The Prescription trends and dosing appropriateness analysis of novel oral anticoagulants in ischemic stroke patients: a retrospective study of 9 cities in China.

Background: Novel oral anticoagulants (NOACs) have been recommended by guidelines as the first-line drugs for preventing cardiogenic stroke. We aimed to provide an overview of the prescription trends and dosing appropriateness of NOACs in China. Methods: We conducted a retrospective analysis of NOAC prescriptions using the Hospital Prescription Analysis Cooperation Project data from 2016 to 2022. Various patient features, such as gender, age, city, year, source, department visited, original diagnosis, dosing, cost, and insurance type, were collected and analyzed to examine the trends and dosing appropriateness of NOAC usage in ischemic stroke patients. Results: 62,014 NOAC prescriptions were analyzed, including 16,602 for dabigatran, 45,253 for rivaroxaban, and 159 for apixaban. 85.14% of the patients were aged 65 or above, and tertiary hospitals accounted for 95.97% of NOAC prescriptions. NOAC prescriptions rose from 1828 in 2016 to 13,998 in 2021 but dropped to 13,166 in 2022. The percentage of annual prescriptions for NOACs among stroke patients has increased from 0.05% in 2016 to 0.37% in 2022. Total drug cost increased from ¥704541.18 in 2016 to ¥4128648.44 in 2021, then decreased to ¥1680109.14 in 2022. Prescriptions were divided into 48,321 appropriate and 11,262 inappropriate dosing groups, showing significant differences in medications, age, year, city type, hospital level, source, insurance type, and department visited (all p < 0.001). The median drug cost for inappropriate dosing was higher than for appropriate dosing (¥55.20 VS ¥83.80). The top comorbidities in ischemic stroke patients were atrial fibrillation (35.30%), hypertension (32.75%), and coronary heart disease (16.48%). Conclusion: The application of NOACs in the Chinese population is increasing. Our findings highlight the frequent deviation from labeled dosing of NOACs in clinical practice. Continued efforts are necessary to promote the appropriate use of NOACs according to the standard dosage in the drug insert.

Knowledge structure and global trends of machine learning in stroke over the past decade: A scientometric analysis.

Objective: Machine learning (ML) models have been widely applied in stroke prediction, diagnosis, treatment, and prognosis assessment. We aimed to conduct a comprehensive scientometrics analysis of studies related to ML in stroke and reveal its current status, knowledge structure, and global trends. Methods: All documents related to ML in stroke were retrieved from the Web of Science database on March 15, 2023. We refined the documents by including only original articles and reviews in the English language. The literature published over the past decade was imported into scientometrics software for influence detection and collaborative network analysis. Results: 2389 related publications were included. The annual publication outputs demonstrated explosive growth, with an average growth rate of 63.99 %. Among the 90 countries/regions involved, the United States (729 articles) and China (636 articles) were the most productive countries. Frontiers in Neurology was the most prolific journal with 94 articles. 234 highly cited articles, each with more than 31 citations, were detected. Keyword analysis revealed a total of 5333 keywords, with a predominant focus on the application of ML models in the early diagnosis, classification, and prediction of "acute ischemic stroke" and "atrial fibrillation-related stroke". The keyword "classification" had the first and longest burst, spanning from 2013 to 2018. 'Upport vector machine' got the strongest burst strength with 6.2. Keywords such as 'mechanical thrombectomy', 'expression', and 'prognosis' experienced bursts in 2022 and have continued to be prominent. Conclusion: The applications of ML in stroke are increasingly diverse and extensive, with researchers showing growing interest over the past decade. However, the clinical application of ML in stroke is still in its early stages, and several limitations and challenges need to be addressed for its widespread adoption in clinical practice.

Effect of an automated dispensing cabinet system on drug distribution effectiveness in a surgical unit.

The use of the automated dispensing cabinet (ADC) for drug distribution in hospitals has become increasingly common and has numerous benefits. This retrospective study assessed the effectiveness of an ADC that uses integrated information technology in the drug distribution process in a surgical unit as part of a smart medical process improvement project at Taichung Veterans General Hospital in 2019. The outcomes include medicine delivery time, working time of healthcare professionals, transportation manpower, dispensing errors, and satisfaction of nursing staff with the medication distribution process. After ADC implementation, the average waiting time of standing orders decreased significantly for both on and off duty periods (40.0 ± 27.6 to 3.0 ± 3.9 min, P < 0.001; 45.2 ± 25.8 to 2.9 ± 2.9 min, P < 0.001; respectively). Similar results were observed with immediate or temporary medication orders (54.4 ± 31.5 to 2.0 ± 3.0 min, P < 0.001; 64.0 ± 47.5 to 1.5 ± 1.8 min, P < 0.001; respectively). The average time spent by operation room and post-operation room (OR/POR) nurses on communicating with ward nurses for medication delivery to OR/POR was shortened by 46.9 ± 4.4 h per month, and the average time pharmacists spent on dispensing immediate or temporary medication orders was shortened by 5.6 ± 0.2 h per month. The satisfaction of nursing staff with the OR/POR drug delivery process was significantly improved after ADC implementation (3.2 ± 0.8 vs 4.2 ± 0.7, P < 0.001). Our results showed that ADC implementation in surgical units simplified drug delivery processes, shortened drug delivery time, improved drug delivery timeliness for surgical patients, decreased dispensing errors, and increased nursing staff satisfaction. In conclusion, the implementation of ADC was beneficial for surgical units. To the best of our knowledge, there have been no studies on a similar ADC system.

Knowledge Mapping of Inflammasome and Pyroptosis in Stroke: A Bibliometric Analysis (2007-2023).

BACKGROUND: Stroke is a major contributor to disability and death worldwide. Studies have demonstrated that inflammasome/pyroptosis and its mediated inflammatory response are important factors aggravating brain injury after stroke. We aimed to investigate and map the knowledge structure and global trends on inflam- masome/pyroptosis in stroke. METHODS: All relevant documents were obtained from the Web of Science on 5 June 2023. Bibliometric visualization diagrams were created using VOSviewer and CiteSpace. Excel was used for statistical analysis and drawing graphs. RESULTS: A total of 1106 publications were included, with more articles published each year, especially since 2014. China (740 papers), Zhejiang University (57 papers), Wang J (25 papers), and the Journal of Neuroinflammation (45 papers) were the most productive countries, institutions, authors, and journals, respectively. The United States was the country with highest centrality (0.56) and total link strength (171), and all of the top 10 institutions were in China. China and the U.S. cooperated closely. The centralities of the top 10 authors were all lower than 0.01; no leader has yet emerged in this field. "NLRP3 inflammasome" ranked first with 447 occurrences among 2136 keywords, of which 56 terms appeared more than 10 times when categorized into four clusters: cluster 1 (inflammation), cluster 2 (pyroptosis), cluster 3 (NLRP3 inflammasome), and cluster 4 (neuroinflammation). The studies focused on the mechanisms of inflammasome/pyroptosis in stroke were mainly limited to cell and animal experiments. CONCLUSION: Interest in inflammasome/pyroptosis in stroke is progressively increasing. The NLRP3 inflammasome is the most extensively studied and has been a research hotspot. The mechanisms of cell death in stroke are complex and future studies are needed to strengthen the clinical research on the relationship between pyroptosis-related processes and stroke, determine at which stage NLRP3 inflammasome activation, and clarify the detailed mechanism of NLRP3 in stroke.

CYP2C19 genotypes and osteoporotic fractures in long-term users of proton pump inhibitors: A hospital-based study.

Proton pump inhibitors (PPIs) are commonly prescribed medications. The existing data suggest that individuals at a high risk of fractures have been exposed to high doses of PPIs for prolonged durations. CYP2C19 plays a pivotal role in metabolism of PPIs and thereby influences their pharmacokinetic profile. Hence, we hypothesize that CYP2C19 genotypes may be associated with fragility fracture among PPIs users due to PPI exposure. This study aimed to investigate the association between CYP2C19 genotypes, bone mineral density (BMD), and osteoporotic fracture in a hospital-based population. This retrospective cohort study enrolled patients who were prescribed long-term PPIs at Taichung Veterans General Hospital using data extracted from the Taiwan Precision Medicine Initiative between January 2010 and April 2021. Associations between CYP2C19 phenotypes, comorbidities, and fractures in PPI users were analyzed. We enrolled 1518 long-term PPI users; 571 (38%), 727 (48%), and 220 (14%) CYP2C19 normal metabolizers (NMs), intermediate metabolizers (IMs), and poor metabolizers (PMs), respectively. Among them, 49 (3.2%) patients developed fractures within the 1-year follow-up period; 20 (3.5%) fractures in NMs, 24 (3.3%) in IMs, and 5 (2.3%) in PMs, respectively. No significant difference was observed among CYP2C19 genotypes and fracture. Additionally, BMD measurements during the 1-year follow-up period were made available among 75 participants. No significant difference in BMD between CYP2C19 PMs and non-PMs was found. This real-world, hospital-based study concludes that CYP2C19 PMs/IMs are not associated with an increased risk for fractures or reduced BMD in individuals on long-term PPI therapy.

The Prevalence and Features of Medications With Actionable Pharmacogenomic Biomarkers Prescribed to Kidney Transplant Recipients.

BACKGROUND: Genetic variants are associated with pharmacokinetic and pharmacodynamic changes, leading to variability in drug effects and safety profiles in the clinical response. The role of genetic variants in kidney transplant recipients (KTRs) has not been extensively studied. Here, we explored the potential of incorporating pharmacogenomic (PGx) gene biomarkers into prescription practices for KTRs. METHODS: This study analyzed 490 KTRs participating in the Taiwan Precision Medicine Initiative program and used medications with actionable PGx biomarkers. The analysis included prescriptions issued between January 2000 and December 2021 with 206 CPIC-recommended level A or B gene-drug pairs, encompassing 363 single or combination drug products. RESULTS: All KTRs had the potential to receive at least one prescription that could be adjusted based on their genetic profiles after the day of surgery. The top 5 medications prescribed within the first 3 months after transplantation were mycophenolic acid, tacrolimus, pantoprazole, labetalol, and tramadol. These findings highlight the significant potential of PGx-guided prescriptions for KTRs. Additionally, some drug-gene pairs, such as tramadol/CYP2D6, pantoprazole/CYP2C19, and atorvastatin/SLCO1B1, were considered high-quality evidence by the Clinical Pharmacogenetics Implementation Consortium and were included in the Food and Drug Administration's drug labels, indicating that they have the potential for clinical application. CONCLUSIONS: Overall, this study demonstrated the potential of incorporating PGx gene biomarkers into prescribing practices for KTRs, which could improve personalized pharmacotherapy for these patients.

Drug-Induced Cardiotoxicity in Children During the Past 30 Years: A Bibliometric Study and Visualization Analysis.

BACKGROUND Drug-induced cardiotoxicity (DICT) is one of the most serious adverse drug reactions, which is an important safety issue in drug development and clinical practice. This study aimed to summarize the knowledge structure and to detect emerging trends, and provide ideas for future research on DICT in children using bibliometric methods. MATERIAL AND METHODS All publications on DICT in children were retrieved through the Web of Science Core Collection up to April 20, 2022. The document type was restricted to articles with the language set to English. CiteSpace and VOSviewer were used to conduct this bibliometric analysis. RESULTS A total of 298 articles were included, and the annual publications decreased since 2021. The United States was the leading country with the most publications (117), the highest centrality (0.39), and total citations (4055). The most influential institution was the University of British Columbia, while Carleton BC and Rassekh SR, both from Canada, were the most productive authors, but there was no leader in this field. The keywords with both high frequency and high centrality after excluding "cardiotoxicity" and "children" were acute lymphoblastic leukemia (Freq=43, Central=0.15), childhood cancer (Freq=42, Central=0.13), toxicity (Freq=33, Central=0.16), and breast cancer (Freq=29, Central=0.19). "Adriamycin cardiotoxicity" was the first burst keyword, while "childhood cancer", "oxidative stress", and "cardiac dysfunction" were emerging research hotspots. CONCLUSIONS Attention to DICT in children was insufficient. This study serves as a breakthrough point, providing a comprehensive overview of the knowledge structure, development landscape, and future opportunities in this field.

Effect of Pharmacist-Led Interventions on Medication Adherence and Glycemic Control in Type 2 Diabetic Patients: A Study from the Chinese Population.

Purpose: Medication adherence plays an important role in glycemic control in type 2 diabetes mellitus (T2DM) self-management. To analyze the factors influencing medication adherence in T2DM patients and the effect of pharmacist-led interventions, we conducted a study in Beijing, China. Patients and Methods: T2DM patients with hypoglycemic drugs for at least 6 months were enrolled. A pharmacist-led survey was conducted followed by individualized interventions for those non-adherent patients monthly within 3 months. FPG, HbA1c, and 2hPG were measured as the comprehensive glycemic control. Medication adherence was determined according to the patient's self-reported compliance with prescribed medication during the last 3 months. Results: A total of 763 T2DM patients were included. The average age was 63.26±11.89 years, with 363 males. After pharmacist intervention, the patients with good adherence increased from 34.21% to 39.06%, while poor adherence decreased from 32.5% to 24.5% (p < 0.001). The average adherence score was a significant increase (p < 0.001) from 27.846±4.185 to 29.831±7.065. Furthermore, our study demonstrated that pharmacist-led interventions significantly increased glycemic control (FPG from 42.33% to 53.60%, p < 0.001; 2hPG from 41.68% to 48.75%, p = 0.005; HbA1c from 24.12% to 29.23%, p = 0.024). The results found that body mass index (OR 0.643, 95% CI 0.437-0.945), use of medications empirically (occasionally (OR=3.066, 95% CI 2.069-4.543); often (OR=2.984, 95% CI 1.107-8.044)), following the doctor's advice to visit (OR 2.129, 95% CI 1.079-4.202) and lifestyle compliance (OR 2.835, 95% CI 1.094-7.346) were the independent risk factors of non-adherence (p < 0.05), the area under the ROC curve was 0.716. Conclusion: Self-reported medication adherence and glycemic control in T2DM patients were poor which can be improved by pharmacist-led interventions. Interventions should focus on empirical medication behavior, non-adherence to lifestyle, and failure to follow the doctor's advice. The recall bias with self-reported results needs further objective data to verify.

Prediction of hyperuricemia in people taking low-dose aspirin using a machine learning algorithm: a cross-sectional study of the National Health and Nutrition Examination Survey.

Background: Hyperuricemia is a serious health problem related to not only gout but also cardiovascular diseases (CVDs). Low-dose aspirin was reported to inhibit uric acid excretion, which leads to hyperuricemia. To decrease hyperuricemia-related CVD, this study aimed to identify the risk of hyperuricemia in people taking aspirin. Method: The original data of this cross-sectional study were obtained from the National Health and Nutrition Examination Survey between 2011 and 2018. Participants who filled in the "Preventive Aspirin Use" questionnaire with a positive answer were included in the analysis. Six machine learning algorithms were screened, and eXtreme Gradient Boosting (XGBoost) was employed to establish a model to predict the risk of hyperuricemia. Results: A total of 805 participants were enrolled in the final analysis, of which 190 participants had hyperuricemia. The participants were divided into a training set and testing set at a ratio of 8:2. The area under the curve for the training set was 0.864 and for the testing set was 0.811. The SHapley Additive exPlanations (SHAP) method was used to evaluate the performances of the modeling. Based on the SHAP results, the feature ranking interpretation showed that the estimated glomerular filtration rate, body mass index, and waist circumference were the three most important features for hyperuricemia in individuals taking aspirin. In addition, triglyceride, hypertension, total cholesterol, high-density lipoprotein, low-density lipoprotein, age, race, and smoking were also correlated with the development of hyperuricemia. Conclusion: A predictive model established by XGBoost algorithms can potentially help clinicians make an early detection of hyperuricemia risk in people taking low-dose aspirin.

Clopidogrel Use and CYP2C19 Genotypes in Patients Undergoing Vascular Intervention Procedure: A Hospital-Based Study.

PURPOSE: Clopidogrel is widely used in coronary artery, peripheral arterial, and cerebrovascular disease. We aimed to study the association of the CYP2C19 phenotype with cardiovascular outcomes and interventional procedures in a hospital-based population. PATIENTS AND METHODS: This cross-sectional, retrospective study enrolled patients with prior exposure to clopidogrel at the Taichung Veterans General Hospital (TCVGH) using data extracted from the Taiwan Precision Medicine Initiative (TPMI). Data on the CYP2C19 phenotype, drug-prescription profile, comorbidities, vascular intervention procedures, and hospitalization due to acute myocardial infarction (AMI) or stroke of clopidogrel users were analyzed. RESULTS: From the 32,728 patients in the TCVGH-TPMI cohort, we selected 2687 clopidogrel users. A total of 400 (14.9%) clopidogrel poor metabolizers (PMs), 1235 (46.0%) intermediate metabolizers (IMs), and 1052 (39.2%) extensive metabolizers (EMs) were identified. The predominant loss-of-function allele is *2. In 2687 patients with clopidogrel exposure, the CYP2C19 PM phenotype was unassociated with hospitalization due to AMI or stroke after adjusting for comorbidities and carotid angiographies. Among the 1554 clopidogrel users who underwent cardiovascular intervention, 193 (12.4%) received two or more types of interventional procedures. Compared with non-PMs, patients with the PM phenotype had a higher risk of multiple carotid interventions (OR: 3.13, 95% CI: 1.19-8.22). CONCLUSION: In this hospital-wide cohort, 8.2% were clopidogrel users, of which 14.9% were CYP2C19 PMs. The result of this study does not support universal genotyping of CYP2C19 in all clopidogrel users to identify risks for stroke and AMI. CYP2C19 PMs are more likely to undergo multiple carotid interventions than non-PMs. Prospective studies to investigate the association of the CYP2C19 genotype and carotid interventions and outcomes are needed to validate our results.

Glycemic control was associated with nonprostate cancer and overall mortalities in diabetic patients with prostate cancer.

BACKGROUND: Diabetes mellitus (DM) can worsen the prognosis or survival in prostate cancer (PC) patients. We investigated whether glycemic control impacts mortality in PC patients with existing diabetes. METHODS: All PC patients with or without preexisting DM were enrolled from 2006 to 2017. Mean hemoglobin A1c (HbA1c) values (<7%, 7%-9%, ≥9%) were used to represent glycemic control. Major outcomes included all-cause, PC-specific, and non-PC mortalities. Statistical analyses were performed using Cox regression models with adjusted mean HbA1c and other related confounders. RESULTS: A total of 831 PC patients were enrolled (non-DM group, n = 690; DM group with a record of mean HbA1c values, n = 141). Results showed that the DM group with mean HbA1c level ≥ 9% (n = 14) had significantly increased risk for all-cause and non-PC mortality (hazard ratio [HR], 3.09; 95% CIs, 1.15-8.32; p=0.025 and HR, 5.49; 95% CIs, 1.66-18.16; p = 0.005, respectively), but not for PC-specific mortality (HR, 1.03; 95% CIs, 0.13-8.44; p = 0.975), compared with the non-DM group. CONCLUSION: Our findings indicate that PC patients with DM who had a mean HbA1c level ≥ 9% had higher risks of all-cause and non-PC mortality compared with non-DM subjects. Further large and long-term studies are needed to verify the effect of glycemic control in PC patients with DM.

Levels of Fibrin Degradation Products at Admission With Acute Ischemic Stroke Correlate With the NIH Stroke Scale Score 1 h After Intravenous Thrombolysis.

Background: Fibrin degradation products (FDPs) are fragments released by the plasmin-mediated degradation of fibrinogen or fibrin. Whether plasma levels of these fragments can predict the thrombolytic effect of recombinant tissue plasminogen activator (r-tPA) remains unknown. Methods: We performed a hospital-based study of patients with acute ischemic stroke (AIS) to explore the relationship between FDP levels at admission and the NIH Stroke Scale (NIHSS) score 1 h after thrombolysis treatment. In this retrospective, single-center study, the data of all patients with AIS who received r-tPA treatment at Beijing Tiantan Hospital from January 2019 to October 2020 were collected and analyzed. Demographic and clinical data, including laboratory examinations, were also analyzed. Results: A total of 339 patients with AIS were included in this study. Of these, 151 showed favorable effects of r-tPA, and 188 showed unsatisfactory effects at 1 h after thrombolysis. Overall, we found an inverse relationship between the FDPs levels at admission and the NIHSS score. A significant difference was observed when using the interquartile range of the FDPs levels (1.31 µg/mL) as a cutoff value (P = 0.003, odds ratio [OR] = 1.95, 95% confidence interval [CI]: 1.26-3.01), even after adjusting for confounding factors (P = 0.003, OR = 2.23, 95% CI: 1.31-3.77). In addition, significant associations were observed in the tertile (T3) and quartile (Q3, Q4) FDP levels when compared with T1 or Q1. A nomogram was also employed to create a model to predict an unsatisfactory effect of r-tPA. We found that FDP levels, white blood cell count, age, D-dimer level, and body mass index could influence the thrombolytic effect of r-tPA. Conclusion: In conclusion, the present study demonstrated that the levels of FDPs at admission can be used as a prognostic factor to predict the curative effect of r-tPA.

Predicting 1-Hour Thrombolysis Effect of r-tPA in Patients With Acute Ischemic Stroke Using Machine Learning Algorithm.

Background: Thrombolysis with r-tPA is recommended for patients after acute ischemic stroke (AIS) within 4.5 h of symptom onset. However, only a few patients benefit from this therapeutic regimen. Thus, we aimed to develop an interpretable machine learning (ML)-based model to predict the thrombolysis effect of r-tPA at the super-early stage. Methods: A total of 353 patients with AIS were divided into training and test data sets. We then used six ML algorithms and a recursive feature elimination (RFE) method to explore the relationship among the clinical variables along with the NIH stroke scale score 1 h after thrombolysis treatment. Shapley additive explanations and local interpretable model-agnostic explanation algorithms were applied to interpret the ML models and determine the importance of the selected features. Results: Altogether, 353 patients with an average age of 63.0 (56.0-71.0) years were enrolled in the study. Of these patients, 156 showed a favorable thrombolysis effect and 197 showed an unfavorable effect. A total of 14 variables were enrolled in the modeling, and 6 ML algorithms were used to predict the thrombolysis effect. After RFE screening, seven variables under the gradient boosting decision tree (GBDT) model (area under the curve = 0.81, specificity = 0.61, sensitivity = 0.9, and F1 score = 0.79) demonstrated the best performance. Of the seven variables, activated partial thromboplastin clotting time (time), B-type natriuretic peptide, and fibrin degradation products were the three most important clinical characteristics that might influence r-tPA efficiency. Conclusion: This study demonstrated that the GBDT model with the seven variables could better predict the early thrombolysis effect of r-tPA.

The effects of a pharmacist-led medication review in a nursing home: A randomized controlled trial.

BACKGROUND: In this study, an intensive review of pharmaceutical care for elderly patients was conducted in a Veterans Administration nursing home in Taiwan and its effects were evaluated. METHODS: One hundred participants were enrolled in this randomized controlled study with even distribution. The inclusion criteria were age 65 years or older, prescriptions for at least 5 oral medicines daily, and ≥2 chronic diseases, for the period May 2013 to October 2014. Subjects were excluded if they had previously been included in an intensive medication review conducted by a pharmacist. The primary outcomes were numbers of drugs prescribed, potential inappropriate medications, and numbers of drug-related problems. The secondary outcomes were self-reported medical usages, measurements of quality of life, results of a satisfaction survey, and health status. RESULTS: A total of 80 cases (42 in the intervention group with medication reconciliation and 38 in the control group without medication reconciliation) completed the study. Baseline characteristics were not statistically different between the 2 groups. The overall prevalence of potential inappropriate medication was 74.3%. There were no differences between the 2 groups, with the exception of "medical problems," which showed a significantly higher prevalence in the intervention group (P < .05). The intervention group reported greater satisfaction regarding pharmacist visits and medication compliance (P < .01). The mean number of drug-related problems was significantly lower after the intervention (P < .01). CONCLUSION: In this study, the intensive review of the elderly patients' medications revealed that the only significant effect of pharmaceutical care was on "all outcomes." A possible reason for this is the rather advanced ages of some patients who needed a considerable number of medications to treat several chronic diseases. Another reason may be the small sample size. However, participants who received the pharmacist intervention did have higher satisfaction with medication reconciliation and fewer drug-related problems.

Efficacy of second-line regimens for Helicobacter pylori eradication treatment: a systemic review and network meta-analysis.

BACKGROUND: Current guidelines recommend bismuth-containing quadruple therapy (BQT) and quinolone-containing therapy after failure of first-line Helicobacter pylori eradication therapy. However, the optimum regimen of second-line eradication therapy remains elusive. We conducted a network meta-analysis to compare the relative efficacy of 16 second-line H. pylori eradication regimens. METHODS: Three major bibliographic databases were reviewed to enrol relevant randomised controlled trials between January 2000 and September 2018. Network meta-analysis was conducted by STATA software and we performed subgroup analysis in countries with high clarithromycin resistance and high levofloxacin resistance, and in patients with documented failure of first-line triple therapy. RESULTS: Fifty-four studies totalling 8752 participants who received 16 regimens were eligible for analysis. Compared with a 7-day BQT, use of probiotic add-on therapy during, before, and after second-line antibiotic regimens, quinolone-based sequential therapy for 10-14 days, quinolone-based bismuth quadruple therapy for 10-14 days, bismuth quadruple therapy for 10-14 days, and quinolone-based triple therapy for 10-14 days were significantly superior to the other regimens. Subgroup analysis of countries with high clarithromycin resistance and high levofloxacin resistance revealed that the ranking of second-line eradication regimens was distributed similarly in each group, as well as in patients with failure of first-line triple therapy. CONCLUSION: We conducted a detailed comparison of second-line H. pylori regimens according to different antibiotic resistance rates and the results suggest alternative treatment choices with potential benefits beyond those that could be achieved using salvage therapies recommended by guidelines.

RNF213 gene polymorphism rs9916351 and rs8074015 significantly associated with moyamoya disease in Chinese population.

BACKGROUND: Gene polymorphism especially Ring Finger Protein 213 (RNF213) p.R4810K is one of the main cause of moyamoya disease (MMD) in Asian populations, especially among Japanese people. However, missense mutation may not explain the reduced frequency of MMD in Chinese patients. We performed a hospital based case-control study in a Chinese population to elucidate the possible underlying reasons. METHODS: Five gene polymorphism loci, namely, rs35692831, rs9916351, rs9913636, rs8074015 and rs112735431, were included. A total of 98 patients and 114 healthy controls were enrolled in the study. Genomic DNA was genotyped by Mass Array methods. RESULTS: A significant difference was observed between patients and healthy controls in rs9916351, rs9913636, and rs8074015 loci under three genotypes and allelic models (P<0.01). Logistic regression analysis revealed the significant differences under the dominant, recessive and additional model in rs9916351 [odds ratio (OR) =4.173, 95% confidence interval (CI): 2.290-7.606, P<0.001; OR =3.152, 95% CI: 1.585-6.267, P=0.001; OR =0.199, 95% CI: 1.727-3.764, P<0.001; respectively] and rs8074015 (OR =0.359, 95% CI: 0.206-0.627, P<0.001; OR =0.348, 95% CI: 0.148-0.81, P=0.015; OR =0.208, 95% CI: 0.311-0.703, P<0.001; respectively), even adjusting for age and gender. In addition, the haplotype rs9913636-rs8074015 under "GACG" showed significant association with MMD. CONCLUSIONS: Our results had revealed the polymorphism of RNF213 rs9916351 and rs8074015 were significantly associated with MMD especially in Chinese population.

Preventing epilepsy after traumatic brain injury: A propensity score analysis.

BACKGROUND: Due to the potential consequences of post-traumatic epilepsy (PTE) exacerbating secondary injury following traumatic brain injury (TBI), the use of antiepileptic drugs (AEDs) is an accepted option for seizure prophylaxis. However, there is only a paucity of data that can be found regarding outcomes surrounding the use of AEDs. The purpose of this retrospective study is to evaluate whether the prophylactic administration of AEDs significantly decreased the incidence of PTE, when considering the severity of TBI. METHODS: All trauma patients who had been newly diagnosed with TBI from January 1, 2010 to December 31, 2017 were retrospectively analyzed. Statistical comparisons were made using the chi-square test, Mann-Whitney U test, and Cox regression modeling. After excluding any exposed subjects with no appropriate match, patients who had received AED prophylaxis were matched by propensity score with those who did not receive AEDs. All of the TBI populations were followed up until June 30, 2018. RESULTS: We identified 1316 patients who met the inclusion and exclusion criteria in our matched cohort through their propensity scores, where 138 patients had been receiving prophylactic AEDs and 138 patients had not. Baseline characteristics were similar in gender, age, Glasgow Coma Scale (GCS) scores, and risk factors of PTE including skull fracture, chronic alcoholism, subdural hematoma, epidural hematoma, and intracerebral hematoma. After adjusting for those risk factors, the relative incidence of seizure was not statistically significant in either of the groups (p = 0.566). CONCLUSION: In our cohort analysis, AED prophylaxis was ineffective in preventing seizures, as the rate of seizures was similar whether patients had been receiving the drugs or not. We therefore concluded that the benefits of routine prophylactic anticonvulsant therapy in patients with TBI need to be re-evaluated.

Risk Factors in and Long-Term Survival of Patients with Post-Transplantation Diabetes Mellitus: A Retrospective Cohort Study.

Post-transplant diabetes mellitus (PTDM) is associated with infection, cardiovascular morbidity, and mortality. A retrospective cohort study involving patients who underwent renal transplantation in a transplantation center in Taiwan from January 2000 to December 2018 was conducted to investigate the incidence and risk factors of PTDM and long-term patient and graft survival rates. High age (45-65 vs. <45 years, adjusted odds ratio (aOR) = 2.90, 95% confidence interval (CI) = 1.64-5.13, p < 0.001), high body mass index (>27 vs. <24 kg/m2, aOR = 5.35, 95% CI = 2.75-10.42, p < 0.001), and deceased organ donor (cadaveric vs. living, aOR = 2.01, 95% CI = 1.03-3.93, p = 0.04) were the three most important risk factors for the development of PTDM. The cumulative survival rate of patients and allografts was higher in patients without PTDM than in those with PTDM (p = 0.007 and 0.041, respectively). Concurrent use of calcineurin inhibitors and mammalian target of rapamycin inhibitors (mTORis) decreased the risk of PTDM (tacrolimus vs. tacrolimus with mTORi, aOR = 0.28, 95% CI = 0.14-0.55, p < 0.001). Investigating PTDM risk factors before and modifying immunosuppressant regimens after transplantation may effectively prevent PTDM development.

A general approach for improving deep learning-based medical relation extraction using a pre-trained model and fine-tuning.

The automatic extraction of meaningful relations from biomedical literature or clinical records is crucial in various biomedical applications. Most of the current deep learning approaches for medical relation extraction require large-scale training data to prevent overfitting of the training model. We propose using a pre-trained model and a fine-tuning technique to improve these approaches without additional time-consuming human labeling. Firstly, we show the architecture of Bidirectional Encoder Representations from Transformers (BERT), an approach for pre-training a model on large-scale unstructured text. We then combine BERT with a one-dimensional convolutional neural network (1d-CNN) to fine-tune the pre-trained model for relation extraction. Extensive experiments on three datasets, namely the BioCreative V chemical disease relation corpus, traditional Chinese medicine literature corpus and i2b2 2012 temporal relation challenge corpus, show that the proposed approach achieves state-of-the-art results (giving a relative improvement of 22.2, 7.77, and 38.5% in F1 score, respectively, compared with a traditional 1d-CNN classifier). The source code is available at https://github.com/chentao1999/MedicalRelationExtraction.

Improving medication safety by cloud technology: Progression and value-added applications in Taiwan.

OBJECTIVES: To develop and implement an integrated cloud technology with the aim of ensuring medication reconciliation during transitions of care and improve medication safety in aged societies. METHODS: PharmaCloud is a new technical platform adopted by the National Health Insurance Administration of Taiwan to collect patients' medication information via cloud technology. Using this platform, healthcare providers can access patients' medication-related information with patient consent. Our hospital applied this technology and developed several approaches to collect and detect medication-related information and alert physicians for the purpose of enhancing patients' medication safety. In addition, pharmacists were involved in the admission process to access medication data and provide optimal suggestions to physicians. Several indicators, including a reduction in the number of drug items in each prescription and medication expenditure, were employed to evaluate the overall effects of the cloud inquiry. RESULTS: After the application of PharmaCloud, the average number of prescribed drug items significantly decreased (change of 0.04 to -0.35 per prescription, p < 0.05), and the median medication expenditure significantly decreased by an average of 3.55 USD, (p < 0.05) per prescription. Intra-hospital medication duplication rates also showed a downward trend. CONCLUSIONS: The use of the cloud technology and value-added applications significantly improved patient medication safety. Further long-term beneficial effects in terms of medication safety and medical cost savings are expected.