OBJECTIVE:To improve the quality of pharmaceutical care and to provide bases for outpatients and medical staff in drug informatin inquiry.METHODS:Information about drug package inserts wre collected and the drug-inquiry system was designed using the database management function of Access.RESULTS & CONCLUSIONS:The drug-inquiry system is easy to operate,clear in interface,humanization in operation,and it faciliats individualized drug information inquiry through daily mantinance and satisfies the requirments for outpatient drug information inquiry.

Objective:To estimate the predictive performance of the population pharmacokinetics software JPKD-vancomycin on predicting the vancomycin steady-state trough concentration, and to analyze the related factors affecting the predictive performance.Methods:The clinical data of patients who were treated with vancomycin and received therapeutic drug monitoring (TDM) admitted to Suzhou Hospital Affiliated to Nanjing Medical University from July 2013 to December 2018 were enrolled. All patients were designed an empirical vancomycin regimen (initial regimen) according to vancomycin medication guidelines. Steady-state trough concentrations of vancomycin were determined at 48 hours after the first dose and 0.5 hour before the next dose. Dosage regimen was adjusted when steady-state trough concentration was not in 10-20 mg/L (adjustment regimen), and then the steady-state trough concentration was determined again 48 hours after adjustment. First, the JPKD-vancomycin software was used to calculate the initial regimen and predict the steady-state trough concentration according to the results calculated by classic pharmacokinetic software Vancomycin Calculator. Second, the JPKD-vancomycin software was used to adjust the vancomycin dosage regime and predict the steady-state trough concentration of adjustment regimen. The weight residual (WRES) between the predicted steady-state trough concentration (C pre) and the measured steady-state trough concentration (C real) was used to evaluate the ability of the JPKD-vancomycin software for predicting the vancomycin steady-state trough concentration. The TDM results of initial regimen were divided into accurate prediction group (WRES < 30%) and the inaccurate prediction group (WRES ≥ 30%) according to the WRES value. Patient and disease characteristics including gender, age, weight, height, the length of hospital stay, comorbidities, vasoactive agent, mechanical ventilation, smoking history, postoperative, obstetric patients, trauma, laboratory indicators, vancomycin therapy and TDM results were collected from electronic medical records. Univariate and multivariate Logistic regression analysis was used to screen the related factors that influence the predictive performance of JPKD-vancomycin software, and the receiver operating characteristic (ROC) curve was drawn to evaluate its predictive value. Results:A total of 310 patients were enrolled, and 467 steady-state trough concentrations of vancomycin were collected, including 310 concentrations of initial regimen and 157 concentrations of adjustment regimen. Compared with the initial regimen, the WRES of adjusted regimen was significantly reduced [14.84 (6.05, 22.89)% vs. 20.41 (11.06, 45.76)%, P < 0.01], and the proportion of WRES < 30% increased significantly [82.80% (130/157) vs. 63.87% (198/310), P < 0.01]. These results indicated that JPKD-vancomycin software had a better accuracy prediction for steady-state trough concentration of the adjusted regimen than the initial regimen. There were 198 concentrations in the accurate prediction group and 112 in the inaccurate prediction group. Univariate Logistic regression analysis showed that women [odds ratio ( OR) = 0.466, 95% confidence interval (95% CI) was 0.290-0.746, P = 0.002], low body weight ( OR = 0.974, 95% CI was 0.953-0.996, P = 0.022), short height ( OR = 0.963, 95% CI was 0.935-0.992, P = 0.014), low vancomycin clearance (CL Van; OR < 0.001, 95% CI was 0.000-0.231, P = 0.023) and postoperative patients ( OR = 1.695, 95% CI was 1.063-2.702, P = 0.027) were related factors affecting the predictive performance of JPKD-vancomycin software. Multivariate Logistic regression analysis indicated that women ( OR = 0.449, 95% CI was 0.205-0.986, P = 0.046), low CL Van ( OR < 0.001, 95% CI was 0.000-0.081, P = 0.015) and postoperative patients ( OR = 2.493, 95% CI was 1.455-4.272, P = 0.001) were independent risk factors for inaccurate prediction of JPKD-vancomycin software. The ROC analysis indicated that the area under ROC curve (AUC) of the CL Van for evaluating the accuracy of JPKD-vancomycin software in predicting vancomycin steady-state trough concentration was 0.571, the sensitivity was 56.3%, and the specificity was 57.1%. The predictive performance of JPKD-vancomycin software was decreased when CL Van was lower than 0.065 L·h -1·kg -1. Conclusions:JPKD-vancomycin software had a better predictive performance for the vancomycin steady-state trough concentrations of adjustment regimen than initial regimen. JPKD-vancomycin software had a poor predictive performance when the patient was female, having low CL Van, and was postoperative. The predictive performance of JPKD-vancomycin software was decreased when CL Van was lower than 0.065 L·h -1·kg -1.

OBJECTIVE： To provide reference for improving the utilization rate of medical resources， the compliance of patients with chemotherapy and the rational use of drugs. METHODS： The working model of day-time chemtherapy center and the model of full-course pharmaceutical care were introduced in our hospital. The effects of full-course pharmaceutical care were summarized. RESULTS： As one of the core team members of the multidisciplinary collaborative working group on day-time chemotherapy， clinical pharmacists participated in the selection of chemotherapy schemes， the formulation of clinical pathways， the formulation of inclusion criteria for patients undergoing day-time chemotherapy， and the participation in information management of day-time chemotherapy centers. With the intervention of clinical pharmacists， prescription pharmacists and dispensing pharmacists， the patients were provided with full-course pharmaceutical care relying on pharmacy intravenous admixture service （PIVAS）， including the combination of pre-reviewing and real-time reviewing to examine and verify the medical orders for chemotherapeutic drugs， standardizing deployment of PIVAS chemotherapeutic drugs， real-time monitoring of the whole process of drug use in the background with the help of closed-loop management information system， actively providing pharmaceutical care， medication education and so on. The full-course pharmaceutical care model could effectively reduce irrational drug use. The qualified rates of chemotherapy pretreatment， hydration rate， administration sequence， flushing tube， placement time， drip rate for chemotherapy injection increased from 76％， 50％， 94％， 50％， 54％， 54％ before providing full-course pharmaceutical course （May-Jul. 2017） to 100％， 100％， 100％， 100％， 94％ and 96％ after providing full-course pharmaceutical course （Sept.-Nov. 2017）. CONCLUSIONS： The development of full-course pharmaceutical care in day-time chemotherapy center can improve the utilization of medical resources， patient compliance and rational drug use.

OBJECTIVE To investigate the clinical efficacy of integrating pharmacists into family health teams （FHTs） for long-term medication therapeutical management （MTM） in stroke patients， and empirically evaluate the service model. METHODS A pharmacist team， jointly established by clinical and community pharmacists from the Affiliated Suzhou Hospital of Nanjing Medical University （hereinafter referred to as “our hospital”）， developed a pharmacist-supported MTM model integrated into FHTs. Using a prospective randomized controlled design， 170 stroke patients discharged from our hospital （July 2022-December 2023） and enrolled in FHTs at Suzhou Runda Community Hospital were randomly divided into trial group （88 cases） and control group （82 cases） according to random number table. The control group received routine FHTs care （without pharmacist involvement in the team collaboration）， while the trial group xhz8405@126.com received 12-month MTM services supported by pharmacists via an information platform. These services specifically included innovative interventions such as personalized medication regimen optimization based on the MTM framework， dynamic medication adherence management， medication safety monitoring， a home medication assessment system， and distinctive service offerings. Outcomes of the 2 grousp were compared before and after intervention， involving medication adherence （adherence rate， adherence score）， compliance rates for stroke recurrence risk factors [blood pressure， low-density lipoprotein cholesterol （LDL-C）]， and incidence of adverse drug reactions （ADR）. RESULTS After 12 months， the trial group exhibited significantly higher medication adherence rates， improved adherence scores， higher compliance rates for blood pressure and LDL-C targets compared to the control group （P＜0.05）. The incidence of ADR in the trial group （4.55%） was significantly lower than that in the control group （8.11%）， though the difference was not statistically significant （P＞ 0.05）. CONCLUSIONS Pharmacist involvement in FHTs to deliver MTM services significantly enhances medication adherence and optimizes risk factor for stroke recurrence， offering practical evidence for advancing pharmaceutical care in chronic disease management under the family doctor system.