Abstract
Background:
Tenofovir diphosphate (TFV-DP) in dried blood spots (DBS) is a measure of tenofovir-based antiretroviral (ART) adherence that is associated with viral suppression and predicts future viremia. However, whether it is associated with medication regimen complexity in persons with human immunodeficiency virus (PWH) remains unknown.
Methods:
DBS for TFV-DP were prospectively collected from PWH receiving tenofovir disoproxil fumarate (TDF)-based ART (up to three visits over 48 weeks). Baseline patient-level medication regimen complexity index (pMRCI) scores were calculated and categorized into three sub-scores (disease-specific [ART], non-ART, and over-the-counter [OTC]). These pMRCI scores were evaluated to assess the association with TFV-DP in DBS <350 fmol/punch after adjusting for clinical covariates. The same pMRCI scores were also categorized to estimate the adjusted relative risk (aRR) of having a TFV-DP <350 fmol/punch between pMRCI quartiles.
Results:
525 participants (1,146 person-visits) were analyzed. Baseline median (interquartile range [IQR]) pMRCI scores for participants with TFV-DP in DBS <350 vs. ≥350 fmol/punch were 4 (3, 8) vs. 4 (2, 6) for ART, 27 (12, 31) vs. 12 (5, 22) for non-ART, and 0 (0, 1) vs. 0 (0, 2) for OTC, respectively. For the non-ART scores, the aRR for having a TFV-DP in DBS <350 fmol/punch was 6.4 (95% CI: 2.0, 20.6; P=0.002) when comparing participants in the highest pMRCI quartile with those in the lowest quartile.
Conclusion:
Higher pMRCI for non-ART medications is associated with lower adherence as measured by TFV-DP in DBS. Future research should investigate whether reducing non-ART medication complexity improves ART adherence and exposure in PWH.
Keywords: Adherence, HIV, Tenofovir, Medication Regimen Complexity Index, Dried Blood Spots
Introduction
Adherence to antiretroviral therapy (ART) is the main predictor of treatment success and positive clinical outcomes in persons with human immunodeficiency virus (PWH).(1) Despite the fact that many modern ART regimens are easy to administer single tablet regimens,(2, 3) PWH still report barriers to ART adherence.(4) Maintaining high ART adherence is vital in order to reach HIV viral suppression, which prevents disease progression and HIV transmission.(5, 6) Among the available measures of ART adherence are self-report, pharmacy refills, electronic monitoring, directly observed therapy (DOT), electronic pill boxes, and pharmacologic biomarkers such as drug concentrations in urine, hair, and dried blood spots (DBS).(7) Previous research on tenofovir diphosphate (TFV-DP) in DBS, a measure of cumulative ART adherence and exposure in PWH taking tenofovir-based regimens, has demonstrated that this adherence biomarker is strongly associated with viral suppression, is predictive of future viremia, and is influenced by several biological, demographic, and social factors.(1, 8–11) Despite the potential simplicity of ART, PWH may be taking numerous additional medications, including over-the-counter agents, which can increase the burden on medication adherence.
Polypharmacy and increased medication regimen complexity have both been associated with poor outcomes in chronic disease states (e.g. depression, diabetes mellitus, and hypertension), elderly patients, and transplant recipients.(12–15) Polypharmacy (defined as a total of ≥5 medications in an individual regimen) has been associated with decreased adherence, increased number of drug-drug interactions, higher frequency of adverse events, and inappropriate medication use (e.g., inappropriate medication choice, mixing up pills, adherence problems, taking medications not as instructed) in PWH.(16–18) Medication regimen complexity includes more than just the number of prescriptions (polypharmacy); it is a measure which includes a weighted score for the types of prescriptions, dosage forms, dosing frequency, and any additional directions. Additional directions include instructions such as taking medications with or without food, time of day, sitting/standing, refrigeration, or other preparations before consumption. A standardized and validated patient-level Medication Regimen Complexity Index (pMRCI) has been developed to look beyond the effects of polypharmacy and give standardized medication regimen complexity values.(19) In previous studies of populations other than PWH, pMRCI has been shown to be associated with significant adverse clinical outcomes such as hospitalizations, readmissions, and poor medication adherence.(20, 21) The focus of this study extends that work by exploring whether the pMRCI is associated with cumulative ART exposure and adherence in PWH (measured using TFV-DP in DBS).
Methods
Study Population and Sampling Strategy
PWH were prospectively recruited and enrolled from the University of Colorado Hospital-Infectious Disease Group Practice (UCH-IDGP) from 2014 through 2017.(1) Enrollment occurred on a first come, first served basis for participants who were attending their usual standard of care clinical visits, 18 years or older, and were taking TDF-based ART therapy for any duration of time. There were no limitations on comorbidities as exclusion criteria. After informed consent, 4–6 mL of whole blood in ethylenediaminetetraacetic acid (EDTA) were obtained from peripheral venipuncture at the time of the participant’s routine clinical blood draw for HIV viral load (VL) monitoring. A maximum of three blood samples were obtained per participant over 48-weeks, and they were compensated $10 per sample. The timeframe between visits was dependent on each participant’s normally-scheduled clinical follow-up, however a minimum of two weeks between visits was required based on the half-life of TFV-DP in DBS.(22, 23) The study was registered with clinicaltrials.gov (NCT02012621) and approved by the Colorado Multiple Institutional Review Board (COMIRB #13–2104). All patients provided informed consent prior to any research activities.(1)
As noted, HIV VL and DBS were collected simultaneously throughout the study. DBS were assayed using a longitudinal case–control extension strategy, where PWH who had a detectable HIV VL (>20 copies/mL) at any of their study visits were considered to be cases, and those with viral suppression at all study visits where considered controls, as previously reported in this cohort. (1, 8, 9)
Patient-level Medication Regimen Complexity Index
To calculate pMRCI scores, each participant’s electronic health record (EHR) was retrospectively reviewed in those with available TFV-DP in DBS. Figure 1 shows an example of the validated pMRCI tool, where each participant’s medication list was entered, accounting for dosage form (i.e., oral, inhaled, injection), frequency (i.e., once a day, three times a day), and any special instructions (e.g., take with food, take at bedtime).(14, 19) The pMRCI allows for the separation of a participant’s medication list into three distinct categories: a) Disease, b) non-ART, and c) Over the Counter (OTC). For the study participants, the “disease” category was any ART medication(s), the “non-ART” category represented any prescription medication(s) that were not antiretrovirals, and the “OTC” category strictly represented OTC medication(s).
Figure 1. (a) Medication list and (b) example pMRCI in a PWH for “non-ART” medications.
In this example, Disease (abacavir, efavirenz and lamivudine) =7; non-ART (ammonium lactate, atorvastatin, azithromycin, betanechol, fluocinonide, lisinopril, methylphenidate, metoclopramide, nystatin, prednisone, sertraline, sulfamethoxazole-trimethoprim and testosterone) = 37; OTC (aspirin, omeprazole, vitamin C, vitamin D and loratadine) =8.
pMRCI= patient-level medication regimen complexity index; PWH= person with HIV.
Quantification of TFV-DP in DBS
To quantify TFV-DP in DBS, 25μL of whole blood were spotted onto a Whatman 903 Protein Saver card five times via micropipette, as previously described.(23) DBS cards were dried at room temperature for a minimum of two hours and up to overnight. Once dried, the DBS cards were stored until analysis in plastic bags (with a humidity indicator) at −80°C. For analysis, a 3-mm punch was obtained, and TFV-DP levels were quantified using liquid chromatography-tandem mass spectrometry (LC-MS/MS) through a previously validated assay.(24–26)
HIV VL Assessment
HIV VL was measured using the UCH clinical laboratory’s Roche cobas 6800 HIV test.(1) The UCH clinical laboratory is certified under the 1988 Clinical Laboratory Improvement Amendments (CLIA) and has a linear detection range of 20 – 10,000,000 copies/mL.
Statistical Analysis
Generalized linear mixed models were used to estimate the likelihood of low TFV-DP in DBS (<350 fmol/punch) relative to the pMRCI score. We selected 350 fmol/punch vs. <700 fmol/punch (next upper threshold) as the adherence cutoff for TFV-DP in DBS based on previous empirical and clinical observations in PWH taking TDF, where participants below this threshold had the lowest odds of viral suppression,(1) and also based on previous data in healthy volunteers, where TFV-DP in DBS <350 fmol/punch corresponded to very low adherence, with an average of <2 doses/week.(22) Adjustment was made for clinical and demographic covariates that were selected a priori as previously described (1, 8, 9, 11), and included race, gender, age, body mass index, ART anchor drug class (i.e., antiretroviral drug class in the participant’s ART regimen in addition to the nucleoside analog backbone), estimated glomerular filtration rate (Modification of Diet in Renal Disease equation), CD4+ T-cell count, and hematocrit. To include only participants who were anticipated to be at steady state, PWH who had been on their current ART for less than three months were excluded. The pMRCI scores were evaluated first as continuous variable and then as categorical (quartiles) to facilitate interpretation. Results are shown as adjusted relative risk (aRR) in lieu of adjusted odds ratio (aOR) due to the outcome of interest, TFV-DP < 350 fmol/punch, being a rare event (4% in the cohort), and because aRR is more easily interpreted than aOR.(27) Research supports using aRR over aOR if the incidence of the rare outcome ≤ 10%.(28) A significance level of 0.05 was assumed for all hypotheses tested, with no adjustment for multiple comparisons as this was a sub-analysis of the original study. Statistical and graphical analyses were conducted in SAS 9.4 (SAS Institute Inc., Cary, NC, USA) and GraphPad Prism 8 (GraphPad Software, La Jolla, CA, USA), respectively.
Results
Population
From the 807 participants enrolled in the original cohort,(1) 532 participants had DBS assayed for TFV-DP and were eligible for this analysis. After listwise deletion of person-visits with missing covariate values, a total of 1,146 person-visits from 525 participants were analyzed. Baseline demographics are presented in Table 1. Among the 525 patients included, the overall cohort had 75 females, 98 Blacks, and 100 Hispanics, with a median age of 45 (Interquartile range [IQR]: 36, 52) years. As highlighted in Table 1, HIV suppression was associated with higher TFV-DP concentrations. Based on classifications of TFV-DP in DBS <350 and ≥350 fmol/punch, the proportion of participants in each HIV VL category were 0% vs. 72% for VL<20 copies/ml, 11% vs. 19% for VL 20–200 copies/mL, and 89% vs. 9% for VL ≥200 copies/mL, respectively. Shown in Table 2, median values for the total pMRCI score in participants with TFV-DP in DBS <350 vs. ≥350 fmol/punch were 30 (IQR: 18, 37) vs. 18 (IQR: 9, 28), respectively. When sub categorized, the median pMRCI were 4 (IQR: 3, 8) vs. 4 (IQR: 2, 6) for ART, 27 (IQR: 12, 31) vs. 12 (IQR: 5, 22) for non-ART prescriptions, and 0 (IQR: 0, 1) vs. 0 (IQR: 0, 2) for OTC, respectively. Figure 2 depicts the pMRCI subscores according to TFV-DP, separating the subcomponents of the pMRCI for each classification and overall.
Table 1.
Demographic characteristics of participants at first available study visit.
| TFV-DP < 350 fmol/punch (N=19) |
TFV-DP ≥ 350 fmol/punch (N=506) |
Total (N=525) |
|
|---|---|---|---|
| Age (median [IQR]) | 43 (34, 55) | 46 (36, 52) | 45 (36, 52) |
| Gender (N [%]) | |||
| Male | 14 (74%) | 436 (86%) | 450 (86%) |
| Female | 5 (26%) | 70 (14%) | 75 (14%) |
| Race/Ethnicity (N [%]) | |||
| White | 10 (53%) | 292 (58%) | 302 (58%) |
| Black | 5 (26%) | 93 (18%) | 98 (19%) |
| Hispanic | 3 (16%) | 97 (19%) | 100 (19%) |
| Other | 1 (5%) | 24 (5%) | 25 (5%) |
| BMI (kg/m2; median [IQR]) | 23 (21, 28) | 26 (23, 29) | 26 (23, 29) |
| eGFR (mL/min/1.73m2; median [IQR]) | 97 (86, 110) | 87 (74, 102) | 87 (74, 102) |
| Hematocrit (%; median [IQR]) | 42 (39, 44) | 45 (42, 47) | 45 (42, 47) |
| CD4+ T-cell count (cells/mm3; median [IQR]) | 288 (168, 722) | 599 (364, 828) | 592 (348, 828) |
| Type of ART (N [%]) | |||
| INSTI-based | 7 (37%) | 182 (36%) | 189 (36%) |
| NNRTI-based | 1 (5%) | 139 (27%) | 140 (27%) |
| b/PI-based | 7 (37%) | 122 (24%) | 129 (25%) |
| Multiclass | 4 (21%) | 63 (12%) | 67 (13%) |
| Pharmacologic booster (N [%]) | |||
| No | 5 (26%) | 253 (50%) | 258 (49%) |
| Yes | 14 (74%) | 253 (50%) | 267 (51%) |
| HIV viral load (copies/mL; N [%]) | |||
| <20 | 0 (0%) | 364 (72%) | 364 (69%) |
| 20 – 200 | 2 (11%) | 95 (19%) | 97 (18%) |
| ≥200 | 17 (89%) | 47 (9%) | 64 (12%) |
TFV-DP, tenofovir diphosphate; DBS, dried blood spots; IQR, interquartile range; BMI, body mass index; eGFR, estimated glomerular filtration rate; ART, antiretroviral therapy; INSTI, integrase strand transfer inhibitor; NNRTI, non-nucleoside analog reverse transcriptase inhibitor; b/PI, boosted protease inhibitor.
Table 2.
Baseline pMRCI in study participants according to TFV-DP concentrations in DBS.
| TFV-DP < 350 fmol/punch (N=19) |
TFV-DP ≥ 350 fmol/punch (N=506) |
Total (N=525) |
|
|---|---|---|---|
| Total pMRCI Score | |||
| Median (IQR) | 30 (18, 37) | 18 (9, 28) | 18 (10, 29) |
| Min, Max | 8 – 82 | 2 – 123 | 2 – 123 |
| Total Disease Score | |||
| Median (IQR) | 4 (3, 8) | 4 (2, 6) | 4 (2, 6) |
| Min, Max | 2 – 16 | 2 – 16 | 2 – 16 |
| Total non-ART Score | |||
| Median (IQR) | 27 (12, 31) | 12 (5, 22) | 12 (5, 22) |
| Min, Max | 4 – 74 | 0 – 119 | 0 – 119 |
| Total OTC Score | |||
| Median (IQR) | 0 (0, 1) | 0 (0, 2) | 0 (0, 2) |
| Min, Max | 0 – 3 | 0 – 30 | 0 – 30 |
pMRCI, patient-level Medication Regimen Complexity Index; TFV-DP, tenofovir diphosphate; DBS, dried blood spots; IQR, interquartile range. OTC, over-the-counter.
Figure 2. Median pMRCI scores by TFV-DP in DBS category.
pMRCI, patient medication regimen complexity index; TFV-DP, tenofovir-diphosphate; ART, antiretroviral therapy. Median over the counter (OTC) score was 0 for all TFV-DP (fmol/punch) categories.
Association of TFV-DP in DBS with pMRCI
Initial analysis showed that for every 5 unit increase in the Total pMRCI score, the aRR for TFV-DP <350 fmol/punch increased by 1.17 (95% confidence interval [CI]: 1.08, 1.26; P <0.0001). Similarly, for every 5 unit increase in the non-ART pMRCI category score, the aRR for TFV-DP <350 fmol/punch increased by 1.19 (95% CI: 1.10, 1.28; P ≤0.0001). For ease of interpretation, the non-ART pMRCI scores were categorized into quartiles (Q1=0–5, Q2=5–12, Q3=12–22, Q4=22+), and the aRR for having a concentration of TFV-DP <350 fmol/punch were estimated. As shown in Table 3, participants with higher pMRCI scores were at greater risk of having low TFV-DP concentrations. Comparing each lower quartile to Q4, participants in the highest pMRCI quartile had an aRR of 6.4 (95% CI: 2.0, 20.6; P= 0.002) for having TFV-DP in DBS <350 fmol/punch compared to those in Q1. Similarly, when compared to Q2 and Q3, participants in Q4 had an aRR for having TFV-DP in DBS <350 fmol/punch of 3.0 (95% CI: 1.1, 8.3; P= 0.031) for Q2 and 3.0 (95% CI: 1.1, 7.9; P= 0.026) for Q3, respectively.
Table 3.
*Adjusted relative risk (aRR) for TFV-DP <350 fmol/punch according to pMRCI “non-ART” medications score quartiles.
| non-ART pMRCI Quartiles (range) | *aRR | 95% CI | P value | |
|---|---|---|---|---|
| Q4 (22+) | Q1 (0, 5) | 6.4 | (2.0, 20.6) | 0.002 |
| Q4 (22+) | Q2 (5, 12) | 3.0 | (1.1, 8.3) | 0.031 |
| Q4 (22+) | Q3 (12, 22) | 3.0 | (1.1, 7.9) | 0.026 |
Adjusted for: gender, age, race, body mass index, type of ART, estimated glomerular filtration rate, CD4+ T-cell count, and hematocrit. Pairwise comparisons between other quartiles were not significant (all P >0.197). pMRCI, patient-level Medication Regimen Complexity Index; TFV-DP, tenofovir diphosphate; DBS, dried blood spots; CI, confidence interval; ART, antiretroviral therapy.
Sensitivity Analysis
Given the low frequency of TFV-DP <350 fmol/punch in persons-visits in the cohort (4%), a sensitivity analysis was performed to estimate the relative risk for TFV-DP <700 fmol/punch (9% of person-visits) based on the previously described pMRCI quartiles. In this analysis, the results showed similar directionality when comparing Q1, Q2, and Q3 to Q4; aRR=3.46 (95% CI: 1.34, 8.92; P=0.01), aRR=1.13 (95% CI: 0.56, 2.26; P=0.74), and aRR=1.33 (95% CI: 0.64, 2.76; P=0.44) for the non-ART pMRCI category, respectively.
Discussion
In this study, higher pMRCI scores, driven primarily by non-ART medications, were associated with lower cumulative ART adherence and exposure quantified using TFV-DP in DBS. These findings suggest that ART adherence in PWH could be influenced by the complexity of their medication regimen which, in the era of modern ART, is predominantly driven by the presence of non-AIDS comorbidities. While previous studies have shown that general polypharmacy (defined as ≥5 medications) is associated with low adherence to medications,(16, 17, 29) this study is unique because it takes into account not only the number of medications, but also the complexity of the entire medication regimen for PWH. Thus, while pMRCI increases with additional medications, it is also a function of the route, dosing frequency and other specific requirements for each drug. As PWH continue to age and accumulate comorbidities, these findings illustrate the potentially adverse clinical consequences of increasing medication complexity in this population. Conversely, one implication is that ART adherence may increase by reducing or simplifying non-ART medications and self-care OTC regimens.
Similar to previous research that evaluated pMRCI in PWH, this study found that non-ART medications were the main driver of adherence rather than ART itself.(14) This is explained by the ease of taking modern ART, which is now mostly once daily fixed-dose combination regimens, which could be further simplified with the expected approval of long-acting ART regimens.(30–32) As PWH age, it is anticipated that the influence of non-ART medications on the pMRCI will be further increased, resulting in highly-complex medication regimens in older PWH. Thus, efforts to simplify medication regimen complexity in this population should be pursued. These efforts, however, may go against medication management programs for chronic conditions such as hypertension where initiation of new therapies may be indicated. Nevertheless, non-ART medication complexity (as shown in this study) are associated with ART non-adherence, which has the urgency of viral suppression, morbidity and mortality.
The results from this study showed significant differences in aRR for TFV-DP <350 fmol/punch between the highest and the three lowest pMRCI quartiles, suggesting that ART adherence is predominantly influenced by a high pMRCI. Interestingly, the aRR for TFV-DP <350 fmol/punch were similar when comparing Q4 vs. Q2 and Q4 vs. Q3, suggesting a potential plateau at a pMRCI value of 12. This is consistent with a recent study that explored optimal pMRCI cut points in older PWH and found that a score of 11.25 was suggestive of polypharmacy.(33) Of note, a concentration of TFV-DP in DBS <350 fmol/punch was selected as the cut point of interest, as it is associated with the lowest odds of viral suppression and high odds of future viremia.(1, 8) Collectively, these findings raise the question whether a pMRCI of 11 or 12 could be considered as a critical threshold to impact ART adherence. Further research is needed to determine the ideal pMRCI cut point and understand their clinical implications.
Regarding clinical relevance, this study results suggest that patient adherence declines when the pMRCI is ≥12. It also suggests that pMRCI could further inform on a patient’s true adherence to ART. For physicians treating PWH, careful consideration of their patients’ pMRCI could impact their decision to prescribe new or complex medications due to concerns about its impact on adherence to ART, and incentivize them to simplify their patient’s medication regimens. In addition, the pMRCI could be used by future researchers hoping to evaluate the cumulative effect of medication regimens on adherence.
Several potential mechanisms could explain the association of pMRCI with TFV-DP in DBS in this study. First, a high pMRCI is a measure of higher complexity, which makes it troublesome for the patient to take medications, thus leading to lower adherence. Second, a more complex medication regimen has the potential for more drug-drug interactions, which could theoretically impact TFV-DP concentrations, however interactions of this magnitude are not likely. (34, 35) Third, a more complex medication regimen is a marker for more comorbidities, which could influence drug concentrations, as was previously observed in this cohort in PWH and diabetes mellitus.(36) Finally, patients who are non-adherent to ART may be more likely to require prophylactic medications for opportunistic infections, which in turn would increase the pMRCI. Similarly, the barriers that patients face to ART are also likely to be extended to non-ART medications, which could lead to additional prescribing by providers. Within this context, non-adherence to ART could become the driver (and not the result) of a high pMRCI. Whether these mechanisms concomitantly influence TFV-DP concentrations in PWH requires further study.
Among the strengths of this study are the large sample size from a real-world clinical cohort (representative of the HIV epidemic in Colorado), the novel application of the pMRCI, and use of an objective measure of cumulative ART adherence and exposure (TFV-DP in DBS). Limitations include the retrospective collection of the pMRCI data, which relies on provider documentation of current medications in the EHR and on self-reporting of OTC medications. In addition, verbal instructions that are not documented in the EHR cannot be included in the calculation of pMRCI scores. The pMRCI could also underestimate the true regimen complexity because it only accounts for medications and not for other medical devices or vaccinations. Furthermore, a relatively low cut point of TFV-DP <350 fmol/punch was selected as the study outcome, which could select for a small subset of patients with very low adherence not representative of the whole spectrum of non-adherence. However, in the sensitivity analysis this cut point was increased to <700 fmol/punch with similar findings, which provided robustness to the results. In addition, the association identified in this study does not necessarily imply causation, and unidentified drivers of low ART adherence in patients with a high pMRCI may not have been identified. Finally, the pMRCI scores were only calculated at baseline and future studies should examine changes in pMRCI over time in relation to changes in TFV-DP in DBS.
In conclusion, this study highlights that higher pMRCI for non-ART medications is associated with lower TFV-DP in DBS. With the increasing complexity and prescription of non-ART medications, ART adherence should continue to be closely monitored to avoid adverse consequences derived from increased medication complexity. Future research should investigate whether reducing non-ART medication complexity improves ART adherence and exposure, particularly among older PWH and those with polypharmacy and/or comorbidities. Whether these findings from participants on TDF extend to those taking the now more commonly-prescribed tenofovir alafenamide (TAF) is also required.
Acknowledgements
We thank the study participants and personnel at the Colorado Antiviral Pharmacology Laboratory for time, support, and assistance throughout this project. Special thanks to the director of the UCH-HIV program (Steven Johnson), the medical assistants (Nancy Olague, Brittany Limon, Ariel Cates, Maureen Sullivan, and Missy Sorrell), and the nursing staff (Joslyn Axinn, Jackie Deavers, and Ann Czyz) at the UCH-IDGP for their contributions and support of this study.
Funding: This work was supported by the National Institutes of Health (K23 AI104315 to J.C.M.; R01 AI122298 to P.L.A.).
Footnotes
Conflicts of Interest: P.L.A. and J.J.K. have received research funding from Gilead Sciences, paid to their institution. All other authors declare no conflicts of interest.
Meeting presentation: Previously presented at IAPAC 2020, Orlando, FL, USA
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