Cytochrome P450 interactions are common and consequential in Massachusetts hospital discharges

Quantifying the economic burden of unintended pregnancies due to drug–drug interactions with hormonal contraceptives from the United States payer perspective

Background: In the United States of America (USA), nearly 10 million women use oral contraceptives (OCs). Concomitant administration of certain medications can result in contraceptive failure, and consequently unintended pregnancies due to drug–drug interactions (DDIs). The objective of this analysis was to estimate the economic impact of unintended pregnancies due to DDIs among women of reproductive age using an OC alone or in combination with an enzyme inducer co-medication in the USA from a payer perspective. Methods: A Markov model using a cohort of 1,000 reproductive-age women was developed to estimate costs due to contraceptive failure for OC alone versus OC with concomitant enzyme inducer drugs. All women were assumed to begin an initial state, continuing until experiencing an unintended pregnancy. Unintended pregnancies could result in birth, induced abortion, spontaneous abortion, or ectopic pregnancy. The cohort was analyzed over a time horizon of 1 year with a cycle length of 1 month. Estimates of costs and probabilities of unintended pregnancy outcomes were obtained from the literature. Probabilities from the Markov cohort trace was used to estimate number of pregnancy outcomes. Results: On average, enzyme inducers resulted in 20 additional unintended pregnancies with additional unadjusted and adjusted costs median (range) of USD136,304 (USD57,436–USD320,093) and USD65,146 (USD28,491–USD162,635), respectively. The major component of the direct cost is attributed to the cost of births. Considering the full range of events, DDIs with enzyme inducers could result in 16–25 additional unintended pregnancies and total unadjusted and adjusted costs ranging between USD46,041 to USD399,121 and USD22,839 to USD202,788 respectively. Conclusion: The direct costs associated with unintended pregnancies due to DDIs may be substantial and are potentially avoidable. Greater awareness of DDI risk with oral contraceptives among payers, physicians, pharmacists and patients may reduce unintended pregnancies in at-risk populations.

Stratified delirium risk using prescription medication data in a state-wide cohort

OBJECTIVE

Delirium is a common condition associated with increased morbidity and mortality. Medication side effects are a possible source of modifiable delirium risk and provide an opportunity to improve delirium predictive models. This study characterized the risk for delirium diagnosis by applying a previously validated algorithm for calculating central nervous system adverse effect burden arising from a full medication list.

METHOD

Using a cohort of hospitalized adult (age 18-65) patients from the Massachusetts All-Payers Claims Database, we calculated medication burden following hospital discharge and characterized risk of new coded delirium diagnosis over the following 90 days. We applied the resulting model to a held-out test cohort.

RESULTS

The cohort included 62,180 individuals of whom 1.6% (1019) went on to have a coded delirium diagnosis. In the training cohort (43,527 individuals), the medication burden feature was positively associated with delirium diagnosis (OR = 5.75, 95% CI 4.34-7.63) and this association persisted (aOR = 1.95; 1.31-2.92) after adjusting for demographics, clinical features, prescribed medications, and anticholinergic risk score. In the test cohort, the trained model produced an area under the curve of 0.80 (0.78-0.82). This performance was similar across subgroups of age and gender.

CONCLUSION

Aggregating brain-related medication adverse effects facilitates identification of individuals at high risk of subsequent delirium diagnosis.

Relative prevalence of 10 types of pharmacodynamic interactions in psychiatric treatment

OBJECTIVE

To assess the relative prevalence and factors affecting the prescription of medication combinations with a theoretical efficacy limiting pharmacodynamic interaction, defined as two medications with opposing indications and side effects or antagonistic action at the primary receptor of mechanism of action.

METHOD

One hundred sixteen combinations were identified for 10 types of pharmacodynamic interactions. PubMed was searched for each combination to assess the quality of evidence either supporting clinical use or verifying reduced efficacy. Micromedex was searched to determine the presence of warnings to prescribers of reduced efficacy. The prevalence in clinical practice was determined by computer review of the Genoa Healthcare database for all prescribers at 10 participating community mental health centers. The expected prevalence was calculated as the product of the probability of each medication prescribed alone and was compared with the actual prevalence of the combination using the test of proportions.

RESULTS

The frequency of prescription of eight combinations met the Bonferroni corrected level of significance of p < 0.001. Four were combinations of amphetamine and D2 antagonists and each were prescribed less often than chance, p = 0.0001 consistent with epidemiological studies and multiple animal studies verifying an efficacy limiting interaction. Despite epidemiological studies indicating increased risk of accidents, alprazolam and amphetamine were prescribed more often than chance, p = 0.0001. Micromedex generated warnings for efficacy limiting interactions for five other combinations, but with no subsequent change in prescription frequency.

CONCLUSIONS

Neither presence of medical evidence nor warnings from Micromedex consistently affect the prescription of combinations with pharmacodynamic efficacy limiting interactions.

Delineating significant genome-wide associations of variants with antipsychotic and antidepressant treatment response: implications for clinical pharmacogenomics

Background

Genome-wide association studies (GWAS) have significantly contributed to the association of many clinical conditions and phenotypic characteristics with genomic variants. The majority of these genomic findings have been deposited to the GWAS catalog. So far, findings uncovering associations of single nucleotide polymorphisms (SNPs) with treatment efficacy in mood disorders are encouraging, but not adequate.

Methods

Statistical, genomic, and literature information was retrieved from EBI’s GWAS catalog, while we also searched for potential clinical information/clinical guidelines in well-established pharmacogenomics databases regarding the assessed drug-SNP correlations of the present study.

Results

Here, we provide an overview of significant genome-wide associations of SNPs with the response to commonly prescribed antipsychotics and antidepressants. Up to date, this is the first study providing novel insight in previously reported pharmacogenomics associations for antipsychotic/antidepressant treatment. We also show that although there are published CPIC guidelines for antidepressant agents, as well as the FDA labels include genome-based drug prescription information for both antipsychotic and antidepressant treatments, there are no specific clinical guidelines for the assessed drug-SNP correlations of this study.

Conclusions

Our present findings suggest that more effort should be implemented towards identifying GWA-significant antipsychotic and antidepressant pharmacogenomics correlations. Moreover, additional functional studies are required in order to characterise the potential role of the assessed SNPs as biomarkers for the response of patients to antipsychotic/antidepressant treatment.