Colchicine for cardiovascular therapy: A drug interaction perspective and a safety meta-analysis

Rhabdomyolysis associated with concomitant use of colchicine and statins in the real world: identifying the likelihood of drug-drug interactions through the FDA adverse event reporting system

Background

Currently, there remains substantial controversy in research regarding whether the concomitant use of colchicine and statins increases the occurrence of rhabdomyolysis, warranting further substantiation.

Objective

This study aimed to identify the likelihood drug-drug interactions (DDIs) for the co-administration of colchicine and statins resulting in rhabdomyolysis.

Methods

A disproportionality analysis was conducted by using data sourced from the US Food and Drug Administration Adverse Event Reporting System (FAERS) to detect rhabdomyolysis signals associated with the combined use of colchicine and statins. The association between (colchicine/statins/colchicine and statins) and rhabdomyolysis were evaluated using information component (IC). DDI signals were calculated based on the Ω shrinkage measure and Bayesian confidence propagation neural network (BCPNN) method. Furthermore, stratification was performed based on colchicine and individual statins agents.

Results

In total, 11,119 reports of rhabdomyolysis were identified in the FAERS database, 255 (2.29%) involved both colchicine and statins. Our analysis showed potential DDI signals of rhabdomyolysis (Ω025 = 1.17) among individuals concurrent use of colchicine and statins. Moreover, further drug-specific analysis suggests DDI signals in the colchicine-atorvastatin pair (Ω025 = 1.12), and colchicine-rosuvastatin pair (Ω025 = 1.05), along with a higher proportion of rhabdomyolysis (IC025 = 5.20) and (IC025 = 4.26), respectively.

Conclusion

The findings suggest that concomitant use of colchicine and statins may increase the risk of rhabdomyolysis, particularly when combined with atorvastatin or rosuvastatin. Therefore, healthcare professionals should pay special attention to life-threatening AE such as rhabdomyolysis, when co-prescribing colchicine statins.

Clinical Significance and Patterns of Potential Drug-Drug Interactions in Cardiovascular Patients: Focus on Low-Dose Aspirin and Angiotensin-Converting Enzyme Inhibitors

Objective: This study assessed the patterns and clinical significance of potential drug-drug interactions (pDDIs) in patients with diseases of the cardiovascular system. Methods: Electronic health records (EHRs), established in 2018-2023, were selected using the probability serial nested sampling method (n = 1030). Patients were aged 27 to 95 years (65.0% men). Primary diagnosis of COVID-19 was present in 17 EHRs (1.7%). Medscape Drug Interaction Checker was used to characterize pDDIs. The Mann-Whitney U test and chi-square test were used for statistical analysis. Results: Drug numbers per record ranged from 1 to 23 in T-List and from 1 to 20 in P-List. In T-List, 567 drug combinations resulted in 3781 pDDIs. In P-List, 584 drug combinations resulted in 5185 pDDIs. Polypharmacy was detected in 39.0% of records in T-List versus 65.9% in P-List (p-value < 0.05). The rates of serious and monitor-closely pDDIs due to 'aspirin + captopril' combinations were significantly higher in P-List than in T-List (p-value < 0.05). The rates of serious pDDIs due to 'aspirin + enalapril' and 'aspirin + lisinopril' combinations were significantly lower in P-List compared with the corresponding rates in T-List (p-value < 0.05). Serious pDDIs due to administration of aspirin with fosinopril, perindopril, and ramipril were detected less frequently in T-List (p-value < 0.05). Conclusions: Obtained data may suggest better patient adherence to 'aspirin + enalapril' and 'aspirin + lisinopril' combinations, which are potentially superior to the combinations of aspirin with fosinopril, perindopril, and ramipril. An abundance of high-order pDDIs in real-world clinical practice warrants the development of a decision support system aimed at reducing pharmacotherapy-associated risks while integrating patient pharmacokinetic, pharmacodynamic, and pharmacogenetic information.

A cloud-based medical device for predicting cardiac risk in suspected coronary artery disease: a rapid review and conceptual economic model

Background

The CaRi-Heart® device estimates risk of 8-year cardiac death, using a prognostic model, which includes perivascular fat attenuation index, atherosclerotic plaque burden and clinical risk factors.

Objectives

To provide an Early Value Assessment of the potential of CaRi-Heart Risk to be an effective and cost-effective adjunctive investigation for assessment of cardiac risk, in people with stable chest pain/suspected coronary artery disease, undergoing computed tomography coronary angiography. This assessment includes conceptual modelling which explores the structure and evidence about parameters required for model development, but not development of a full executable cost-effectiveness model.

Data sources

Twenty-four databases, including MEDLINE, MEDLINE In-Process and EMBASE, were searched from inception to October 2022.

Methods

Review methods followed published guidelines. Study quality was assessed using Prediction model Risk Of Bias ASsessment Tool. Results were summarised by research question: prognostic performance; prevalence of risk categories; clinical effects; costs of CaRi-Heart. Exploratory searches were conducted to inform conceptual cost-effectiveness modelling.

Results

The only included study indicated that CaRi-Heart Risk may be predictive of 8 years cardiac death. The hazard ratio, per unit increase in CaRi-Heart Risk, adjusted for smoking, hypercholesterolaemia, hypertension, diabetes mellitus, Duke index, presence of high-risk plaque features and epicardial adipose tissue volume, was 1.04 (95% confidence interval 1.03 to 1.06) in the model validation cohort. Based on Prediction model Risk Of Bias ASsessment Tool, this study was rated as having high risk of bias and high concerns regarding its applicability to the decision problem specified for this Early Value Assessment. We did not identify any studies that reported information about the clinical effects or costs of using CaRi-Heart to assess cardiac risk. Exploratory searches, conducted to inform the conceptual cost-effectiveness modelling, indicated that there is a deficiency with respect to evidence about the effects of changing existing treatments or introducing new treatments, based on assessment of cardiac risk (by any method), or on measures of vascular inflammation (e.g. fat attenuation index). A de novo conceptual decision-analytic model that could be used to inform an early assessment of the cost effectiveness of CaRi-Heart is described. A combination of a short-term diagnostic model component and a long-term model component that evaluates the downstream consequences is anticipated to capture the diagnosis and the progression of coronary artery disease.

Limitations

The rapid review methods and pragmatic additional searches used to inform this Early Value Assessment mean that, although areas of potential uncertainty have been described, we cannot definitively state where there are evidence gaps.

Conclusions

The evidence about the clinical utility of CaRi-Heart Risk is underdeveloped and has considerable limitations, both in terms of risk of bias and applicability to United Kingdom clinical practice. There is some evidence that CaRi-Heart Risk may be predictive of 8-year risk of cardiac death, for patients undergoing computed tomography coronary angiography for suspected coronary artery disease. However, whether and to what extent CaRi-Heart represents an improvement relative to current standard of care remains uncertain. The evaluation of the CaRi-Heart device is ongoing and currently available data are insufficient to fully inform the cost-effectiveness modelling.

Future work

A large (n = 15,000) ongoing study, NCT05169333, the Oxford risk factors and non-invasive imaging study, with an estimated completion date of February 2030, may address some of the uncertainties identified in this Early Value Assessment.

Study registration

This study is registered as PROSPERO CRD42022366496.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR135672) and is published in full in Health Technology Assessment; Vol. 28, No. 31. See the NIHR Funding and Awards website for further award information.

Colchicine in atherosclerotic cardiovascular disease

Inflammation has a direct role in the development of atherosclerotic vascular disease, and oral colchicine displays broad anti-inflammatory properties. Several large, randomised controlled trials (RCTs) have evaluated colchicine's impact on cardiovascular outcomes. Results from a meta-analysis of these trials demonstrate that colchicine reduces the risk of recurrent major adverse cardiovascular events (MACEs) by 25%, leading to its recent approval by the Food and Drug Administration for the treatment and prevention of cardiovascular disease. Despite this, colchicine has not been shown to confer any survival benefit in these trials. The non-significant reduction in cardiovascular death of 18% (95% CI: 45% decrease to 23% increase) is outweighed by a more prominent, borderline non-significant increase in the risk of non-cardiovascular death by 38% (95% CI: 1% decrease to 92% increase). Key populations including those with heart failure, those undergoing surgical revascularisation, women, elderly individuals and non-Caucasians are under-represented in completed trials, which limits generalisability. C reactive protein has been proposed as a biomarker for colchicine response and shows promise for identifying a high-risk population where the benefit on MACE reduction and specifically reduced cardiovascular death might outweigh any real increased risk of non-cardiovascular death; however, this approach is still to be validated in ongoing RCTs. In conclusion, while colchicine shows promise in reducing MACE, its net risk-benefit profile requires further elucidation before its widespread adoption into clinical practice for the secondary prevention of atherosclerotic cardiovascular disease. Much more large-scale, long-term trial data are still needed in this space.

Prescribed Versus Taken Polypharmacy and Drug-Drug Interactions in Older Cardiovascular Patients during the COVID-19 Pandemic: Observational Cross-Sectional Analytical Study

The study aimed to assess clinical pharmacology patterns of prescribed and taken medications in older cardiovascular patients using electronic health records (EHRs) (n = 704) (2019-2022). Medscape Drug Interaction Checker was used to identify pairwise drug-drug interactions (DDIs). Prevalence rates of DDIs were 73.5% and 68.5% among taken and prescribed drugs, respectively. However, the total number of DDIs was significantly higher among the prescribed medications (p < 0.05). Serious DDIs comprised 16% and 7% of all DDIs among the prescribed and taken medications, respectively (p < 0.05). Median numbers of DDIs between the prescribed vs. taken medications were Me = 2, IQR 0-7 vs. Me = 3, IQR 0-7 per record, respectively. Prevalence of polypharmacy was significantly higher among the prescribed medications compared with that among the taken drugs (p < 0.05). Women were taking significantly more drugs and had higher prevalence of polypharmacy and DDIs (p < 0.05). No sex-related differences were observed in the list of prescribed medications. ICD code U07.1 (COVID-19, virus identified) was associated with the highest median DDI number per record. Further research is warranted to improve EHR structure, implement patient engagement in reporting adverse drug reactions, and provide genetic profiling of patients to avoid potentially serious DDIs.

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.