Objective To investigate the incidence trend of migraine in Chinese from 1990 to 2019 in the global burden of disease database (GBD2019) and its effect on three factors: age, period and cohort. Methods Data were obtained from GBD2019. Joinpoint software was used to analyze the changes in migraine incidence. We analyzed the age-period-cohort (APC) model of migraine in the Chinese population with Stata 17.0 software and estimated the effect of age, period, and cohort on migraine incidence. Results From 1990 to 2019, the standardized incidence of migraine in the general population, and in men and women in China showed a fluctuating upwards trend, and the average annual percentage changes were 0.23%, 0.27% and 0.21%, respectively. The APC model showed that from 1990 to 2019, the risk of migraine in China decreased with the increase of age, showed a downward trend with the increase of the period, and increased with the increase of the birth cohort, indicating that the cohort effect played a dominant role in the risk of migraine in the current period, and the risk of women in the rear of the birth cohort began to be gradually higher than that of men. Conclusion The incidence of migraine in Chinese population is on the rise from 1990 to 2019, and the younger the age, the earlier the period, and the lower the birth cohort, the greater the risk of migraine, suggesting that the prevention and treatment of migraine in women aged 10 to 54 years should be strengthened to further reduce the incidence of migraine in China.
In recent years, wearable devices have seen a booming development, and the integration of wearable devices with clinical settings is an important direction in the development of wearable devices. The purpose of this study is to establish a prediction model for postoperative pulmonary complications (PPCs) by continuously monitoring respiratory physiological parameters of cardiac valve surgery patients during the preoperative 6-Minute Walk Test (6MWT) with a wearable device. By enrolling 53 patients with cardiac valve diseases in the Department of Cardiovascular Surgery, West China Hospital, Sichuan University, the grouping was based on the presence or absence of PPCs in the postoperative period. The 6MWT continuous respiratory physiological parameters collected by the SensEcho wearable device were analyzed, and the group differences in respiratory parameters and oxygen saturation parameters were calculated, and a prediction model was constructed. The results showed that continuous monitoring of respiratory physiological parameters in 6MWT using a wearable device had a better predictive trend for PPCs in cardiac valve surgery patients, providing a novel reference model for integrating wearable devices with the clinic.
Breathing pattern parameters refer to the characteristic pattern parameters of respiratory movements, including the breathing amplitude and cycle, chest and abdomen contribution, coordination, etc. It is of great importance to analyze the breathing pattern parameters quantificationally when exploring the pathophysiological variations of breathing and providing instructions on pulmonary rehabilitation training. Our study provided detailed method to quantify breathing pattern parameters including respiratory rate, inspiratory time, expiratory time, inspiratory time proportion, tidal volume, chest respiratory contribution ratio, thoracoabdominal phase difference and peak inspiratory flow. We also brought in “respiratory signal quality index” to deal with the quality evaluation and quantification analysis of long-term thoracic-abdominal respiratory movement signal recorded, and proposed the way of analyzing the variance of breathing pattern parameters. On this basis, we collected chest and abdomen respiratory movement signals in 23 chronic obstructive pulmonary disease (COPD) patients and 22 normal pulmonary function subjects under spontaneous state in a 15 minute-interval using portable cardio-pulmonary monitoring system. We then quantified subjects’ breathing pattern parameters and variability. The results showed great difference between the COPD patients and the controls in terms of respiratory rate, inspiratory time, expiratory time, thoracoabdominal phase difference and peak inspiratory flow. COPD patients also showed greater variance of breathing pattern parameters than the controls, and unsynchronized thoracic-abdominal movements were even observed among several patients. Therefore, the quantification and analyzing method of breathing pattern parameters based on the portable cardiopulmonary parameters monitoring system might assist the diagnosis and assessment of respiratory system diseases and hopefully provide new parameters and indexes for monitoring the physical status of patients with cardiopulmonary disease.
ObjectiveTo explore the impact of number of positive regional lymph nodes (nPRLN) in N1 stage on the prognosis of non-small cell lung cancer (NSCLC) patients. MethodsPatients with TxN1M0 stage NSCLC who underwent lobectomy and mediastinal lymph node dissection from 2010 to 2015 were screened from SEER database (17 Regs, 2022nov sub). The optimal cutoff value of nPRLN was determined using X-tile software, and patients were divided into 2 groups according to the cutoff value: a nPRLN≤optimal cutoff group and a nPRLN>optimal cutoff group. The influence of confounding factors was minimized by propensity score matching (PSM) at a ratio of 1∶1. Kaplan-Meier curves and Cox proportional hazards models were used to evaluate overall survival (OS) and lung cancer-specific survival (LCSS) of patients. ResultsA total of 1316 patients with TxN1M0 stage NSCLC were included, including 662 males and 654 females, with a median age of 67 (60, 73) years. The optimal cutoff value of nPRLN was 3, with 1165 patients in the nPRLN≤3 group and 151 patients in the nPRLN>3 group. After PSM, there were 138 patients in each group. Regardless of before or after PSM, OS and LCSS of patients in the nPRLN≤3 group were superior to those in the nPRLN>3 group (P<0.05). N1 stage nPRLN>3 was an independent prognostic risk factor for OS [HR=1.52, 95%CI (1.22, 1.89), P<0.001] and LCSS [HR=1.72, 95%CI (1.36, 2.18), P<0.001]. ConclusionN1 stage nPRLN>3 is an independent prognostic risk factor for NSCLC patients in TxN1M0 stage, which may provide new evidence for future revision of TNM staging N1 stage subclassification.
As a low-load physiological monitoring technology, wearable devices can provide new methods for monitoring, evaluating and managing chronic diseases, which is a direction for the future development of monitoring technology. However, as a new type of monitoring technology, its clinical application mode and value are still unclear and need to be further explored. In this study, a central monitoring system based on wearable devices was built in the general ward (non-ICU ward) of PLA General Hospital, the value points of clinical application of wearable physiological monitoring technology were analyzed, and the system was combined with the treatment process and applied to clinical monitoring. The system is able to effectively collect data such as electrocardiogram, respiration, blood oxygen, pulse rate, and body position/movement to achieve real-time monitoring, prediction and early warning, and condition assessment. And since its operation from March 2018, 1 268 people (657 patients) have undergone wearable continuous physiological monitoring until January 2020, with data from a total of 1 198 people (632 cases) screened for signals through signal quality algorithms and manual interpretation were available for analysis, accounting for 94.48 % (96.19%) of the total. Through continuous physiological data analysis and manual correction, sleep apnea event, nocturnal hypoxemia, tachycardia, and ventricular premature beats were detected in 232 (36.65%), 58 (9.16%), 30 (4.74%), and 42 (6.64%) of the total patients, while the number of these abnormal events recorded in the archives was 4 (0.63%), 0 (0.00%), 24 (3.80%), and 15 (2.37%) cases. The statistical analysis of sleep apnea event outcomes revealed that patients with chronic diseases were more likely to have sleep apnea events than healthy individuals, and the incidence was higher in men (62.93%) than in women (37.07%). The results indicate that wearable physiological monitoring technology can provide a new monitoring mode for inpatients, capturing more abnormal events and provide richer information for clinical diagnosis and treatment through continuous physiological parameter analysis, and can be effectively integrated into existing medical processes. We will continue to explore the applicability of this new monitoring mode in different clinical scenarios to further enrich the clinical application of wearable technology and provide richer tools and methods for the monitoring, evaluation and management of chronic diseases.