distress syndrome (ARDS) is a serious threat to human life and health disease, with acute onset and high mortality. The current diagnosis of the disease depends on blood gas analysis results, while calculating the oxygenation index. However, blood gas analysis is an invasive operation, and can’t continuously monitor the development of the disease. In response to the above problems, in this study, we proposed a new algorithm for identifying the severity of ARDS disease. Based on a variety of non-invasive physiological parameters of patients, combined with feature selection techniques, this paper sorts the importance of various physiological parameters. The cross-validation technique was used to evaluate the identification performance. The classification results of four supervised learning algorithms using neural network, logistic regression, AdaBoost and Bagging were compared under different feature subsets. The optimal feature subset and classification algorithm are comprehensively selected by the sensitivity, specificity, accuracy and area under curve (AUC) of different algorithms under different feature subsets. We use four supervised learning algorithms to distinguish the severity of ARDS (P/F ≤ 300). The performance of the algorithm is evaluated according to AUC. When AdaBoost uses 20 features, AUC = 0.832 1, the accuracy is 74.82%, and the optimal AUC is obtained. The performance of the algorithm is evaluated according to the number of features. When using 2 features, Bagging has AUC = 0.819 4 and the accuracy is 73.01%. Compared with traditional methods, this method has the advantage of continuously monitoring the development of patients with ARDS and providing medical staff with auxiliary diagnosis suggestions.
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.
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.
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.
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.