Barriers to Adoption of Tailored Drug-Drug Interaction Clinical Decision Support.

OBJECTIVE: Despite the benefits of the tailored drug-drug interaction (DDI) alerts and the broad dissemination strategy, the uptake of our tailored DDI alert algorithms that are enhanced with patient-specific and context-specific factors has been limited. The goal of the study was to examine barriers and health care system dynamics related to implementing tailored DDI alerts and identify the factors that would drive optimization and improvement of DDI alerts. METHODS: We employed a qualitative research approach, conducting interviews with a participant interview guide framed based on Proctor's taxonomy of implementation outcomes and informed by the Theoretical Domains Framework. Participants included pharmacists with informatics roles within hospitals, chief medical informatics officers, and associate medical informatics directors/officers. Our data analysis was informed by the technique used in grounded theory analysis, and the reporting of open coding results was based on a modified version of the Safety-Related Electronic Health Record Research Reporting Framework. RESULTS: Our analysis generated 15 barriers, and we mapped the interconnections of these barriers, which clustered around three entities (i.e., users, organizations, and technical stakeholders). Our findings revealed that misaligned interests regarding DDI alert performance and misaligned expectations regarding DDI alert optimizations among these entities within health care organizations could result in system inertia in implementing tailored DDI alerts. CONCLUSION: Health care organizations primarily determine the implementation and optimization of DDI alerts, and it is essential to identify and demonstrate value metrics that health care organizations prioritize to enable tailored DDI alert implementation. This could be achieved via a multifaceted approach, such as partnering with health care organizations that have the capacity to adopt tailored DDI alerts and identifying specialists who know users' needs, liaise with organizations and vendors, and facilitate technical stakeholders' work. In the future, researchers can adopt the systematic approach to study tailored DDI implementation problems from other system perspectives (e.g., the vendors' system).

A machine learning evaluation of patient characteristics associated with prescribing of guideline-directed medical therapy for heart failure.

Introduction/background: Patients with heart failure and reduced ejection fraction (HFrEF) are consistently underprescribed guideline-directed medications. Although many barriers to prescribing are known, identification of these barriers has relied on traditional a priori hypotheses or qualitative methods. Machine learning can overcome many limitations of traditional methods to capture complex relationships in data and lead to a more comprehensive understanding of the underpinnings driving underprescribing. Here, we used machine learning methods and routinely available electronic health record data to identify predictors of prescribing. Methods: We evaluated the predictive performance of machine learning algorithms to predict prescription of four types of medications for adults with HFrEF: angiotensin converting enzyme inhibitor/angiotensin receptor blocker (ACE/ARB), angiotensin receptor-neprilysin inhibitor (ARNI), evidence-based beta blocker (BB), or mineralocorticoid receptor antagonist (MRA). The models with the best predictive performance were used to identify the top 20 characteristics associated with prescribing each medication type. Shapley values were used to provide insight into the importance and direction of the predictor relationships with medication prescribing. Results: For 3,832 patients meeting the inclusion criteria, 70% were prescribed an ACE/ARB, 8% an ARNI, 75% a BB, and 40% an MRA. The best-predicting model for each medication type was a random forest (area under the curve: 0.788-0.821; Brier score: 0.063-0.185). Across all medications, top predictors of prescribing included prescription of other evidence-based medications and younger age. Unique to prescribing an ARNI, the top predictors included lack of diagnoses of chronic kidney disease, chronic obstructive pulmonary disease, or hypotension, as well as being in a relationship, nontobacco use, and alcohol use. Discussion/conclusions: We identified multiple predictors of prescribing for HFrEF medications that are being used to strategically design interventions to address barriers to prescribing and to inform further investigations. The machine learning approach used in this study to identify predictors of suboptimal prescribing can also be used by other health systems to identify and address locally relevant gaps and solutions to prescribing.

Cost-effectiveness analysis of five anti-obesity medications from a US payer's perspective.

BACKGROUND AND AIMS: To determine the cost-effectiveness of anti-obesity medications (AOM): tirzepatide, semaglutide, liraglutide, phentermine plus topiramate (PpT), and naltrexone plus bupropion (NpB). METHODS AND RESULTS: From a U.S. perspective we developed a Markov model to simulate weight change over a 40-year time horizon using results from clinical studies. According to the body mass index (BMI), cardiovascular diseases, diabetes and mortality risk were the health states considered in the model, being mutually exclusive. Costs of AOM, adverse events, cardiovascular events, and diabetes were included. We applied a 3% per-year discount rate and calculated the incremental cost-effectiveness ratios (ICERs) of cost per quality-adjusted life-year (QALY) gained. Probabilistic sensitivity analyses incorporated uncertainty in input parameters. A deterministic analysis was conducted to determine the robustness of the model. The model included a cohort of 78.2% females with a mean age of 45 years and BMI of 37.1 (SD 4.9) for females and 36.8 (SD 4.9) for males. NpB and PpT were the least costly medications and, all medications differed no more than 0.5 QALYs. Tirzepatide ICER was $355,616 per QALY. Liraglutide and semaglutide options were dominated by PpT. CONCLUSION: Compared to other AOM, PpT was lowest cost treatment with nearly identical QALYs with other agents.

Caregiver survey in glioblastoma focused on cognitive dysfunction: development and results from a multicenter study.

Aim: To develop a cognitive dysfunction (CD) focused questionnaire to evaluate caregiver burden in glioblastoma. Materials & methods: The survey was developed from stakeholder consultations and a pilot study, and disseminated at eight US academic cancer centers. Caregivers self-reported caring for an adult with glioblastoma and CD. Results: The 89-item survey covered demographics, CD symptoms and caregiver burden domains. Among 185 caregivers, most were white, educated females and reported memory problems as the most common CD symptom. An exposure-effect was observed, with increase in number of CD symptoms significantly associated with greater caregiver burden. Conclusion: This questionnaire could guide caregiver interventions and be adapted for use longitudinally, in community cancer settings, and in patients with brain metastases.

Glioblastoma (GBM) is a very aggressive brain cancer. People who have GBM have trouble remembering things and are unable to do things they used to do. These changes can be very hard. Researchers are trying to better understand what it is like for people who take care of people with GBM (or caregivers). In this study, researchers created a new survey for caregivers. The survey included questions about what caregivers see happening in their loved one with GBM. Caregivers said that memory problems were common. Also, when the patient had more problems the caregiver had a harder time, too. Researchers hope to improve the survey and use it in the future for more studies.

Drugs That Interact With Colchicine Via Inhibition of Cytochrome P450 3A4 and P-Glycoprotein: A Signal Detection Analysis Using a Database of Spontaneously Reported Adverse Events (FAERS).

BACKGROUND: Colchicine has a narrow therapeutic index. Its toxicity can be increased due to concomitant exposure to drugs inhibiting its metabolic pathway; these are cytochrome P450 3A4 (CYP3A4) and P-glycoprotein (P-gp). OBJECTIVE: To examine clinical outcomes associated with colchicine drug interactions using the spontaneous reports of the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). METHODS: We conducted a disproportionality analysis using FAERS data from January 2004 through June 2020. The reporting odds ratio (ROR) and observed-to-expected ratio (O/E) with shrinkage for adverse events related to colchicine's toxicity (ie, rhabdomyolysis/myopathy, agranulocytosis, hemorrhage, acute renal failure, hepatic failure, arrhythmias, torsade de pointes/QT prolongation, and cardiac failure) were compared between FAERS reports. RESULTS: A total of 787 reports included the combined mention of colchicine, an inhibitor of both CYP3A4 and P-gp drug, and an adverse event of interest. Among reports that indicated the severity, 61% mentioned hospitalization and 24% death. A total of 37 ROR and 34 O/E safety signals involving colchicine and a CYP3A4/P-gp inhibitor were identified. The strongest ROR signal was for colchicine + atazanavir and rhabdomyolysis/myopathy (ROR = 35.4, 95% CI: 12.8-97.6), and the strongest O/E signal was for colchicine + atazanavir and agranulocytosis (O/E = 3.79, 95% credibility interval: 3.44-4.03). CONCLUSION AND RELEVANCE: This study identifies numerous safety signals for colchicine and CYP3A4/P-gp inhibitor drugs. Avoiding the interaction or monitoring for toxicity in patients when co-prescribing colchicine and these agents is highly recommended.

Cancer incidence in immunocompromised patients: a single-center cohort study.

BACKGROUND: Diminished immune defense plays an important role in cancer development. Cancer risk in immunocompromised patients may differ. Identifying individuals with elevated cancer risk can inform strategies for routine cancer screening. This study aimed to understand and compare cancer incidence and risk in three patient groups: recipients of solid organ transplant (SOT) or hematopoietic stem cell transplant (HSCT); diagnosis of primary or secondary immunodeficiency disorder (PID/SID); and recipients of tumor necrosis factor inhibitor (TNF-i) therapy. METHODS: This retrospective cohort study used the University of Utah Health System database and Huntsman Cancer Institute tumor registry. Patients aged ≥18 years with SOT/HSCT, PID/SID or ≥ 3 months of TNF-i therapy were included. The date of transplant, diagnosis of PID/SID, or 1st TNF-i medication order date was defined as the index date. We calculated cumulative cancer incidence by Kaplan-Meier method. A Cox-proportional hazard regression model with a stepwise variable selection process was used to identify independent risk factors associated with the time to onset of a new primary cancer. RESULTS: In total, 13,887 patients were included which comprised of 2982 (21%) SOT/HSCT, 7542 (54%) PID/SID and 3363 (24%) patients receiving TNF-i. The mean (SD) age ranged from 46.8 (15) years - 50.4 (18.2) years. The proportion of white patients ranged from 72.3-84.8%. The estimated cumulative cancer incidence was 11.5% in the SOT/HSCT cohort, 14.3% in the PID/SID cohort, and 8.8% in the TNF-i cohort. The multivariable model adjusted for age, benign in-situ disease, Charlson Comorbidity Index, hypertension/cardiovascular disease/end stage renal disease, gender, race/ethnicity, and renal cyst as significant risk factors. The adjusted hazard ratios for cancer development in SOT/HSCT and PID/SID cohorts compared to the TNF-i cohort over the full follow-up period were 1.57 (95% CI: 1.16-2.13) and 2.14 (95% CI: 1.65-2.77), respectively. CONCLUSION: A significantly increased risk of cancer was observed in PID/SID patients and SOT/HSCT patients compared to TNF-i patients. Age ≥ 50 years, male gender, and clinical comorbidities were additional factors impacting cancer risk. PID/SID and SOT/HSCT patients may benefit from more intensive cancer screening.

Colchicine Drug Interaction Errors and Misunderstandings: Recommendations for Improved Evidence-Based Management.

Colchicine is useful for the prevention and treatment of gout and a variety of other disorders. It is a substrate for CYP3A4 and P-glycoprotein (P-gp), and concomitant administration with CYP3A4/P-gp inhibitors can cause life-threatening drug-drug interactions (DDIs) such as pancytopenia, multiorgan failure, and cardiac arrhythmias. Colchicine can also cause myotoxicity, and coadministration with other myotoxic drugs may increase the risk of myopathy and rhabdomyolysis. Many sources of DDI information including journal publications, product labels, and online sources have errors or misleading statements regarding which drugs interact with colchicine, as well as suboptimal recommendations for managing the DDIs to minimize patient harm. Furthermore, assessment of the clinical importance of specific colchicine DDIs can vary dramatically from one source to another. In this paper we provide an evidence-based evaluation of which drugs can be expected to interact with colchicine, and which drugs have been stated to interact with colchicine but are unlikely to do so. Based on these evaluations we suggest management options for reducing the risk of potentially severe adverse outcomes from colchicine DDIs. The common recommendation to reduce the dose of colchicine when given with CYP3A4/P-gp inhibitors is likely to result in colchicine toxicity in some patients and therapeutic failure in others. A comprehensive evaluation of the almost 100 reported cases of colchicine DDIs is included in table form in the electronic supplementary material. Colchicine is a valuable drug, but improvements in the information about colchicine DDIs are needed in order to minimize the risk of serious adverse outcomes.

Relationship between a risk score for QT interval prolongation and mortality across rural and urban inpatient facilities.

OBJECTIVES: To evaluate the relationship between a modified Tisdale QTc-risk score (QTc-RS) and inpatient mortality and length of stay in a broad inpatient population with an order for a medication with a known risk of torsades de pointes (TdP). BACKGROUND: Managing the risk of TdP is challenging due to the number of medications with known risk of TdP and the complexity of precipitating factors. A model to predict risk of mortality may be useful to guide treatment decisions. METHODS: This was a retrospective observational study using inpatient data from 28 healthcare facilities in the western United States. This risk score ranges from zero to 23 with weights applied to each risk factor based on a previous validation study. Logistic regression and a generalized linear model were performed to assess the relationship between QTc-RS and mortality and length of stay. RESULTS: Between April and December 2020, a QTc-RS was calculated for 92,383 hospitalized patients. Common risk factors were female (55.0%); age > 67 years (32.1%); and receiving a medication with known risk of TdP (24.5%). A total of 2770 (3%) patients died during their hospitalization. Relative to patients with QTc-RS < 7, the odds ratio for mortality was 4.80 (95%CI:4.42-5.21) for patients with QTc-RS = 7-10 and 11.51 (95%CI:10.23-12.94) for those with QTc-RS ≥ 11. Length of hospital stay increased by 0.7 day for every unit increase in the risk score (p < 0.0001). CONCLUSION: There is a strong relationship between increased mortality as well as longer duration of hospitalization with an increasing QTc-RS.

Drug-drug interaction between dexamethasone and direct-acting oral anticoagulants: a nested case-control study in the National COVID Cohort Collaborative (N3C).

OBJECTIVE: The goal of this work is to evaluate if there is an increase in the risk of thromboembolic events (TEEs) due to concomitant exposure to dexamethasone and apixaban or rivaroxaban. Direct oral anticoagulants (DOACs), as well as corticosteroid dexamethasone, are commonly used to treat individuals hospitalised with COVID-19. Dexamethasone induces cytochrome P450-3A4 enzyme that also metabolises DOACs apixaban and rivaroxaban. This raises a concern about possible interaction between dexamethasone and DOACs that may reduce the efficacy of the DOACs and result in an increased risk of TEE. DESIGN: We used nested case-control study design. SETTING: This study was conducted in the National COVID Cohort Collaborative (N3C), the largest electronic health records repository for COVID-19 in the USA. PARTICIPANTS: Study participants were adults over 18 years who were exposed to a DOAC for 10 or more consecutive days. Exposure to dexamethasone was at least 5 or more consecutive days. PRIMARY AND SECONDARY OUTCOME MEASURES: Our primary exposure variable was concomitant exposure to dexamethasone for 5 or more days after exposure to either rivaroxaban or apixaban for 5 or more consecutive days. We used McNemar's Χ2 test and adjusted logistic regression to evaluate association between concomitant use of dexamethasone with either apixaban or rivaroxaban. RESULTS: McNemar's Χ2 test did not find a discernible association of TEE in patients concomitantly exposed to dexamethasone and a DOAC (χ2=0.5, df=1, p=0.48). In addition, a conditional logistic regression model did not find an increase in the risk of TEE (adjusted OR 1.15, 95% CI 0.32 to 4.18). CONCLUSION: This nested case-control study did not find evidence of an association between concomitant exposure to dexamethasone and a DOAC with an increase in risk of TEE. Due to small sample size, an association cannot be completely ruled out.

Shared Decision-Making for Drug-Drug Interactions: Formative Evaluation of an Anticoagulant Drug Interaction.

BACKGROUND: Warnings about drug-drug interactions (DDIs) between warfarin and nonsteroidal anti-inflammatory drugs (NSAIDs) within electronic health records indicate potential harm but fail to account for contextual factors and preferences. We developed a tool called DDInteract to enhance and support shared decision-making (SDM) between patients and physicians when both warfarin and NSAIDs are used concurrently. DDInteract was designed to be integrated into electronic health records using interoperability standards. OBJECTIVE: The purpose of this study was to conduct a formative evaluation of a DDInteract that incorporates patient and product contextual factors to estimate the risk of bleeding. METHODS: A randomized formative evaluation was conducted to compare DDInteract to usual care (UC) using physician-patient dyads. Using case vignettes, physicians and patients on warfarin participated in simulated virtual clinical encounters where they discussed the use of taking ibuprofen and warfarin concurrently and determined an appropriate therapeutic plan based on the patient's individualized risk. Dyads were randomized to either DDInteract or UC. Participants completed a postsession interview and survey of the SDM process. This included the 9-item Shared Decision-Making Questionnaire (SDM-Q-9), tool usability and workload National Aeronautics and Space Administration (NASA) Task Load Index, Unified Theory of Acceptance and Use of Technology (UTAUT), Perceived Behavioral Control (PBC) scale, System Usability Scale (SUS), and Decision Conflict Scale (DCS). They also were interviewed after the session to obtain perceptions on DDInteract and UC resources for DDIs. RESULTS: Twelve dyad encounters were performed using virtual software. Most (n=11, 91.7%) patients were over 50 years of age, and 9 (75%) had been taking warfarin for more than 2 years (75%). Regarding scores on the SDM-Q-9, participants rated DDInteract higher than UC for questions pertaining to helping patients clarify the decision (P=.03), involving patients in the decision (P=.01), displaying treatment options (P<.001), identifying advantages and disadvantages (P=.01), and facilitating patient understanding (P=.01) and discussion of preferences (P=.01). Five of the 8 UTAUT constructs showed differences between the 2 groups, favoring DDInteract (P<.05). Usability ratings from the SUS were significantly higher (P<.05) for physicians using DDInteract compared to those in the UC group but showed no differences from the patient's perspective. No differences in patient responses were observed between groups using the DCS. During the session debrief, physicians indicated little concern for the additional time or workload entailed by DDInteract use. Both clinicians and patients indicated that the tool was beneficial in simulated encounters to understand and mitigate the risk of harm from this DDI. CONCLUSIONS: Overall, DDInteract may improve encounters where there is a risk of bleeding due to a potential drug-drug interaction involving anticoagulants. Participants rated DDInteract as logical and useful for enhancing SDM. They reported that they would be willing to use the tool for an interaction involving warfarin and NSAIDs.

Colchicine and macrolides: a cohort study of the risk of adverse outcomes associated with concomitant exposure.

Colchicine is increasingly used as the number of potential indications expands. However, it also has a narrow therapeutic index that is associated with bothersome to severe side effects. When concomitantly use with medications inhibiting its metabolism, higher plasma levels will result and increase likelihood of colchicine toxicity. We conducted a cohort study using electronic health records comparing encounters with colchicine plus a macrolide and colchicine with an antibiotic non-macrolide. We assessed the relationship between the two groups using adjusted multivariate logistic regression models and the risk of rhabdomyolysis, pancytopenia, muscular weakness, heart failure, acute hepatic failure and death. 12670 patients on colchicine plus an antibiotic non-macrolide were compared to 2199 patients exposed to colchicine plus a macrolide. Patients exposed to colchicine and a macrolide were majority men (n = 1329, 60.4%) and white (n = 1485, 67.5%) in their late sixties (mean age in years 68.4, SD 15.6). Heart failure was more frequent in the colchicine plus a macrolide cohort (n = 402, 18.3%) vs the colchicine non-macrolide one (n = 1153, 9.1%) (p < 0.0001) and also had a higher mortality rate [(85 (3.87%) vs 289 (2.28%), p < 0.0001 macrolides vs non-macrolides cohorts, respectively]. When the sample was limited to individuals exposed to either clarithromycin or erythromycin and colchicine, the adjusted OR for acute hepatic failure was 2.47 (95% CI 1.04-5.91) and 2.06 for death (95% CI 1.07-3.97). There is a significant increase in the risk of hepatic failure and mortality when colchicine is concomitantly administered with a macrolide. Colchicine should not be used concomitantly with these antibiotics or should be temporarily discontinued to avoid toxic levels of colchicine.

Risk of Bleeding Among Individuals on Direct-Acting Oral Anticoagulants: An Academic Medical Center Cohort Study.

ABSTRACT: Previous research has identified risk factors that may affect the risk of bleeding when individuals are exposed to oral anticoagulants. It is unclear if the risk continues to exist with the direct oral anticoagulants (DOACs). The purpose of this study was to assess the risk of bleeding in patients on DOACs (apixaban, rivaroxaban, dabigatran, edoxaban, and betrixaban) based on known risk factors including demographics, medical conditions, and concomitant medications. This study was a retrospective analysis using electronic health record data from the University of Utah Hospital (Division of Cardiovascular Medicine) of individuals receiving a DOAC from 2015 to 2020. The primary outcome of interest was bleeding events [gastrointestinal (GI) bleeding, other anatomical site bleeding (excluding GI), and any bleeding] recorded in the electronic health record that codes using International Classification of Diseases 9th and 10th codes. Known risk factors were used to predict bleeding using multivariate logistic regression. A total of 5492 patients received a DOAC during the study period. Less than half the study population were female (2287, 41.6%). During the follow-up, there were 988 patients (18.0%) experiencing a bleeding event. Of them, 351 patients (35.5%) had a GI bleeding event. Significant risk factors of GI bleeding included clopidogrel [odds ratio (OR) 1.71; 95% confidence interval (95% CI), 1.16-2.52] and previous GI bleeding episodes (OR 7.73; 95% CI, 5.36-11.16). Exposure to corticosteroids (OR 1.50; 95% CI, 1.20-1.87) and previous GI bleeding (OR 1.61; 95% CI, 1.10-2.35) were associated with an increase in bleeding at other anatomical sites (not GI included).

A Disproportionality Analysis of Drug-Drug Interactions of Tizanidine and CYP1A2 Inhibitors from the FDA Adverse Event Reporting System (FAERS).

INTRODUCTION: Tizanidine is primarily metabolized via cytochrome P450 (CYP) 1A2 and therefore medications that inhibit the enzyme will affect the clearance of tizanidine, leading to increased plasma concentrations of tizanidine and potentially serious adverse events. OBJECTIVES: Our aim was to study the occurrence of adverse events reported in the FDA Adverse Event Reporting System (FAERS) involving the combination of tizanidine and drugs that inhibit the metabolic activity of CYP1A2. METHODS: A disproportionality analysis of FAERS reports from 2004 quarter 1 through 2020 quarter 3 was conducted to calculate the reporting odds ratio (ROR) of reports mentioning tizanidine in a suspect or interacting role or having any role, a CYP1A2 inhibitor, and the following adverse events: hypotension, bradycardia, syncope, shock, cardiorespiratory arrest, and fall or fracture. RESULTS: A total of 89 reports were identified mentioning tizanidine, at least one CYP1A2 inhibitor, and one of the adverse events of interest. More than half of the reports identified tizanidine as having a suspect or interacting role (n = 59, 66.3%), and the reports more frequently involved women (n = 58, 65.1%). The median age was 56.1 years (standard deviation 17.1). Some of the important safety signals included interactions between tizanidine in a suspect or interacting role and ciprofloxacin (ROR for hypotension 28.1, 95% confidence interval [CI] 19.2-41.2) or fluvoxamine (ROR for hypotension 36.9, 95% CI 13.1-103.4), and also when reported in "any role" with ciprofloxacin (ROR for hypotension 6.3, 95% CI 4.7-8.5), fluvoxamine (ROR for hypotension 11.4, 95% CI 4.5-28.8), and zafirlukast (ROR for falls 16.0, 95% CI 6.1-42.1). CONCLUSIONS: Reports involving tizanidine and a CYP1A2 inhibitor have higher odds of reporting hypotension. This study suggests that concurrent use of tizanidine with CYP1A2 inhibitors may lead to serious health consequences associated with low blood pressure such as falls and fractures.

Clinician Responses to a Clinical Decision Support Advisory for High Risk of Torsades de Pointes.

Background Torsade de pointes (TdP) is a potentially fatal cardiac arrhythmia that is often drug induced. Clinical decision support (CDS) may help minimize TdP risk by guiding decision making in patients at risk. CDS has been shown to decrease prescribing of high-risk medications in patients at risk of TdP, but alerts are often ignored. Other risk-management options can potentially be incorporated in TdP risk CDS. Our goal was to evaluate actions clinicians take in response to a CDS advisory that uses a modified Tisdale QT risk score and presents management options that are easily selected (eg, single click). Methods and Results We implemented an inpatient TdP risk advisory systemwide across a large health care system comprising 30 hospitals. This CDS was programmed to appear when prescribers attempted ordering medications with a known risk of TdP in a patient with a QT risk score ≥12. The CDS displayed patient-specific information and offered relevant management options including canceling offending medications and ordering electrolyte replacement protocols or ECGs. We retrospectively studied the actions clinicians took within the advisory and separated by drug class. During an 8-month period, 7794 TdP risk advisories were issued. Antibiotics were the most frequent trigger of the advisory (n=2578, 33.1%). At least 1 action was taken within the advisory window for 2700 (34.6%) of the advisories. The most frequent action taken was ordering an ECG (n=1584, 20.3%). Incoming medication orders were canceled in 793 (10.2%) of the advisories. The frequency of each action taken varied by drug class (P<0.05 for all actions). Conclusions A modified Tisdale QT risk score-based CDS that offered relevant single-click management options yielded a high action/response rate. Actions taken by clinicians varied depending on the class of the medication that evoked the TdP risk advisory, but the most frequent was ordering an ECG.

Serum potassium changes due to concomitant ACEI/ARB and spironolactone therapy: A systematic review and meta-analysis.

PURPOSE: To provide evidence of serum potassium changes in individuals taking angiotensin-converting enzyme inhibitors (ACEIs) and/or angiotensin receptor blockers (ARBs) concomitantly with spironolactone compared to ACEI/ARB therapy alone. METHODS: PubMed, Embase, Scopus, and Web of Science were searched for studies including exposure to both spironolactone and ACEI/ARB therapy compared to ACEI/ARB therapy alone. The primary outcome was serum potassium change over time. Main effects were calculated to estimate average treatment effect using random effects models. Heterogeneity was assessed using Cochran's Q and I2. Risk of bias was assessed using the revised Cochrane risk of bias tool. RESULTS: From the total of 1,225 articles identified, 20 randomized controlled studies were included in the meta-analysis. The spironolactone plus ACEI/ARB group included 570 patients, while the ACEI/ARB group included 547 patients. Treatment with spironolactone and ACEI/ARB combination therapy compared to ACEI/ARB therapy alone increased the mean serum potassium concentration by 0.19 mEq/L (95% CI, 0.12-0.26 mEq/L), with intermediate heterogeneity across studies (Q statistic = 46.5, P = 0.004; I2 = 59). Sensitivity analyses showed that the direction and magnitude of this outcome did not change with the exclusion of individual studies, indicating a high level of reliability. Reporting risk of bias was low for 16 studies (80%), unclear for 3 studies (15%) and high for 1 study (5%). CONCLUSION: Treatment with spironolactone in combination with ACEI/ARB therapy increases the mean serum potassium concentration by less than 0.20 mEq/L compared to ACEI/ARB therapy alone. However, serum potassium and renal function must be monitored in patients starting combination therapy to avoid changes in serum potassium that could lead to hyperkalemia.