Development and validation of the nomogram to predict the risk of hospital drug shortages: A prediction model.

INTRODUCTION: Reasons for drug shortages are multi-factorial, and patients are greatly injured. So we needed to reduce the frequency and risk of drug shortages in hospitals. At present, the risk of drug shortages in medical institutions rarely used prediction models. To this end, we attempted to proactively predict the risk of drug shortages in hospital drug procurement to make further decisions or implement interventions. OBJECTIVES: The aim of this study is to establish a nomogram to show the risk of drug shortages. METHODS: We collated data obtained using the centralized procurement platform of Hebei Province and defined independent and dependent variables to be included in the model. The data were divided into a training set and a validation set according to 7:3. Univariate and multivariate logistic regression were used to determine independent risk factors, and discrimination (using the receiver operating characteristic curve), calibration (Hosmer-Lemeshow test), and decision curve analysis were validated. RESULTS: As a result, volume-based procurement, therapeutic class, dosage form, distribution firm, take orders, order date, and unit price were regarded as independent risk factors for drug shortages. In the training (AUC = 0.707) and validation (AUC = 0.688) sets, the nomogram exhibited a sufficient level of discrimination. CONCLUSIONS: The model can predict the risk of drug shortages in the hospital drug purchase process. The application of this model will help optimize the management of drug shortages in hospitals.

A Direct Injection Technique to Improve Biosafety to Analyze Levetiracetam Concentrations in Human Serum and Its Application in Therapeutic Drug Monitoring.

BACKGROUND: With the outbreak of COVID-19, it has become very important to improve biosafety measures taken by medical staff. Fewer pretreatment steps correspond to lower chances of infection. The authors established a direct injection technique to analyze levetiracetam (LEV) concentrations in human serum and studied its application in therapeutic drug monitoring. METHODS: Serum samples were prepared by hollow fiber centrifugal ultrafiltration and the filtrate was directly injected into a ultra-high performance liquid chromatography apparatus (Waters UPLC BEH C18 column: 50 × 2.1 mm, 1.7 µm) for analysis. The mobile phase consisted of acetonitrile and water (8:92) at a flow rate of 1.0 mL/min. The column temperature was maintained at 30°C. The detected wavelength was 210 nm. RESULTS: A linear relationship was obtained for LEV from 0.625 to 80 mcg/mL (r2 = 0.999). The limit of detection for the analysis of LEV was 0.125 mcg/mL. The analysis time was shortened to 4 minutes. The recovery rate of LEV based on the current method was 96.6%-100.1%, whereas the absolute recovery rate was 93.2%-96.8%. The relative SD of intraday and interday precision was <7.3%. Stability was achieved at room temperature for 24 hours after 3 freeze-thaw cycles and at -80°C for 21 days. The method was successfully applied to determine LEV concentrations in the serum of 19 patients. CONCLUSIONS: The present method is simple, accurate, and sensitive, and can improve biosafety with the direct injection technique. It is suitable for the analysis of LEV concentrations in therapeutic drug monitoring.

Association between social support and anxiety among pregnant women in the third trimester during the coronavirus disease 2019 (COVID-19) epidemic in Qingdao, China: The mediating effect of risk perception.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a public health emergency of international concern and poses a threat to the mental health of pregnant women. AIM: The purpose of this study was to investigate the relationship between social support and anxiety, and the mediating effect of risk perception during the COVID-19 epidemic in the third trimester pregnant women in Qingdao, China. METHODS: From 16 to 21 February 2020, an online survey was conducted, which collected the information on demographic data, anxiety, social support and risk perception to COVID-19 of women with established medical records in the ambulatory of the Department of Obstetrics at the Affiliated Hospital of Qingdao University. Anxiety was assessed by the Self-Rating Anxiety Scale (SAS), social support was assessed by the Social Support Rating Scale (SSRS) and risk perception was assessed by a self-designed questionnaire. RESULTS: This study had 308 participants with an average of 31.02 ± 3.91 years. During the period of prevention and control of the epidemic, most pregnant women adopted protective measures, such as wearing masks (97.4%), washing hands frequently (88.3%) and staying at home (76.3%). The average SAS, SSRS and risk perception scores of the participants were 42.45 ± 6.98, 44.60 ± 7.00 and 21.60 ± 5.74, respectively. The total effect of maternal social support on anxiety was -2.63 (95% confidence interval (CI): -4.40 ~ -1.44, p < .001), the direct effect was -1.44 (95% CI: -2.74 ~ -0.35, p < .05) and the indirect effect was -1.19 (95% CI: -2.49 ~ -0.51, p < .001). CONCLUSION: The third trimester pregnant women had a high level of social support, a medium level of risk perception to COVID-19 and were susceptible to anxiety. Risk perception played a mediating role between social support and anxiety.