Patient-reported common symptoms as an assessment of interventions in medication reviews: a randomised, controlled trial
International Journal of Clinical Pharmacy
Patient‑reported common symptoms as an assessment of interventions in medication reviews: a randomised, controlled trial
Tim W. A. Schoenmakers 0 1 2 4
Michel Wensing 0 1 2 4
Peter A. G. M. De Smet 0 1 2 4
Martina Teichert 0 1 2 4
0 Department of Clinical Pharmacy, Radboud Institute for Health Sciences, Radboud University Medical Center , Nijmegen , The Netherlands
1 Department of General Practice and Health Services Research, University Hospital Heidelberg , Heidelberg , Germany
2 Zorgapotheek Nederland BV , Utrecht , The Netherlands
3 Tim W. A. Schoenmakers
4 Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center , Leiden , The Netherlands
Background A 'Patient-Reported Outcome Measure, Inquiry into Side Effects' (PROMISE) instrument was developed for patients to report common symptoms in clinical medication reviews. Objective To determine changes in patient-reported drug-associated symptoms collected by PROMISE before and after community pharmacist-led clinical medication reviews compared with usual care. Setting Community pharmacies in the Netherlands. Methods Patients were randomised into an intervention group (IG) and a control group (CG). PROMISE was used to collect symptoms experienced during the previous month, and any suspected drug-associated symptoms from both groups at baseline and at follow-up after 3 months. IG patients received a one-time clinical medication review, while CG patients received usual care. Main outcome measure Mean number of drug-associated symptoms at follow-up. Results Measurements were completed by 78 IG and 67 CG patients from 15 community pharmacies. Mean numbers of drug-associated symptoms per patient at follow-up were 4.0 in the IG and 5.0 in the CG, with an incident rate ratio between the groups of 0.90 (95% CI 0.62-1.33). Mean numbers of drug-associated symptoms per patient reported at follow-up and persisting since baseline were 2.1 in the IG and 2.6 in the CG, with an incident rate ratio of 0.85 (95% CI 0.43-1.42). The lowest percentages of persisting drug-associated symptoms detected in the IG were 'palpitations' and 'stomach pain, dyspepsia' while in the CG they were 'stomach pain, dyspepsia' and 'trembling, shivering'. Conclusion PROMISE provided meaningful information on drug-associated symptoms in clinical medication reviews, however the number of drug-associated symptoms was not reduced by performing clinical medication reviews compared with usual care.
Adverse drug events; Community pharmacies; Medication review; Patient-reported outcomes; Pharmaceutical care; PROMISE; Side effects; Netherlands
Department of IQ Healthcare, Radboud Institute for Health
Sciences, Radboud University Medical Center, PO
Box 9101, 6500 HB Nijmegen, The Netherlands
Impacts on practice
• The PROMISE instrument as a patient-reported
symptom list is helpful in detecting drug-associated symptoms
experienced by patients with multiple drugs in chronic
• The integration of PROMISE into the context of a clinical
medication review may be useful to detect and improve
drug-associated symptoms experienced by patients.
Beside their desired effects, drugs can cause adverse
symptoms. Health professionals mainly pay attention to
potentially serious drug-related symptoms to prevent major harms
to patients [
]. In contrast, potentially severe drug-related
symptoms tend to be neglected or acknowledged by
practitioners as inevitably connected to the drug effect [
Nevertheless, such common symptoms could substantially
impact patient quality of life. For example, muscle pain may
reduce physical activity  and diarrhoea may impact social
]. Thus, strategies to detect and reduce common
drug-related symptoms are needed.
The reduction of drug-related symptoms is one of the
aims in a Clinical Medication Review (CMR), which is
a structured evaluation of a patient‘s medicines based on
medication history, patient information and clinical data
]. While studies on CMRs have focused on various
outcomes, such as drug therapy-related problems (DTPs)
], patient-reported outcomes, such as bothersome
common symptoms, are underrepresented . Some
studies included patient-suspected drug-related symptoms [
], although only Sorensen et al.  identified these by
patient reporting through open-ended questioning. However,
identification by means of a list of specific symptoms may
be preferable as this has higher sensitivity [
Similarly to DTPs, patient-reported outcome measures
(PROMs) could potentially be used as outcome measures
for CMRs, as is done in other areas of clinical research [
In the context of patient-centred care, PROMs could prompt
a discussion between patients and healthcare professionals
on important issues for patients and their healthcare needs
]. Specifically, information on a patient’s beliefs and
concerns about and the use of his/her drugs could be collected
to optimise and individualise drug treatment [
the ‘Patient-Reported Outcome Measure, Inquiry into Side
Effects’ (PROMISE) instrument was developed to
facilitate the gathering of meaningful information from patients
in CMRs to support pharmacists in detecting drug-related
], and to use this information as PROMs in
CMRs. In this trial the ability of the instrument to detect
changes was assessed.
Aim of the study
The aim of this study was to determine changes in
patientreported drug-associated symptoms collected by PROMISE
before and after community pharmacist-led CMRs compared
with usual care.
The Arnhem-Nijmegen medical ethical committee waived
ethical approval for this study (registration number
Study design and setting
This non-blinded, randomised, controlled trial was
conducted in the Netherlands (registered in the Netherlands
Trial Register under number 4895, www.trialregister.nl).
Patients eligible for a CMR according to the guidelines
were invited to participate in the study by their community
]. After providing written informed
consent, patients were randomised into an intervention group
(IG) and a control group (CG). On average 13% of the
patients in the Netherlands use five or more drugs long
term . In the Netherlands, community pharmacists
collect patients’ medication history in information
systems. These data are used for regular medication
surveillance, and for performing CMRs combined with patient
information and clinical information. At present,
community pharmacists have to conduct a specified number
of CMRs annually for susceptible elderly on behalf of
the Dutch Healthcare Inspectorate, which are reimbursed
by the health insurance companies. The performance of
CMRs is defined in guidelines and comprises six steps for
patient selection, patient interview, medication analysis,
intervention plan, implementation of treatment changes,
and evaluation after 3 months of follow-up; the patient
and the general practitioner (GP) also contribute to these
PROMISE was developed as a paper-based instrument to
collect patient information in six domains for the patient
interview in CMRs and for the follow-up evaluation
(Online Resource 1). The main domain comprised 22
common predefined symptoms, with the option of reporting
additional symptoms [
]. Patients were asked to report all
symptoms experienced in the previous month (yes/no) and
to report any suspicion that these symptoms were
associated with the drugs they were using (yes/do not know/
no). Symptoms with the answer ‘yes’ or ‘do not know’
in the second component were further evaluated as
drugassociated symptoms (DAS). In PROMISE, additional
information was collected in four other domains based on
existing validated instruments: general health perception
], a question about self-rated health that can serve as a
global measure of health status [
]; necessity and
concern beliefs, five of the ten items in the Beliefs about
Medicines Questionnaire reflecting the current and future
necessity and concern beliefs, and the concern beliefs
about the knowledge of the medicines ; self-efficacy
in understanding and using medicines, one item for both
subscales of the eight-item MUSE scale [
adherence from the patient’s perspective according to the
frequently used Medication Adherence Rating Scale [
]. Finally, patients could propose other issues to be
discussed in the interview with the pharmacist .
Intervention and usual care
For the IG, the completed PROMISE instrument at the start
of the CMR was used to identify DTPs, such as
patientreported DAS, and to collect additional information relevant
to the patient’s drug use. During the patient interview in the
CMR, the answers to the PROMISE instrument were
discussed between the patient and pharmacist. The information
on DAS was used by pharmacists to elucidate the burden
for the patient, assess the potential cause, and decide on
an intervention regarding drug therapy [
]. With this
information, the following steps of the CMR were followed
to agree on an improved treatment plan together with the
GP and patient. The CG completed PROMISE without a
subsequent CMR. After 3 months of follow-up, PROMISE
was repeated by the IG and CG.
All participating pharmacists were invited from pharmacists
affiliated to ‘Pluriplus’, a Dutch pharmaceutical care support
organisation providing an online tool called ‘Nexus
Medication Check’ which facilitates the implementation of
medication reviews. All participating pharmacists were already
trained and experienced in performing CMRs according
to the Dutch guidelines for CMR [
pharmacists received written and oral instructions for sampling of
patients, using PROMISE in the CMR, and collecting study
Patients were eligible for study inclusion if they met the
guideline-based inclusion criteria for CMRs [
chronic use of at least five drugs was determined by means of
the online tool. Subsequently, further sampling was applied
by pharmacists based on additional risk factors like age over
65 years, comorbidities, decreased adherence, and use of
risk medication. Finally, pharmacists applied additional
criteria such as cooperation with the relevant GP. Patients were
excluded if they met one of the following criteria: cognitive
impairment, personal or health issues hindering participation
according to the GPs; recent participation in other
pharmacotherapy interventions; unwilling or unable to participate in
a CMR according to the pharmacist. Pharmacists recruited
patients by telephone or by mail, aiming to form a group of
20 participants. All patients in a pharmacy who provided
written informed consent were randomised into the IG or CG
by a research collaborator in blocks of four patients using
computer-generated code lists.
The primary outcome was the mean number of DAS at
follow-up, as measured with PROMISE in the IG compared
with the CG.
Additional outcomes were the mean number and types of
DAS reported at baseline that persisted at follow-up as well
as the number of patients who reported at least one DAS at
follow-up in the IG compared with the CG. Furthermore, for
persisting DAS a number needed to treat (NNT) was
calculated to express the number of patients needed to participate
in the intervention to solve one additional DAS.
Additionally, the patient-reported scores of the other domains of
PROMISE were measured as outcomes at follow-up: health
perception, necessity beliefs (mean of two items), concern
beliefs (mean of three items), self-efficacy (mean of two
items), and medication adherence (sum of five items).
Sample size calculation
We designed the trial to detect a provisionally estimated
difference of 10% in the number of DAS between the IG and
CG at follow-up. With an alpha of 5%, a power of 80%,
and a intraclass correlation coefficient of 0.05 to correct for
potential dependencies within a pharmacy a number of 90
subjects per group were needed. Assuming a drop-out rate
of 10% during follow-up we aimed to enrol 100 patients per
group, giving a total of 200 subjects.
The pharmacists sent the completed PROMISE and other
documents with anonymised information on actual drug use
and patient sex and age to the researchers. All dispensed
drugs covering the month before completing PROMISE
were considered to be in use and were recorded [
according to the 2013 version of the Anatomic Therapeutic
Chemical classification system of the World Health Organisation
All data from PROMISE, the actual medication status
and the interview protocols were recorded in a Microsoft
Access database, version 2007 (Microsoft Corp., Redmond,
WA) and were analysed with SPSS version 22 (IBM Corp.,
Armonk, NY). All patients who completed PROMISE at
baseline and follow-up were included for analysis.
Descriptive statistics were applied to patient characteristics, drugs
in use, and data from PROMISE. In a sensitivity analysis
only symptoms reported by patients with certainty as DAS
(yes) were used.
A negative-binomial log linear regression model was built to
assess differences between the IG and CG in mean numbers
of DAS per patient at follow-up, expressed as incident rate
ratios (IRR) where an IRR of 1 signifies no difference.
Differences were adjusted for mean number of DAS at baseline
and potential confounders (sex, age and number of drugs in
use at baseline) were added to the model. If a patient
clustering effect per pharmacy was detected, differences in primary
outcome were assessed using mixed model regression
analysis with negative-binomial distribution and log link function.
Differences between the IG and CG in mean number of DAS
persisting at follow-up per patient adjusted for mean number
of DAS at baseline (expressed as IRR) were assessed with
a negative-binomial log linear regression model. A logistic
regression model was used to assess differences between
numbers of patients reporting a specific DAS persisting at
follow-up, adjusted for differences in numbers of patients
reporting that particular DAS at baseline. The NNT for
persisting DAS was calculated as the inverse of the absolute risk
reduction, where the absolute risk reduction was defined as
the difference between the CG event rate and the IG event
rate; these event rates were expressed as the number of
persisting DAS at follow-up divided by the number of DAS at
For general health perception, the scores were
dichotomised; the patient scores ‘very good’ and ‘good’ were
considered as ‘healthy’, and ‘fair’ (‘relatively healthy’), and
‘bad’ or ‘very bad’ (‘unhealthy’) were considered as ‘other’
]. For self-efficacy a mean score was calculated only for
patients who answered at least two items. The mean scores
were dichotomised as follows: a mean score of four (totally
agree) was considered as ‘good’, while all other mean scores
were considered as ‘other’. For the Medication Adherence
Rating Scale, a sum score ranging from 5 to 25 was
calculated only for patients who answered all items. The sum
score was dichotomised, and a sum score of 22 or lower was
considered as non-adherent according to earlier studies [
]. For these domains, differences in scores between the IG
and CG at follow-up (adjusted for differences at baseline)
were assessed using logistic regression analysis.
For necessity beliefs (two items) and concern beliefs
(three items), means were calculated only for patients who
answered at least two items. Differences in mean scores
between the IG and CG at follow-up were assessed using a
linear regression model adjusted for differences at baseline.
Potential confounders (sex, age and number of drugs in
use at baseline) were added to the model for all additional
outcomes. When an effect of a pharmacy for patient
clustering was detected for a specific outcome, differences were
assessed using the corresponding mixed regression model
A total of 228 patients from 15 community pharmacies
provided informed consent and were invited to participate in the
study between September 2014 and October 2015. Patient
numbers per pharmacy varied from 6 to 29 included patients
and 4 to 24 patients who completed PROMISE at baseline
and follow-up. Information from 48 patients could not be
included due to withdrawal after randomisation or
incomplete baseline data. From the remaining 180 participants,
145 (80.6%) patients completed the measurement at
followup between January 2015 and June 2016 (78 in the IG and
67 in the CG) (Fig. 1). Of these patients, 53.1% were female,
and the mean age was 73 years (range 49–89) (Table 1). The
IG and CG did not differ in sex, age, mean numbers and drug
classes in use. The types of most frequently reported DAS
at baseline were comparable in both groups, except for ‘dry
mouth, thirst, mouth complaints’ and ‘muscle pain, joint
pain’, which were reported less often in the CG.
The mean numbers of DAS per patient at follow-up were
4.0 in the IG (5.1 at baseline) and 5.0 in the CG (4.8 at
baseline) (Table 2). The IRR between the IG and CG was
0.90 [95% confidence interval (CI) 0.62–1.33], implying a
higher reduction in the IG. In the sensitivity analysis, mean
numbers of DAS (answer ‘yes’) per patient at follow-up were
1.0 in the IG (1.4 at baseline) and 1.8 in the CG (2.4 at
baseline), and the IRR for the mean numbers of DAS (answer
‘yes’) between the IG and CG was 0.72 (95% CI 0.45–1.15).
The mean numbers of persisting DAS per patient at
follow-up were 2.1 in the IG and 2.6 in the CG, which meant
a reduction for both groups compared with baseline
measurements (5.1 and 4.8, respectively). The incidence rate
ratio between the IG and CG was 0.85 (95% CI 0.43–1.42)
(Table 2). For the persisting DAS, the NNT was nine,
implying that nine patients had to receive a CMR with PROMISE
to solve one persisting DAS at follow-up.
The percentage of persisting DAS, reported at baseline
and again at follow-up, was 43% in the IG and 54% in the
CG. For separate symptoms, the percentage of persisting
DAS reported by at least 10 patients at baseline varied at
follow-up from 11 to 89% (Table 3).
The total number of patients who reported at least one
DAS at follow-up was 56 (72%) in the IG and 51 (76%) in
the CG. The IG was 15% less likely to report at least one
DAS at follow-up; however, this difference was not
statistically significant [odds ratio (OR) 0.85; 95% CI 0.38–1.88]
Of the other domains in the PROMISE instrument only
‘self-efficacy’ showed a statistically significant improvement
in the IG compared with the CG. More patients in the IG
reported ‘good’ self-efficacy at follow-up compared with
the CG (OR 2.91, 95% CI 1.20–7.06).
Our study did not show a statistically significant reduction
in numbers of DAS at follow-up for patients participating in
a CMR with the PROMISE instrument compared with those
receiving usual care. However, there might be a potential
benefit for the use of PROMISE in CMRs in reducing DAS,
as on average one DAS within nine patients participating in
a CMR was resolved.
Absence of reduction in drug‑associated symptoms
Our findings are in line with the earlier results of Sorensen
et al. [
] who also did not find a statistically significant
reduction in patient-reported drug-related symptoms after a
CMR. However, it might be difficult to detect specific effects
of a complex intervention in complex cases. First, patients
to receive usual care
84 patientsincluded for
follow up measure
228 patientsrandomised after
29 patientsnot included
10 Withdrew before baseline
19 No baseline data
17 patientsdrop out
8 No questionnaire returned
3 Withdrew from MR
2 Moved to other community
2 Personal circumstances
1Admission to nursing home
67 patientscompleting the
78 patientscompleting the
to receive intervention
96 patientsincluded for
follow up measure
19 patientsnot included
6 Withdrew before baseline
13 No baseline data
18 patientsdrop out
11 No questionnaire returned
2 Withdrew from MR
2 Withdrew for health
1 Moved to other community
1 Personal circumstances
eligible for CMRs are likely to consult more healthcare
practitioners than patients who are ineligible for CMRs as they
are often affected by a range of diseases or minor illnesses.
The reported DAS could be manifestations of symptoms
related to these ailments. Second, some DAS, such as
stomach pain or constipation, may already have been resolved
as part of usual care [
]. Third, some side effects, such
as headache caused by dihydropyridin derivatives, may be
transient in nature and thus resolve without intervention
]. Finally, the structured questioning may have increased
the awareness of DAS among CG patients, which may have
encouraged them to act. All these aspects may explain the
notable decrease in persisting DAS in the CG at follow-up
(from 4.8 to 2.6).
Increase in self‑efficacy
Of the other domains in PROMISE, only self-efficacy in
using medication showed a positive effect of the CMR
compared with usual care. This may be because a pharmacist can
easily improve a patient’s ability to use a drug by
providing additional instructions; however, uniform registration
of pharmacists’ interventions are needed to confirm this.
Furthermore, the literature indicates an association between
concern beliefs and patient-reporting of a DAS [
], but this
could not be confirmed due to our small sample size.
Potential benefit of PROMISE in practice
Although using PROMISE within CMRs had no
detectable effect on the number of DAS at follow-up, we believe
that the PROMISE instrument could be useful to draw the
Intervention N = 78
MARS The Medication Adherence Rating Scale; DAS drug-associated symptoms
aPatients reporting ‘very good’ or ‘good’ are considered healthy
bMean of two items, for each item: 1 = totally disagree, 5 totally agree; higher scores on scale indicate higher beliefs of necessity
cMean of three items, for each item: 1 = totally disagree, 5 totally agree; reverse scored so lower scores on scale indicate less concerns
dMean score of 4 (totally agree) from two items was considered as ‘good’ self-efficacy
eA sum score of five items (for each item: 1 = always, 5 = never) < = 22 was considered as non-adherent
attention of healthcare professionals to common DAS as
symptom management is a cornerstone of care for patients
with chronic conditions [
]. Furthermore, Willeboordse
et al. [
] reported that except for vulnerable patients
(characterized by > 4 chronic diseases, > 10 drugs used,
and low health literacy) a questionnaire may be as
effective as an interview in determining the patient perspective.
Hence, the use of PROMISE may improve the feasibility
DAS drug-associated symptoms, IG intervention group, CG control group, CI confidence interval
aNegative-binomial log linear regression analysis adjusted for number of DAS at baseline, sex, age and number of drugs in use at baseline
bLogistic regression analysis adjusted for differences at baseline, sex, age and number of drugs in use at baseline
Table 3 Number of
drugassociated symptoms at
baseline and percentage of these
persisting at follow-up
Intervention (N = 78)
Control (N = 67)
T = 0
T = 1
% Baseline DAS
T = 0
T = 1
% Baseline DAS
of a CMR as a replacement for or in support of the patient
Our study was not without limitations. First, the number of
participants was smaller than intended. In practice, it was
difficult to achieve the targeted number of 20 patients per
pharmacy. The extra informed consent and randomisation
step complicated the usual procedure involved in inviting
patients to participate in a CMR. The study period was
prolonged in an attempt to reach a sufficient number of
participants, but sufficient numbers could not be reached
within the frame of the study. The achieved sample size may
have been insufficient to prove a possible effect. Second, to
reduce additional work for the pharmacists, instructions for
pharmacists’ registrations (e.g. the evaluation and follow up
actions of DAS), were kept to a minimum; however, this also
reduced the possibility to evaluate the plausibility of DAS
and potential follow-up actions. A variation in follow-up
actions on DAS may be plausible as guidelines on
interventions are lacking. Furthermore, pharmacists’
recommendations also have to be accepted by GPs and patients, which is
likely to vary between patients and settings [
]. Finally, the
pharmacists had little experience with PROMs which may
have hindered potential outcomes.
The PROMISE instrument provided meaningful information
on DAS in CMRs; however, the number of DAS was not
reduced by the application of CMRs compared with usual
care. Further research with larger numbers of patients is
needed to investigate the factors that can facilitate the use of
PROMISE as a tool to effectively deal with common DAS.
Acknowledgements The authors like to thank all community
pharmacists and their staff for participating in this trial, Joyce Verschoor
from Pluriplus for her coordinating activities, Mariska van der Ham
from the Royal Dutch Pharmacists Association for data-entry work,
and Reinier Akkermans from IQ Healthcare for advice and support in
Funding This study received an unrestricted research fund by the Royal
Dutch Pharmacists Association (KNMP).
Conflicts of interest The authors declare that they have no conflict of
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