目的 對糖尿病急性代謝并發癥小劑量胰島素的應用方法及技巧進行探討和分析。 方法 以2009年1月－2012年2月收治的50例糖尿病急性代謝并發癥患者為研究對象,按照數字隨機表方法分為對照組和試驗組，每組25例患者。對照組患者在小劑量胰島素的使用上采用常規技巧和方法；試驗組患者在小劑量胰島素的使用上結合實際情況和相關規范進行綜合治療，比較兩組患者小劑量胰島素的應用效果。 結果 經治療，對照組18例患者的臨床癥狀得到明顯改善，顯效率為72%；試驗組25例患者的臨床癥狀均得到有效緩解，顯效率為100%，兩組患者的顯效率比較，差異有統計學意義（P＜0.05）。 結論 使用小劑量胰島素治療糖尿病急性代謝并發癥患者時，結合實際情況和相關規范進行綜合治療臨床效果滿意。
Objective To analyze the substitution mechanism of surrogate endpoints for traditional Chinese medicine (TCM) clinical efficacy evaluation of chronic heart failure (CHF). Methods To obtain data from the occurrence of surrogate endpoints and cardiogenic death of patients with CHF in 7 hospitals. The causal relationship between surrogate endpoints and cardiogenic mortality was inferred by the Bayesian network model, and the interaction among surrogate endpoints was analyzed by non-conditional logistic regression model. Results A total of 2 961 patients with CHF were included. The results of Bayesian network causal inference showed that cardiogenic mortality had a causal relationship with the surrogate endpoints including NYHA classification (P=0.46), amino-terminal pro-B-type natriuretic peptide (NT-proBNP) (P=0.24), left ventricular ejaculation fraction (LVEF) (P=0.19), and hemoglobin (HB) (P=0.11); non-conditional logistic regression analysis showed that NYHA classification had interaction with NT-proBNP, LVEF, and HB prior to and after adjusting confounders. Conclusions The substitution capability of surrogate endpoints for TCM clinical efficacy evaluation of CHF for cardiogenic mortality are NYHA classification, NT-proBNP, LVEF, and HB in turn, and there is a multiplicative interaction between the main surrogate endpoint NYHA classification and the secondary surrogate endpoints including NT-proBNP, LVEF, and HB, suggesting that when the two surrogate endpoints with interaction exist at the same time, it can enhance the substitution capability of surrogate endpoints for cardiogenic mortality.
目的：糖尿病周圍神經病變是糖尿病最常見的并發癥，我們對治療2型糖尿病周圍神經病變的有效方法進行了償試。方法:將66例2型糖尿病周圍神經病變患者隨機分為兩組。在常規應用口服降糖藥或胰島素治療的基礎上，治療組加用彌可保500 μg肌注每周3次，前列腺素E1100 μg加入生理鹽水250 mL中靜脈滴注，每日1次；對照組加用維生素B12500 μg肌注每周3次，復方丹參注射液20 mL加入生理鹽水250 mL中靜脈滴注，每日1次，療程均為4周。結果:治療組總有效率82.3%，與對照組比較差異有顯著性（Plt;0.05）。臨床癥狀改善方面，治療組肢體麻木、肢體疼痛改善率分別達72%和75%，明顯高于對照組的37%和44%，感覺減退、多汗、口干、腹瀉、便秘、排尿障礙改善也優于對照組（分別為53%比26%、51%比16%、53%比19%、57%比26%、51%比28%、61%比26%）。結論:彌可保與前列腺素E1合用治療糖尿病周圍神經病變是一種有效的方法。
ObjectiveTo systematically evaluate the predictive models for re-admission in patients with heart failure (HF) in China. MethodsStudies related to the risk prediction model for HF patient re-admission published in The Cochrane Library, PubMed, EMbase, CNKI, and other databases were searched from their inception to April 30, 2024. The prediction model risk of bias assessment tool was used to assess the risk of bias and applicability of the included literature, relevant data were extracted to evaluate the model quality. ResultsNineteen studies were included, involving a total of 38 predictive models for HF patient re-admission. Comorbidities such as diabetes, N-terminal pro B-type natriuretic peptide/brain natriuretic peptide, chronic renal insufficiency, left ventricular ejection fraction, New York Heart Association cardiac function classification, and medication adherence were identified as primary predictors. The area under the receiver operating characteristic curve ranged from 0.547 to 0.962. Thirteen studies conducted internal validation, one study conducted external validation, and five studies performed both internal and external validation. Seventeen studies evaluated model calibration, while five studies assessed clinical feasibility. The presentation of the models was primarily in the form of nomograms. All studies had a high overall risk of bias. ConclusionMost predictive models for HF patient re-admission in China demonstrate good discrimination and calibration. However, the overall research quality is suboptimal. There is a need to externally validate and calibrate existing models and develop more stable and clinically applicable predictive models to assess the risk of HF patient re-admission and identify relevant patients for early intervention.
ObjectiveTo systematically summarize and evaluate the existing evidence of Qishen Yiqi dropping pill (QSYQ) in the treatment of chronic heart failure (CHF), and to evaluate its quality. MethodsThe PubMed, Cochrane Library, EMbase, Web of Science, CNKI, CBM, WanFang Data databases were electronically searched to collect systematic reviews/meta-analyses(SRs/MAs) related to objectives from inception to December 31, 2022. Two researchers independently screened the literature and extracted data, and assessed the methodological quality, risk of bias, reporting quality, and quality of evidence of included SRs/MAs by using Assessment of Multiple Systematic Reviews 2 (AMSTAR-2), the Risk of Bias in Systematic(ROBIS) scale, the list of Preferred Reporting Items for Systematic Reviews and Meta-Analysis(PRISMA), and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system. ResultsThis overview included 17 SRs/MAs. The methodological quality, reporting quality, risk of bias, and quality of evidence for outcome measures of SRs/MAs were all unsatisfactory. All SRs/MAs were of low quality according to the results of the AMSTAR-2 assessment. And only a small number of SRs/MAs were assessed as low risk of bias based on the results of the ROBIS assessment. The evaluation results of the PRISMA checklist showed that the report quality of the 24 studies included was relatively complete. According to the GRADE system evaluation results, 94% of the 84 outcome indicators were low-quality and very low-quality evidence. Limitations were the main factors leading to their degradation, followed by publication bias, inconsistency, imprecision and indirectness. ConclusionAt present, QSYQ has good clinical efficacy in the treatment of CHF, but the standardization and scientificity of clinical research and secondary research reports are insufficient, resulting in low quality of clinical recommendations evidence. In the future, it is necessary to further standardize and improve the quality of clinical and secondary research.
ObjectiveTo systematically review the application status of Delphi method in clinical research of traditional Chinese medicine (TCM).MethodsPubMed, EMbase, CNKI, WanFang Data and VIP databases were electronically searched to collect original research and methodological research on Delphi method in TCM from inception to August 30th, 2020. Two reviewers independently screened literature, extracted data, and then, descriptive analysis was performed by using qualitative methods.ResultsA total of 612 articles involving 573 original studies and 39 methodological studies were included, which involved 167 types of diseases. The primary research purposes were disease diagnosis and treatment, syndrome research, scale development, evaluation research, index research, clinical investigation, methodology research, and other 8 categories. 487 papers reported the implementation process and results of Delphi method in varying degrees.ConclusionsDelphi method is widely used in clinical research of TCM, however, there are deficiencies in the specific implementation process.
The development and validation of clinical prediction models based on artificial intelligence (AI) and machine learning (ML) methods have become increasingly widespread. However, the prediction model bias risk and applicability evaluation tool developed in 2019 (i.e., PROBAST-2019) has shown significant limitations. Therefore, an expanded and updated version of the PROBAST-2019 tool was released in 2025, known as the PROBAST+AI tool. The tool is divided into two parts including model development and model evaluation. It aims to comprehensively and systematically evaluate potential methodological quality issues in model development, bias risks in model evaluation, and the applicability of models, regardless of the modeling method used. This paper provides a systematic interpretation of the PROBAST+AI tool's items and case analyses, with the aim of guiding and assisting researchers engaged in related studies and promoting the high-quality development of clinical predictive model research.