Medication adherence, medical record accuracy, and medication exposure in real-world patients using comprehensive medication monitoring
Medication adherence, medical record accuracy, and medication exposure in real- world patients using comprehensive medication monitoring
Timothy P. Ryan 0 2 3
Ryan D. Morrison 0 2 3
Jeffrey J. Sutherland 0 2 3
Stephen B. Milne 0 2 3
Kendall A. Ryan 0 2 3
J. Scott Daniels 0 2 3
Anita Misra-Hebert 0 1 3
J. Kevin Hicks 0 3
Eric Vogan 0 3
Kathryn Teng 0 3
Thomas M. Daly 0 3
0 a Current address: Division of Population Science, Moffitt Cancer Center, Tampa, Florida, United States of America ¤b Current address: Internal and Community Medicine, Metro Health , Cleveland, Ohio , United States of America
1 Department of Internal Medicine, Cleveland Clinic , Cleveland , Ohio, United States of America, 3 Medicines Department, Cleveland Clinic , Cleveland , Ohio, United States of America , 4 Reporting and Analytics , Cleveland Clinic , Cleveland , Ohio, United States of America, 5 Medicines Department, Cleveland Clinic , Cleveland , Ohio, United States of America, 6 Robert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic , Cleveland, Ohio , United States of America
2 Sano Laboratories , Sano Informed Prescribing, Franklin, Tennessee , United States of America
3 Editor: Christophe Leroyer, Universite de Bretagne Occidentale , FRANCE
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
Funding: Sano Informed Prescribing provided
support in the form of salaries for TPR, RDM, JJS,
SBM, KAR, and JSD. Study design and sample
collection was performed by the investigator site
(AMH, JKH, EV, TMD), and blinded sample
analysis was performed as indicated in Methods by
RDM, SBM, and KAR. All authors agreed to publish
Poor adherence to medication regimens and medical record inconsistencies result in incom
plete knowledge of medication therapy in polypharmacy patients. By quantitatively
identifying medications in the blood of patients and reconciling detected medications with the
medical record, we have defined the severity of this knowledge gap and created a path
toward optimizing medication therapy.
Methods and findings
We validated a liquid chromatography-tandem mass spectrometry assay to detect and/or
quantify 38 medications across a broad range of chronic diseases to obtain a
comprehensive survey of patient adherence, medical record accuracy, and exposure variability in two
patient populations. In a retrospectively tested 821-patient cohort representing U.S. adults,
we found that 46% of medications assessed were detected in patients as prescribed in the
medical record. Of the remaining medications, 23% were detected, but not listed in the
medical record while 30% were prescribed to patients, but not detected in blood. To determine
how often each detected medication fell within literature-derived reference ranges when
taken as prescribed, we prospectively enrolled a cohort of 151 treatment-regimen adherent
patients. In this cohort, we found that 53% of medications that were taken as prescribed, as
determined using patient self-reporting, were not within the blood reference range. Of the
medications not in range, 83% were below and 17% above the lower and upper range limits,
results and analysis, with joint manuscript
organization and composition taking place after
Competing interests: I have read the journal's
policy and the authors of this manuscript have the
following competing interests: TPR, RDM, JJS,
SBM, and JSD are employees of and own stock in
Sano Informed Prescribing. This does not alter our
adherence to PLOS ONE policies on sharing data
respectively. Only 32% of out-of-range medications could be attributed to short oral
halflives, leaving extensive exposure variability to result from patient behavior, undefined drug
interactions, genetics, and other characteristics that can affect medication exposure.
This is the first study to assess compliance, medical record accuracy, and exposure as
determinants of real-world treatment and response. Variation in medication detection and
exposure is greater than previously demonstrated, illustrating the scope of current therapy
issues and opening avenues that warrant further investigation to optimize medication
The United States spends more on healthcare and prescribes more medications per patient
than any other country [
]. Despite this, health outcomes in the United States are poor
compared to other industrialized countries. The greatest portion of expenditure is for chronic
conditions; for example, in 2013 diabetes ranked first in overall healthcare spending at over $100
Billion, and of that cost, more than 57% was driven by pharmaceuticals . Although diabetes
medications have proven to be efficacious in clinical studies, the effectiveness of these and
other medications must be improved, as there is a disconnect between drug efficacy in
controlled clinical trials and effectiveness in real-world patient settings [
]. Lack of medication
effectiveness may result from poor patient behavior, healthcare delivery flaws, inter-individual
variability in medication response, or a combination of these factors [
]. To better
understand medication effectiveness, it is vital to know if patients are compliant with prescribed
medication regimens, if the medical record used by the healthcare provider is accurate, and if
medication concentrations are within target blood ranges. Knowing the medication
concentration in blood is particularly relevant to medication effectiveness and has demonstrated
treatment utility, particularly in the field of psychiatry . Levels below the therapeutic
reference range may not provide therapeutic benefit, while levels above the therapeutic reference
range may increase the risk of adverse events without offering additional benefit.
While adherence to test medications in clinical trials is typically high, the post-FDA
approval reality is that real-world patient adherence is variable and difficult to measure [
Adherence to medication treatment regimens is driven by economic, health literacy, side effect
profiles, or a host of other factors [
]. Approximately 25% of patients do not pick up their
medications after the initial prescription, and 40% do not refill prescriptions for medications
prescribed for chronic conditions. The cost to the healthcare system of nonadherence is
staggering, estimated to be greater than $200 billion, largely driven by avoidable hospitalizations
]. A recent study by Kymes et. al., demonstrated the benefit of addressing patient
adherence, showing cost savings in the thousands of dollars annually for co-morbid patients
when adherence was improved. Moreover, this study and others have demonstrated that
persistenceÐkeeping adherent patients adherentÐwas largely responsible for the savings
The electronic monitoring of medication container usage may represent the gold standard for assessing medication adherence, surpassed only by direct observation of medication intake . Objective direct methods, such as unscheduled blood monitoring, may be attractive, but these methods have been mostly limited to testing for drugs of abuse. Furthermore, there are
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documented studies of improved adherence shortly before physician appointments,
demonstrating the need to measure adherence in real-world workflows to determine the impact on
hospitalizations, ED visits, and other outcomes. Indeed, improving how current medicines are
taken could have far reaching implications on outcomes; maybe more so than newly developed
Each patient's accounting of medications is located within their electronic health record
(EHR). Complex patients often have multiple healthcare professionals using separate EHR
systems, each of which provide an incomplete view of the patient's care. Using patient pharmacy
records, the EHR, and patient interviews, discrepancies were observed in over 33% of patients
when assessed at hospital admission [
]. When reconciliation was led by a trained
pharmacist, post-hospitalization healthcare utilization was improved, including hospital revisits,
emergency department visits and hospital readmissions [
]. When delivered as an integrated
solution, adherence intervention and medical record reconciliation represent opportunities
for innovation that can un-blind the healthcare provider to the patient's true treatment
Therapeutic drug monitoring has been an effective means to improve therapy for select
medications, typically those with narrow therapeutic margins. When coupled with genetics,
therapeutic drug monitoring can identify causes as to why medications do not fall within
therapeutic reference ranges, and can be used to guide medication selection or dosage changes
[22±25]. A properly attained circulating exposure measurement offers a surrogate biomarker
of drug action and can minimize the guess-work often associated with dose selection [
measurement of medication concentrations takes into consideration all sources that impact
exposure, as these measurements are the manifestation of variability in patient treatment and
response. Historically, therapeutic drug monitoring has been impractical for polypharmacy
patients due to cost, pharmacokinetic considerations, and sample volume necessary to cover
the wide spectrum of medicines. In addition, current approaches to therapeutic drug
monitoring are limited in their scope and can be criticized as ªlooking under the streetlightº, missing
medications that are unknown to the physician. Improvements in medication monitoring
technology using sensitive, high-throughput approaches [
] have now made it possible to
comprehensively assess multiple medications simultaneously and assess total medication
burden in the polypharmacy patient.
Herein we utilized a liquid chromatography-tandem mass spectrometry (LC/MS/MS) assay
capable of quantifying 38 medications from multiple medication classes in a single blood
sample. We assessed medication exposure at the time of sample collection, and subsequently
matched the detected medications with the primary medical record. We quantified
medications in two distinct patient cohorts, each to answer a different question. First, by performing
the comprehensive medication test during visits to healthcare facilities where medication
testing was not anticipated, we explored the use of medication detection as an unambiguous
measure of real-world adherence to ascertain the fidelity of the medical record. In a second cohort,
we measured medication concentrations in prospectively enrolled, adherent patients with
reconciled medical records, comparing the measured concentration of each detected medication
to established reference ranges. By enrolling adherent patients and reconciling records prior to
testing, we were able to explore exposure variability for the 38 drugs queried. The present
investigation is the first to empirically assess compliance, medical record accuracy, and
exposure as determinants of real-world treatment and response in complex patients, providing
insight to the scope of current therapy issues and potential avenues to optimize medication
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Materials and methods
The two studies included in this report were conducted at the Cleveland Clinic, Cleveland OH.
Trials were conducted by Cleveland Clinic personnel and approved by the Cleveland Clinic
Institutional Review Board. All patients provided written informed consent, and for patients
below the age of 18, informed consent was obtained by parent or legal guardian. Patient
enrollment began in April 2015 and last patient visit was in September 2015. All samples were
collected from both cohorts within this timeframe. Sample analysis was performed by Sano
Informed Prescribing Inc., Franklin, TN.
A patient cohort representative of U.S. hospital patients (Residuals Cohort) was obtained
by randomly selecting residual samples from patients receiving a Vitamin D test. Vitamin D
testing was chosen because it is a high-volume test that is routinely ordered in otherwise
generally healthy outpatients. The Cleveland Clinic central electronic health record database was
utilized to match medication lists with residual serum samples from 1000 subjects. Samples with
non-unique identifiers or origin numbers that did not match extraction criteria were excluded
from the analysis. The resulting cohort consisted of 821 patients with available serum and a
matching medication list. A second patient cohort (Reconciled Cohort) with improved
adherence and demonstrated polypharmacy was obtained by prescreening medication lists from
patients prescribed at least five overall medications, including at least two medicines
represented in the test panel and one medicine of the psychotropic drug class. These enrollment
criteria, coupled with an interview-based reconciliation of the medical record prior to admission,
blood draw, and analysis created a biased cohort with improved medication adherence and
demonstrated polypharmacy. Adherence improvement was likely a result of: 1) removing
medications within the EHR no longer taken by the patient based on interview and 2) consent
bias toward more adherent patients. Approximately 500 patients were approached based on
pre-enrollment criteria resulting in a final cohort of 151 patients.
For both study cohorts, serum samples were transferred into microsample tubes bearing
study-specific identifiers. The key linking study-specific identifiers to EHR information was
maintained by study personnel at the Cleveland Clinic and not shared externally. Serum
samples were stored at -70ÊC, until shipping to Sano Informed Prescribing Laboratories for
LC/MS/MS analysis. The medications measured in the assay were prescribed for the treatment
of psychiatric disorders, idiopathic or anatomical pain, cardiovascular disease, diabetes, and
gastrointestinal complications. Sano Informed Prescribing, Inc. is accredited through the
College of American Pathology (CAP# 9265097) and CLIA registered (44D2096427). Sample
analysis was executed under the guidelines set forth by the CAP and standard operating
procedures commensurate with CLIA-registered operations.
Sano laboratory personnel were blinded to study participants' records and reported
medications during the measurement phase of the studies. After measurement, deidentified
medication lists from the EHRs were compared to LC/MS/MS measured results and classified into
one of the following three categories: 1) detected and prescribed (DAP); 2) prescribed, but not
detected (PND); or 3) detected, but not prescribed (DNP). Additional analyses included the
comparison of quantitative measurements for each detected medication to serum reference
ranges available in the literature (S1 Table).
Reagents and standards
Optimal grade methanol and acetonitrile were obtained from Fisher Scientific (Waltham,
MA). Formic acid, ammonium acetate, ammonium formate, and water were all LC/MS grade
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and obtained from Sigma-Aldrich (St. Louis, MO). Dimethylsulfoxide was obtained from
Sigma-Aldrich. Ammonium hydroxide was obtained from Thermo Fisher Scientific. Drug
naïve human serum used in validation studies was obtained from Bioreclamation IVT
(Westbury, NY). All analytical standards were obtained at the highest purity available. Stock
solutions were prepared individually in DMSO, water, methanol, or acetonitrile, then combined.
Standard Curve and Quality Control samples were prepared in drug naïve human serum.
Serum samples were collected in red top gel barrier-free microsample tubes, frozen, and
shipped on dry ice to Sano Informed Prescribing for processing. Samples were thawed, mixed,
and transferred to 96-well plates for processing. Internal standard working solution was added
and protein precipitation was performed using Phenomenex Impact Protein Precipitation
Plates. Eluate was transferred to a new plate and dried under Nitrogen. Sample was reconstituted for LC/MS/MS analysis.
Reconstituted samples were processed using a Shimadzu Nexera X2 liquid chromatography
system (Columbia, MD)) fitted with a 2.1 x 50 mm, 1.7um C18 column (Phenomenex,
Torrence, CA)). Sample analysis was performed on a Sciex 5500 QTrap Mass Spectrometer
(Framingham, MA) with TurboV ion source and polarity switching. Data collection was
performed with Sciex Analyst software, version 1.6.2, and data analysis was performed using
Indigo BioAutomation Ascent software (Indianapolis, IN).
Assay linearity, precision, accuracy, and detection were validated by adding various
amounts of each test drug to human serum. Each of the 38 drugs assayed passed strict
analytical validation criteria. Three medications originally intended to be included in the multi-plex
assay exhibited poor analytical performance and were excluded from analysis. Bupropion
exhibited plasma instability, and lovastatin and phenytoin exhibited poor performance near
the lower levels of the therapeutic reference range necessary for data interpretation. The final
number of medications tested and included in all analyses was 38 (S1 Table).
Quantitative medication reporting
Reference ranges for each of the 38 parent drugs were obtained using triaged data sources
as indicated in S1 Table. The primary information source was obtained from the AGNP
Consensus Guidelines for Therapeutic Drug Monitoring in Psychiatry, which is a comprehensive,
evidence-based summary of therapeutic reference ranges for 128 marketed medications. If
the medication was not listed in this primary source, secondary sources derived from primary
literature were utilized. Finally, if no literature values could be obtained, drug label
information was utilized [29±35]. Medications were mapped to drug classes according to the
NHANES resource (https://wwwn.cdc.gov/nchs/nhanes/1999-2000/RXQ_DRUG.htm; accessed 3/9/2017.
We developed a multiplex assay for the quantitative assessment of serum concentrations for
medications used clinically in the management of chronic disease. The 38-medication panel
was biased toward medications that target the central nervous system, with the balance
prescribed for cardiovascular, metabolic, or gastrointestinal indications. Over-the-counter and
non-centrally acting medications were selected that are known to be co-prescribed at high
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rates with psychotropic medications [
], known perpetrators of drug interactions, or
metabolized through pathways with documented genetic influence. Both acute-acting and
chronicacting medications were included in the test panel. The average coefficient of variation (CV)
established for quality control was less than 20% for the lower (17.3%) and upper range
(16.8%) of quality control samples. The therapeutic reference range, as defined in Hiemke
], was determined for each medication from literature. Measures of inter-assay
precision and accuracy for each analyte and corresponding range parameters are presented in
S1 Table. Nearly all medications in the assay were detected in at least one patient, except for
gemfibrozil, which was prescribed three times but never detected, and clozapine/phenytoin
that were not prescribed or detected in either patient cohort.
Two patient cohorts were selected to answer separate questions pertaining to medication
treatment and pharmacokinetic response. The first cohort consisted of 821 patients randomly
selected from routine clinical testing for serum Vitamin D levels (Residuals Cohort). Patients
ranged in age from 5 to 103 years, with an average age of 54. In 39% of patients, zero panel
medications were detected and 4% of patients had five or more panel medications detected. A
second cohort consisting of 151 patients with documented polypharmacy, including at least
one psychotropic medication, was prospectively enrolled based upon prescreening criteria
(Reconciled Cohort). Owing to the selection criteria, 19% of patients had five or more detected
panel medications. Enrollment criteria for this cohort created a strong bias of 78% female
patients with an average age of 57. Patient characteristics and summary medication results are
listed in Table 1.
The distribution of total number of detected medications differed significantly across the
cohorts (p = 1e-14, Mann-Whitney U-test; Fig 1), with more medications detected per patient
in the prospectively enrolled Reconciled Cohort. Across individual patients, the number of
detected drugs was correlated with the number of prescribed drugs (Spearman ρ = 0.61 and
0.69 in Residuals and Reconciled cohorts, respectively; S1 Dataset). The rate of detection for
individual drugs was correlated in the two cohorts (Spearman ρ = 0.81), although the median
rate of detection in the Reconciled Cohort was 2.4 times greater (Fig 2). Psychotropic
medicines were detected at an even greater rate in the Reconciled Cohort, which required at least
one psychotropic medication for enrollment.
Fig 1. Distribution of total detected medications for two cohorts. Percent of patients having between 0 and 8 detected medications in
the Residuals vs. Reconciled cohorts.
We tabulated drugs across categories denoting whether each detected medication was
consistent with the medication list in the patient's EHR (Table 2). There were three potential
scenarios. A medication could be detected and prescribed (DAP), prescribed but not detected
(PND), or detected but not prescribed (DNP). For drugs that were prescribed but not detected,
we identified and removed the subset that were prescribed on an `as needed' basis (PND prn),
because failure to detect such medications could not be used as a surrogate measure of
nonadherence. We noted that the proportion of prescribed medications that were detected was
significantly higher (Fig 3A, p = 3e-13, two-sided χ2-test), and the proportion of detected
medications not in the medical record was significantly lower (Fig 3B, p = 7e-14, two-sided χ2-test) in
the Reconciled Cohort relative to the Residuals Cohort. These trends further illustrate bias
from the Reconciled Cohort enrollment criteria. Within this Cohort the number of
medications prescribed not detected was similar for males vs. females (93% vs. 86%, p = 0.06,
We examined frequency trends for drugs that were detected in both cohorts. A higher pro
portion of prescribed metabolic agents, such as statin medications, were detected in the
Residuals Cohort, while a larger proportion of prescribed antidepressants, including paroxetine and
trazodone, were detected in the Reconciled Cohort (Fig 4A). Conversely, the proportion of
detected medications not in the medical record was higher for over-the-counter analgesics
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Fig 2. Detection rate for panel medications in two cohorts. Percent of patients for whom a given medication is detected in Residuals vs.
Reconciled Cohorts. The dotted line indicates equal detection rates in both cohorts, while the solid line indicates the ratio of overall detection
rate in both cohorts: 1.3 detected drugs per patient in Residuals Cohort vs. 3.2 detected drugs per patient in Reconciled Cohort.
such as ibuprofen and acetaminophen, and drugs of abuse, including benzodiazepines, in the
Residuals Cohort than in Reconciled Cohort (Fig 4B).
Several drugs with lower levels of detection relative to prescribing rates have short oral
halflives, making them theoretically difficult to detect upon q.d. dosing. Therefore, we examined
the proportion of detected medications as a function of drug half-life in the Reconciled Cohort,
where we gathered self-reported time of dosing and where patients exhibited overall higher
medication adherence (Fig 5). The percentage detected was generally lower for simvastatin,
pravastatin and omeprazole, but not for acetaminophen and metoprolol. All these medications
have average literature half-lives less than three hours (Table 2). Comparing simvastatin
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a detected and prescribed (DAP),
b detected not prescribed (DNP),
c prescribed not detected (PND)
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Fig 3. Medication prescriptions according to EHR vs. medication detection in two cohorts. A) Percent of prescribed
medications that are detected and B) percent of detected medication that are non-prescribed (i.e. not in the EHR). Error bars
were calculated from Bernoulli trials.
(t1/2 = 2.5 hours) and pravastatin (t1/2 = 2.9 hours) to atorvastatin (t1/2 = 20 hours) was
instructive, as atorvastatin would be predicted to reach steady state blood concentrations upon
q.d. dosing, whereas simvastatin and pravastatin would not based on oral half-life. The
detection rate for atorvastatin (93%) exceeded the detection rates of the short-lived statins (55%
simvastatin and 40% pravastatin). For drugs with half-lives less than four hours, we evaluated
the percentage detected vs. time since last dose (S1 Fig). A decreasing trend of single point
exposure vs. time since last dose for simvastatin was observed, but no such trend was observed
with other short half-life medications, such as oxycodone. These empirical data show that
many such drugs can be detected 12 hours or more after dosing.
A central tenet in pharmacology is to optimize drug concentrations at the target to elicit the intended effect. In practice, measuring drug concentrations in blood is a useful surrogate for most medications, and the optimal blood levels have been established for many drugs. We compared the concentration of each medication detected to the published therapeutic
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Fig 4. Adherence and non-prescribed medication use in two cohorts. A) Percent of prescribed medications
that are detected (adherence), for medications having 10 or more prescriptions in each cohort. B) Percent of
detected medications not in the EHR (non-prescribed), for medications having 10 or more detections in each
cohort. The solid diagonal line indicates equality in both cohorts, and the dashed line indicates the overall ratio of
adherence or non-prescribed use between cohorts, calculated across all medications. Markers are sized
proportionally to log10 of prescriptions or detections.
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Fig 5. Percent of prescribed medications that are detected vs. medication half-life for Reconciled Cohort. Medications with
halflife > 24 hours are shown at 24 hours on the abscissa. The fit denotes the least-squares power curve; the functional form was selected due
to expected exponential decay of medication concentration with time.
reference range (Table 3; S2 Fig), focusing on the Reconciled Cohort, where patients took
prescribed medications at a high rate. In this cohort 53% of detected drugs were observed to lie
outside these ranges (Fig 6). Medications were more frequently detected at concentrations
below the therapeutic reference range than at concentrations above the therapeutic reference
range, and the percentage of drugs within, above, or below the therapeutic reference range was
remarkably consistent between patient cohorts (Table 3). We explored the impact of dose and
time since dose, and found modest predictive utility in explaining variation in drug levels
We developed a 38-medication LC/MS/MS assay that crosses therapeutic indications for the
detection and quantitation of medications in serum. We used the assay as a surrogate of
medication adherence, a tool to improve medical record accuracy, and as a comprehensive method
to measure exposure in patients. When reconciled with patient's EHRs, medication
measurement in serum offers an empirical measure of adherence and insight into EHR fidelity.
Further, quantitative measurement in serum allowed for comparison of each detected medication
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a Number of drugs per patient in each category, and
b percentage of drugs in each category.
c Each prescribed and/or detected drug was assigned to one of 4 categories: detected and prescribed (DAP), prescribed not detected (PND), PND drugs
taken as needed (PND-prn), and detected but not prescribed (DNP) drugs.
d For detected and prescribed drugs that were measured quantitatively, tabulation by drug level compared to therapeutic drug ranges
concentration relative to the therapeutic reference range, elucidating the extent of patient
exposure variability patients.
We queried the systemic circulation in two patient cohorts. The first, 821-patient cohort (Residuals Cohort) was designed to obtain samples from de-identified outpatients blinded to the medication testing paradigm. As such, comparisons between medications detected
Fig 6. Medication detections vs. therapeutic monitoring ranges in Reconciled Cohort. Percent of medications detected quantitatively
below, within or above ranges established in the therapeutic drug monitoring literature, for drugs that were listed in the patients EHR. Error
bars were calculating from Bernoulli trials.
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empirically and those in the prescription record would not be biased by patient behaviors
associated with knowledge of drug testing. The second cohort of 151 patients (Reconciled Cohort)
was prospectively enrolled, had medical records reconciled in a self-reporting interview, and
consented to have blood tested for the presence of medications. In this cohort, we queried
patients with reconciled records and a propensity to adhere to complex medication paradigms
on how often medications would fall within the desired therapeutic reference range. Overall
medication usage and detection rates were higher in this cohort and fewer medications were
detected that were not listed in the medical record (Figs 1±3), producing the desired cohort to
investigate quantitative aspects of medication exposure in complex patients that take
medications as indicated.
In the residuals cohort, we found that 71% of prescribed drugs were detected in patients, a
result slightly higher than estimates of compliance using pill counting and other methods of
adherence measurement [
]. Blood concentrations from most medications remain at
detectable levels for several days post-ingestion, therefore slightly higher `adherence' rates
using medication monitoring relative to indirect methods likely results from patients that are
partially adherent. The most frequently detected medications were drugs prescribed for
metabolic and cardiovascular disease. The most disproportionately detected medications were of
the psychotropic class, as enrollment criteria for the Reconciled Cohort required one
psychotropic medication in the patient record prior to enrollment (Fig 2). Acetaminophen was the
most often detected medication in circulation (Table 2). The frequency of detection and
cumulative dose of this drug can become unintentionally high in patients, as this medication is
found in at least 650 over-the-counter products, many of which are over-the-counter
combination products taken simultaneously.
The rate of detection for medications that were not in the prescription record, the converse
of the adherence measure discussed above, is novel information for the healthcare provider.
Overall, 33% of detected medications in the Residuals Cohort were not in the medical record,
with higher rates for over-the-counter medications, such as ibuprofen, and abused
medications, such as benzodiazepines (Fig 4). This proportion decreased to 15% in the Reconciled
Cohort, demonstrating that adherence and medical record omissions go hand in hand for the
polypharmacy patient. Detected medications not in the EHR also create treatment issues, as
drug-drug interactions with current treatment or future prescribing cannot be addressed
when the medications are unbeknownst to the physician. This offers the opportunity for
improving the medication reconciliation process and patient literacy [39±42].
The Reconciled Cohort was used to assess the impact of polypharmacy and biological factors
on medication blood levels by testing in patients adherent to complex pharmacy regimens.
Reference ranges were derived from published values for each medication in the assay panel, some
of which had more supporting literature than others. Serum concentrations below the
therapeutic reference range lower limit are unlikely to elicit a therapeutic response and concentrations
above the upper limit exhibit tolerability decreases or no evidence that therapeutic
improvement will be enhanced. This range is meant to be an orienting value, and is not necessarily
applicable to all patients for each individual medication (26). More than half of the medications
detected in this cohort were not within the therapeutic range (Fig 6). This finding deserves
further study, including investigation into caveats associated with this type of measurement.
Therapeutic drug monitoring has been performed with antipsychotic medications as
singlemedication studies in a variety of healthcare settings, and it has been consistently observed
that medications are often out of range [
26, 43, 44
]. We now extend these findings to
nonpsychotropic medications, including medications more frequently prescribed to US patients
alone or in combination [
]. Typically, therapeutic drug monitoring studies are conducted
with patient medications at steady state and samples taken at trough levels. Although we did
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not replicate true trough sample collection with the present study design, given the multiplicity
of medications tested at one time, our data demonstrate that almost 50% of medications are
below the intended therapeutic reference range. This suggests that a significant number of
patients have sub-therapeutic levels of medication when multiple medications prescribed.
We collected self-reported time of dosing in Reconciled Cohort patients. Although
individual medication half-lives are expected to be important criteria when monitoring medication
levels, the correlation of medication exposure with time of dosing varied widely (S1 Fig). The
percentage of prescribed medications detected in patients was generally lower for simvastatin,
pravastatin and omeprazole, but not for acetaminophen and oxycodone (Fig 5); these drugs
have average literature oral half-lives less than 3 hours (Table 2). Reasons patients may be
below the reference range are multifactorial and include; 1) patients may be partially adherent,
with medication persistence lacking, 2) the therapeutic range, which is often developed in
clinical trial patients lacking real-world diversity, may be inaccurate, or 3) pharmacokinetic
drugdrug or drug-gene interactions may be manifesting in these polypharmacy patients. There are
countless other reasons, including patient health and biological makeup, but the finding of
extensive variability in medication exposure is important for optimizing medication therapy.
As data accumulate with each medication measured, we will begin to address these issues by
comparing measured data to patient outcomes, and de-convolute behavioral vs. biological
factors underlying variability in drug treatment and response.
The current study included 38 medications, offering a comprehensive survey of the most
frequently prescribed psychotropic medications and select over-the-counter and
non-psychotropic medications used to treat other chronic diseases. In theory, the approach applied herein
could be scaled to detect several hundred cross-therapeutic medications simultaneously,
detecting a very high percentage of written prescriptions. Measuring the majority of frequently
taken medications provides the healthcare professional a comprehensive view of therapy for
the complex patient that cannot be obtained without empirical measurement, although one
must consider the pharmacokinetic limitations that may hamper the detection or quantitation
of a particular drug, such as topical administration or short half-life.
There are several limitations in the current study. First, the use of exposure as a surrogate of
medication adherence, medical record accuracy, and therapeutic range has caveats given the
current state of real-world medication exposure knowledge. Except for medications that are
frequently monitored, such as digoxin or phenytoin, published information is lacking
information on medication exposure relative to outcomes. For some medications, there have yet to
be published studies linking blood levels to outcomes, and in a few, no association was shown
to exist when assessed. The measurement of medications using the LC/MS/MS methodology
deployed herein is highly precise and accurate, but there are a multitude of reasons a
medication prescribed may not be detected. Finally, medication persistence, drug interactions,
genetics, disease state, and many other factors contribute to whether a medication detected falls
within published therapeutic reference ranges, and with errors in self-reported medication
ingestion and therapeutic range derivation issues, it would be premature to use this
information quantitatively as stand-alone decision criteria in medication management as it stands
today. The best way to circumvent these issues is to collect real-world exposure information
on more medications relative to patient outcomes, and build empirical measurement data into
largely theoretical clinical decision support on medication exposure relative to response.
These studies demonstrate using a novel and empirical surrogate approach that patients do not take all their prescribed medications, that the medication lists in EHRs are often
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erroneous, and that medication exposure is more variable than previously demonstrated. In
these studies, only 37% of prescribed or ingested medications were fully in line with the
medical record that the healthcare provider was working from. Ours is the first study to empirically
measure cross therapy medication levels regardless of prescription record, and illustrates the
scope of multifactorial problem underlying medication therapy management. We have shown
with 38 medications that the issue of adherence and medical record accuracy is substantial,
and expanding these studies to more complex patients, measuring more simultaneous
medications, and gathering requisite genetic, wellness, and outcome data will prove valuable in
explaining sources of medication exposure and its relevance to treating disease. The
quantitative aspect of blood-based medication measurement deserves further study, and with increased
sample size driving model building, can ultimately extend this approach beyond simple
adherence and record reconciliation into exposure-based prescribing.
S1 Fig. Percent of prescribed medications that are detected vs. time since ingestion. Percent
of prescribed medications that are detected for a given range of hours since taking (x-axis),
using patient-reported medication ingestion times from Reconciled Cohort. Values on bars
denote number of observations in the given time range. The absence of a count label indicates
that there are no observations in that time range.
S2 Fig. Distribution of log10 (concentration) aggregated across both cohorts. Vertical
reference lines denote the low/high therapeutic drug range according to the literature. Value below
the drug name denote its half-life in hours. Only drugs with 10 or more detections are shown.
S1 Table. Reference ranges and assay performance for the medication panel.
S2 Table. Relationship between drug concentration and patient-reported dose and time
since taking medication in cohort 2. a Only drugs detected and prescribed 10 or more times;
b patient-reported doses for detected drugs; c Spearman rho correlation between concentration
vs. dose or time since dosing; d hydrochorothiazide.
S1 Dataset. De-identified patients, gender, age and summary of prescribed and detected
S2 Dataset. Prescribed and/or detected drugs for two patient cohorts.
The authors gratefully acknowledge initial insight from Rebecca Heltsley and Lucas Marshall, and administrative support from Amy Ryan in the preparation of this manuscript.
Conceptualization: Timothy P. Ryan, J. Scott Daniels, J. Kevin Hicks, Kathryn Teng, Thomas
16 / 19
Data curation: Jeffrey J. Sutherland, Thomas M. Daly.
Formal analysis: Ryan D. Morrison, Jeffrey J. Sutherland, Thomas M. Daly.
Funding acquisition: Timothy P. Ryan.
Investigation: Anita Misra-Hebert, J. Kevin Hicks, Eric Vogan, Kathryn Teng, Thomas M.
Methodology: Ryan D. Morrison, Stephen B. Milne, Kendall A. Ryan, J. Scott Daniels.
Project administration: Thomas M. Daly.
Supervision: Timothy P. Ryan.
Validation: Ryan D. Morrison, Jeffrey J. Sutherland, Stephen B. Milne, Kendall A. Ryan.
Visualization: Jeffrey J. Sutherland.
Writing ± original draft: Timothy P. Ryan, Jeffrey J. Sutherland, Thomas M. Daly.
Writing ± review & editing: Timothy P. Ryan, Jeffrey J. Sutherland, J. Scott Daniels, Anita
Misra-Hebert, J. Kevin Hicks, Kathryn Teng, Thomas M. Daly.
17 / 19
18 / 19
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