ABSTRACT
Background@#High-alert medications (HAMs) are medications that bear a heightened risk of causing significant patient harm if used in error. To facilitate safe use of HAMs, identifying specific HAM lists for clinical setting is necessary. We aimed to develop the national level HAM list for acute care setting. @*Methods@#We used three-step process. First, we compiled the pre-existing lists referring HAMs. Second, we analyzed medication related incidents reported from national patient safety incident report data and adverse events indicating medication errors from the Korea Adverse Event Reporting System (KAERS).We also surveyed the assistant staffs to support patient safety tasks and pharmacist in charge of medication safety in acute care hospital. From findings from analysis and survey results we created additional candidate list of HAMs. Third, we derived the final list for HAMs in acute care settings through expert panel surveys. @*Results@#From pre-existing HAM list, preliminary list consisting of 42 medication class/ingredients was derived. Eight assistant staff to support patient safety tasks and 39 pharmacists in charge of medication safety responded to the survey. Additional 44 medication were listed from national patient safety incident report data, KAERS data and common medications involved in prescribing errors and dispensing errors from survey data. A list of mandatory and optional HAMs consisting of 10 and 6 medication classes, respectively, was developed by consensus of the expert group. @*Conclusion@#We developed national level HAM list for Korean acute care setting from pre-existing lists, analyzing medication error data, survey and expert panel consensus.
ABSTRACT
Background@#Levothyroxine is an essential drug for the treatment of hypothyroidism or related diseases. Several studies have reported an association between the effects of levothyroxine treatment and time of administration, which can be inconsistent. @*Objective@#This study was conducted to compare the levels of thyroid-stimulating hormone or free thyroxine between morning and nighttime dosing of levothyroxine. @*Methods@#We reviewed previously reported relevant articles and conducted a meta-analysis. @*Results@#In total, five studies were included in this meta-analysis. Results showed that thyroid-stimulating hormone (standard difference in means [SE]=0.321; 95% confidence interval [CI], -0.016 to 0.657) and free thyroxine (SE= −1.367; 95% CI, -2.943 to 0.210) levels did not differ significantly between morning (before breakfast) and nighttime (before bedtime) administration. @*Conclusion@#This is the first meta-analysis to evaluate the effects of time of administration on levothyroxine levels in patients with hypothyroidism. Based on our results, we suggest considering patients’ lifestyles or daily routines when counselling them on the optimal time of administration for levothyroxine.
ABSTRACT
Background@#Drug-drug interactions (DDIs) in patients using oral anticancer treatment are more common than in those using injectable anticancer agents. In addition, DDIs related to anticancer treatment are known to cause clinically significant outcomes, such as treatment failure and severe toxicity. To prevent these negative outcomes, significant DDIs are monitored and managed using the information provided in drug databases. We aimed to evaluate the consistency of information on clinically significant DDIs for tyrosine kinase inhibitors (TKIs) between representative drug databases. @*Methods@#We selected clinically significant DDIs involving medications that are co-prescribed with TKIs and met the following criteria: the severity level of DDIs was equal or greater than “D” in Lexicomp® or “major” in Micromedex® . We then analyzed the consistency of the severity classification and evidence level between the drug databases. Spearman’s correlation coefficient was used to identify the relationship between DDI information in the drug databases. @*Results@#In total, 627 DDI pairs were identified as clinically significant; information on these was provided by Lexicomp® and Micromedex® for 571 and 438 pairs, respectively, and both drug databases provided information on 382 DDI pairs.There was no correlation between the severity and evidence level of DDIs provided in the two databases; Spearman’s correlation coefficient for Lexicomp® and Micromedex® was -0.009 (p=0.861) and -0.064 (p=0.209), respectively. @*Conclusion@#To judge the significance of DDIs, healthcare providers should consider that the information on DDIs may be different between drug information databases; hence, clinical factors must be considered concurrently.
ABSTRACT
Background@#Drug-drug interactions (DDIs) in patients using oral anticancer treatment are more common than in those using injectable anticancer agents. In addition, DDIs related to anticancer treatment are known to cause clinically significant outcomes, such as treatment failure and severe toxicity. To prevent these negative outcomes, significant DDIs are monitored and managed using the information provided in drug databases. We aimed to evaluate the consistency of information on clinically significant DDIs for tyrosine kinase inhibitors (TKIs) between representative drug databases. @*Methods@#We selected clinically significant DDIs involving medications that are co-prescribed with TKIs and met the following criteria: the severity level of DDIs was equal or greater than “D” in Lexicomp® or “major” in Micromedex® . We then analyzed the consistency of the severity classification and evidence level between the drug databases. Spearman’s correlation coefficient was used to identify the relationship between DDI information in the drug databases. @*Results@#In total, 627 DDI pairs were identified as clinically significant; information on these was provided by Lexicomp® and Micromedex® for 571 and 438 pairs, respectively, and both drug databases provided information on 382 DDI pairs.There was no correlation between the severity and evidence level of DDIs provided in the two databases; Spearman’s correlation coefficient for Lexicomp® and Micromedex® was -0.009 (p=0.861) and -0.064 (p=0.209), respectively. @*Conclusion@#To judge the significance of DDIs, healthcare providers should consider that the information on DDIs may be different between drug information databases; hence, clinical factors must be considered concurrently.
ABSTRACT
We aimed to assess one-year persistence with antihypertensive therapy (AHT) among newly treated uncomplicated hypertensive patients in Korea and to evaluate the effect of initial therapeutic classes on persistence. We retrospectively analyzed a random sample of 20% of newly treated uncomplicated hypertensive patients (n = 45,787) in 2012 from the National Health Insurance claims database. This group was classified into six cohorts based on initial AHT class. We then measured treatment persistence, allowing a prescription gap of 60 days. Adherence to AHT was assessed with the medication possession ratio. Calcium channel blockers (CCB, 43.7%) and angiotensin receptor blockers (ARB, 40.3%) were most commonly prescribed as initial monotherapy. Overall, 62.1% and 42.0% were persistent with any AHT and initial class at one year, respectively, and 64.2% were adherent to antihypertensive treatment. Compared with ARBs, the risk of AHT discontinuation was significantly increased with initial use of thiazide diuretics (hazard ratio [HR], 3.16; 95% confidence interval [CI] 2.96-3.74) and beta blockers (HR, 1.86; CI, 1.77-1.95) and was minimally increased with CCBs (HR, 1.12; CI, 1.08-1.15). In conclusion, persistence and adherence to AHT are suboptimal, but the differences are meaningful in persistence and adherence between initial AHT classes.