Objective:To construct a diagnostic prediction model for childhood asthma and conduct a preliminary evaluation based on the test results of specific IgE (sIgE) for airborne allergens and in combination with clinical data.Methods:This study is a case-control study. A total of 4 338 cases that completed the sIgE test for airborne allergens in the Allergy Department of Beijing Children′s Hospital Affiliated to Capital Medical University from January to December 2023 were selected as the research subjects. They were divided into the asthma group and the non-asthma group based on the diagnostic information. Age, gender, cough and wheezing symptoms, and the classification results of sIgE concentrations of 15 airborne allergens were collected as the predictor variables of the asthma diagnostic prediction model. Differential analysis and LASSO regression were employed for the screening of predictor variables. The multivariate logistic regression method was applied to construct the nomogram prediction model. The data set was randomly split at a ratio of 7∶3 into a training set (3 036 cases) for constructing the prediction model and a validation set (1 302 cases) for testing the predictive efficacy of the model. The area under the receiver operating characteristic (ROC) curve (AUC), the Hosmer-Lemeshow calibration curve were utilized to assess the discrimination and goodness of fit of the model, and the clinical decision curve (DCA) was adopted to evaluate the clinical application value of the model.Results:Among 4 338 pediatric cases, children aged 0 to <3 years accounted for 10.17% (441 cases), those aged 3 to <6 years accounted for 36.49% (1 583 cases), those aged 6 to <12 years accounted for 46.98% (2 038 cases), and those aged 12 to 18 years accounted for 6.36% (276 cases). Males constituted 65.17% (2 827 cases), and females 34.83% (1 511 cases). The proportion of children without wheezing symptoms was 41.47% (1 799 cases), while those with wheezing symptoms was 58.53% (2 539 cases). The asthma group accounted for 41.77% (1 812 cases), and the non-asthma group for 58.23% (2 526 cases). Statistically significant differences were observed between the asthma group and the non-asthma group in 18 predictive variables including age, gender, wheezing symptoms, d1, d2, e1, e5, g2, g6, m6, t11, t3, t6, w1, w22, w6, wx5, and m3 ( P<0.05). LASSO regression analysis identified six predictor variables: age (calculated in months), cough and wheezing symptoms, and sIgE of four airborne allergens, namely, Dermatophagoides pteronyssinus (d1), Canis familiaris dander (e5), Aspergillus fumigatus (m3), and Artemisia vulgaris pollen (w6).Multifactorial regression analysis revealed that the contribution degrees of the above-mentioned predictor variables to the asthma diagnosis prediction model were ranked as follows: cough and wheezing symptoms ( OR=24.37, P<0.001), m3 ( OR=1.34, P<0.001), d1 ( OR=1.22, P<0.001), e5 ( OR=1.12, P=0.028), w6 ( OR=1.11, P<0.001), and age ( OR=1.01, P<0.001).The AUCs of the nomogram prediction model for the training set and the validation set were 0.853 (95% CI: 0.840-0.866) and 0.838 (95% CI: 0.817-0.860), respectively. The Hosmer-Lemeshow calibration curve indicated a good fit ( P=0.215 for the training set; P=0.352 for the validation set). The DCA of the validation set demonstrated that when the probability threshold for predicting the occurrence of childhood asthma was 8%-92%, the model had the best applicability. Conclusion:By combining age, cough and wheezing symptoms, and sIgE of the four airborne allergens (d1, e5, m3, and w6) selected from 15 airborne allergens, a childhood asthma diagnosis prediction model with good predictive performance and clinical practicability was constructed. It can serve as a simple and convenient tool for accurately identifying asthma and provides a practical basis for the application of artificial intelligence big data analysis models in the prevention, treatment, and management of childhood asthma.

Objective:To construct a diagnostic prediction model for childhood asthma and conduct a preliminary evaluation based on the test results of specific IgE (sIgE) for airborne allergens and in combination with clinical data.Methods:This study is a case-control study. A total of 4 338 cases that completed the sIgE test for airborne allergens in the Allergy Department of Beijing Children′s Hospital Affiliated to Capital Medical University from January to December 2023 were selected as the research subjects. They were divided into the asthma group and the non-asthma group based on the diagnostic information. Age, gender, cough and wheezing symptoms, and the classification results of sIgE concentrations of 15 airborne allergens were collected as the predictor variables of the asthma diagnostic prediction model. Differential analysis and LASSO regression were employed for the screening of predictor variables. The multivariate logistic regression method was applied to construct the nomogram prediction model. The data set was randomly split at a ratio of 7∶3 into a training set (3 036 cases) for constructing the prediction model and a validation set (1 302 cases) for testing the predictive efficacy of the model. The area under the receiver operating characteristic (ROC) curve (AUC), the Hosmer-Lemeshow calibration curve were utilized to assess the discrimination and goodness of fit of the model, and the clinical decision curve (DCA) was adopted to evaluate the clinical application value of the model.Results:Among 4 338 pediatric cases, children aged 0 to <3 years accounted for 10.17% (441 cases), those aged 3 to <6 years accounted for 36.49% (1 583 cases), those aged 6 to <12 years accounted for 46.98% (2 038 cases), and those aged 12 to 18 years accounted for 6.36% (276 cases). Males constituted 65.17% (2 827 cases), and females 34.83% (1 511 cases). The proportion of children without wheezing symptoms was 41.47% (1 799 cases), while those with wheezing symptoms was 58.53% (2 539 cases). The asthma group accounted for 41.77% (1 812 cases), and the non-asthma group for 58.23% (2 526 cases). Statistically significant differences were observed between the asthma group and the non-asthma group in 18 predictive variables including age, gender, wheezing symptoms, d1, d2, e1, e5, g2, g6, m6, t11, t3, t6, w1, w22, w6, wx5, and m3 ( P<0.05). LASSO regression analysis identified six predictor variables: age (calculated in months), cough and wheezing symptoms, and sIgE of four airborne allergens, namely, Dermatophagoides pteronyssinus (d1), Canis familiaris dander (e5), Aspergillus fumigatus (m3), and Artemisia vulgaris pollen (w6).Multifactorial regression analysis revealed that the contribution degrees of the above-mentioned predictor variables to the asthma diagnosis prediction model were ranked as follows: cough and wheezing symptoms ( OR=24.37, P<0.001), m3 ( OR=1.34, P<0.001), d1 ( OR=1.22, P<0.001), e5 ( OR=1.12, P=0.028), w6 ( OR=1.11, P<0.001), and age ( OR=1.01, P<0.001).The AUCs of the nomogram prediction model for the training set and the validation set were 0.853 (95% CI: 0.840-0.866) and 0.838 (95% CI: 0.817-0.860), respectively. The Hosmer-Lemeshow calibration curve indicated a good fit ( P=0.215 for the training set; P=0.352 for the validation set). The DCA of the validation set demonstrated that when the probability threshold for predicting the occurrence of childhood asthma was 8%-92%, the model had the best applicability. Conclusion:By combining age, cough and wheezing symptoms, and sIgE of the four airborne allergens (d1, e5, m3, and w6) selected from 15 airborne allergens, a childhood asthma diagnosis prediction model with good predictive performance and clinical practicability was constructed. It can serve as a simple and convenient tool for accurately identifying asthma and provides a practical basis for the application of artificial intelligence big data analysis models in the prevention, treatment, and management of childhood asthma.

Objective:To establish the post competency evaluation index system for extracorporeal membrane oxygenation (ECMO) nurses.Methods:Based on semi-structured interviews, a preliminary post competency evaluation index system for ECMO nurses was developed. From May to June 2022, the Delphi method was used to conduct two rounds of consultation with 19 experts in critical illness field from 15 ClassⅢGrade A hospitals in 6 provinces and municipalities across the country to determine the evaluation index system. The analytic hierarchy process was used to determine the weights and consistency coefficients of indexes at all levels. The positivity of experts was expressed by the effective response rate of the questionnaire, the coordination degree of expert opinions was expressed by Kendall's W and coefficient of variation, the authority of experts was expressed by the authority coefficient of experts, and the concentration of expert opinion was expressed by the mean importance assignment and the perfect score rate. Results:In two rounds of expert consultations, the effective response rates of the questionnaires well all 100.00% (19/19), with expert authority coefficients of 0.834 and 0.879. The Kendall's W for the overall indicator in the second round of expert correspondence was 0.281 ( P<0.01), and the coefficient of variation of the indicators at all levels was 0.05 to 0.17. In the second round of consultation, the mean importance scores assigned to indicators at all levels was 4.05 to 4.95, with a perfect score of 21.05% to 94.74%. The final constructed post competency evaluation index system for ECMO nurses included 4 first-level indicators, 28 second-level indicators, and 59 third-level indicators. Conclusions:The post competency evaluation index system for ECMO nurses is scientific, reliable, and reasonable, which can provide reference for optimizing the ECMO nurse training system and improving evaluation standards.

Extracorporeal membrane oxygenation is an extracorporeal life support technique used to rescue patients with respiratory and (or) heart failure. Infection is one of the most serious complications of extracorporeal membrane oxygenation, which can affect patients' clinical outcomes. This article reviews the definition, diagnosis, incidence, site of infection, pathogenic bacteria, risk factors, prevention and treatment measures of extracorporeal membrane oxygenation associated nosocomial infection, so as to provide reference for the prevention and treatment of extracorporeal membrane oxygenation associated nosocomial infection.

This paper reviews the research status of the training and certification of extracorporeal membrane oxygenation nurses at home and abroad and their job competencies, and analyzes its limitations, so as to provide a reference for the training of extracorporeal membrane oxygenation nurses in my country.

Objective:To investigate the feasibility and safety of right-sided abdominal evisceration in retroperitoneal liposarcoma.Methods:The clinical data of 16 cases of retroperitoneal liposarcoma performed with right-sided abdominal evisceration at Sarcoma Center of Peking University Cancer Hospital from Sep 2015 to Feb 2019 were analyzed retrospectively.Results:Complete resection were successfully performed in all 16 cases.The median tumor size was 29cm(13-43 cm), the median operative time was 660 min(429-940 min), the median estimated blood loss was 2 000 ml(300-6 000 ml). The major postoperative complications rate (Clavien-Dindo classification Ⅲ-Ⅴ) was 38%. Median overall survival is 41.0 months while the median disease-free survival is 32.6 months.Conclusions:Right-sided abdominal evisceration is a favorable procedure to attain complete resection with acceptable complication rate.

Objective To find the best level of transducer when continuous invasive arterial blood pressure monitoring using evidence-based approach. Methods Searched in three databases (Cochrane, PubMed,sinoMED), meanwhile the website of related academic societies,supplemented with Citation Index. Results There were 11 articles after selected. The majority of evidences (9 articles) suggested leveling with the heart, one suggested leveling with the catheter tip,and the other one regarded the matter as nothing important. Conclusions Transducer should be leveled to align the heart rather than the tip of arterial catheter when continuous invasive arterial blood pressure monitoring, but it doesn't matter which surface landmark of the heart. It was suggested that changing the level of transducer when changing the patient position or head of bed.

Objective To explore the hospitalization status of patients with chronic obstructive pulmonary disease ( COPD ) . Method In total, 12,838 COPD patients hospitalized from January 2008 to December 2014 were involved in the study and their hospitalization status were analyzed. Results Among the 12,838 patients, 2,499 were hospitalized for critical conditions (19.47%), 5,455 for acute attack (42.49%), 4,884 for acute exacerbation (38.04%). The ratio of male/female was 5.32:1. Those in 71 to 80 years old were at the highest risk. They were hospitalized at least for 1 time, at most for over 38 times, averaged (3.52 ± 4.05) times. Conclusions The COPD patients were hospitalized due to acute attack and acute exacerbation. The patients'age ranged from 71 to 80 years. The male patients had a predominant incidence than the female ones. The times of hospitalization were related with possible complications of other chronic diseases. Therefore, nursing staff should draw up individual continuing nursing strategies based on the patients′ hospitalization reasons to reduce the hospitalization rate of acute attack and acute exacerbation. Meanwhile, we should formulate the pre-hospital rescue plan for the hospitalized patients at the peak age and implement prospective nursing.