scholarly journals 1074. Evaluation of a Pharmacist-led Antimicrobial and Anticoagulant Monitoring Initiative

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S380-S381
Author(s):  
Wei Hsiang Lin ◽  
Amanda Binkley ◽  
Christo L Cimino ◽  
Naasha J Talati ◽  
Jimish M Mehta ◽  
...  

Abstract Background Adverse drug events are associated with an increase in hospital stay and cost. Risks from these events are minimized by adjusting a medication’s dose or frequency, and changes in renal function may necessitate adjustments. Currently, there is no formal procedure for a prospective audit of renal function over the weekend at our institution. This pharmacist-driven initiative will evaluate if a prospective review identified by real-time clinical decision support alerts over the weekend will reduce the time from change in renal function to dose adjustment of select antimicrobials and/or anticoagulants. Methods This monitoring initiative is comprised of a pre- and post-cohort population. The pre-cohort population included patients admitted to Penn Presbyterian Medical Center (PPMC) from January to March of 2018 on select antimicrobials and/or anticoagulants, who were identified to have a change in renal function (serum creatinine change of 0.3 mg/dL or greater) over the weekend. The post-cohort population was identified with a clinical decision support system (ILÚM Health Solutions, Kenilworth, NJ) and included patients admitted to PPMC from January to March of 2019. A pharmacy resident reviewed alerts in the clinical decision support system over the weekend and contacted providers with dose adjustment recommendations. The Mann–Whitney U test was used to analyze the primary endpoint while descriptive statistics were used for the secondary endpoints Results Eighteen interventions were completed within the 3-month post-cohort intervention period, with a time to dose adjustment between the pre/post-cohort being reduced by 50 hours (P = 0.0001) resulting in a median time to change of 11 hours in the post-cohort. All pharmacy recommendations were accepted by the provider, and 94% of medication adjustments were antimicrobials. Conclusion The application of this prospective weekend initiative utilizing a clinical decision support system demonstrated a clinically and statistically significant reduction in the time to dose adjustments for antimicrobials and/or anticoagulants. Implementation of this initiative will further establish a role for pharmacist-led evaluations and could potentially be expanded to other clinical areas. Disclosures All authors: No reported disclosures.

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1309-P
Author(s):  
JACQUELYN R. GIBBS ◽  
KIMBERLY BERGER ◽  
MERCEDES FALCIGLIA

2020 ◽  
Vol 16 (3) ◽  
pp. 262-269
Author(s):  
Tahere Talebi Azad Boni ◽  
Haleh Ayatollahi ◽  
Mostafa Langarizadeh

Background: One of the greatest challenges in the field of medicine is the increasing burden of chronic diseases, such as diabetes. Diabetes may cause several complications, such as kidney failure which is followed by hemodialysis and an increasing risk of cardiovascular diseases. Objective: The purpose of this research was to develop a clinical decision support system for assessing the risk of cardiovascular diseases in diabetic patients undergoing hemodialysis by using a fuzzy logic approach. Methods: This study was conducted in 2018. Initially, the views of physicians on the importance of assessment parameters were determined by using a questionnaire. The face and content validity of the questionnaire was approved by the experts in the field of medicine. The reliability of the questionnaire was calculated by using the test-retest method (r = 0.89). This system was designed and implemented by using MATLAB software. Then, it was evaluated by using the medical records of diabetic patients undergoing hemodialysis (n=208). Results: According to the physicians' point of view, the most important parameters for assessing the risk of cardiovascular diseases were glomerular filtration, duration of diabetes, age, blood pressure, type of diabetes, body mass index, smoking, and C reactive protein. The system was designed and the evaluation results showed that the values of sensitivity, accuracy, and validity were 85%, 92% and 90%, respectively. The K-value was 0.62. Conclusion: The results of the system were largely similar to the patients’ records and showed that the designed system can be used to help physicians to assess the risk of cardiovascular diseases and to improve the quality of care services for diabetic patients undergoing hemodialysis. By predicting the risk of the disease and classifying patients in different risk groups, it is possible to provide them with better care plans.


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