A retrospective, matched cohort study of potential drug-drug interaction prevalence and opioid utilization in a diabetic peripheral neuropathy population initiated on pregabalin or duloxetine

Challenging to patients, as well as providers, diabetes has the potential to cause a specific group of nerve disorders categorized as autonomic, peripheral proximal, focal, and peripheral neuropathies [1,2]. A common complication of diabetes, painful diabetic peripheral neuropathy (pDPN) affects approximately 1 in 4 people with this disease. Reduced glycemic control, advanced age of the patient, and the length of time the patient has diabetes are all documented risk factors for developing pDPN [3-6]. Diagnosis of the condition involves documentation of the symptoms of peripheral nerve dysfunction after exclusion of other potential causes of pain. The development of pDPN has been linked to signaling impairment in the central nervous system, although the process driving this change remains inadequately understood [7,8].

While the advancement of pDPN has been shown to be decreased through glycemic control [5], a variety of treatment options are available for pDPN; however, only a limited number have been proven effective in appropriate clinical trial settings [9,10]. There are a number of drug classes recommended and available including anticonvulsants, tricyclic antidepressants, and serotonin-norepinephrine reuptake inhibitors (SNRIs) [9,11]. Guidelines are available for treatment of pDPN; however, these can differ, and the recommended medications may generate adverse effects in conjunction with diabetic treatments [11,12]. Only three drugs, pregabalin, duloxetine, as well as the recently approved tapentadol, have been approved by the Food and Drug Administration for treatment of pDPN [13-20]. Evidence has been put forth for the use of opioids such as oxycodone and tramadol in treatment of pDPN through randomized controlled trials, although there is no long-term evidence of their impact on the disease or on quality of life of patients [9,11,21-24]. Stemming from the variable course of pDPN, the risk of opioid addiction in this group is increased, and a number of side effects are associated with opioids including hypogonadism and lowered immunity [25-27]. Consequences of opioid overdosing, especially in older patients, can be severe and clinical decision-making must balance the risk of such adverse effects by achieving pain control at the lowest possible combined dose(s) of opioid [28]. Previous retrospective studies across various patient populations and utilizing disparate definitions of opioid utilization have incongruent findings regarding the opioid-sparing effects of pregabalin [29-32] and duloxetine [29,30,33]. While clarity on the opioid-sparing effect of pregabalin and duloxetine should continue to be sought, it must be done so in light of introducing potential drug-drug interactions (DDIs) through use of either agent.

As the occurrence of pDPN increases with the degree of hyperglycemia and age of the patient, the total number of prescriptions a patient may be taking has the potential to rise dramatically due to the need to manage the effects of advancing diabetes and comorbidities related to the disease. As such, anticipating and controlling DDIs in patients with pDPN presents a significant challenge to providers, particularly in an older population. The consequences of these drug interactions have the potential to impact the quality of life of the patient, as well as increase the cost of the disease [9,34].

While some studies have examined aspects of treatment outcomes such as healthcare utilization and economic comparisons between pregabalin and duloxetine (where the costs were found to be comparable between the two drugs), few studies have documented the costs incurred from DDIs related to treatment of patients with pDPN [29,34-36]. Recently, Johnston et al. [34] examined the frequency and financial impact of DDIs and drug-condition interactions (DCIs) in predominantly commercially-insured pDPN patients prescribed pregabalin or duloxetine and found patients taking duloxetine had a substantially higher potential for DDIs and DCIs than patients taking pregabalin. Furthermore, the costs associated with potential DDIs and DCIs in patients taking duloxetine were found to be significantly increased.

The present study continues and builds on previous research by examining the impact of newly initiated pregabalin or duloxetine treatment on Medicare Advantage Prescription Drug plan (MAPD) pDPN members’ encounters with potential drug-drug interactions, the healthcare cost and utilization consequences of those interactions, and opioid utilization.

The present study adds to the limited understanding of potential DDI prevalence, as well as the healthcare cost and utilization consequences of those interactions, in a predominantly older MAPD patient population newly initiated on pregabalin or duloxetine for the treatment of pDPN.

This present study found that pDPN subjects initiating treatment with pregabalin were significantly less likely to encounter potential DDIs than patients initiating duloxetine. The magnitude of DDI prevalence for duloxetine patients observed (56.7%) is likely driven by factors inherent to the medication itself and by the population in which it was studied. Duloxetine undergoes hepatic metabolism by cytochrome P-450 (CYP450) isoenzymes 2D6 and 1A2. Further, it inhibits isoenzymes 2D6 and 1A2 and is highly plasma protein bound resulting in many potential DDIs with other medications [44]. Alternatively, pregabalin has minimal pharmacokinetic or pharmacodynamic interactions with other medications as signified in this study by the low prevalence of potential DDIs found (2.9%) [45]. The comorbidity burden and associated medication use of the matched pDPN pregabalin and duloxetine cohorts align with previous studies of pDPN patients [29,34-36] and it has been shown that pain treatment itself is an independent risk factor for exposure to potential DDIs [46,47].

A recent study by Johnston et al. found the frequency of potential contraindicated, major, and moderate DDIs in DPN patients newly initiated on duloxetine to be 3.9%, 36.9%, and 33.8%, respectively [34]. Conversely, no DPN patients newly initiated on pregabalin encountered a potential DDI of any severity. Methodological variations between the aforementioned study [34] and present study may explain the differences in reported potential DDI prevalence, with the most noteworthy variation being the insurance plan case-mix amongst the study populations. The former study [34] was conducted in a younger, predominantly commercially-insured population while the present study was conducted in a Medicare population with a mean age of 66.7 years for the matched cohorts. This observation highlights the age-related vulnerability to drug interactions experienced by older patients [48,49].

Previous research reported no statistically significant differences in post-initiation all-cause healthcare costs for pDPN patients newly initiated on pregabalin or duloxetine [29,36]. The present study endeavored to determine the impact of potential pregabalin and duloxetine DDIs on 6-month post-initiation costs. Among duloxetine subjects, mean all-cause total healthcare, total medical, inpatient-related, emergency room, and total pharmacy costs were significantly higher for those with a potential DDI than those without. Non-statistically significant cost increases were found between the pregabalin members with and without a potential DDI. These findings are substantiated by the Johnston et al. study that found the combined presence of potential DDIs and potential drug-condition interactions (DCIs) were associated with significantly higher all-cause healthcare costs in duloxetine users but not so in pregabalin users [34].

The non-significant findings of the within-pregabalin cohort comparisons and in the difference-in-difference analysis must be tempered by the small number of pregabalin subjects with a potential DDI (n = 13). The increase in costs associated with potential DDIs within the duloxetine cohort, however, cannot be overlooked. Prior studies of chronic pain conditions have found that CYP450 interactions between specific opioids (codeine, fentanyl, hydrocodone, methadone, oxycodone, and tramadol) and concomitant medications that serve as substrates or inhibitors of CYP450 isoenzymes 3A4 and 2D6, including duloxetine, result in higher costs and greater healthcare utilization [50-52].

A retrospective analysis of 170,086 patients using claims data from 2004 to 2008 found significantly higher mean total 6-month costs in chronic pain subjects with an exposure to a potential DDI compared to matched subjects without an exposure ($8165 vs. $7498, respectively; p < 0.01) [50]. This same study found that potential DDI exposed patients versus non-exposed patients experienced significantly more office visits (19.10 vs 18.29; p < 0.01), outpatient visits (6.71 vs 6.39; p < 0.01), emergency department visits (0.46 vs 0.43; p < 0.01), and inpatient hospitalizations (0.13 vs 0.12; p < 0.01) [50]. The large sample sizes allowed for detection of small differences, making the statistically significant findings difficult to interpret in terms of clinical relevance. The increases in healthcare utilization in the presence of potential DDI exposure is directionally consistent with the duloxetine cohort in the present study with regard to percent of patients with inpatient hospitalization and ER visits and mean numbers of visits per member for inpatient hospitalizations, ER visits, and non-physical therapy outpatient visits.

Changes in opioid utilization after initiation of pregabalin or duloxetine, regardless of potential DDI occurrence, were also assessed in the present study. No changes in pre- to post-initiation of long-acting opioid, short-acting opioid, strong opioid, weak opioid, or “any” opioid use were found for pregabalin or duloxetine, as defined by the percentage of patients with one or more pharmacy claims, in the lone head-to-head retrospective analysis of these drugs in a pDPN population [29]. However, when defined as the number of prescriptions filled by opioid users, both pregabalin and duloxetine patients experienced statistically significant increases in mean number of prescriptions filled for short-acting, weak, and “any” opioids [29]. Duloxetine patients also exhibited significantly greater post-index use of long-acting opioids [29]. The difference between the increases observed for pregabalin and duloxetine were not compared. As in the aforementioned study [29], the present study found no difference between pregabalin and duloxetine patients in their respective pre-versus-post differences in the percentage of members with ≥1 pharmacy claims for long-acting opioids, short-acting more potent opioids, or short-acting less potent opioids.

Given that opioids within the same category, such as “long-acting,” vary in terms of potency, the present study expanded on the aforementioned research [29] by assessing opioid use as a function of morphine equivalents. A robust difference-in-difference analysis of 6-month pre-index and 6-month post-index morphine equivalents used identified a trend toward greater mean morphine equivalents per 30-day post-index increase in use in the duloxetine cohort relative to the pregabalin cohort (176.9 mg vs. 60.2 mg, respectively; p = 0.058). As opioids vary not only in duration of action but also in potency, this finding, while not providing clarity on the opioid-sparing differences of pregabalin compared to duloxetine, does support the use of morphine equivalents as a preferred method of comparing changes in opioid utilization.

Several limitations must be noted for studies involving administrative claims data. Causal effect cannot be assigned given the observational nature of this study. The use of secondary data is limited by the threat of validity posed by inaccurate data within healthcare information technology systems as well as by differences in coding and documentation practices at the provider, region, or site-specific level. Actual patient behaviors regarding medication consumption cannot be assessed through paid pharmacy claims. Further, reliance on paid pharmacy claims for medication utilization does not capture the use of over-the-counter medications or remedies impactful to the disease state in question. Indirect costs were not considered as part of this study. A reduction in confounding was attempted through the use of propensity score matching and multivariate modeling. These approaches are limited by only the covariates included in the statistical models. Even with this attempt at bias reduction, residual confounding from included covariates can still occur and confounding from unknown or unmeasured covariates remains a possibility. The use of “potential” DDIs does not imply the actual occurrence of clinically-impactful DDIs, and therefore limits the interpretation and applicability of the findings. Lastly, given the focus on pDPN patients, the results of this study should not be considered generalizable to other FDA-approved indications or off-label uses.

The results of this retrospective analysis suggest that the real-world prevalence of potential duloxetine DDIs in a MAPD pDPN population is substantially greater than that of potential pregabalin DDIs. While it is desirable to find infrequent DDIs, the low prevalence of potential pregabalin DDIs places limits on both the performance and sensitivity of within or between drug comparisons. From a clinical perspective, the importance of considering DDIs during treatment selection should be considered due to the significantly higher prevalence of potential DDIs found in pDPN duloxetine users.