Objective:To understand the quality of adverse drug reaction (ADR) reports in Lu′an Hospital of Traditional Chinese Medicine (our hospital) and its change trend in recent years, and explore the methods of objectively evaluating the quality of ADR reports.Methods:According to the 20 evaluation indicators of the ADR report quality evaluation scoring table in the Appendixes 5 of Provisions for Adverse Drug Reaction Reporting and monitoring, the ADR reports submitted to the National Center for ADR Monitoring from 2013 to 2022 by our hospital were evaluated. The weighted technique for order preference by similarity to ideal solution (TOPSIS) combined with rank-sum ratio (RSR) model was used to rank the quality of ADR reports into the following 5 grades: excellent, good, medium, qualified and unqualified, according to the weight of each evaluation index. The quality grading results were tested to determine the rationality of grading. Results:A total of 3 947 ADR reports were included in the analysis, including 1 361 new/serious ADR reports (34.5%), and the average score of quality evaluation index was 87.9. After 2016, the number of ADR reports and the proportion of reports with scores ≥ 80 increased significantly. Among the 20 evaluation indicators, 10 had a high pass rate, 7 had a medium or upper pass rate, and 3 had a low pass rate. The TOPSIS-RSR model was used to classify the quality of ADR reports. The overall proportions of excellent, good, moderate, qualified, and unqualified reports were 4.7% (186/3 947), 23.0% (908/3 947), 45.3% (1 787/3 947), 23.4% (925/3 947), and 3.6% (141/3 947), respectively. The homogeneity of variance test showed that each grade met the homogeneity of variance, and the analysis of variance results showed that the differences between every 2 grades were statistically significant ( P<0.001), indicating that the quality grading was reasonable. Conclusions:After 2016, the quantity and quality of ADR reports in our hospital have significant improvement, but there are still some evaluation indicators with low pass rate. Using the weighted TOPSIS-RSR model to grade the quality of ADR reports can more objectively reflect the quality of ADR reports.

Objective:To understand the quality of adverse drug reaction (ADR) reports in Lu′an Hospital of Traditional Chinese Medicine (our hospital) and its change trend in recent years, and explore the methods of objectively evaluating the quality of ADR reports.Methods:According to the 20 evaluation indicators of the ADR report quality evaluation scoring table in the Appendixes 5 of Provisions for Adverse Drug Reaction Reporting and monitoring, the ADR reports submitted to the National Center for ADR Monitoring from 2013 to 2022 by our hospital were evaluated. The weighted technique for order preference by similarity to ideal solution (TOPSIS) combined with rank-sum ratio (RSR) model was used to rank the quality of ADR reports into the following 5 grades: excellent, good, medium, qualified and unqualified, according to the weight of each evaluation index. The quality grading results were tested to determine the rationality of grading. Results:A total of 3 947 ADR reports were included in the analysis, including 1 361 new/serious ADR reports (34.5%), and the average score of quality evaluation index was 87.9. After 2016, the number of ADR reports and the proportion of reports with scores ≥ 80 increased significantly. Among the 20 evaluation indicators, 10 had a high pass rate, 7 had a medium or upper pass rate, and 3 had a low pass rate. The TOPSIS-RSR model was used to classify the quality of ADR reports. The overall proportions of excellent, good, moderate, qualified, and unqualified reports were 4.7% (186/3 947), 23.0% (908/3 947), 45.3% (1 787/3 947), 23.4% (925/3 947), and 3.6% (141/3 947), respectively. The homogeneity of variance test showed that each grade met the homogeneity of variance, and the analysis of variance results showed that the differences between every 2 grades were statistically significant ( P<0.001), indicating that the quality grading was reasonable. Conclusions:After 2016, the quantity and quality of ADR reports in our hospital have significant improvement, but there are still some evaluation indicators with low pass rate. Using the weighted TOPSIS-RSR model to grade the quality of ADR reports can more objectively reflect the quality of ADR reports.

Background: Diabetes can complicate hypertension management by increasing the risk of cardiovascular disease (CVD) and all-cause mortality. Studies targeting diabetes detection in hypertensive individuals demonstrating an increased risk of diabetes are lacking.We aimed to assess the performance of hemoglobin A1c (HbA1c) and its cut-off point in detecting diabetes in the abovementioned population. Methods: Data from 4,096 community-dwellers with hypertension but without known diabetes were obtained from the Study on Evaluation of iNnovated Screening tools and determInation of optimal diagnostic cut-off points for type 2 diaBetes in Chinese muLti-Ethnic (SENSIBLE) study; these data were randomly split into exploration (70% of the sample) and internal validation (the remaining 30%) datasets. The optimal HbA1c cut-off point was derived from the exploration dataset and externally validated using another dataset from 2,431 hypertensive individuals. The oral glucose tolerance test was considered the goldstandard for confirming diabetes. Results: The areas under the ROC curves for HbA1c to detect diabetes were 0.842, 0.832, and 0.829 for the exploration, internal validation, and external validation datasets, respectively. An optimal HbA1c cut-off point of 5.8% (40 mmol/mol) yielded a sensitivity of 76.2% and a specificity of 74.5%. Individuals who were not diagnosed as having diabetes by HbA1c at 5.8% (40 mmol/mol) had a lower 10-year CVD risk score than those diagnosed as having diabetes (P = 0.01). HbA1c ≤ 5.1% (32 mmol/mol) and ≥ 6.4% (46 mmol/mol) could indicate the absence and presence of diabetes, respectively. Conclusions HbA1c could detect diabetes effectively in community-dwellers with hypertension.