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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.

Supplemental Digital Content is Available in the Text.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).

Treatment intensification (TI) may help patients with type 2 diabetes mellitus (T2DM) achieve target hemoglobin A1c (A1c) < 7.0%. This study aimed to measure the influence of TI on A1c outcome in patients who insufficiently responded to two classes of oral antidiabetic drugs (2OADs).

Introduction:Prolongation of the heart rate-corrected QT interval (QTc) is associated with increased mortality. We hypothesized that incorporation of a QTc prolongation risk score (QTc-RS) into a clinical decision support (CDS) advisory may reduce mortality in patients in intensive care units (ICUs).Objective:To evaluate the effectiveness of a QTc-RS risk score CDS on inpatient mortality in ICUs within hospitals in a wide geographical area.Methods:A QTc-RS CDS advisory incorporating a validated risk score using patient information available in the electronic health record was implemented throughout urban and rural facilities in the Banner Health system. Risk factors were assigned points as follows: female (1 point), age > 67 years (1), administration of a loop diuretic (1), diagnosis of acute myocardial infarction (2), serum K+≤ 3.5 mEq/L (2), sepsis (3), diagnosis of heart failure (3), one medication with a known risk of torsades de pointes (TdP) (3), two or more medications with known risk of TdP (3), and a Fridericia-corrected QTc > 500 ms (4). The QTc-RS was automatically calculated in the background each time an order for medication with a known risk of TdP was initiated. The pre-implementation period consisted of hospitalized patients from January 1, 2020 to March 31, 2020, with QTc-RS ≥12. However, clinicians did not receive a QTc-RS CDS advisory when placing an order for a medication with a known risk of TdP for these patients. The post-implementation period was from April 1, 2020 to December 13, 2020 and included patients where clinicians received an advisory when placing an order for a medication with a known risk of TdP and the QTc-RS ≥12. Logistic regression was conducted to assess the impact of implementing the QTc-RS advisory on inpatient mortality.Results:We evaluated 620 unique hospitalized patients during the pre-implementation period and 3,112 hospitalized patients post-implementation of the CDS. Mortality was lower in the post-implementation cohort compared to that in the pre-implementation cohort (odds ratio =0.61, 95% confidence interval 0.49-0.76). Increasing QTc-RS was associated with increasing mortality [(OR=1.08 (1.02-1.14)].Conclusion:Implementation of a QTc-RS CDS advisory was associated with lower mortality.

Introduction:Torsades de pointes (TdP) is a life-threatening arrhythmia associated with prolongation of the heart rate-corrected QT (QTc) interval. QTc prolongation is associated with increased mortality. A QTc Risk Score (QTc-RS) advisory has been developed, but the ability of the QTc-RS to predict mortality is unknown.Objective:To evaluate the relationship between a validated QTc-RS and mortality among hospitalized patients.Methods:An advisory incorporating a previously validated QTc-RS was implemented as a clinical decision support tool at 28 inpatient facilities. When an order was initiated for a known QTc-prolonging medication, a QTc-RS (maximum score = 21) was electronically calculated and, if the score was > 11, prescribers received a warning that included patient-specific risk factors. The advisory also provided single click actions for laboratory tests, electrocardiogram orders, cancelling incoming medication orders or discontinuing existing QTc-prolonging medications.Results:Between April and November 2020, there were 144,148 QTc-RS scores calculated. Among the 7923 warnings with a score >11, the majority (51%) had a score of 12, 29% had a score of 13, 12% had a score of 14, and 8% had a score ≥ 15. The most common risk factors were: age > 67 years (66%); sepsis (65%); ≥ 1 QTc-prolonging medication(s) (60%); female (59%); heart failure (34%); serum potassium < 3.5 mEq/L (28%); and a loop diuretic (21%). Among those with a QTc-RS >11, 11.9% (944/7923) expired during the inpatient encounter. The proportion of deaths increased as the risk score increased (Table 1). Relative to individuals with QTc-RS 12-14, the odds ratio for inpatient mortality in those with scores >17 was 3.0 (95% CI:1.7-5.5).Conclusions:An increasing QTc-RS was associated with an increased likelihood of in-hospital mortality

Introduction:Patients with heart failure and reduced ejection fraction (HFrEF) are consistently under-prescribed guideline-directed medications. Many barriers to prescribing are known, including patient characteristics (e.g., higher potassium values within normal limits predict under-prescribing of some medications). However, identification of these barriers has relied on traditional a priorihypotheses or qualitative methods. To overcome the limitations of traditional methods, we used a supervised machine learning (ML) approach to identify potential new barriers to prescribing.Methods:We used electronic health record data to evaluate characteristics of adults with HFrEF within the UCHealth system. Characteristics evaluated included insurance type, medications, diagnoses and lab values. We evaluated the predictive performance of ML algorithms and statistical models based on area under the receiver operating characteristic curve (AUC) to predict prescription of 4 types of medications: angiotensin converting enzyme inhibitor/angiotensin receptor blocker (ACE/ARB), angiotensin receptor-neprilysin inhibitor (ARNI), beta blocker (BB), or mineralocorticoid receptor antagonist (MRA). We identified top characteristics associated with prescribing each medication type from the models with the best predictive performance.Results:For 3,835 patients meeting inclusion criteria, 70% were prescribed an ACE/ARB, 8% an ARNI, 75% a BB and 40% a MRA. The best-predicting model for each medication type was a random forest. Figure 1 describes the top characteristics associated with each medication type.Conclusions:In this hypothesis generating ML study, we identified additional patient characteristics that may be associated with under-prescribing for HFrEF. These results also align with some patient characteristics that are known barriers to prescribing. Additional research is needed to further elucidate barriers to prescribing for HFrEF.