Objective:To analyze the influencing factors of type 2 diabetes patients with mild cognitive impairment by Logistic regression and decision tree analysis, so as to provide reference for the prevention of these patients.Methods:The cross sectional investigation and convenience sampling method was used for the observational study, patients with type 2 diabetes hospitalized in the Endocrinology Department of the Drum Tower Hospital Affiliated of Nanjing University Medical School from April 2019 to August 2022 were selected as the objects, they were divided into the cognitive normal group and the mild cognitive impairment group, single factor analysis and Lasso analysis were used to screen variables. Logistic regression and decision tree analysis of diabetes with mild cognitive impairment were established and evaluated respectively.Results:Logistic regression analysis and decision tree analysis both showed that age, years of education, and insulin sensitivity index were effective early warning indicators of mild cognitive impairment with type 2 diabetes ( Z = - 9.39, 12.21, - 4.62, all P<0.05), and the decision tree model analysis showed that the number of years of education had the highest correlation with mild cognitive impairment. The different influencing factors of the two models were peripheral neuropathy, abnormal bone metabolism, and lower limb macroangiopathy. The specificity (62.7%) of the Logistic regression model was lower than that of the decision tree model (81.6%), and the sensitivity (77.3%) was higher than that of the decision tree model (54.9%). The AUC of the logistic regression model was 0.763 (95% CI 0.737-0.790), and the AUC of the decision tree model was 0.743 (95% CI 0.715-0.771). There was no difference in the predictive performance of the two models ( Z = 1.05, P = 0.295). Conclusions:The prediction ability of Logistic regression analysis model is similar to that of decision tree model. The Logistic regression analysis model can be used to screen out meaningful main effect early warning indicators, and further analysis of the correlation between indicators and research outcomes, as well as the interaction between various research factors, using a decision tree model, providing a reference for the prevention and control of diabetes patients with mild cognitive impairment.

Objective:To construct endocrinology nursing subspecialty model and explore its clinical effect.Methods:In December 2018, the organization structure of endocrinology nursing subspecialty was constructed in the Drum Tower Hospital Affiliated of Nanjing University Medical School and applied in clinic. In this model, the data of 2018 were taken as the data before application and the data of 2020 were taken as the data after application. The comprehensive ability of nurses, nurse satisfaction, related nursing workload and scientific research ability of nurses were compared before and after the application of the model.Results:After the application of subspecialty nursing mode, nurses′ comprehensive ability score was (92.00 ± 2.36) points. Compared with (84.25 ± 3.24) points before implementation, the difference was statistically significant ( t=-9.46, P<0.01); nurses′ satisfaction evaluations including specialty development (7.92 ± 1.41), self-quality improvement (8.00 ± 1.69), work pressure (6.42 ± 2.67), salary and welfare (3.96 ± 0.85), compared with (5.79 ± 2.31), (6.17 ± 2.82), (8.33 ± 1.50), (2.88 ± 1.59) before implementation, the difference was statistically significant ( t values were -3.86--2.73, all P<0.05). The annual workload of related nursing increased and the scientific research ability of nurses was improved. Conclusions:The application of endocrinology nursing subspecialty mode is beneficial to improve nurses′ comprehensive ability of clinical work, improve the level of specialized nursing, improve the quality of nursing service and promote the improvement of economic benefits, which is worthy of clinical promotion.

目的:系统评价预见性护理对糖尿病足的应用效果.方法:计算机检索PubMed、EMbase、the Cochrane Library、中国知网(CNKI)、维普数据库和万方数据库(WanFang Database),检索时间为建库至2020年7月1日,由2位评价员按照纳入与排除标准独立筛选文献,并将最终纳入的文献进行评估和信息提取,采用RevMan 5.3软件进行数据处理.结果:最终纳入13篇随机对照试验文献,Meta分析结果显示,相较于常规护理干预,预见性护理干预应用于糖尿病足病人能更有效地降低空腹血糖[SMD=-0.88,95％CI(-1.23,-0.53),P<0.00001],提高自我管理能力评分[SMD=1.35,95％CI(0.87,1.83),P<0.00001],足部护理行为评分[SMD=1.46,95％CI(1.18,1.73),P<0.00001]及治疗的总有效率[OR=4.36,95％CI(2.61,7.27),P<0.00001].结论:现有证据表明,预见性护理干预在提高糖尿病足治疗总有效率、病人的自我管理和足部护理行为以及降低空腹血糖方面效果显著.

Objective To build an intelligent cloud inpatient health education platform and explore the application effect with diabetes patients preliminary. Methods The public cloud and private cloud computing technology were integrated to build an intelligent cloud inpatient health education platform. The platform has three ports:clinical nursing,patient management and nursing management. It has the functions of sending diabetes multimedia education content,receiving health education evaluation feedback,and grading health education management. A diabetes health education resource library was built,taking diabetic patients as a pilot and classified them based on types. In addition to in-person education,nurses sent health education information to patients via the platform according to patients’ needs and path-based forms and make sure patients read it. The head nurse checked nurses’ implementation and health education feedback through the platform. The study compared the nurses’ time spent on patient health education and patients’ satisfaction before（30 patients from September to November 2018 as the control group） and after（30 patients from September to November 2020 as the test group）the application of the platform. Also,25 nurses were surveyed with a questionnaire about their satisfaction with the platform. Results After application of the platform,time of nursing health education was（12.25±7.38） min,which was significantly lower compared to（26.36±8.07） min before using the platform（t=6.933,P＜0.001）. No significant difference was founded in patients’ satisfaction of health education（43.13 ±4.63 vs 41.50 ±6.28,t=2.406,P=0.257）.The average satisfaction score from nurses of the platform was ≥4. Conclusion The intelligent cloud inpatient health education platform is helpful in reducing the time spent on health education and improving the patients’ satisfaction. Besides,nurses were satisfied with the intelligent cloud inpatient health education platform.

Background This updated systematic review and meta-analysis was performed to compare clinical efficacy and safety of locking plate fixation (LPF) and hemiarthroplasty (HAP) for surgical treatment of complex proximal humeral fractures (PHFs). Methods Five electronic databases (PubMed, EMBASE, CNKI, Wanfang database and the Cochrane Library) were searched from their start dates to July 2020 to identify all relevant studies. Our main endpoints were Constant–Murley score (efficacy), and method-related complications and revisions (safety). Cochrane Collaboration’s RevMan 5.3 was used for meta-analysis. Results Sixteen retrospective trials and one randomized controlled trial involving a total of 936 patients (506 patients in the LPF group and 430 patients in the HAP group) were included in this analysis. The Constant–Murley score was significantly higher with LPF than with HAP [SMD=0.73, 95%CI: (0.23, 1.22)]. In subgroup analysis however, there was no significant difference in Constant-Murley score between LPF and HAP for four-part fractures [SMD=0.35, 95%CI (-0.07, 0.77)] or for subjects over 60 years of age [SMD=0.54, 95%CI: (-0.45, 1.52)]. Revision rate [OR=3.61, 95%CI (1.99, 6.56)] and postoperative complications [OR=1.80, 95%CI (1.24, 2.61)] were significantly lower with HAP than with LPF. Conclusions In general, for treatment of complex PHFs, LPF was superior to HAP in postoperative shoulder joint function assessed by the Constant–Murley score. However, there was no significant difference in efficacy for patients with four-part fractures or those older than 60 years of age. Since LPF was associated with significantly higher revision and postoperative complications rates, we suggest that HAP should be considered the preferred procedure for patients older than 60 years with four-part proximal humeral fractures.

目的 构建新生儿早期诊疗护理预警模型的架构体系,保障新生儿早期诊疗护理质量安全.方法 梳理出以护理程序为基础的新生儿早期诊疗护理的工作流程,构建基于临床决策支持系统(CDSS)的新生儿早期诊疗护理预警模型.于2018年6-9月构建知识库,并于2018年10月开始进行临床应用.评价基于CDSS系统的新生儿早期诊疗护理预警模型的应用效果(新生儿风险评估及时率、预防措施落实与风险评估合格率)、护士对系统满意度及早产儿家属满意度.结果 护士应用新生儿早期诊疗护理预警的临床决策支持系统后,新生儿风险评估及时率(96.94％)高于系统应用前(85.18％),差异有统计学意义(P＜0.001).预防措施落实与风险评估合格率(98.13％)高于系统应用前(90.35％),差异有统计学意义(P＜0.001).护士对系统的可用性评价为(18.63±1.13)分,数据可靠性为(14.22±1.42)分,系统支持力度为(13.51±2.23)分,使用意向为(8.51±1.52)分,总体满意度为(4.42±0.51)分,净收益为(4.19±0.63)分.早产儿家属满意度处于较高水平.结论 基于临床决策系统的新生儿早期诊疗护理预警模型的构建可以为临床护士在照护新生儿时对疾病早期识别和护理干预提供正确高效的决策支持,提升护士对临床常规执行的依从性和评判性思维能力,提高护士护理质量管理能力,提高医护患多角色满意度.