ObjectiveTo systematically evaluate the efficacy of high-flow nasal cannula oxygen therapy (HFNC) in Post-extubation acute exacerbation of chronic obstructive pulmonary disease (AECOPD) patients. MethodsThe Domestic and foreign databases were searched for all published available randomized controlled trials (RCTs) about HFNC therapy in post-extubation AECOPD patients. The experimental group was treated with HFNC, while the control group was treated with non-invasive positive pressure ventilation (NIPPV). The main outcome measurements included reintubation rate. The secondary outcomes measurements included oxygenation index after extubation, length of intensive care unit (ICU) stay, mortality, comfort score and adverse reaction rate. Meta-analysis was performed by Revman 5.3 software. ResultA total of 20 articles were enrolled. There were 1516 patients enrolled, with 754 patients in HFNC group, and 762 patients in control group. The results of Meta-analysis showed that there were no significant difference in reintubation rate [RR=1.41, 95%CI 0.97 - 2.07, P=0.08] and mortality [RR=0.91, 95%CI 0.58 - 1.44, P=0.69]. Compared with NIPPV, HFNC have advantages in 24 h oxygenation index after extubation [MD=4.66, 95%CI 0.26 - 9.05, P=0.04], length of ICU stay [High risk group: SMD –0.52, 95%CI –0.74 - –0.30; Medium and low risk group: MD –1.12, 95%CI –1.56- –0.67; P<0.00001], comfort score [MD=1.90, 95%CI 1.61 - 2.19, P<0.00001] and adverse reaction rate [RR=0.22, 95%CI 0.16 - 0.31, P<0.00001]. ConclusionsCompared with NIPPV, HFNC could improve oxygenation index after extubation, shorten the length of ICU stay, effectively improve Patient comfort, reduce the occurrence of adverse reactions and it did not increase the risk of reintubation and mortality. It is suggested that HFNC can be cautiously tried for sequential treatment of AECOPD patients after extubation, especially those who cannot tolerate NIPPV.
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.
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.
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.
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.
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.
ObjectiveTo evaluate the clinical value of a combined diagnostic model integrating circulating cell-free DNA (cfDNA) methylation markers and CT imaging features for differentiating benign and malignant lung nodules and for early lung cancer detection. This study pioneers a two-step multi-omics modeling approach to construct a robust diagnostic model. MethodsA retrospective cohort of 140 patients (70 malignant and 70 benign, confirmed by postoperative pathology) with lung nodules who underwent surgical treatment at West China Hospital, Sichuan University, from January 2014 to December 2024 was included. Methylation profiles of 54 cfDNA regions were detected via targeted methylation sequencing. CT imaging features (e.g., nodule size, type, and signs) were extracted. A two-step modeling strategy was applied: ① imaging features were modeled directly using binary logistic regression, while methylation features were selected via LASSO regression before modeling; ② a combined model was constructed using the scores from both models. Model performance was evaluated using receiver operating characteristic (ROC) curves, with internal validation via Bootstrap (1000 iterations). ResultsAll patients were split into a training set (n=84) and a test set (n=56). In the test set, the combined model achieved an area under the ROC curve (AUC) of 0.86 [95% confidence interval (CI): 0.74-0.95], with both sensitivity and specificity reaching 82%. This outperformed the individual imaging model (AUC=0.74) and methylation model (AUC=0.82). ConclusionThe multi-omics combined diagnostic model significantly improved the ability to distinguish benign from malignant lung nodules, particularly for early-stage lesions like ground-glass opacities. Its non-invasive and high-sensitivity features provide a promising translational tool for lung cancer screening, with promising clinical application prospects.
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.
Objective To investigate the feasibility and effectiveness of a comprehensive minimally invasive approach for pulmonary nodule day surgery, utilizing non-invasive localization techniques. Methods A retrospective analysis was conducted on the clinical data of patients diagnosed with peripheral pulmonary nodules and undergoing video-assisted thoracoscopic wedge resection at the Department of Thoracic Surgery, the University of Hong Kong-Shenzhen Hospital, from January 2020 to May 2024. Patients were divided into a conventional surgery group and a day surgery group based on different treatment approaches. The perioperative data between the two groups were compared. Results A total of 40 patients were included, comprising 19 males and 21 females, with an average age of (47.4±12.5) years. The day surgery group consisted of 20 patients, and the conventional surgery group consisted of 20 patients. There were no statistically significant differences in baseline demographic characteristics between the two groups (P>0.05). All patients successfully completed the surgery without any deaths or serious complications. The two groups showed statistically significant differences (P<0.05) in key indicators such as pulmonary nodule localization time, incidence of localization-related complications, operative time, blood loss, duration of postoperative chest tube placement, total length of hospital stay, and patient satisfaction on the day of discharge. Conclusion Pulmonary nodule day surgery based on a comprehensive minimally invasive approach with non-invasive localization techniques can maximize the reduction of hospital stay and operative time, reduce surgery-related complications, and improve patient satisfaction and recovery speed while ensuring safety and effectiveness. This model not only meets the needs of patients but also optimizes the allocation of medical resources, demonstrating significant clinical application value and broad potential for promotion.