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\begin{document}

\title{Medication changes implemented during medication reviews and factors
related to overprescribing: post-hoc analyses of a randomized clinical
trial in geriatric outpatients with polypharmacy}



\author[1]{Jonatan Kornholt}%
\author[1]{Shafika Feizi}%
\author[1]{Alexandra Hansen}%
\author[1]{Jannie Laursen}%
\author[1]{Karl Johansson}%
\author[1]{Lene Reuther}%
\author[1]{Tonny Petersen}%
\author[1]{Eckart Pressel}%
\author[1]{Mikkel Christensen}%
\affil[1]{Copenhagen University Hospital, Bispebjerg and Frederiksberg}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\selectlanguage{english}
\begin{abstract}
\textbf{Aims:} To describe the medication changes implemented during
physician-led medication reviews that resulted in improved
health-related quality of life; and to explore factors that could
identify patients with overprescription.

\textbf{Methods:} Post-hoc analyses of data from a pragmatic,
non-blinded, randomized clinical trial investigating a medication review
intervention (NCT03911934) in 408 geriatric outpatients taking
\(\geq\)9 medicines.

\textbf{Results:} The most frequent medicine change in the medication
review group (n=196) was discontinuation (26\% of the medicines) due to
lack of indication (72\% of the discontinuations). After 13 months, 82\%
of the discontinued medicines were persistently discontinued. The
medicines most often discontinued in the medication review group
compared with usual care included: metoclopramide (11/5=73\%
discontinued vs 1/12=8\% in usual care), acetylsalicylic acid
(20/48=42\% vs 2/47=4\%), simvastatin (18/48=38\% vs 2/58=3\%),
zopiclone (23/59=39\% vs 4/54=7\%), quinine (9/14=64\% vs 6/16=38\%),
citalopram (4/18=22\% vs 0/20=0\%), and tramadol (18/37=49\% vs
8/30=27\%). Factors associated with the number of overprescribed
medicines included: number of prescribed medicines (8\% increase per
medicine), Drug Burden Index (15\% increase per 1 increase), and patient
motivation for medicine changes (26\% less if not motivated).
Prescriptions of metoclopramide, iron preparations, antidepressants
other than SSRIs, NSAIDs, or drugs for urinary incontinence were
associated with a higher number of overprescribed medicines.

\textbf{Conclusion:} Medication reviews can be used to persistently
discontinue overprescribed medicines in older polypharmacy patients.
Motivation for having their medicine changed, treatment with a higher
number of medicines, and a higher burden of sedative and anticholinergic
drugs characterized patients most likely to benefit from physician-led
medication reviews.%
\end{abstract}%



\sloppy


\pagebreak

\section*{Title page}

{\label{title-page}}

\textbf{Title:} Medication changes implemented during medication reviews
and factors related to overprescribing: post-hoc analyses of a
randomized clinical trial in geriatric outpatients with polypharmacy

\textbf{Authors:}

Jonatan Kornholt, MD\textsuperscript{1}

Shafika Tapia Feizi, MD\textsuperscript{2}

Alexandra Storm Hansen, MD\textsuperscript{1}

Jannie Thaysen Laursen, PhD\textsuperscript{1}

Karl Sebastian Johansson, MD\textsuperscript{1}

Lene \selectlanguage{ngerman}Ørskov Reuther, MD\textsuperscript{1,3}

Tonny Studsgaard Petersen, MD, PhD\textsuperscript{1,3}

Eckart Pressel, MD\textsuperscript{2}

\textsuperscript{}Mikkel Bring Christensen, MD,
PhD\textsuperscript{1,3,4,5}

\textbf{Affiliations:}

\textsuperscript{1}Copenhagen University Hospital, Bispebjerg and
Frederiksberg, Department of Clinical Pharmacology

\textsuperscript{2}Copenhagen University Hospital, Bispebjerg and
Frederiksberg, Department of Geriatric and Palliative Medicine

\textsuperscript{3}University of Copenhagen, Department of Clinical
Medicine

\textsuperscript{4}Copenhagen University Hospital, Herlev and Gentofte,
Center for Clinical Metabolic Research

\textsuperscript{5}Copenhagen University Hospital, Bispebjerg and
Frederiksberg, Center for Translational Research

\textbf{Corresponding author:~}Mikkel Bring Christensen, MD, PhD, Center
for Translational Research, Copenhagen University Hospital Bispebjerg,
Bispebjerg Bakke 23, 2400 Copenhagen NV, Denmark
(mikkel.bring.christensen@regionh.dk)

\emph{}\textbf{Bullet point summary:}

\emph{What is already known about this subject:}

\begin{itemize}
\tightlist
\item
  Physician-led medication reviews improved health-related quality of
  life in geriatric outpatients with polypharmacy
\item
  Medication reviews reduced the total number of medicines, but which
  medicines were reduced, why the medicines were reduced, and which
  patients had the most medicines reduced are not known
\end{itemize}

\emph{What this study adds:}

\begin{itemize}
\tightlist
\item
  The most frequent change during medication reviews was discontinuation
  due to lack of indication
\item
  The medicines most often discontinued may cause adverse effects in
  older patients, e.g. metoclopramide, zopiclone, citalopram, and
  tramadol
\item
  Patient-related factors including number of medicines and motivation
  may identify patients most likely to benefit from medication reviews
\end{itemize}

\pagebreak

\section*{Abstract}

{\label{abstract}}

\textbf{Aims:} To describe the medication changes implemented during
physician-led medication reviews that resulted in improved
health-related quality of life; and to explore factors that could
identify patients with overprescription.

\textbf{Methods:} Post-hoc analyses of data from a pragmatic,
non-blinded, randomized clinical trial investigating a medication review
intervention (NCT03911934) in 408 geriatric outpatients taking
~\(\geq\) 9 medicines.

\textbf{Results:} The most frequent medicine change in the medication
review group (n=196) was discontinuation (26\% of the medicines) due to
lack of indication (72\% of the discontinuations). After 13 months, 82\%
of the discontinued medicines were persistently discontinued. The
medicines most often discontinued in the medication review group
compared with usual care included: metoclopramide (11/5=73\%
discontinued vs 1/12=8\% in usual care), acetylsalicylic acid
(20/48=42\% vs 2/47=4\%), simvastatin (18/48=38\% vs 2/58=3\%),
zopiclone (23/59=39\% vs 4/54=7\%), quinine (9/14=64\% vs 6/16=38\%),
citalopram (4/18=22\% vs 0/20=0\%), and tramadol (18/37=49\% vs
8/30=27\%). Factors associated with the number of overprescribed
medicines included: number of prescribed medicines (8\% increase per
medicine), Drug Burden Index (15\% increase per 1 increase), and patient
motivation for medicine changes (26\% less if not motivated).
Prescriptions of metoclopramide, iron preparations, antidepressants
other than SSRIs, NSAIDs, or drugs for urinary incontinence were
associated with a higher number of overprescribed medicines.

\textbf{Conclusion:} Medication reviews can be used to persistently
discontinue overprescribed medicines in older polypharmacy patients.
Motivation for having their medicine changed, treatment with a higher
number of medicines, and a higher burden of sedative and anticholinergic
drugs characterized patients most likely to benefit from physician-led
medication reviews.

\pagebreak

\section*{Introduction}

{\label{introduction-1}}

Medication reviews are used as a tool to combat inappropriate
polypharmacy and associated adverse effects.\textsuperscript{1} In
numerous trials, medication reviews have decreased the number of
potentially inappropriate medicines and increased the prescribing
appropriateness.\textsuperscript{2}

We have previously reported on the clinical effects of medication
reviews as a supplement to usual care in a geriatric outpatient
clinic.\textsuperscript{3} In a randomized clinical trial, medication
reviews reduced the number of medicines after 4 months and led to
relative increases in the patient's health-related quality of life
(HRQoL) and reduced mortality. The combined beneficial effect on HRQoL
and mortality support that the discontinued and dose-reduced medicines
were in fact inappropriate and not merely
\emph{potentially~}inappropriate. Thus, a detailed description of these
medicines seem warranted as these medicines could be discontinued in a
pragmatic clinical trial setting and thus are of clinical relevance for
medication reviews in geriatric outpatients.

The purpose of this paper is twofold: 1) to provide a detailed
description of the medication changes implemented during medication
reviews to better understand the observed beneficial clinical effects
and to guide future medication review interventions; and 2) to explore
patient- and medication-related factors that may identify patients that
will benefit most from medication reviews. To fulfill these purposes,
the detailed descriptions include: 1) the actual changes to the
medicine; 2) the persistence of the changes; 3) the medicines most often
discontinued; 4) the reasons for discontinuing; 5) the medicines that
most often were prescribed again after discontinuation; and 6) an
exploratory analysis of factors that predicted the number of
overprescribed medicines to help identify patients that may benefit the
most from medication reviews.

\section*{Methods}

{\label{methods}}

All results in this paper are post-hoc analyses of data from a
pragmatic, non-blinded, single-center, randomized clinical trial with
follow-up 4 months and 13 months after the initial visit. For a detailed
description of the trial see the primary publication.\textsuperscript{3}
Briefly, participants taking 9 or more different medicines were
recruited from the geriatric outpatient clinic at Copenhagen University
Hospital, Bispebjerg and Frederiksberg from June 2017 to December 2019.
Participants were randomized to usual care or usual care plus a
medication consultation including a medication review and increased
cross-sectoral communication (termed the medication review group). The
medication reviews were prepared and implemented by a physician from the
Department of Clinical Pharmacology. Proposed changes to the medicine
were discussed with the patient's primary care physician and the
treating geriatrician before an in-person consultation with the patient
where the changes were implemented if the patient agreed to the changes.
All included participants provided written, informed consent to the
collection of data and the study was approved by the Danish Data
Protection Agency (BFH-2017-031).

\subsection*{Baseline data}

{\label{944560}}

The following baseline data were collected: Age, sex, number of
different medicines, Drug Burden Index\textsuperscript{4} calculated
using the Irish Drug Burden Index list\textsuperscript{5} as an estimate
of anticholinergic and sedative drug exposure, number of admissions the
last 3 months, any falls the last 3 months (yes or no), weight, height,
body mass index, nursing home resident (yes or no), home care (including
frequency of visits), who dispensed the patient's medicine (the patient,
relative, home nurse, nursing home or other), who referred the patient
to the outpatient clinic (the patient's general practitioner, the
geriatric department or another department), what was the patient
referred to (geriatric assessment, follow up after admission or the
falls clinic), diagnoses registered in the electronic health record,
Charlson Comorbidity Index,\textsuperscript{6} FRAIL
scale,\textsuperscript{7} and whether the patient was motivated for
medicine changes (yes or no).

\subsection*{Medicine data}

{\label{326941}}

Detailed prescription data were obtained from the Shared Medicine
Record\textsuperscript{8} at baseline, immediately after the visit, and
4 and 13 months after the visit. The following medicines were excluded
when tallying the number of different medicines during inclusion and
were not registered during the trial: Topical treatments (such as eye
drops, ear drops, and creams), antibiotics with limited treatment
duration, multivitamins, and protein drinks. The data collected for each
medicine included product name, dosage, and any changes to the medicine
at the specified time points. When available, the reasons for the
medicine changes were obtained from the physician's note in the
electronic medical record.

\subsection*{Statistical methods}

{\label{145219}}

All medicines were aggregated per patient according to the fifth level
codes (substance) in the Anatomical Therapeutic Chemical (ATC)
Classification System.\textsuperscript{9} Based on these aggregated
data, the medicine changes were computed from the differences in the
prescribed medicines at the different time points (baseline, after first
visit, follow-up after 4 months, and follow-up after 13 months). If a
medicine was still discontinued or prescribed with precisely the same
dosage \emph{after} the first visit and at a follow-up visit, then the
medicine change at the first visit was described as
\emph{persistent}.~\emph{Overprescribed} medicines were defined as
medicines that were discontinued or reduced in dosage at the first visit
in the outpatient clinic. \emph{Underprescribed} medicines were defined
as medicines that were prescribed or increased in dosage at the first
visit in the outpatient clinic. \emph{Rebounds} were defined as
medicines that were prescribed again following discontinuation.

The number of medicines prescribed at baseline was summarized according
to the therapeutic subgroups (second level ATC). The number of
underprescribed and overprescribed medicines were then plotted as a
function of the total number of medicines at baseline with trend lines
using loess regression.\textsuperscript{10} The medicine changes per
group at the first visit were summarized descriptively along with the
persistence of these changes. To identify the medicines that were more
often discontinued during the medication review, we calculated the
absolute difference in the proportions of discontinuations per medicine
between groups. Only medicines prescribed to at least ten patients at
baseline were included in the calculation. Rebounds were summarized for
medicines with at least five discontinuations during the first visit. To
compare the proportion of discontinuations and rebounds between groups,
we plotted the ratio of the number of medicines prescribed at each time
point to the number of medicines prescribed at baseline for
pharmacological subgroups (third level ATC). Only subgroups with at
least 40 medicines prescribed in both groups at baseline and at least 10
discontinuations in the medication review group during the first visit
were included.

The reason(s) for discontinuations in the medication review group were
registered prospectively, and based on these the \emph{primary} reason
for discontinuation was determined using the following hierarchy: 1)
Treatment not indicated; 2) Treatment with no or poor effect; 3)
Safety-related issues; 4) Patient preferences and circumstances; and 5)
Unknown reason.

Lastly, to identify factors related to the number of overprescribed
medicines, we created two exploratory models using all the subjects from
the medication review group. One model included all patient baseline
characteristics (to identify patient-related factors) and the other
included all medicine groups (ATC fourth level, chemical subgroup)
prescribed at baseline (to identify medicines that were associated with
overprescribing). As the predicted variable was a count of
overprescribed medicines, we fitted generalized linear models with a
quasi-Poisson distribution (log link) using R version
3.6.3\textsuperscript{11} with the tidymodel\textsuperscript{12} and
poissonreg\textsuperscript{13} packages. For both models, we first
fitted a full model using all variables excluding variables with
near-zero variance. The statistically significant variables (defined as
P \textless{} 0.05) from the full models were then further explored in
univariate models. The models were purely exploratory and confidence
limits and P-values were not adjusted for multiple comparisons. To
illustrate the results from the models, model predictions using
estimated marginal means\textsuperscript{14} were plotted for all
patient baseline characteristics that were statistically significant in
both the full and univariate models.

\section*{Results}

{\label{results-1}}

The study included 196 patients randomized to medication review and 212
to usual care. The included patients had a median age of 81 years (IQR
75 to 85 years), were prescribed a median of 12 medicines (IQR 10 to 14)
at baseline, and 71\% were females. Detailed patient characteristics and
patient flowchart have previously been reported.\textsuperscript{3}
Medicines prescribed at baseline are listed in Supporting Information
Table S1. As reported in the primary publication,\textsuperscript{3}
medication review reduced the number of prescribed medicines by 18\%
after the first visit, by 16\% after 4 months, and by 11\% after 13
months compared with 5\%, 5\% and 2\% in the usual care group. The
medicine changes, and their corresponding persistence, that led to these
differences in the number of prescribed medicines are described in
detail in Table 1. The number of overprescribed and underprescribed
medicines as a function of the number of baseline medicines is
visualized in Figure~{\ref{633516}}. The primary
reasons for discontinuations in the medication review group are listed
in Table 2. The medicines that were more often discontinued in the
medication review group compared with usual care are listed in Table 3.
The medicines that were most often restarted after being discontinued in
the medication review group are listed in Table 4. The proportions of
prescribed medicines in pharmacological subgroups in the two groups at
the different time points are shown in
Figure~{\ref{222994}}. The numbers from the plots in
Figure~{\ref{222994}} are listed in Supporting
Information Table S2. The factors that predicted the number of
overprescribed medicines in the two models are listed in Table 5. The
effect of the patient-related factors on predicted number of
overprescribed medicines is visualized in
Figure~{\ref{907792}}. For reference, the number of
overprescribed medicines for patients referred from the geriatric
department was a median (IQR) of 3 (2 to 5) compared with 4 (2 to 6) for
patients referred from the GP, and for patients not motivated for
medicine changes 3 (1 to 4) compared with 4 (2 to 6) for patients
motivated for medicine changes.

\section*{Discussion}

{\label{304804}}

In this study, we deep-dived into the medicine changes that were
implemented during the medication review intervention leading to
improved HRQoL and reduced mortality.\textsuperscript{3} The results in
Table 1 show that even though there were three times the number of
discontinuations (26\% vs 8.7\%) in the medication review group, the
persistence of the discontinuations after 4 and 13 months were similar
between groups (medication review: 91\% at 4 months and 82\% at 13
months; usual care: 83\% at 4 months and 86\% at 13 months). The high
persistence in the medication review group despite the larger number of
changes could be due to improved cross-sectoral communication as the
patient's general practitioner (GP) was involved before implementation
of the changes and notified of the changes after implementation.

While the overall persistence of changes was high, some medicines were
more prone to rebound than others as evidenced in Table 4. Of note, even
the highest rebound rates were roughly 1/3 meaning that even for these
medicines, discontinuation attempts were successful for 2/3 of the
patients. The rebounded medicines were almost exclusively symptomatic
treatments where recurrence of symptoms could easily be identified and
therapy reinitiated, while the two preventive medicines with high
rebound rates both have easy monitoring options (bone mineral density
for alendronate and blood pressure for bendroflumethiazide).
Interestingly, discontinuation of opioids and benzodiazepines, which we
consider symptomatic treatments, were more often successful than the
opposite despite the added risk of withdrawal symptoms.

In Figure {\ref{222994}}, other patterns regarding
discontinuation and rebounding are apparent. For some medicine groups,
the medication review results in fast and lasting reductions compared
with usual care, i.e. for proton pump inhibitors (drugs for peptic ulcer
and gastro-oesophagael reflux disease), opioids,
paracetamol/acetaminophen (other analgesics and antipyretics),
antithrombotic agents, antiepileptics, furosemide (high ceiling
diuretics), and potassium (which follows furosemide prescriptions). For
other medicines, the difference in discontinuations is not persistent,
suggesting that the same changes will happen in the usual care group but
over a longer time period, i.e. for drugs against constipation, vitamin
B12 and folic acid, blood glucose-lowering drugs excl. insulins, statins
(lipid modifying agents, plain), selective calcium channel blockers with
many cardiovascular effects, and hypnotics and sedatives. For
antidepressants and adrenergic inhalants, the medication review
discontinuations seem to be unsuccessful with discontinuation of
antidepressants being the least successful. Lastly, the calcium
supplement discontinuations are outnumbered by new prescriptions leading
to an overall and similar increase in prescriptions in both groups.

The reasons for the discontinuations in the medication review group
listed in Table 2 clearly show that lack of indication is by far the
most common reason to discontinue. Note that only the primary reason is
listed, hence safety-related concerns may also apply when the primary
reason for discontinuation was lack of indication. Nonetheless, it is
evident that to reduce overprescription, a thorough review of the
patient's medical history is needed to understand the (original)
indication for the patient's treatments. In cases where the indication
is not obvious, the patient or the patient's family or GP may have this
valuable information, further highlighting the need for a strong patient
involvement and cross-sectoral collaboration when conducting medication
reviews.

The results in Table 3 show that many of the medicines that were more
often discontinued in the medication review group than in the usual care
group are medicines which can negatively impact, especially older
patients', HRQoL. For example, metoclopramide, citalopram, tramadol,
tiotroprium and zopiclone may commonly cause dizziness and other
important adverse effects. Even low dose aspirin may cause significant
bleeding\textsuperscript{15} and quinine may increase mortality risk in
heart failure patients.\textsuperscript{16} So, while a medication
review is a complex intervention, the changes to the medicine may very
well be one of the causes of the observed positive effects on HRQoL and
mortality.

Medication reviews are labor intensive as they require a comprehensive
evaluation of the patient's medical history and prescriptions to
accurately evaluate their current medication use and indications.
Therefore, it is important to identify the patients that would benefit
most from medication reviews to optimize the allocation of health
resources. Table 5 lists the factors that were statistically
significantly associated with the number of overprescribed medicines for
this population. Only two factors were associated with fewer
overprescribed medicines; not being motivated for medicine changes or
being referred from the geriatric department. Patients who were not
motivated for medication changes had 26\% fewer overprescribed medicines
at the first visit. It is unknown whether this is due to them having
fewer overprescribed medicines prescribed or an unwillingness to consent
to the proposed medicine changes. Still, even patients that were not
motivated for medicine changes had a median of 3 medicines discontinued
or reduced in dosage. Patients referred from the geriatric department
had 25\% fewer overprescribed medicines, most likely due to some extent
of medication reviews being performed prior to referral. Multiple
factors were associated with an increased number of overprescribed
medicines. The number of medicines was an important factor with 8\% more
overprescribed medicines per additional medicine prescribed at baseline.
This is similar to the finding by Steinman et
al.\textsuperscript{17}~and intuitively makes sense as more medicines
impair overview while offering more opportunities for mistakes. However,
not only the number of medicines but also the sedative and
anticholinergic burden of the medicines (as measured with the Drug
Burden Index) impacted the number of overprescribed medicines with
15\%--26\% (full model or univariate model) more overprescribed
medicines per one increase in Drug Burden Index. Besides the patient
characteristics, we also identified some medicines that seem to be
proxies for suboptimal medicine treatment. Thus, a prescription of
metoclopramide was associated with a 42\%--73\% (full model or
univariate model) increase in overprescribed medicines. Thus,
metoclopramide, non-steroid anti-inflammatory drugs (propionic acid
derivatives), and drugs for urinary frequency and incontinence are all
medicines that should serve as an alarm bell that may prompt medication
reviews in patients exposed to polypharmacy.

\subsection*{Strengths and limitations}

{\label{202717}}

The strength of this study is the completeness of data and long
follow-up of medicine changes due to medication reviews
that\emph{overall} resulted in improved clinical outcomes for the
patients. The main limitation is that we cannot correlate individual
medicine changes to these outcomes due to the design of the study. Also,
all analyses in this paper are post-hoc explorative studies that require
further confirmative studies.

\section*{Conclusion}

{\label{conclusion}}

In conclusion, the most frequent medicine change was discontinuation due
to lack of indication and most of the discontinued medicines were
medicines that may cause adverse effects in older patients. A higher
number of medicines, higher sedative and higher anticholinergic burden
of the medicines, if the patient was motivated for medicine changes or
had a prescription of metoclopramide, an iron preparation, other
antidepressant (i.e. not selective serotonin reuptake inhibitors),
non-steroid anti-inflammatory drug, or drug for urinary frequency and
incontinence was associated with a higher number of overprescribed
medicines. These patient- and medication-related factors could aid in
identifying patients that will benefit most from medication reviews.

\section*{Acknowledgements}

{\label{acknowledgements}}

We would like to thank Ditte-Marie Schärfe Kjærgaard and Linea Fugmann
Thamdrup for assistance with data collection. We would like to thank all
the physicians, nurses and medical secretaries at the geriatric
outpatient clinic at Copenhagen University Hospital, Bispebjerg and
Frederiksberg for assistance with the conduct of this study. This work
was supported by the Velux Foundation {[}00025835{]} and the Capital
Region of Denmark {[}E-12825-01-12-01{]}. The funders had no role in the
design and conduct of the study, the collection, management, analysis
and interpretation of the data, the preparation, review or approval of
the manuscript or the decision to submit the manuscript for publication.

\section*{Conflict of interest
statement}

{\label{conflict-of-interest-statement}}

The authors have no conflicts of interest to declare.

\section*{Funding information}

{\label{funding-information}}

This study was funded by the Velux Foundation (00025835) and the Capital
Region of Denmark (E-12825-01-12-01).

\section*{Data availability statement}

{\label{data-availability-statement}}

Data available on request from the authors.

\section*{References}

{\label{references}}

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2. Rankin A, Cadogan, CA, Patterson, SM, Kerse, N, Cardwell, CR,
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. 2018;(9). doi:10.1002/14651858.CD008165.pub4

3. Kornholt J, Feizi ST, Hansen AS, et al. Effects of a comprehensive
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other clinical outcomes in geriatric outpatients with polypharmacy: A
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4. Hilmer SN, Mager DE, Simonsick EM, et al. A Drug Burden Index to
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8. Munck LK, Hansen KR, Mølbak AG, Balle H, Kongsgren S. The use of
shared medication record as part of medication reconciliation at
hospital admission is feasible. \emph{Dan Med J} . 2014;61(5):A4817.

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doi:https://doi.org/10.1111/j.1532-5415.2006.00889.x

\pagebreak

\section*{Tables}

{\label{tables}}\selectlanguage{english}
\begin{table}[!h]
\label{tbl:tbl1}
\caption{{Changes to the medicine and persistence of changes. For the change to be persistent, the medicine must be prescribed at the same dosage at follow-up as after the first visit. This means that if e.g., a medicine is reduced in dosage at the first visit and then discontinued at 4 months, then the change is not persistent. Medicine data is only available for patients that were alive at follow-up. When calculating the proportion of persistent changes, the denominator is therefore not identical to the number of changes at first visit. \textit{Other change} includes changes in medicines with multiple prescriptions with the same substance(s) where the changes are in opposite directions, e.g. reducing regular and increasing as-needed at the same time.}}
\begin{center}
\addtolength{\leftskip} {-4cm} % increase (absolute) value if needed
\addtolength{\rightskip}{-4cm}
\begin{tabular}{p{3cm}
                >{\raggedleft}p{2cm}
                >{\raggedleft}p{2cm}
                >{\raggedleft}p{2cm}
                >{\raggedleft}p{2cm}
                >{\raggedleft}p{2cm}
                >{\PBS\raggedleft}p{2cm}
}
\toprule
\multirow{2}{*}{} &
  \multicolumn{2}{p{4cm}}{\textbf{Changes at first visit, No. (\% of medicines prescribed at baseline)}} &
  \multicolumn{2}{p{4cm}}{\textbf{Persistent changes at 4   months, No. (\% of changes at first visit)}} &
  \multicolumn{2}{p{4cm}}{\textbf{Persistent changes at 13   months, No. (\% of changes at first visit)}} \\ \cmidrule(l){2-7} 
                                   & \textbf{Medication review} & \textbf{Usual care} & \textbf{Medication review} & \textbf{Usual care} & \textbf{Medication review} & \textbf{Usual care} \\ \cmidrule(lr){1-7}
\textbf{Overprescribed medicines}  &                   &            &                   &            &                   &            \\
~~Discontinued                     & 628 (26)          & 224 (8.7)  & 565 (91)          & 191 (86)   & 481 (82)          & 176 (83)   \\
~~Reduced dosage                   & 202 (8.3)         & 83 (3.2)   & 154 (78)          & 61 (76)    & 96 (54)           & 45 (61)    \\
\textbf{Underprescribed medicines} &                   &            &                   &            &                   &            \\
~~New medication                   & 175               & 92         & 132 (75)          & 62 (67)    & 103 (62)          & 43 (49)    \\
~~Increased dosage                 & 52 (2.1)          & 27 (1)     & 39 (75)           & 21 (78)    & 28 (59)           & 15 (58)    \\
\textbf{Other}                     &                   &            &                   &            &                   &            \\
~~Other change                     & 113 (4.7)         & 24 (0.9)   & 86 (76)           & 22 (92)    & 63 (59)           & 13 (59)    \\
~~Unchanged                        & 1429 (59)         & 2230 (86)  & 1159 (81)         & 1751 (82)  & 929 (68)          & 1329 (67)  \\ \bottomrule
\end{tabular}
\end{center}
\end{table}


\pagebreak\selectlanguage{english}
\begin{table}[!h]
\label{tbl:table2}
\caption{{Primary reasons for discontinuation of medicine in the medication review group. Primary was determined using the following hierarchy: 1) Indication; 2) Efficacy; 3) Safety; 4) Patient preferences and circumstances; and 5) Unknown reason.}}
\begin{tabular}{l@{}r}
\toprule
\textbf{Primary reason for discontinuation} & \textbf{Number of medicines} \\ \midrule
Indication                                  & 455 (72\%)                                       \\
~~Treatment no longer indicated               &                                                  \\
Unknown                                     & 48 (8\%)                                         \\
~~Reason for discontinuation not described    &                                                  \\
Safety                                      & 47 (7\%)                                         \\
~~Adverse drug reaction                       &                                                  \\
~~Risk of adverse drug reaction               &                                                  \\
~~Risk due to drug-drug interactions          &                                                  \\
~~Risk due to drug-disease interactions       &                                                  \\
Efficacy                                    & 39 (6\%)                                         \\
~~Poor or no effect of the treatment          &                                                  \\
Patient preferences and circumstances       & 39 (6\%)                                         \\
~~Treatment too expensive                     &                                                  \\
~~Patient does not wish to take the medicine  &                                                  \\
~~Patient cannot administer the medicine      &                                                  \\ \bottomrule
\end{tabular}
\end{table}

\pagebreak\selectlanguage{english}
\begin{table}[!h]
\label{tbl:table3}
\caption{{Top 10 medicines with the highest absolute difference in the proportion of discontinuations between the groups. Only medicines prescribed to at least 10 patients at baseline are included.}}
\begin{center}
\addtolength{\leftskip} {-4cm} % increase (absolute) value if needed
\addtolength{\rightskip}{-4cm}
\begin{tabular}{
        b{4.2cm}
        >{\raggedleft}b{3.5cm}
        >{\raggedleft}b{2.2cm}
        >{\PBS\raggedleft}b{4cm}
}
\toprule
\multirow{2}{4.2cm}[-3.5em]{\textbf{Medicine}} &
  \multicolumn{2}{m{5.7cm}}{\textbf{Discontinued at first visit, No. discontinued / No. at   baseline = \% discontinued}} & \multirow{2}{4cm}[-3em]{\PBS\raggedleft\textbf{Absolute difference, No. (\%)}}
   \\ \cmidrule(lr){2-3}           & \textbf{Medication review (n = 196)} & \textbf{Usual care (n = 212)} &         \\ \cmidrule(lr){1-4}
Metoclopramide              & 11 / 15 = 73\%                & 1 / 12 = 8\%         & 10 (65) \\
Acetylsalicylic   acid      & 20 / 48 = 42\%                & 2 / 47 = 4\%         & 18 (37) \\
Simvastatin                 & 18 / 48 = 38\%                & 2 / 58 = 3\%         & 16 (34) \\
Formoterol and   budesonide & 5 / 15 = 33\%                 & 0 / 16 = 0\%         & 5 (33)  \\
Zopiclone                   & 23 / 59 = 39\%                & 4 / 54 = 7\%         & 19 (32) \\
Tiotropium   bromide        & 4 / 14 = 29\%                 & 0 / 17 = 0\%         & 4 (29)  \\
Allopurinol                 & 3 / 11 = 27\%                 & 0 / 12 = 0\%         & 3 (27)  \\
Quinine                     & 9 / 14 = 64\%                 & 6 / 16 = 38\%        & 3 (27)  \\
Citalopram                  & 4 / 18 = 22\%                 & 0 / 20 = 0\%         & 4 (22)  \\
Tramadol                    & 18 / 37 = 49\%                & 8 / 30 = 27\%        & 10 (22) \\ \bottomrule
\end{tabular}
\end{center}
\end{table}

\pagebreak\selectlanguage{english}
\begin{table}[!h]
\label{tbl:table4}
\caption{{Rebounds after discontinuation of medicines during medication review. Only includes data from the medication review group and medicines with at least 5 discontinuations at the first visit. The denominator in \textit{proportion restarted} can differ from the number discontinued at first visit since medicine data was not collected for patients who died.}}
\begin{center}
\addtolength{\leftskip} {-4cm} % increase (absolute) value if needed
\addtolength{\rightskip}{-4cm}
\begin{tabular}{
    >{\raggedright}b{3.5cm}
    >{\raggedleft}b{5.5cm}
    >{\raggedleft}b{3.5cm}
    >{\PBS\raggedleft}b{4cm}
}
\toprule
\multirow{2}{=}[-3em]{\textbf{Medicine}} & \multirow{2}{=}[-1em]{\raggedleft\textbf{Discontinued   at first visit, No. discontinued / No. at baseline = \% discontinued}}
   &
  \multicolumn{2}{m{8cm}}{\textbf{Proportion restarted, No.   restarted / No. discontinued at first visit = \% restarted}} \\ \cmidrule(l){3-4} 
                                  &               & \textbf{Follow-up at 4   months} & \textbf{Follow-up at 13 months} \\ \cmidrule(lr){1-4}
Paracetamol   (acetaminophen)       & 26 / 180 = 14\% & 2 / 24 = 8\%            & 9 / 24 = 38\%          \\
Alendronic acid                     & 6 / 30 = 20\%   & 1 / 6 = 17\%            & 2 / 6 = 33\%           \\
Quinine                             & 9 / 14 = 64\%   & 1 / 9 = 11\%            & 3 / 9 = 33\%           \\
Furosemide                          & 15 / 59 = 25\%  & 3 / 14 = 21\%           & 4 / 14 = 29\%          \\
Metoprolol                          & 8 / 68 = 12\%   & 1 / 8 = 12\%            & 2 / 8 = 25\%           \\
Zopiclone                           & 23 / 59 = 39\%  & 3 / 21 = 14\%           & 5 / 21 = 24\%          \\
Macrogol,   combinations            & 27 / 96 = 28\%  & 2 / 27 = 7\%            & 6 / 27 = 22\%          \\
Morphine                            & 5 / 23 = 22\%   & 1 / 5 = 20\%            & 1 / 5 = 20\%           \\
Codeine                             & 5 / 16 = 31\%   & 0 / 5 = 0\%             & 1 / 5 = 20\%           \\
Bendroflumethiazide   and potassium & 6 / 21 = 29\%   & 2 / 6 = 33\%            & 1 / 6 = 17\%           \\
Amlodipine                          & 7 / 40 = 18\%   & 2 / 6 = 33\%            & 1 / 6 = 17\%           \\
Oxazepam                            & 5 / 21 = 24\%   & 1 / 5 = 20\%            & 0 / 5 = 0\%            \\ \bottomrule
\end{tabular}
\end{center}
\end{table}

\pagebreak

\thispagestyle{empty}\selectlanguage{english}
\begin{table}[!h]
\small
\captionsetup{font=footnotesize}
\label{tbl:table5}
\caption{{Factors predicting the number of overprescribed medicines in the medication review group. Results from the full and univariate analysis are presented for the model with patient-related factors and the model with medication-related factors. All models were generalized linear models with quasi-Poisson distribution (log link). The full models included all medicine groups or all patient-related baseline characteristics without near-zero variance. Confidence limits and P-values are not adjusted for multiple comparisons. The exponentiated \selectlanguage{greek}β \selectlanguage{english}is the ratio between the predicted number of overprescribed medicines for patients "having" the factor versus patients not "having" the factor (with all other factors being equal). The reference level (i.e. what is meant by not "having" the factor) is listed in parentheses for binary and categorical factors. For example, patients referred from the geriatric department have 74\% (or 26\% fewer) the number of overprescribed medicines compared with patients referred from their GP. For continuous variables, an increase of one is used, e.g. patients with a Drug Burden Index of 1 have 115\% (or 15\% more) overprescribed medicines compared with patients with a Drug Burden Index of 0, and patients with a Drug Burden Index of 2 have 15\% more overprescribed medicines compared with patients with a Drug Burden Index of 1 (or 115\% × 115\% = 132\% overprescribed medicines compared with a patient with a Drug Burden Index of 0).}\selectlanguage{ngerman}}
\begin{center}
\addtolength{\leftskip}{-7cm} % increase (absolute) value if needed
\addtolength{\rightskip}{-7cm}
\begin{tabular}{
    >{\raggedright}b{6cm}
    >{\raggedleft}b{2cm}
    >{\raggedleft}b{3cm}
    >{\raggedleft}b{1.5cm}
    >{\raggedleft}b{3cm}
    >{\PBS\raggedleft}b{1.5cm}
}

\toprule
\multirow{2}{=}{} &
  \multirow{2}{=}[-1.75em]{\raggedleft\textbf{Descriptive   statistics}} &
  \multicolumn{2}{c}{\textbf{Full model}} &
  \multicolumn{2}{c}{\textbf{Univariate model}} \\ \cmidrule(lr){3-6} 

                                           &             & \textbf{Exponentiated  \boldmath$\beta$ (95\% CI)} & \textbf{\textit{P} value} & \textbf{Exponentiated \boldmath$\beta$ (95\% CI)} & \textbf{\textit{P} value}        \\ \cmidrule(r){1-6}
{\textbf{Model:   Baseline characteristics}} & & & & & \\
Not motivated for medicine changes (ref.   Motivated for medicine changes), No. (\%) &
  39 (19.9\%) &
  0.74 (0.59 to 0.92) &
  .009 &
  0.72 (0.55 to 0.93) &
  .016 \\
Referred from the geriatric department   (ref. Referred from GP), No. (\%) &
  59 (30.1\%) &
  0.75 (0.58 to 0.96) &
  .024 &
  0.78 (0.63 to 0.97) &
  .026 \\
Age in years   (continuous), median (IQR) &
  80 (74 to 85) &
  1.02 (1.00 to 1.03) &
  0.009 &
  1.003 (0.99 to 1.02) &
  .63 \\
No. of medicines at baseline, median (IQR) &
  12 (10 to 14) &
  1.08 (1.05 to 1.11) &
  \textless .001 &
  1.09 (1.07 to 1.12) &
  \textless .001 \\
Drug Burden Index, median (IQR) &
  0.5 (0 to 1) &
  1.15 (1.03 to 1.29) &
  .015 &
  1.26 (1.13 to 1.40) &
  \textless .001 \\
\textbf{Model: Medicine   groups} &
  \multirow{2}{*}{} &
  \multirow{2}{*}{} &
  \multirow{2}{*}{} &
  \multirow{2}{*}{} &
  \multirow{2}{*}{} \\
(ref. Medicine   group not used), No. (\%) &             &                             &         &                           &                \\
Propulsives                                & 15 (7.7\%)  & 1.42 (1.02 to 1.97)         & .038    & 1.73 (1.30 to 2.26)       & \textless .001 \\
~~Metoclopramid                              & 15          &                             &         &                           &                \\
Iron bivalent, oral preparations           & 28 (14.3\%) & 1.51 (1.14 to 2.01)         & .005    & 1.40 (1.07 to 1.80)       & .013           \\
~~Ferrous fumarate                           & 16          &                             &         &                           &                \\
~~Ferrous sulphate                           & 11          &                             &         &                           &                \\
~~Ferrous tartrate                           & 1           &                             &         &                           &                \\
Other antidepressants                      & 26 (13.3\%) & 1.52 (1.13 to 2.04)         & .006    & 1.66 (1.29 to 2.11)       & \textless .001 \\
~~Mirtazapin                                 & 14          &                             &         &                           &                \\
~~Venlafaxin                                 & 5           &                             &         &                           &                \\
~~Duloxetin                                  & 4           &                             &         &                           &                \\
~~Mianserin                                  & 2           &                             &         &                           &                \\
~~Agomelatin                                 & 1           &                             &         &                           &                \\
Propionic acid derivatives                 & 19 (9.7\%)  & 1.54 (1.14 to 2.07)         & .005    & 1.47 (1.11 to 1.91)       & .006           \\
~~Ibuprofen                                  & 18          &                             &         &                           &                \\
~~Dexibuprofen                               & 1           &                             &         &                           &                \\
Drugs for urinary frequency and   incontinence &
  14 (7.1\%) &
  1.97 (1.37 to 2.79) &
  \textless{}.001 &
  1.36 (0.95 to 1.87) &
  .076 \\
~~Mirabegron                                 & 8           &                             &         &                           &                \\
~~Tolterodine                                & 4           &                             &         &                           &                \\
~~Solifenacin                                & 1           &                             &         &                           &                \\
~~Trospium                                   & 1           &                             &         &                           &                \\ \bottomrule
\end{tabular}
\end{center}
\end{table}

\pagebreak

\section*{Figures}

{\label{figure-legends}}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.98\columnwidth]{figures/Figure-11/Figure-11}
\caption{{Observed number of overprescribed medicines and underprescribed
medicines in the medication review group as a function of the number of
medicines prescribed at baseline. Overprescribed medicines are medicines
that were reduced in dosage or discontinued during the first visit.
Underprescribed medicines are medicines that were increased in dosage or
prescribed during the first visit. The fitted lines are loess
regressions with 95\% confidence intervals shaded in grey. All points
are jittered slightly to reduce overplotting.
{\label{633516}}%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.98\columnwidth]{figures/Figure-21/Figure-21}
\caption{{The ratio of the number of medicines prescribed at each timepoint
relative to the number prescribed at baseline. The figures present
pharmacological subgroups (third level ATC groups) with at least 40
medicines prescribed in both groups at baseline and at least 10
discontinuations in the medication review group during the first visit.
Abbreviations: GORD, gastro-oesophageal reflux disease.
{\label{222994}}%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.98\columnwidth]{figures/Figure-31/Figure-31}
\caption{{Visualization of the results from the full model using the
patient-related baseline characteristics. The lines show the predicted
number of overprescribed medicines (i.e., medicines with reduced dosage
or discontinuation at first visit) for different combinations of all the
variables that were statistically significant in both the full and
univariate models. The full model was a generalized linear model with
quasi-Poisson distribution (log link) including all patient-related
baseline characteristics without near-zero variance.
{\label{907792}}%
}}
\end{center}
\end{figure}

\section*{Supporting information}

{\label{267198}}

\textbf{Hosted file}

\verb`supporting_information.docx` available at \url{https://authorea.com/users/522596/articles/595019-medication-changes-implemented-during-medication-reviews-and-factors-related-to-overprescribing-post-hoc-analyses-of-a-randomized-clinical-trial-in-geriatric-outpatients-with-polypharmacy}

\selectlanguage{english}
\FloatBarrier
\end{document}

