OBJECTIVE To investigate the application status of prescription pre-review systems in healthcare institutions of Yunnan province， evaluate their system functions and management capabilities， and provide a practical basis for promoting rational drug use. METHODS A questionnaire survey was conducted among public healthcare institutions at or above the secondary level in Yunnan province to investigate the deployment status of the systems. A capability maturity assessment framework was constructed， encompassing 6 dimensions and 39 indicators， including real-time prescription review， prescription correlation review， rule setting， evidence-based information support， prescription authority management， and system operation management. This framework was then used to evaluate the institutions that had implemented the pre-review systems. RESULTS A total of 100 valid questionnaires were collected， with 37 institutions having adopted prescription pre-review systems， mainly tertiary hospitals. The system predominantly adopted a modular architecture and was embedded into the hospital information system through application programming interfaces and middleware， providing certain capabilities for real-time prescription risk identification. Evaluation results indicated that basic functions such as reviewing indications， contraindications， and drug compatibility performed well， while deficiencies remained in functions related to parenteral nutrition prescription， review of drug dosage for specific diseases， individual patient characteristic recognition， and rule setting. Moreover， the construction of review centers and establishment of management systems were also not well-developed. CONCLUSIONS The overall application rate of prescription pre-review systems in Yunnan province remains low. System functions and management mechanisms require further improvement. It is recommended to enhance information infrastructure in lower-level institutions and explore regionally unified review models to promote standardized and intelligent development of prescription review practices.

Objective To develop and validate a prediction model for risk of amputation in patients with diabetic foot ulcers(DFU)based on systematic review and meta-analysis.Methods The studies on the risk factors of amputation in DFU patients was retrieved by using subject words+free words.After screening,37 cohort studies were finally included,and the Newcastle-Ottawa scale(NOS)was used for quality evaluation.Meta-analysis was performed on the risk factors of amputation in DFU.Then a prediction model for DFU amputation risk were constructed based on the statistically significant risk factors in the meta-analysis.The corresponding β value was calculated based on the combined odds ratio(OR)value of each risk factor,and each risk factor was scored to establish a scoring system model.The clinical data of 453 DFU patients hospitalized in our department from 2021 to 2023 were collected as a validation cohort.Receiver operating characteristic(ROC)curve analysis was used to evaluate the model performance.The area under the curve(AUC)was calculated,and the optimal cutoff score was determined by calculation of the maximum Youden index through sensitivity and specificity.Results Our meta-analysis showed a cumulative amputation rate of approximately 34.65%in 11 779 DFU patients.The final risk prediction models include gangrene[OR=11.92(5.86～24.24)],ulcer depth[OR=4.93(2.52～9.64)],osteomyelitis[OR=3.19(2.36～4.29)],previous amputation history[OR=3.19(2.00～5.09)]and lower extremity arterial disease[OR=3.10(2.31～4.17)].According to the weights of each risk factor,the total score of the model is 76,and the optimal cut-off score is 36.5.The prediction model performed well,with an AUC value of 0.864(0.824,0.903),a sensitivity of 0.743,a specificity of 0.859,and an accuracy rate of 83.00%.Conclusion A prediction model for DFU amputation risk is developed based on risk factor scoring,and has good discrimination and calibration,providing effective scientific basis for clinical research and clinical decision-making related to DFU amputation.

Objective To investigate the clinical application of perioperative human serum albumin(HSA)in cardiac surgery in multiple regions in China,and to evaluate the rationality of its clinical application in conjunction with the clinical guidelines,in order to provide a reference for promoting the rational application of HSA.Methods The medical records of patients who underwent cardiac surgery from April to June 2019 in eight hospitals across the country were retrospectively collected.The statistical information on patients'general information,the dosage,course of treatment,and cost of HSA,and the serum albumin level before and after medication was analyzed to evaluate the use of HSA.Relevant evaluation criteria were established,and the rationality of its medication was evaluated.Results Data from a total of 449 patients were included for analysis,the appropriate rate of medication was 81.1％.The course of medication was mostly＞2-5 days and the total amount of HSA was mostly 50-99 g.The main purpose of medicaiton were improving colloid osmotic pressure,reducing exudation to improve interstitial edema,postoperative volume expansion.Conclusion Clinical attention should be paid to ensure the rational application of HSA in cardiac surgery during the perioperative period and prevent the abuse of blood products.

Human exhaled breath has great application prospects,e.g.,monitoring pharmacokinetics,disease diagnosis,due to its advantages such as non-invasive and high-frequency sampling.Breath samples can be collected from the oral and nasal cavity.However,the oral and nasal environment affect the chemical composition of breath sample.Therefore,the investigation on the chemical composition of mouth-exhaled breath and nose-exhaled breath is crucial for selection of appropriate sampling strategy for individual studies.In this work,secondary electrospray ionization-high resolution mass spectrometry(SESI-HRMS)was applied to analysis of respiratory metabolomics in real time.A quantitative analysis approach was established for 9 kinds of volatile organic compounds(VOCs)e.g.2-butanone,2-pentanone,ethyl acetate,methyl methacrylate,toluene,styrene,mesitylene,isoprene and limonene.The limit of detection was 2.3?240.8 ng/m3.The intra-day(n=6)and inter-day(n=18)relative standard deviations were 0.6%?4.6%and 4.3%?12.2%,respectively.Nine healthy subjects were recruited to investigate the chemical composition of mouth-exhaled and nose-exhaled breath.The results showed the good performance in quantitative analysis of 9 VOCs in breath air.It was found that the number of unique component(m/z)detected in mouth-exhaled breath(167)was 2.2 times greater than that detected in nose-exhaled breath(76),which might result from the complex environment in oral cavity.The signal intensity of commun component(163)was significantly different between mouth-exhaled breath and nose-exhaled breath.Additionally,the elemental composition analysis showed that the proportion of polar compounds detected in nose-exhaled breath was higher than that in mouth-exhaled breath.This study demonstrated that there was significant differences in the chemical composition between mouth-exhaled and nose-exhaled breath,which provided a theoretical basis for selection of exhalation mode.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

The Taiwan Initiative for Geriatric Epidemiological Research (TIGER) was founded in 2011 to elucidate the interrelationships among various predictors of global and domain-specific cognitive impairment, with the aim of identifying older adults with an increased risk of dementia in the preclinical phase. TIGER, a population-based prospective cohort, recruited 605 and 629 (total of 1,234) older adults (aged 65 and above) at baseline (2011-2013 and 2019-2022) of phase I and II, respectively. Participants have undergone structured questionnaires, global and domain-specific cognitive assessments, physical exams, and biological specimen collections at baseline and biennial follow-ups to date. By 2022, TIGER I has included 4 biennial follow-ups, with the participants comprising 53.9% female and having a mean age of 73.2 years at baseline. After an 8-year follow-up, the annual attrition rate was 6.1%, reflecting a combination of 9.9% of participants who passed away and 36.2% who dropped out. TIGER has published novel and multidisciplinary research on cognitive-related outcomes in older adults, including environmental exposures (indoor and ambient air pollution), multimorbidity, sarcopenia, frailty, biomarkers (brain and retinal images, renal and inflammatory markers), and diet. TIGER’s meticulous design, multidisciplinary data, and novel findings elucidate the complex etiology of cognitive impairment and frailty, offering valuable insights into factors that can be used to predict and prevent dementia in the preclinical phase.