Potential Multidrug Interactions in Elderly Ambulatory Patients
Tara V. Anand, BA1, Brendan K. Wallace,
BS2, and Herbert S. Chase, MD, MA1*
1Department of Biomedical informatics, Columbia
University Medical Center; 2Vagelos College of
Physicians and Surgeons, Columbia University, New York, NY 10032 USA
*Corresponding author: email hc15@cumc.columbia.edu
ABSTRACT
Aim. Polypharmacy may increase the prevalence of potential multidrug
interactions (pMDIs), where one drug interacts with two or more other
drugs, possibly amplifying the risk of a potential adverse drug event
(pADE). The major goal of this study was to estimate the prevalence of
amplifying pMDIs in an ambulatory cohort of older patients.
Methods. Current medication lists of 22033 randomly chosen outpatients
≥50 years old were extracted from the New York Presbyterian Hospital
(NYP) data warehouse. Network analysis identified patients prescribed
three or more interacting drugs from their current medication lists.
Potentially harmful interactions were identified from the NYP drug-drug
interaction alerting system. pMDIs were considered amplifying if the
interactions increased the probability of a pADE through
pharmacokinetic, pharmacodynamic or conditional mechanisms.
Results. pMDIs were identified in 5.1% of the medication lists; 3.4%
were three-drug and 1.1% were four-drug pMDIs. The most common drugs
involved were psychotropic, comprising 23.3% of the total drugs. The
most common pADEs associated with the interactions were serotonin
syndrome (17.2%), seizures (14.4%), prolonged QT interval (15.8%) and
bleeding (14.4%). pADE amplification risk was identified in 71.8% of
three-drug pMDIs when one drug interacted with two others, 97.8% when
all three interacted with each other, and 93% for four-drug pMDIs.
Conclusion. Our data suggest that approximately 5% of elderly
ambulatory patients may be exposed to pMDIs which amplify the
probability of associated adverse drug events. The recent and persistent
rise in polypharmacy will likely increase the prevalence of pMDIs and
potential exposure to serious adverse events.
1. What is already known about this subject
The steady rise in polypharmacy in elderly patients has increased the
prevalence of serious adverse events resulting from drug-drug
interactions.
It has been hypothesized that polypharmacy will also increase the
prevalence of multidrug interactions where one drug interacts with two
or more, potentially amplifying the probability of an adverse event.
2. What this study adds:
Using network analysis, we determined that 5% of the medication lists
of a cohort of elderly ambulatory patients contained three and four-drug
multidrug interactions with amplification potential.
Psychotropic drugs were most often involved in multidrug interactions,
placing patients at risk for serotonin syndrome, prolonged QT interval
or seizures.
1.0 INTRODUCTION
For several decades there has been a concerted effort to identify
potentially harmful drug-drug interactions and to develop and implement
decision support prompts to warn prescribing clinicians of these
possible interactions. Drug-drug interactions are an increasingly common
cause of morbidity and mortality in the elderly, accounting for nearly
5% of hospital admissions from the emergency room [1]. The steady
increase in polypharmacy in the elderly population, resulting in an
average of five drugs per patient [2, 3], increases the likelihood
that a patient will experience one or more adverse events due to a
drug-drug interaction [4, 5].
Most of the research efforts to identify the impact of drug-drug
interactions have focused on two-drug interacting pairs. With the
increasing prevalence of polypharmacy, however, there is an effort to
identify potential multidrug interactions (pMDIs), where one drug
interacts with two or more other drugs, and their potential to amplify
the probability of an adverse event [6, 5, 7]. A patient taking four
drugs, for example, could be exposed to two different adverse events
associated with two-drug interacting pairs if the pairs acted
independently from one another. However, if each of the four drugs
interacted with all others, the potential for patients to experience
serious adverse events could be significantly amplified if several of
the drugs were associated with the same adverse event or if two or more
drugs altered the metabolism of the other drugs in the multidrug
interaction.
Although several studies have explored the prevalence of potential
two-drug interactions in elderly ambulatory patients, there are few
studies of multidrug interactions and the potential consequences [8,
9]. The major goals of this study were to estimate the prevalence of
multidrug interactions in an ambulatory cohort of older patients, to
identify the most common drugs involved in the interactions, to identify
the potential serious adverse events associated with the interactions
and to identify mechanisms by which the interactions could lead to
amplification of the risk of associated adverse events. We explored
these issues by analyzing the current medication lists of individual
ambulatory patients cared for at the New York Presbyterian Hospital
(NYP), using network analysis to identify multidrug interactions and
natural language processing to identify the potential adverse events
associated with the interactions from the NYP drug-drug interaction
database.
2.0 METHODS
2.1 Source of patient data. The current medication lists of 22033
randomly chosen outpatients from 2015-2020, were extracted from the NYP
data warehouse. The dates of the oldest and newest prescriptions on the
medication list were within a five-year time window or shorter.
Medications were recorded with either generic or brand names, which were
converted to generic names. Topical and ophthalmic preparations were
excluded. Patients prescribed any of the following medications were
excluded because these drugs are largely prescribed to hospitalized
patients: lactulose, enoxaparin, vancomycin, neomycin, dalteparin, or
heparin. The study was approved by the Columbia University Irving
Medical Center institutional review board (protocol AAAC8273).
2.2 List of potential two-drug interacting pairs. The NYP hospital
system utilizes a decision support tool (Allscripts) that consults a
list of potential two-drug interactions from a Cerner Multum table
modified by NYP [10]. The severity of the potential drug
interactions is graded from one to seven: 1- contraindicated, 2-
generally avoid; 3- monitor closely; 4- adjust dosing interval; 5-
adjust dose; 6- additional contraception recommended; 7-might be a
potential drug-drug interaction (pDDI). For this study we extracted
those interactions that were graded 1, 2 and 3.
2.3 Identification of multidrug interactions. pMDIs and associated
potential adverse events (pADEs) were identified by finding interacting
two-drug pairs on patients’ medication lists and then determining
whether there were drugs that participated in more than one drug-drug
interaction (overlapping interacting drug-drug pairs) using network
analysis and standard depth-first search graph traversal [11]. Drugs
in this list were represented as the network nodes and potential
interactions between the drugs as the edges. The algorithm determined an
individual network for each patient’s medication list and identified
multidrug interaction sub-graphs, if present. The steps involved are
described in Fig. 1
2.4 Identification of pADEs associated with pMDIs. A master table of
significant potential adverse events was developed through manual review
of a selection of NYP database pDDI descriptors. Potential adverse
events were identified for each of the drug-drug pairs that comprised
the pMDI by parsing the description of the interaction in the NYP tables
and determining if an adverse event listed in the master table appeared
in the description using regular expressions. A pADE associated with a
drug-drug pair was identified as a “pADE-instance.”
2.5 Amplification. Multidrug interactions were classified as either
amplifying or non-amplifying. In amplifying and non-amplifying pMDIs,
the pADEs are the collection of the individual pADEs associated with
each drug-drug pair. However, in amplifying pMDIs, the probability of
pADEs is greater than the probability of the pADE associated with the
drug-drug pair in isolation because of the multiple interactions between
the drugs. We explored three mechanisms of amplification:
pharmacodynamic, where the pMDI is composed of two or more drug-drug
interactions which are associated with the same pADE; pharmacokinetic,
where two or more drugs inhibit the metabolism of a third or fourth; and
conditional, where two or more drugs alter the patient’s physiology,
increasing the risk of a pADE caused by a third or fourth drug.
3.0 RESULTS
3.1 Potential multidrug interactions. The medication lists of 22033
randomly chosen outpatients from the NYP data warehouse had a mean age
of 72.5 (SD 8.2) of which 53% were female. Of these patients 5142
(23.3%) were exposed to at least one pDDI pair, 3369 (15.2%) were
exposed to only one pDDI pair, and 1773 (8.0%) were exposed to two or
more pDDI pairs (Table 1). The number of drugs participating in all
pDDIs for a given patient (Table 1, “Actual,” third column from right)
was lower than that required if the pDDI pairs were independent of each
other, where each drug interacted with only one other drug (Table 1,
“Expected,” fourth column from the right). In patients exposed to five
pDDIs the average number of drugs participating in the pDDIs was 5.71
(SD 1.31), approximately half of the ten drugs necessary if
participating drugs interacted with only one other drug. The higher the
number of prescribed drugs on a medication list (Table 1, “# drugs on
medication list”) the greater the proportion (%) of drugs involved in
a pDDI (Table 1, “% all drugs participating in a DDI”).
3.2. Potential multidrug interactions. We used network analysis to count
the number of multidrug interactions in 1129 patients with at least one
multidrug interaction. There were 752 (66.6%) three-drug interactions
(Table 2, column “3”). In 610 pMDIs one drug interacted with two other
drugs (Table 2, line “2”, column “3”). In 142 pMDIs all three drugs
interacted with each other (line “3”, column “3”, Table 2). There
were 233 (20.6%) four-drug interactions (Table 2, column “4”). Most
commonly, one drug interacted with three others (150 of 233). There were
also four-drug pMDIs that resulted in four, five or six interactions. In
fourteen of the four-drug pMDIs and one five-drug pMDI, all drugs
interacted with each other forming a complete graph.
3.3 Drugs involved in multidrug interactions. A total of 277 unique
drugs were involved in pMDIs. The top 15 most common drugs involved in
over 40% of the interactions and their associated drug classes are
listed in Table 3. Although the classes of participating drugs were
diverse, psychotropic drugs were the most involved representing 23% of
the total number of interactions of the top 50 drugs. The main
psychotropic drugs involved in pMDIs were bupropion, escitalopram and
trazadone (Table 3).
3.4. Potential adverse events associated with multidrug interactions.
The most common pADEs were serotonin syndrome, seizures, prolonged QT
and bleeding (Table 4). Of the 1129 pMDIs, there were 2222
pADE-instances demonstrating that some of the drug-drug pairs in a pMDI
were associated with more than one pADE, the most common of which were
[seizures-serotonin syndrome], [seizures-prolonged QT], and
[prolonged QT-serotonin syndrome]. Psychotropic drugs accounted for
over 40% of the total drugs involved in these pMDIs.
A significant proportion of pMDIs had two or more drug-drug pairs
associated with the same pADE. In the three and four-drug pMDIs, 76.9%
and 92.7% were associated with two or more instances of the same pADE,
respectively (Table 5). The pMDIs in patients A, B and C in Table 6, for
example, were exposed to three pADE-instances associated with bleeding,
serotonin syndrome and prolonged QT, respectively. In patients with
four-drug pMDIs, 96 of the 233 (41.2%) contained two pADE-instances of
two different adverse events.
Of the 1127 pMDIs, 47% contained drugs that inhibited the metabolism of
the other drugs. Of the four-drug interactions in which the pMDI
included two pADE-instances of two different adverse events, over 40%
contained two or more drugs that inhibited the metabolism of the other
drugs. Patients D, E, G and H of Table 6 are examples of the pMDIs which
included drugs that could increase the concentration of the other drugs
associated with pADEs. Patient E has been prescribed warfarin and
aspirin which place the patient at risk for hemorrhage. The patient has
also been prescribed amiodarone and metronidazole, both of which inhibit
the metabolism of warfarin, thereby further amplifying the risk of
bleeding.
Approximately 10% of the 1127 pMDIs contained conditional interactions.
Patients G and H (Table 6) were exposed to drugs that lower the seizure
threshold increasing the likelihood of bupropion-induced seizures. The
risk is further amplified by bupropion’s inhibition of the metabolism of
the other drugs. Patient F was exposed to two drugs, hydrochlorothiazide
and furosemide, which by causing hypokalemia, would increase the
probability of prolonged QT caused by amiodarone and ranolazine. Of the
four-drug interactions in which the pMDI included two pADE-instances of
two different adverse events, in 9.3% of the pMDIs the four drugs
consisted of two diuretics (loop and thiazides) and one or two drugs
associated with prolonged QT.
3.5 Amplification of potential pADEs. We counted the number of pMDIs
where pADEs could be amplified either as a result of pharmacodynamic,
pharmacokinetic, or conditional mechanisms, or a combination of the
three. In the three-drug pMDIs where one drug interacted with two
others, pADE amplifications were seen in 71.8%. When all three drugs
interacted with each other, 97.8% of the pMDIs were potentially
amplified. Of 233 four-drug pMDIs, 93% of the interactions could result
in amplification. The most common amplified pADEs were serotonin
syndrome, prolonged QT, and seizures.
4.0 DISCUSSION
4.1 . Potential multidrug interactions. Given the rising prevalence of
polypharmacy, patients are likely to be at increased risk for
experiencing adverse events due to multidrug interactions where one or
more drugs interact with two or more other drugs [6]. The goal of
this project was to establish that patients prescribed multiple
medications are at risk for pMDIs, to identify the pADEs associated with
the interactions, and determine to what extent risk of pADEs might be
amplified by the multiple interactions amongst the drugs. Using network
analysis of individual patient’s medication lists, we demonstrated
multidrug interactions in 1129 (5%) of the 22033 randomly chosen
mediation lists from an older cohort of ambulatory patients. The
majority (67%) of pMDIs were observed in patient taking three drugs,
where one drug interacted with two other drugs or all three interacted
with each other (Table 2). Patients with four-drug interactions
comprised 20% of the pMDIs where there were between three and six
connections between the drugs. A small percentage of the pMDIs (12%)
were identified in patients taking five to ten drugs resulting in four
to 13 interactions.
4.2 Amplification of risk. The rationale for identifying pMDIs was based
on the hypothesis that overall risk of pADEs would be amplified as a
result of the multiple interactions and be greater than the combined
risks associated with the individual drug-drug pairs that comprised the
pMDI. We reviewed the pMDIs to identify potential amplifications defined
as multiple drugs with the same pADE (pharmacodynamic), combinations of
interacting drugs which included those that reduced the metabolism of
the other drugs (pharmacokinetic) and combinations where one or more
drugs altered the patient’s physiology, increasing the probability of
experiencing a pADE associated with the other drug (conditional). We
found that the majority of pMDIs were comprised of combinations that
could result in amplified risk of associated pADEs. In the three-drug
pMDIs, pADE amplifications were seen in 71.8%, where one drug
interacted with two others, and 97.8% when all three drugs interacted
with each other. In four-drug pMDIs, 93% of the interactions could
result in amplification. Examples of potentially amplifying interactions
are presented in Table 6.
4.3 Psychotropic medications. Drugs for psychiatric conditions were the
most frequently involved in multidrug interactions, accounting for
23.3% of the drugs involved (Table 3). Several prior studies of
two-drug interacting pairs made similar observations [12-17, 8]. The
preponderance of psychotropic drugs in multidrug interactions may
reflect the rising incidence of psychotropic polypharmacy [18]. Many
psychotropic medications pose a dual risk in that they are potent
inhibitors, substrates, or both of CYP 1A2, 2D6 and CYP 3A4 as well as
associated with potentially lethal pADEs, such as serotonin syndrome and
torsades de pointe [19, 20]. Fluvoxamine has been shown to inhibit
four CYP450 enzymes (1A2, 2C19, 2D6 and 3A4) all of which are involved
in the metabolism of other psychotropic drugs [21, 22]. Age-related
changes in pharmacokinetics in the elderly make them especially
sensitive to these interactions [23]. Increasing the levels of
substrate drugs which are involved in a pharmacodynamic pMDIs to prolong
the QT will further amplify the risk of torsades de pointes given the
additive effect on the QT interval for each drug [24]. The high
prevalence of involvement of psychotropic drugs is particularly
worrisome given the known association between psychotropic medication
and an increased risk of death [25-32].
4.4 Relationship between actual and potential multidrug interactions.
While there are many studies of the prevalence of patients who are
prescribed drugs that participate in potential drug-drug interactions,
there are limited studies of the prevalence of documented adverse events
that result from multidrug interactions in ambulatory patients [33,
34, 15, 35, 8]. Multidrug interactions are expected in hospitalized
patients who are acutely ill and are treated with multiple medications.
A majority of patients in the intensive care units experience drug-drug
related adverse events [33, 36-38]. Hospitalized patients,
especially those in the ICU, are monitored for adverse events, such as
QT prolongation or hemorrhage and can promptly receive appropriate
interventions. Ambulatory patients, prescribed multiple drugs for
chronic conditions, who experience adverse reactions due to multidrug
interactions would do so outside of the hospital and be unaccounted for
if they were to suffer a lethal reaction.
There are currently no studies that have prospectively followed patients
with potential multidrug interactions to determine the proportion that
actually develop adverse events predicted by the interactions and
mechanisms of interaction. However, the FDA adverse events reporting
system (FAERS) has recently been mined to identify multidrug
interactions and the resulting adverse events experienced by patients
[7]. Two of the five examples of multidrug interactions and
associated adverse events were composed of three psychotropic
medications. One of the two MDIs was associated with a cardiovascular
event. While this approach cannot determine the prevalence of the
multidrug associated adverse events, it can establish that the potential
multidrug interactions are associated with documented serious adverse
events.
5.0 LIMITATIONS
A major limitation of this study is that there is no assurance that the
patients were told to take all the drugs on the current medication
lists, or that they actually took the medications. It is possible that
some of the drugs on the list, although listed as active, were not
intended to be continued and should have been removed from the list
during medication reconciliation. A home visit or thorough medication
reconciliation during an ambulatory visit would provide a more accurate
measure of the prevalence of potential multidrug interactions [2].
Nevertheless, the fact that the potentially interacting drugs are on the
active medication lists, even if not taken by the patients, may
demonstrate inattention of the clinicians as to what other medications
have been prescribed, as well as incomplete medication reconciliation.
Medication discrepancies have been well documented and often occur
during transition to nursing facilities [39, 40].
A second limitation is that the measurements of prevalence of three and
four multidrug interactions are specific to NYP. The NYP-computerized
physician order entry system has a drug-drug interaction prompt that
utilizes the NYP-Multum database. The identification and rating of
various interactions might differ across institutions given that there
is little agreement across the various drug-interaction databases
[41-45]. Furthermore, if NYP physicians were more (or less)
responsive to interaction warning prompts, then the observed prevalence
of the multidrug interactions might be lower (or higher) than that
observed at other institutions. Thus, the prevalence rate, too, is
institution specific.
A third limitation is that we did not take into consideration the doses
of the medications that were involved in the potential multidrug
interactions. It is possible that patients were prescribed lower doses
of a particular medication than recommended because a second or third
drug could influence its metabolism, thus mitigating any potentially
amplifying interaction.
6. CONCLUSIONS
The current rating of the severity of drug-drug interactions is based on
drug-drug pairs and does not consider multidrug interactions in which
amplification may increase the likelihood of an associated adverse
event. Using network analysis of ambulatory patients’ current medication
lists, we identified potential multidrug interactions that, acting
through pharmacodynamic, pharmacokinetic and conditional mechanisms,
expose patients to amplified risks of serious adverse events.
Psychotropic drugs, known to cause sudden death and serotonin syndrome,
were the most common drugs involved in the multidrug interactions
identified. Estimating risk of drug-related adverse events should be
adjusted for potentially amplifying multidrug interactions.
ACKNOWLEDGMENTS
The authors are deeply grateful to Matthew Spotnitz, MD for his
meticulous review of the manuscript and insightful comments.
DECLARATIONS
Funding : Tara V. Anand was chosen as a Clare Booth Luce Scholar
and supported by a grant from Barnard College, Columbia University.
Conflicts of interest/Competing interests : NA
Availability of data and material : Not available because of HIPAA
guidelines
Code availability : NA; code is published
Authors’ contributions All authors contributed to the study
conception and design. Material preparation, data collection and
analysis were performed by Tara V. Anand, Brendan K. Wallace and Herbert
S. Chase. The first draft of the manuscript was written by Herbert S.
Chase and all authors commented on previous versions of the manuscript.
All authors read and approved the final manuscript.
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Table 1 Expected and observed average number of drugs per
patient with pDDIs on medication lists. The total number of patients
with pDDIs was 5142 representing 23.3% of the 22033 total patients.