Objective To determine the diagnostic value of fractional exhaled nitric ( FeNO)measurement in diagnosis of bronchial asthma. Methods The patients with unkown-cause respiratory symptoms including wheezing, cough, and breathlessness were enrolled from August to September in 2008.FeNO was measured by nitric oxide analyzer ( NIOX; Aerocrine AB; Solna, Sweden) . Bronchial challenge test ( BCT) or bronchodilator test was defined as golden standard for asthma diagnosis. The value of FeNO was assessed and the optimal operating point of FeNO testing was determined by the means of the receiver operating characteristic ( ROC) curves. Results A total of 101 patients were enrolled, in which 48 cases were diagnosed as asthma by positive yield in BCT ( in 38 cases) or bronchodilator test ( in 10 cases) . The severity of airway hyperresponsiveness ( AHR) judged by BCT was mild in 15 cases, moderate in 15 cases and severe in 8 cases. The levels of FeNO of asthma group were higher than those of non-asthma group [ ( 68. 19 ±43. 00) ppb vs ( 19. 52 ±10. 60) ppb, P lt; 0. 05] . A linear correlation of FeNO with lnPD20 FEV1 was revealed in the cases with AHR. Area under ROC curve was 0. 9. The optimal diagnostic cutoff point was 36. 5 ppb which was capable of differentiating asthma and non-asthma with sensitivity of 92. 7% ,specificity of 83. 3% , positive predictive value of 79. 17% , negative predictive value of 94. 34% and accuracy of 87. 13% . Conclusion FeNO test may be helpful in the diagnosis of asthma with high sensitivity and specificity.
The risk of bias assessment tool 2.0 (RoB 2.0) for analyzing cluster randomized trials and crossover trials (revised version 2021) has been updated. The current paper briefly delineates the history of the RoB 2.0 tool and includes an explanation and interpretation of the updated contents and software operation process for use with cluster randomized trials and crossover trials. Compared with the previous versions, the updated RoB 2.0 tool (revised version 2021) has the advantage of precise language and is easily understood. Thus, the updated RoB 2.0 tool merits popularization and further general application.
Brain natriuretic peptide (BNP) and amino-terminal pro-brain natriuretic peptide (NT-proBNP) were the main members of the natriuretic peptide family. BNP has the effects of diuretic sodium, reducing sympathetic nervous system activity, dilating blood vessels, and improving the pathological remodeling of heart. Plasma BNP/NT-proBNP levels have been widely used in the diagnosis, severity assessment, prognosis prediction and treatment guidance of heart failure. In recent years, BNP/NT-proBNP has become a research hotspot in the diagnosis and and prognosis judgment of atrial fibrillation, recurrence of atrial fibrillation after radiofrequency ablation and cardioversion and congenital heart disease in infants and children, prediction of postoperative complications, and drug development. This article reviews the latest advances in clinical application and research progress on BNP/NT-proBNP.
ObjectiveTo systematically review the qualitative research on the obstacles and promoting factors of artificial intelligence implementation in the real perioperative world. MethodsComputer searches were conducted on PubMed, CINAHL, Scopus, Web of Science, ACM Digital Library, Cochrane Library, CNKI, WanFang Data, and VIP databases to collect perioperative studies related to the clinical application of artificial intelligence. The search period was from database establishment until December 31, 2023. Based on the SPIDER model, the quality of the included literature was evaluated using the JBI Epidemiological Scale. The NASSS framework was used to integrate and analyze the qualitative factors discovered during the implementation of the perioperative artificial intelligence system, and a problem item pool was established. ResultsA total of 22 articles were included, and perioperative stakeholders mainly focused on perioperative artificial intelligence technology users such as anesthesiologists, anesthesiologists, and surgeons. The field of perioperative artificial intelligence services mainly focused on robot surgery. The JBI evaluation score was 4-8 points. The NASSS implementation factor framework consisted of 7 core themes and 27 secondary items. ConclusionIt is undeniable that perioperative artificial intelligence has a positive impact on the prognosis, medical quality, and efficiency of surgical patients. However, its clinical application will face influences from adopters, organizational structures, social culture, and other aspects, which will ultimately affect its implementation effect. The existing qualitative research on the influencing factors of perioperative artificial intelligence systems in clinical implementation has problems such as limited quantity, moderate quality, and lack of scientific research based on a systematic implementation factor framework. Conducting scientific and standardized application research will have a guiding effect on the future use of perioperative artificial intelligence and is expected to improve its final service effectiveness.