The inspiratory impedance threshold device (ITD) was put forward by Lurie in 1995, and was assigned as a class II a recommendation by the International Liaison Committee on Resuscitation (ILCOR) resuscitation guidelines in 2005. The ITD is used to augment negative intrathoracic pressure during recoil of the chest so as to enhance venous return and cardiac output, and to decrease intracranial pressure. In the recent years many researches on the ITD have been1 carried out, but all the researches can not take out a clear evidence to support or refute the use of the ITD. This paper introduces the structure and working principle of the ITD in detail, the research results and the debates about the use of the ITD for the past years.
Cardiopulmonary Resuscitation ; instrumentation ; Electric Impedance ; Humans ; Pressure

BACKGROUND:Osteoporotic vertebral compression fracture is a common fracture secondary to osteoporosis.At present,there is no effective prediction index and method for osteoporotic vertebral compression fracture. OBJECTIVE:To investigate the predictive effect of the comprehensive index of lumbar vertebral body bone strength on osteoporotic vertebral compression fracture. METHODS:233 patients with osteoporosis were divided into a fracture group and a non-fracture group according to whether a vertebral fracture occurred.The demography,body mass index,vertebral bone mineral density and other details were collected.Lateral X-ray films of the lumbar spine were photographed.The vertebral body width,vertebral body length,sacral slope,pelvic tilt,pelvic incidence,lumbar compressive strength index and the lumbar impact strength index were measured,calculated,and analyzed by univariate and multivariate,and the receiver operating characteristic curve was analyzed.The survival analysis was conducted according to the cut-off value. RESULTS AND CONCLUSION:(1)All patients were followed up for 2-4 years,with an average of 3.1 years.During the follow-up period,99 cases(38 cases of L1 vertebral body,61 cases of L2 vertebral body)had fractures(fracture group),and 134 cases(52 cases of L1 vertebral body,82 cases of L2 vertebral body)had no fractures(non-fracture group).Univariate analysis showed that there was no significant difference in age,sex,height,body mass,body mass index and fracture segment between the two groups(P>0.05).(2)Lumbar compressive strength index and lumbar impact strength index in the fracture group were lower than those in the non-fracture group(P<0.05).Pelvic incidence and pelvic tilt in the fracture group were higher than those in the non-fracture group(P<0.05).(3)Multivariate analysis showed that lumbar compressive strength index,lumbar impact strength index and pelvic tilt were risk factors for osteoporotic vertebral compression fractures(P<0.05).(4)Receiver operating characteristic curve analysis showed that the cutoff values of vertebral bone mineral density,lumbar compressive strength index,lumbar impact strength index,pelvic tilt and pelvic incidence were 0.913 5 g/cm2,1.932,0.903,21.5° and 55°,respectively;areas under the curve were 0.630,0.800,0.911,0.633 and 0.568,respectively.(5)According to the survival analysis(with osteoporotic vertebral compression fracture as the end point),the average survival time of the patients with lumbar impact strength index≥0.903 was significantly longer than that of the patients with lumbar impact strength index<0.903(P<0.05).(6)These findings conclude that the comprehensive index of lumbar vertebral body bone strength is more accurate than the bone mineral density of the vertebral body and spine-pelvis sagittal parameters in predicting osteoporotic vertebral compression fractures,which is helpful for early prevention and treatment of osteoporotic vertebral compression fractures.

BACKGROUND:Osteoporosis vertebral compression fracture is a common fracture secondary to osteoporosis,and there is currently a lack of effective predictive indicators and methods for osteoporosis vertebral compression fracture. OBJECTIVE:To investigate the predictive effects of paravertebral muscle degeneration,functional cross-sectional area,and percentage of fat infiltration on osteoporotic vertebral compression fractures. METHODS:The 224 patients with osteoporosis diagnosed from January 2018 to June 2022 were included.They were followed up for more than 2 years.They were divided into fracture group and non-fracture group according to the presence and absence of vertebral fracture.The detailed information of demographics,body mass index,bone mineral density and so on were collected.The functional cross-sectional area and percentage of fat infiltration of bilateral Psoas major muscle and extensor dorsi(Erector spinae muscles muscle and multifidus muscle)at the level of lower endplate of L2 vertebral body were measured and calculated. RESULTS AND CONCLUSION:(1)224 patients were ultimately included,of which 126 had fractures as the fracture group and 98 had no fractures as the non-fracture group.There was no statistically significant difference in age,gender,height,body mass,body mass index,and fracture segment between the two groups(P>0.05).(2)The bone mineral density of the fracture group was significantly lower than that of the non-fracture group(P<0.05).Functional cross-sectional areas of Psoas major muscle and extensor dorsi in the fracture group were significantly lower than those in the non-fracture group(P<0.05).The percentage of fat infiltration of the extensor dorsi in the fracture group was significantly higher than that in the non-fracture group(P<0.05).There was no significant difference in percentage of fat infiltration of Psoas major muscle between the two groups(P>0.05).(3)Receiver operating characteristic analysis showed that the vertebral bone mineral density,percentage of fat infiltration of extensor dorsi,functional cross-sectional area of extensor dorsi and percentage of fat infiltration of Psoas major muscle were 0.903 g/cm2,35.426%,418.875 mm2,and 6.375%,respectively.The areas under curve were 0.634,0.755,0.876,and 0.585,respectively.(4)These findings indicate that paravertebral muscle degeneration is strongly associated with the occurrence of osteoporotic vertebral compression fractures.The functional cross-sectional area of extensor dorsi muscle can effectively predict the occurrence of osteoporotic vertebral compression fractures,which is helpful for early prevention and treatment of osteoporotic vertebral compression fractures.

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.
Algorithms ; Area Under Curve ; Blood Gas Analysis ; Humans ; Machine Learning ; Monitoring, Physiologic ; methods ; ROC Curve ; Respiratory Distress Syndrome, Adult ; diagnosis ; Sensitivity and Specificity