OPTImization of Medication by Transdisciplinary Assessment of Drug Treatment in Elderly Hospitalized Patients: Application of a Definitive Intervention by Physicians or Clinical Pharmacists
Phase: NA
Conditions: Multimorbidity, Polypharmacy
Sponsor: University College Cork

OPTIMATE  Study Protocol 
Optimate Trial Protocol  
Version 6.0                  13 June 2023 
1 
Cover Page:  
 
Official Title: 
OPTImization of Medication by transdisciplinary Assessment of drug Treatment 
in Elderly hospitalized patients: application of a definitive intervention by 
physicians or clinical pharmacists. 
(OPTIMATE) 
Date:  
13 June 2023 
NCT number: NCT05387096 
 
 
 

OPTIMATE Trial  
Principal Investigator: Prof. Denis O’Mahony 
 
OPTIMATE trial protocol 
V6.0         13 June 2023  
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Study Protocol OPTIMATE 
 
 
Study/ Trial Title: OPTImization of Medication by transdisciplinary Assessment of drug 
Treatment in Elderly hospitalized patients: application of a definitive intervention by 
physicians or clinical pharmacists. 
 
 
Principal Investigator: 
Prof. Denis O’Mahony 
Email/Contact details:  
denis.omahony@ucc.ie 
 
Sponsor:  
 
 
University College Cork 
Contact name: 
 
Dr Muiris Dowling   
Email:  
 
 
PFRsponsor@ucc.ie  
 
Sponsor Protocol Number:  
19166 
 
PI at Study Site: 
 
Prof. Denis O’Mahony (Cork University Hospital) 
  
 
 
Additional Study Sites and Local Co-Investigators (if applicable):  
University Hospital Waterford (Prof. John Cooke) 
Ghent University Hospital (Prof. Mirko Petrovic) 
Ghent University & Ghent University Hospital (Prof. Annemie 
Somers) 
Sub-Investigators and Additional Project Staff: 
Prof. Stephen Byrne, School of Pharmacy, UCC 
Prof. Joe Eustace, UCC Clinical Research Facility-Cork 
Dr. Darren Dahly, UCC Clinical Research Facility-Cork 
Dr. Kieran Dalton, School of Pharmacy, UCC 
Ms. Jen O’Sullivan, Dept of Medicine, UCC  
Dr. Brendan Palmer, UCC Clinical Research Facility-Cork 
Dr. Ruth Daunt, Dept of Medicine, CUH 
Dr. Siobhán McGettigan, Dept of Medicine, CUH 
Ms. Paula Fuller, School of Pharmacy, UCC 
Dr. Alice O’Donoghue, University Hospital Waterford  
Mr. Kevin Casey, University Hospital Waterford 
Ms. Ine Simal, Ghent University Hospital 

OPTIMATE Trial  
Principal Investigator: Prof. Denis O’Mahony 
 
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Ms Hilde Vlieghe, Ghent University Hospital  
Dr Valerie Walsh, Economist, College of Business and Law, UCC 
 
 
1.0 Study Synopsis 
 
Brief Title  / 
Acronym 
OPTIMATE 
Full Study Title
OPTImization of Medication by transdisciplinary Assessment of drug 
Treatment in Elderly hospitalized patients: application of a definitive 
intervention by physicians or clinical pharmacists. 
Study Sponsor
University College Cork (UCC)
Abstract (Brief) 
Background: Recurrent hospitalization and unplanned emergency 
department (ED) attendance resulting from potentially inappropriate 
medication is an increasingly common phenomenon in older people with 
multi-morbidity and associated polypharmacy. With a growing older 
population with multi-morbidity/polypharmacy, there is a pressing need 
to address the increasing challenge of potentially inappropriate 
medications (PIMs) and potential prescribing omissions (PPOs) and 
associated problems that accentuate adverse drug reactions/events and 
avoidable excess morbidity. There is also an imperative to curb excess 
healthcare expenditure related to preventable medication-related 
problems. 
Objective: To test the clinical and economic impact of a multi-faceted 
medication optimization definitive intervention (DI) on avoidable 
rehospitalization and unscheduled ED attendance in multi-morbid 
patients aged ≥ 70 years hospitalized with acute illness. 
Design: A randomized controlled clinical trial is proposed in which it is 
anticipated to randomize 3 x 463 patients to one of 3 groups: (a) standard 
pharmaceutical care, or (b) trained physician-implemented DI, or (c) 
clinical pharmacist-implemented DI.  
Setting: Acute care environment in 3 large tertiary referral teaching 
hospitals with similar custom and practice relating to management of 
older people with acute illness. 

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Principal Investigator: Prof. Denis O’Mahony 
 
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Participants: Patients aged ≥ 70 years with multi-morbid illness i.e. ≥ 3 
chronic medical conditions and associated polypharmacy i.e. ≥ 5 daily 
prescription medications admitted with acute unselected illness. 
Intervention: The definitive multi-faceted intervention will consist of the 
following components: (i) modified structured history of medication 
(mSHiM), (ii) Screening Tool of Older Persons’ Prescriptions (STOPP) and 
Screening Tool to Alert to Right Treatment (START) screening for PIMs and 
PPOs using STOPP/START version 3, (iii) drug-drug interactions screening, 
using Stockley’s Drug Interaction Checker (iv) face-to-face consultation 
with attending hospital physicians to discuss PIMs, PPOs, interactions and 
other issues, (v) pre-discharge medication review and adjustment, (vi) 
detailed medication adjustment discharge report to patients’ general 
practitioners (GPs), (vii) follow-up contact with patients’ GPs and 
community pharmacists at 1 week and 1 and 6 months post-discharge. 
Patients in the control arm (standard pharmaceutical care) will receive a 
sham intervention     i.e.  modified Medication Adherence Rating Scale 
(MARS) questionnaire. 
Outcome measures: Primary endpoints will include: (i) unscheduled 
readmission within 30 days post-discharge, (ii) unscheduled readmission 
within between 90 and 180 days post-discharge, and (iii) ED attendance 
within 30 days and between 90 and 180 days post-discharge. A primary 
composite endpoint of unscheduled readmission or ED attendance within 
30 days and between 90 and 180 days post-discharge will also be 
ascertained. Secondary endpoints are: (i) quality of life measured by 
EQ5D-5L instrument (incorporating pain control) at 30 days and at 
between 90 and 180 days post-discharge, (ii) all-cause mortality at 30 days 
and at between 90 and 180 days post-discharge, (iii) first admission to 
residential care facility for long-term nursing care at 30 days and at 
between 90 and 180 days post-discharge.  Treatment effects for each of 
the two active intervention arms (versus control) will be estimated and 
equivalence tests comparing the two active arms (for each outcome) will 
be undertaken to determine the effectiveness of the DI as delivered by a 
physician compared to a pharmacist. 
An economic evaluation of the DI as delivered by a trained physician and 
by a trained pharmacist will also be undertaken, involving 
- 
Quality Life Adjusted Year (QALY),  
- 
cost per hospital readmission avoided, and  
- 
cost per ED attendance avoided. 
Time frame: 36 months from project start to final report submission. 

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Principal Investigator: Prof. Denis O’Mahony 
 
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Participating centres: UCC/Cork University Hospital, University Hospital 
Waterford, University of Ghent/Ghent University Hospital, Health 
Research Board Clinical Research Facility, Cork (HRB CRF-C). 
Study Design 
Interventional Randomized Controlled trial.
Study Population  
1389 older people presenting to hospital with acute illness for 
unscheduled admission lasting > 48 hours.  
Entry Criteria
Inclusion Criteria: 
a. Age ≥ 70years  
b. 3 or more chronic conditions. 
c. ≥ 5 daily medications pre-admission, all medications taken for at 
least 4 weeks continuously. 
d. Can speak and understand English (in the two Irish medical 
centres), and Dutch or French in Ghent University Hospital (Ghent 
is predominantly Dutch-speaking). 
e. Can give written informed consent, or give witnessed verbal 
consent or have a suitable proxy who is able to give informed 
assent on the patient’s behalf. 
f. Agrees to follow-up contact post-discharge up to 180 days post-
discharge. 
g. Agrees to primary researcher contacting the GP and community 
pharmacist post-discharge. 
 
Exclusion Criteria:  
a. Terminal illness. 
b. Severe dementia and clearly unable to understand the purpose of 
the trial or give consent to participation. 
c. Severe communication disorder, making informed consent 
impossible. 
d. Likely to be discharged from hospital within      48 hours of arrival. 
e. Intensive Care Unit (ICU) admission. 

OPTIMATE Trial  
Principal Investigator: Prof. Denis O’Mahony 
 
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f. Primary psychiatric presenting illness.
g. Unavailable for post-discharge follow-up for any reason. 
h. Non-accidental poisoning. 
i. Previous participation in medication optimization trials. 
j. Active participation in another clinical trial  
k. Infectious illness requiring strict isolation (including COVID-19 
infection) blocking access of the primary researcher to the patient 
for enrolment. 
l. End-stage renal, liver or lung disease requiring organ replacement 
therapy. 
m. Admitted under the care of specialists in Clinical Pharmacology, 
Palliative Medicine, Clinical Oncology or Haematology. 
n. Admitted under the care of specialists in Geriatric Medicine in 
Ghent University Hospital. 
o. Trial participation refusal. 
 
 
Estimated Study 
start and End 
Dates  
Start date:      26/09     /2022 
End date: 15/07/2024 
 
Data collection 
and Statistical 
Analysis.  
All OPTIMATE trial data will be collected electronically and entered on a 
bespoke trial proforma. Once verified as fully correct and complete, all 
individual participant trial data will be stored on a fully secure clinical trial 
database, the Castor electronic data capture system (Castor EDC) on the 
server located in the Netherlands. The Castor EDC system is compliant 
with ICH E6 GCP, GDPR, ISO 27001 and ISO 9001 standards and 
regulations. On completion of all OPTIMATE trial data collection, the 
database will be locked down and trial statistical analysis will commence. 
The data analysis will be completed no later than 30/11/2024 when a Final 
Project Report will be submitted to the Health Research Board, the trial 
funding body. 

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Principal Investigator: Prof. Denis O’Mahony 
 
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Dissemination of 
results 
The results of this trial will be presented at national and international 
conferences and published in relevant journals. 
 
 
 
2.0 Background and Significance of the question  
2.1 
Introduction  
Research consistently shows that 5-6% of all acute hospitalizations result from adverse drug reactions 
(ADRs; Kongkaew et al.). This proportion approximately doubles in older people with multimorbidity 
and related polypharmacy (i.e. ≥ 5 daily prescription medications; Oscanoa et al.). Polypharmacy which 
results from multimorbidity predisposes to drug-related problems (DRPs), potentially inappropriate 
medications (PIMs), potential prescribing omissions (PPOs), medication non-adherence, medication 
errors and accidental overdose, all of which heighten risk of ADRs (O’Mahony). These medication-
related problems can individually or in combination lead to hospitalization, more commonly in older 
people (Al Hamid et al.; Thomsen et al.). A recent systematic review indicates that approximately two-
thirds of hospital readmissions relating to adverse medication in older people are preventable (El 
Morabet et al.). Previous studies show that older people in transition from hospital to the community 
experience prescribing errors and problems with medication information transfer more commonly, 
resulting in various DRPs that are commonly detrimental to patients (Laugaland et al.). Recurrent 
hospitalizations (including 1-month readmissions affecting at least 21.9%-22.3% of patients (McAuliffe 
et al.; Ravn-Nielsen et al.) and unplanned emergency department (ED) attendances arising from 
potentially inappropriate medication are common in multimorbid older people, and costly (Hyttinen 
et al.; Leendertse et al.), but preventable. Previous studies also show that older people exposed to 
PIMs and PPOs in the community experience excess hospitalization and ED attendance (Xing et al.; 
Moriarty et al.). Hence, any definitive intervention for minimizing rehospitalization/ED attendance in 
multi-morbid older people should include components for minimizing PIMs and PPOs. 
A recent multi-centre randomized clinical trial (RCT) called SENATOR (O’Mahony et al., 2020) 
examined the effect of a software intervention for applying STOPP/START PIM and PPO criteria 
(version 2; O’Mahony et al., 2015) along with potentially adverse drug-drug and drug-disease 
interaction identification in hospitalized multi-morbid older patients on incident ADRs within 14 days 
of randomization. The trial which eventually randomized 1537 patients showed no difference in ADR 
incidence between intervention patients (24.5%) and control patients receiving standard 
pharmaceutical care (24.8%). Importantly, the overall implementation rate of software-generated 
medication recommendations by attending clinicians in their patients' medication lists was only 15% 
in the intervention arm. A previous single centre clinical trial had shown that delivery of STOPP/START 
recommendations in person by a trained physician resulted in 81.2% implementation of STOPP 
recommendations and 87.4% implementation of START recommendations (O’Connor et al.); the 
number needed to treat (NNT) to prevent one non-trivial incident ADR in that trial was eleven. A 
parallel study of STOPP/START recommendations (as part of a multi-faceted, software-supported 
medication optimizing intervention) being delivered in person by a clinical pharmacist resulted in 
STOPP recommendations being applied in 39.2% and START recommendations in 29.5% of cases 

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(O’Sullivan et al.). This disparity has raised important questions about the impact of pharmacists in 
optimizing hospitalized older multi-morbid patients’ medications (Dalton et al.). 
More recently, a Danish RCT (OPTIMIST trial) examined the effect of a multi-faceted clinical 
pharmacist-delivered intervention (basic and extended) based on medication review during 
hospitalization compared with standard pharmaceutical care (Ravn-Nielsen et al.). The trial concluded 
that applying the extended clinical pharmacist intervention which included a motivational interview 
with patients and post-discharge follow-up with the patients’ GPs and community pharmacists 
significantly reduced all-cause readmission within 30 days (hazard ratio [HR] of 0.62, 95% CI 0.46-0.84) 
and within 180 days (HR 0.75, 95% CI 0.62-0.90) and the proportion of patients experiencing the 
primary composite endpoint of readmission or emergency department attendance within 180 days 
(HR 0.77, 95% CI 0.64-0.93) compared to matched patients receiving standard pharmaceutical care. 
The NNT to prevent the composite primary endpoint with the extended pharmacist intervention was 
12. The cost-benefit ratio of the extended pharmacist intervention is, however, unknown. The 
OPTIMIST trial included adult patients over 18 and randomized a total of 1498 patients. Importantly, 
less than 50% of randomized patients were aged over 65. In the over-65 age group, the impact of the 
extended pharmacist intervention was statistically significant whilst its impact in the under-65s was 
not significant. Furthermore, the extended intervention showed a significant effect on the primary 
composite endpoint in patients of all ages taking > 8 daily drugs at admission but was not significant 
in patients taking ≤ 8 daily drugs. That is, the extended pharmacist intervention was particularly 
effective in patients over 65 taking > 8 daily medications. 
A cost-benefit analysis of the physician-delivered STOPP/START intervention RCT of O’Connor et al. 
indicates that it was not cost-effective (O’Brien et al.); in contrast, the pharmacist-delivered 
intervention in the parallel RCT by O’Sullivan et al. was found to be cost-effective. However, this 
analysis focused only on in-hospital ADR prevention. The OPTIMATE study proposes to measure cost 
effectiveness of the DI from the wider public health services perspective, principally the possible 
benefits derived from avoidance of unscheduled rehospitalization and emergency department 
attendance. The cost effectiveness analysis will focus on these events, as they are the costliest 
consequences in financial terms of adverse medication in older multimorbid patients. Quality of life 
improvement arising from medication optimization interventions is an important theoretical but so 
far unproven potential benefit for multimorbid older people. This aspect of the DI in OPTIMATE will 
also be assessed by measurement of quality of life in all randomized patients during post-discharge 
follow-up period. 
From the OPTIMIST trial findings, it is likely that a definitive intervention (DI) most likely to succeed in 
optimizing medication in hospitalized multi-morbid older people with polypharmacy will be multi-
faceted and sustained both during and after the index hospitalization. Furthermore, the DI should 
incorporate detailed discussion of proposed medication adjustments with the patient, the attending 
hospital physician, the GP and the community pharmacist in order to maximize its implementation 
and impact. Additionally, based on well-established published evidence, the DI should be focused on 
medication optimization at all points of care transition in older people, especially hospital discharge 
when the risk of inappropriate medication exposure is highest (Cardwell; Weir et al.). 
Given the positive results of the OPTIMIST trial arising from an extended pharmacist intervention 
designed to optimize medication and the positive results of the RCT by O’Connor et al., direct 
comparison of physician versus pharmacist delivery of the DI is logical. Furthermore, direct 
comparison between physician- and pharmacist-delivered DI is important since the particular 
healthcare professional training of the person performing the DI would have an important bearing on 

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Principal Investigator: Prof. Denis O’Mahony 
 
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the roll-out of the DI into routine clinical practice, if shown to have significant benefit for multi-morbid 
older people compared to usual pharmaceutical care. It is also logical to use easily measurable 
endpoints that are both clinically and economically important as well as being comparable to similar 
trials in multimorbid older people exposed to polypharmacy. 
In summary, a recent multi-centre trial (OPTIMIST) showed important positive effects of the 
structured pharmacist intervention on post-discharge readmission/ED attendance among older 
patients with polypharmacy. The previous single-centre trials in Cork also showed the positive impact 
of physician-delivered and pharmacist-delivered medication advice interventions, both based on 
STOPP/START criteria. The larger-scale, international, multi-centre SENATOR and OPERAM trials based 
on computerized STOPP/START interventions did not show a positive impact on incident ADRs within 
14 days (SENATOR) or drug-related hospitalizations within 12 months (OPERAM, Blum et al.) largely  
resulting from suboptimal adherence with medication advice among attending physician prescribers. 
2.2 
Aims of the study 
(i) Primary aim: to determine if a medication optimization DI can significantly reduce all-cause re-
hospitalization and unscheduled emergency department attendance at 30 days and between 90 and 
180 days in multi-morbid older people exposed to polypharmacy. To achieve this, we will measure the 
following outcomes: Unscheduled readmission within 30 days and between 90 and 180 days post-
discharge, ED attendance within 30 and between 90 and 180 days post-discharge, and a primary 
composite endpoint of unscheduled readmission or ED attendance within 30 and between 90 and 
180 days post-discharge. 
(ii) Secondary aims:  
(a) To determine the effects of the DI on secondary endpoints, including quality of life (EQ5D-5L), 
all-cause mortality and referral to nursing home for long-term care. 
(b) To compare endpoints achieved through standard pharmaceutical care (control) with those 
achieved through physician-delivered DI and pharmacist-delivered DI. This is to determine if 
DI delivery by a trained pharmacist is more/less/equally effective compared to DI delivery by 
a trained physician. 
 
2.3 
Research Question /Hypothesis  
Hypothesis 1: A multi-faceted medication optimization definitive intervention (DI) based on explicit 
inappropriate prescribing criteria, potential prescribing omission criteria and drug-drug and drug-
disease interaction surveillance applied following admission and pre-discharge from hospital in older 
people with multi-morbid illness and associate polypharmacy supplemented by structured follow-up 
with patients’ GPs and community pharmacists as well with patients themselves results in significantly 
lower rates of unscheduled rehospitalization or emergency department attendance or both compared 
to standard current pharmaceutical care. 
Hypothesis 2: The same multi-faceted medication optimization DI has an equal effect on rates of 
unscheduled rehospitalization or emergency department attendance or both when delivered by a 
trained pharmacist compared to a trained physician. 
 

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Principal Investigator: Prof. Denis O’Mahony 
 
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3.0 Trial Objectives and Purpose  
3.1 
Primary objective(s)  
● To test by RCT the effects of the definitive prescribing optimization intervention delivered to 
attending hospital physician/prescribers on unscheduled rehospitalization or emergency 
department attendance or both compared to standard current pharmaceutical care. 
● To compare by RCT the effects of the definitive prescribing optimization intervention 
delivered by trained physicians, with the effects of the definitive prescribing optimization 
intervention delivered by trained clinical pharmacists on unscheduled rehospitalization or 
emergency department attendance or both. 
● To complete the RCT (including all patient follow-ups) within 24 months. 
● To deliver an RCT results report within 6 months of completion of patient follow-up. 
● To publish the RCT findings in a high impact biomedical journal within 6 months of completion 
of all trial data analysis. 
3.2 
Secondary objective(s) (if applicable)  
● To examine the effectiveness of the definitive intervention on quality of life as measured by 
EQ5D-5L. 
● To examine the effectiveness of the definitive intervention on all-cause mortality. 
● To examine the effectiveness of the definitive intervention on nursing home disposition. 
● To examine the cost-effectiveness of the definitive intervention as measured by Quality Life 
Adjusted Year (QALY), cost per hospital readmission avoided and cost per ED attendance 
avoided. 
3.3  
Safety Objectives (if applicable)  
● To minimize the prevalence of medications to be avoided in multimorbid older people. 
  
 
 
 
4.0 Description of methodology 
 
4.1 Type of Study- study design 
OPTIMATE is designed as a prospective, single-blinded, randomized controlled multi-centre study. 
4.2 Recruitment of study participants  
OPTIMATE will focus on patients admitted with acute medical or surgical illness admitted to Cork 
University Hospital, University Hospital Waterford and Ghent University Hospital. The OPTIMATE trial 
will be multi-centre, involving individual level patient randomization. The 3 participating centres are 
large university medical centres of comparable size, diversity of medical and surgical specialties and 
acute admission and discharge procedures. This is to ensure that the proposed trial can recruit 
appropriate patients who meet the inclusion criteria efficiently so that the trial can be completed 
within the proposed time frame. 
 

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Principal Investigator: Prof. Denis O’Mahony 
 
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V6.0         13 June 2023  
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 4.2.1 Subjects Eligibility Criteria 
Inclusion criteria: 
I. 
Age ≥ 70 years (male or female). 
II. 
≥ 3 chronic medical conditions. 
III. 
≥ 5 daily medications pre-admission, all medications taken for at least 4 weeks continuously. 
IV. 
Can speak and understand English (in the two Irish medical centres), and Dutch or French in 
Ghent University Hospital (Ghent is predominantly Dutch-speaking). 
V. 
Can give written informed consent, or give witnessed verbal consent or have a suitable proxy 
who can give informed assent on the patient’s behalf.             
VI. 
Agrees to follow-up contact post-discharge up to 180 days post-discharge. 
VII. 
Agrees to primary researcher contacting the GP and community pharmacist post-discharge.  
Exclusion criteria 
I. 
Terminal illness. 
II. 
Severe dementia and clearly unable to understand the purpose of the trial or give consent to 
participation.  
III. 
Severe communication disorder, making informed consent impossible. 
IV. 
Likely to be discharged from hospital within 48 hours of arrival. 
V. 
ICU admission. 
VI. 
Primary psychiatric presenting illness. 
VII. 
Unavailable for post-discharge follow-up for any reason. 
VIII. 
Non-accidental poisoning. 
IX. 
Previous participation in medication optimization trials. 
X. 
Participation in another clinical trial.  
XI. 
Infectious illness requiring strict isolation  (including COVID-19 infection) blocking access of 
the primary researcher to the patient for enrolment. 
XII. 
End-stage renal, liver or lung disease requiring organ replacement therapy. 
XIII. 
Admitted under the care of specialists in Clinical Pharmacology, Palliative Medicine, Clinical 
Oncology or Haematology. 
XIV. 
Admitted under the care of specialists in Geriatric Medicine in Ghent University Hospital. 
XV. 
Trial participation refusal. 
 

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4.3 Sampling and Randomization (if applicable) 
To test the main study hypothesis i.e. that the definitive intervention (DI) yields significantly better 
outcomes than standard pharmaceutical care (control), patients will be randomized in a 1:1:1 ratio to 
one of 3 trial arms: (a) standard pharmaceutical care as it contemporaneously exists in that hospital, 
or (b) trained physician-delivered intervention or (c) clinical pharmacist-delivered intervention. The 3-
way randomization to control, Physician-DI and Pharmacist DI is fundamental to addressing the 
important secondary question: does physician-delivered DI offer any advantage over pharmacist-
delivered DI?       
The sample size is based on a known 180-day risk of medication-related readmission of approximately 
20%. At a bilateral 5% significance threshold and 80% power, for detection of an 8% absolute reduction 
in readmissions, with a 1:1:1 randomization, each group must contain 324 patients i.e. 972 patients in 
total. With the expectation of up to 30% patient attrition, we aim to randomize 972/(1-0.3) = 1389 
patients. 
The randomization will be designed by the trial statistician, but the code to create the resulting 
randomization lists will be executed by an independent statistician. The resulting lists will be 
integrated into the electronic data capture system in a manner such that trial staff never have access 
to the lists. Allocation of enrolled patients into treatment arms will then be delivered using a web-
based system, such that the allocation remains concealed until a patient is unambiguously consented 
and enrolled onto the trial and cannot be altered in the underlying database once it is revealed. While 
the intervention will be known to clinical staff, the trial statistician and outcome assessors will remain 
blinded until the results are finalized. 
Within 72 hours of admission, patients who are potentially suitable for trial enrolment will be 
approached by the primary researchers (PRs) and informed about OPTIMATE in outline. If they meet 
inclusion criteria and do not have exclusion criteria, they will be asked to provide informed consent 
to trial enrolment. Recommended best practice for assessment of decision-making capacity of older 
patients who may be suitable for the OPTIMATE trial, involves formal assessment of capacity. Where 
necessary, the patient’s mental capacity will be evaluated using the OPTIMATE Decision-Making 
Capacity Functional Assessment guidelines document in the OPTIMATE Manual of Operations. The 
patient will then be randomized to either the control arm (usual pharmaceutical care), pharmacist-
delivered DI arm or physician-delivered DI arm of the trial. Randomization will proceed in a 1:1:1 
ratio. The randomization process will be performed electronically using Castor EDC’s block 
randomization algorithm.      Patient enrolment will be sequential. With individual level 
randomization, there is the theoretical risk of control group contamination.  This is where both 
control and intervention patients could be under the care of the same physician, thereby presenting 
a risk of medication advice learnings in intervention patients being applied unwittingly to control 
patients. However, in practice, as was seen in the pre-SENATOR observation study (Lavan et al.), 
variation in reported incidence of ADRs across various medical and surgical specialties in the 6 multi-
national participating clinical sites was so large that meaningful cluster randomization was 
considered both impractical and unnecessary. Furthermore, in the SENATOR trial itself (O’Mahony et 
al.), although uptake of medication optimization advice was generally poor, there was no evidence 
of contamination of the control population.  
Randomization will be stratified by study site and by admitting service type (i.e. medical vs. surgical).      
The stratum-specific randomization lists will be generated electronically within Castor EDC, using a 
validated variable block randomization model.  Once a patient has given consent and been enrolled 
onto the study, their data will be irreversibly added to the study database. From this point, the      

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Principal Investigator: Prof. Denis O’Mahony 
 
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V6.0         13 June 2023  
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primary researchers will receive randomization outcomes      immediately upon activating the 
randomization request on the electronic Case Report Form (eCRF) within the Castor EDC. 
 
 
 
4.0 Study Procedures and Schedule of Events 
 
The schedule of trial procedures for patient participants in the intervention arm of the trial are shown 
in Tables 1a and 1b below. To mimic the intervention for blinding purposes of the participants, both 
control and intervention arm patients will receive a sham intervention in the form of a modified 
version of the Medication Adherence Rating Scale (MARS; Thompson et al.).  
 
Table 1a: Schedule of events for intervention patients 
Timepoints 
0/1 
2 
3 
4 
5 
Study Visit 
 Baseline 
(within 72 
hours of 
admission)  
Pre-
discharge 
(within 48 hours 
pre-discharge) 
1 Week 
Follow-up 
(within 10 days 
post-discharge) 
1 Month 
Follow-up 
(within 30 days 
post-discharge) 
6 Month 
Follow-up 
(between 90 and 
180 days post-
discharge) 
 
Day 1  
Day variable 
Day 10  (+/-3 
days 
post-
discharge)  
Day 30 (+/- 7 days 
post-discharge) 
Between Day 90 
and Day 180 post-
discharge 
Inclusion / Exclusion 
criteria 
X 
 
  
  
 
Decision-Making 
Capacity Functional 
Assessment 
X 
X** 
 
X** 
X** 
Informed consent/ proxy 
assent 
X 
X** 
  
 X** 
X** 
Patient Demographics 
X 
 
 
 
 
Medical History 
X 
X 
 
X 
X 
Medication 
Reconciliation 
X 
X 
 
X 
X 

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Principal Investigator: Prof. Denis O’Mahony 
 
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FORTA* criteria 
X 
X 
 
X 
X 
Laboratory Results 
X 
X 
 
 
 
Electrocardiogram 
review 
X 
 
 
 
 
EQ5D-5L 
 
 
 
X 
X 
Randomization 
X 
 
 
 
 
Medication History 
(modified SHiM)  
X 
 
 
 
 
Modified Medication 
Adherence      Rating 
Scale (mMARS) 
X 
 
 
 
 
Barthel Index Score 
X 
X 
      
      
      
Drug-drug interactions 
(Stockley’s)  
X 
X 
 
X 
X 
STOPP/START criteria 
(PIMs & PPOs*) 
X 
X 
 
X 
X 
Face-to-face discussion 
with senior attending 
physician or senior 
resident to highlight 
potentially 
inappropriate PIMs and 
PPOs 
X 
X 
 
 
 
Face-to-face discussion 
with patient or 
nominated proxy  
(where patient does not 
have decision-making 
capacity) to explain 
details of recommended 
medication changes. 
 
X 
 
 
 

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Pre-discharge 
medication review, 
documentation of 
agreed medication 
adjustments and 
preparation of report for 
patient’s GP with 
detailed explanation of 
recommended 
medication changes. 
 
X 
 
 
 
Telephone contact with 
patient’s GP to discuss 
medication changes. 
 
 
X 
 
 
Telephone contact with 
patient or nominated 
proxy (where patient 
does not have decision-
making capacity)       to 
define current list of 
medications, discuss any 
medication issues and 
ascertain endpoint 
information. 
 
 
 
X 
X 
Telephone contact with 
patient’s pharmacist to 
define medication 
currently dispensed. 
 
 
X 
X (if necessary) 
X (if necessary) 
Telephone contact with 
patient’s GP for 
endpoint ascertainment 
(if not available from 
patient or proxy).  
 
 
 
X (if necessary) 
X (if necessary) 
Unscheduled 
Readmission 
Determination 
 
 
 
X 
X 
Unscheduled Emergency 
Department Attendance 
Determination 
 
 
 
X 
X 

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Admission to residential 
care facility (long-term) 
 
 
 
X 
X 
All-cause mortality 
check via registries or if 
necessary, with the 
patient’s GP. 
 
 
 
X 
X 
 
Table 1b: Schedule of events for control patients 
 
Timepoints 
1 
2 
3 
4 
5 
Study Visit 
 Baseline 
(within 72 
hours of 
admission)  
Pre-
discharge 
(within 48 hours 
pre-discharge) 
1 Week 
Follow-up 
(within 10  days 
post-discharge) 
1 Month 
Follow-up 
(within 30 days 
post-discharge) 
6 Month 
Follow-up 
 (between 90 and 
180 days post-
discharge) 
 
Day 1  
Day variable 
Day 10  (+/-3 
days 
post-
discharge)  
Day 30 (+/- 7 days 
post-discharge) 
 Between Day 90 
and Day 180 post-
discharge 
Inclusion / Exclusion 
criteria 
X 
 
  
  
 
Decision-Making 
Capacity Functional 
Assessment 
X 
X** 
 
X** 
X** 
Informed consent/ proxy 
assent 
X 
X ** 
  
 X** 
X** 
Patient Demographics 
X 
 
 
 
 
Medical History 
X 
X 
 
X 
X 
Medication 
Reconciliation 
X 
X 
 
X 
X 
FORTA* criteria 
X 
X 
 
X 
X 

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Laboratory Results 
X 
X 
 
 
 
Electrocardiogram 
review 
X 
 
 
 
 
EQ5D-5L 
 
 
 
X 
X 
Randomization 
X 
 
 
 
 
Modified Medication 
Adherence rating Scale 
(mMARS) 
X 
 
 
 
 
Barthel Index 
X 
X 
 
      
      
Telephone contact with 
patient or next-of-kin to 
define current list of 
medications, discuss any 
medication issues and 
ascertain endpoint 
information. 
 
 
 
X 
X 
Telephone contact with 
patient’s pharmacist to 
define medication 
dispensed. 
 
 
X 
X (if necessary) 
X (if necessary) 
Telephone contact with 
patient’s GP for 
endpoint ascertainment 
(if not available from 
patient or proxy).  
 
 
 
X (if necessary) 
X (if necessary) 
Unscheduled 
Readmission 
Determination 
 
 
 
X 
X 
Unscheduled Emergency 
Department Attendance 
Determination 
 
 
 
X 
X 

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Admission to residential 
care facility (long-term) 
 
 
 
X 
X 
All-cause Mortality 
check via registries or if 
necessary, with the 
patient’s GP. 
 
 
 
X 
X 
* PIMs – Potentially Inappropriate Medications; PPOs – Potentially inappropriate Prescribing 
Omissions; FORTA Fit fOR The Aged 
** If necessary i.e. applies to participants who initially lacked decision- 
making capacity at the time of their enrolment. 
 
 
6.0 Outcome measures  
6.1 Primary Outcome  
(i) 
unscheduled readmission within 30 days post-discharge,  
(ii) 
unscheduled readmission between 90 and 180 days  post-discharge, and  
(iii) 
ED attendance within 30 and  between 90 and 180 days post-discharge. 
A primary composite endpoint of unscheduled readmission or ED attendance within 30 and between 
90 and 180 days post-discharge will also be ascertained. 
Treatment effects for each of the two intervention arms versus the control arm will be estimated for 
each outcome. Equivalence tests comparing the two active arms (for each outcome) will be done to 
compare DI delivery by physician with DI delivery by pharmacist.   
 
6.2 Secondary Outcome(s) 
(i) 
quality of life measured by EQ5D-5L instrument (incorporating pain control) at 30 days 
and at between 90 and 180 days post-discharge,  
(ii) 
all-cause mortality at  30 and between 90 and 180 days post-discharge,  
(iii) 
occurrence of first admission to residential care facility for long-term nursing care at 30 
and between 90 and 180 days post-discharge.  
The EQ-5D-5L is a self-assessed, health related, quality of life questionnaire which brings the patient 
(and often their carer) to the fore when attempting to measure the impact of the intervention (see 
Appendix 1). The scale measures quality of life on a 5-component scale including mobility, selfcare, 
usual activities, pain/discomfort, and anxiety/depression. It offers the patient the opportunity to 
express their satisfaction (or not) with the intervention received and is used to determine and quantify 
the benefits of the intervention. 
 
6.3 Tertiary endpoint(s) 
 
Our study, in line with current recommended practice (HIQA guideline, see references), proposes to 
examine the DI cost-effectiveness from the wider public health services perspective including possible 
costs and benefits accrued via the community services and rehospitalizations. The study aims to assess 

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the following cost-effectiveness measures of the DI as delivered by a trained physician and by a trained 
pharmacist:  
(i) Quality Life Adjusted Year (QALY),  
(ii) cost per hospital readmission avoided, and  
(iii) cost per ED attendance avoided.  
For the economic analysis, all ED attendances and all unscheduled readmissions of all randomized 
patients will be taken into account, as well as the quality-of-life status of all followed up randomized 
patients (EQ5D-5L; see Appendix 1)     ). 
6.3 Safety outcomes (if relevant)  
FORTA criteria (Kuhn-Thiel et al.) will be applied at baseline, at discharge, at day 30 and at day 180. 
FORTA criteria are validated explicit indicators of medication appropriateness. FORTA criteria category 
D in particular identify those medications that should be avoided in individual cases. Other FORTA 
labels range from A (indispensable) to B (beneficial), to C (questionable). FORTA criteria will provide 
an objective measure of medication safety in OPTIMATE. Patients in the 3 arms of the trial will be 
compared in terms of proportions with FORTA category A, B, C and D medications as indicators 
appropriateness/inappropriateness of medication. 
 
 
 
7.0 Data Management and Statistical Analysis  
 
7.1. Data Management 
The research team will engage closely with HRB data stewards based in the Statistics and Data Analysis 
Unit (SDAU) of the UCC HRB CRF-C. Before study initiation, study staff will be instructed by members 
of the SDAU on data collection, organization and      eCRF entry techniques that adhere to the FAIR 
data principles and will guide development of the study data management plan. 
The data obtained in OPTIMATE will include details of patients' age, sex, physical function, 
cognitive function, clinical diagnoses, medications, drug doses, laboratory test results indicating 
kidney and liver function, quality of life and survival at follow-up. Overall, the study will enroll 
approximately 1400 patients across three participating medical centres. The entire data collection is 
not expected to exceed 100 GB of digital storage. 
Data collection will be performed using a third-party electronic data capture (EDC) system 
ensuring consistent data entry, documentation and security between research sites. Where 
applicable, variable data measurements will be recorded using international standardized units. All 
variables are to be recorded within Comma Separated Value (.csv) data tables. Data files will include 
metadata identifying title, creator, keywords etc. in line with the Dublin Core Metadata Initiative 
alongside accompanying data dictionaries which will be developed in parallel. Medical coding of 
adverse events (MedRA) and medications (WHODrug) will be enacted as part of the EDC platform. 
Throughout the OPTIMATE study, routine data visualization and exploration approaches using the R 
statistical computing and graphics language will be performed. This will allow for the development of 
standardized documentation of both data review and reporting processes, in collaboration with the 
SDAU, that is both reproducible and linked to version control strategies. 
Data storage and back-up will be dictated by the EDC supplier allowing for two-factor 
authentication and establishment of defined user access permissions and audit trails. All OPTIMATE 
trial data will be collected electronically and entered on a bespoke trial proforma. Once verified as 

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fully correct and complete, all individual participant trial data will be stored on a fully secure clinical 
trial database, the Castor electronic data capture system (Castor EDC) on the server located in the 
Netherlands. The Castor EDC system is compliant with ICH E6 GCP, GDPR, ISO 27001 and ISO 9001 
standards and regulations. Pre-existing UCC IT research infrastructure protocols will provide for 
secondary backups of the OPTIMATE trial data. All personally identifiable data relating to participating 
patients is subject to General Data Protection Regulation (GDPR) and the Irish Health Research 
Regulations. Accordingly, all staff with data access permissions will be required to undergo training in 
GDPR to ensure that all data collected for the purposes of the OPTIMATE trial will be treated with the 
highest standards of security and confidentiality. Ethical approval for this study was granted by the 
Clinical Research Ethics Committee of the Cork Teaching Hospitals , HSE South Eastern Area Research 
Ethics Committee for the University Hospital Waterford Centre and from the : Commissie voor 
Medische Ethiek van het UZ Gent/Committee on Medical Ethics Ghent University Hospital for Ghent 
University & Ghent University Hospital. This study will also follow the guidelines set out by the UCC 
code of Research Conduct and Research Integrity. 
Participation in the OPTIMATE trial and the identity of the subjects, both those screened and 
those enrolled and randomized, will be treated as confidential and no participant identifiable records 
or results relating to the study will be disclosed to any third party other than the authorized 
investigators. Personal identifiers will undergo pseudonymization. An encryption key, held securely 
away from the data, will be accessible to the project Principle Investigator at the coordinating site 
only. 
Consent from patients participating in OPTIMATE will be obtained for all data to be shared 
publicly, such as data used in the generation of publications arising from the study, and in accordance 
with CRF-C Standard Operating Procedures. Data sharing repositories will be formerly identified via 
careful alignment of the expected data object outputs and evaluated using the re3data resource 
(re3data.org). This is to ensure maximum utility and interoperability of the final data package(s) and 
assignment of a persistent digital object identifier (DOI). Additional post-study data provenance will 
be enacted through sharing of analysis scripts and study protocols via Open Science Framework 
projects with an accompanying DOI(s) and/or through the HRB Open Research publishing platform. 
 
7.2. Statistical Analysis  
Treatment effects for each of the two active intervention arms (versus control) in terms of binary 
outcomes (e.g. readmission) will be estimated using logistic regression, while Health Related QoL (EQ-
5D 5L) will be analyzed using ordinal regression with a logit link function (i.e. a proportional odds 
model). We will report two models for each outcome: one adjusted for centre and admitting service 
type; and another further adjusted for      age (years), sex, number of comorbidities at baseline, and 
number of prescribed medications at baseline. Effect estimates will be reported as odds-ratios with 
95% confidence intervals and exact p-values. There will be no correction for multiplicity, but we will 
report results for all outcomes regardless of the result and provide enough information for the reader 
to make whatever corrections they may consider appropriate. Equivalence tests comparing the two 
active arms (for each outcome) will be done using a “two one-sided tests” procedure based on a 90% 
confidence interval (which equates to a 5% type 1 error rate). All analyses will be conducted on an 
intention-to-treat basis and will be conducted under the quality system and SOPs of the HRB CRF-C 
Statistics and Data Analysis Unit. A complete statistical analysis plan (SAP) will be pre-registered on 
the Open Science Framework prior to database lock. Any necessary deviations from this SAP will be 
documented and explained in the trial report. There will be only one single trial data analysis at the 
end of the project. All reporting will be carried out in accordance with CONSORT guidelines for clinical 
trials. 
 
 
 

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8.0. Data Protection and Confidentiality 
All relevant national and local requirements regarding data protection, including GDPR and Health 
Research Regulations, will be adhered to. Patient data in OPTIMATE will be pseudonymized. 
Pseudonymity of patient data is assured by means of the unique study ID number allocated on 
enrolment. Throughout the study, participants will be identified by this study ID number. The master 
key that links clinical data to participants’ identities will be stored securely in each study centre.       
Participants will receive a copy of the Sponsor Data Protection Notice (DPN). The DPN will inform them 
of their rights about their data and provide contacts for Sponsor Data controller and Data protection 
commissioner.  
 
Participants will also be informed that study monitors, representatives of the sponsor and the Ethics 
Committee may inspect their medical records to verify the information collected, and that all personal 
information made available for inspection will be handled in strictest confidence and in accordance 
with legal data protection requirements.  
 
UCC research Data protection officer will review the protocol and study documents as required by 
UCC policies and procedures.  
 
 
 
9.0 REC approvals and Governance   
This trial has been reviewed by and received approval from the Clinical Research Ethics Committee of 
the Cork Teaching Hospitals      the HSE South Eastern Area Research Ethics Committee for the 
University Hospital Waterford Centre and from the Commissie voor Medische Ethiek van het UZ 
Gent/Committee on Medical Ethics Ghent University Hospital for Ghent University & Ghent University 
Hospital. A consent declaration was applied for, for the patients that lack decision-making capacity, 
through the Health Research Consent Declaration Committee (HRCDC). The application was made and 
conditional approval was granted on 17th July 2022. The PI has confirmed acceptance of the conditions. 
In accordance with recommended practice relating to definitive clinical trials, the following oversight 
committees will be established for OPTIMATE: (i)Trial Management Group and (ii) Trial Steering 
Committee (TSC). The TMG will include the trial co-PI’s, the UCC CRF-C director, the trial statistician 
and the trial manager. The TMG will meet formally monthly. Formal minutes of each TMG meeting 
will be kept and approved at each follow-on meeting. The TMG will address all matters relating to the 
preparation, organization, running and completion of OPTIMATE. The TMG will monitor patient 
recruitment and ensure that recruitment targets are achieved in order to avoid serious delays and 
avoidable deviations from the trial timeline. 
The TSC will consist of two senior academic physicians in Geriatric Medicine and General Practice, a 
senior academic pharmacist      and two non-clinical older persons (as per PPI description). There will 
be a gender balance within the TSC. The TSC will ensure a high standard of research and monitor the 
progress of the trial. It is proposed that the TSC will meet every 3 months and that all meetings will be 
minuted for the trial log. The TSC will also advise on dissemination of trial results. 
A separate Data Monitoring Committee (DMC) is not considered necessary for the OPTIMATE trial 
because it is fundamentally a low-risk trial i.e. all advice points provided as part of the definitive 
intervention are evidence-based already, comply with current established best practice and are left 

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to the clinical judgement of the attending physician in terms of what to adopt or reject during 
medication review of patients enrolled in the trial. It is proposed that the TSC will also undertake the 
core roles and responsibilities of a DMC i.e. to regularly assess and advise on the data emerging from 
the trial, relevant safety data, the critical efficacy endpoints, and whether to recommend to the trial 
sponsor to continue, modify or stop the trial. 
The entire OPTIMATE members will have up-to-date GCP training and certification (this is provided by 
the UCC CRF-C). The PI/coordinator will meet weekly with the trial manager to ensure that all current 
issues and project targets are discussed and achieved on time. All project targets (see Gantt chart) will 
be monitored closely by the PI/coordinator, the trial manager and a UCC CRF-C management team 
member. 
The TMG will meet by teleconference on a monthly basis to discuss progress. There will be a rolling 
agenda with formal minutes to facilitate internal trial progress scrutiny. Issues relating to patient 
enrolment raised by primary researchers will form part of the monthly teleconference to ensure that 
any recurring or systematic problems relating to OPTIMATE trial recruitment are discussed 
comprehensively and corrected efficiently. 
 
 
 
10.0 Insurance  
The sponsor of the study, University College Cork, has appropriate insurance in place to cover litigation 
that may arise as a result of patients who are harmed as a result of participation in this trial.   
 
 
11.0 Potential Risks of the intervention and the study procedures: (if 
applicable)  
 
11.1 Adverse Event reporting (if applicable)  
Adverse clinical events are expected to occur with a substantially high frequency in the patient 
population participating in OPTIMATE, given their age, comorbidity and frailty profile. 
The definitive intervention (DI) to be deployed in OPTIMATE is considered intrinsically low risk, given 
that all of the advice points to be offered to patients’ attending hospital medical staff and GPs are 
evidence-based and may be accepted or rejected by the patients’ hospital doctors or GPs as they see 
fit.  
Nevertheless, errors may possibly occur if primary researchers use patients’ data incorrectly when 
applying STOPP/START criteria or Stockley’s drug interaction software in the detection of drug-drug 
and drug-disease interactions. 
Any actual or suspected significant adverse events experienced by patients arising out of application 
of the DI will be reported by the primary researcher to the following personnel within 24 hours of 
detection: 

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(i) 
The trial PI. 
(ii) 
The local trial co-PI where the adverse event has been detected. 
(iii) 
The independent trial monitor. 
 
The PI will co-ordinate an online discussion of the suspected adverse event within 48 hours of receiving 
notice of the event with the local trial PI, the primary researcher who has reported the adverse event 
and the independent trial monitor. If there is consensus that a clinically significant adverse clinical 
event arising out of applying the intervention has occurred, the adverse event will be reported to the 
TSC). The TSC chairperson will convene a meeting of the TSC within one week of notification to 
examine the details of reported potential adverse event and report back to the TMG within 24 hours 
of its deliberations.  
Any adverse event deemed potentially attributable to incorrect/erroneous application of the DI, in the 
judgement of the local trial co-PI, will be investigated fully by the independent trial monitor within 2 
weeks of the adverse event report. 
An electronic record of all reported adverse events possibly relating to the application of the DI will 
be kept securely at the UCC Clinical Research Facility as part of the OPTIMATE trial record for further 
scrutiny/examination.  
 
11.2 Deviations from the Protocol  
Any Deviations from the protocol will be noted by the PI or researchers. Any deviations which have 
the potential to affect participant safety, or the integrity of the study data will be reported to the 
sponsor i.e. Health Research Board. 
 
19 
 
12.0 Withdrawal of Participants from the study - (If relevant)  
Patient participants in the OPTIMATE trial may withdraw from the study at any time. Their data up to 
the time of withdrawal will be retained for later analysis unless a participant explicitly indicates that 
he/she wishes to have their data removed from the trial database. 
 
 
 
13.0 Bibliography  
 
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medicine-related problems in adult patients. Br J Clin Pharmacol. 2014 Aug;78(2):202-17. 
 
Baxter K, Preston CL (eds), Stockley’s Drug Interactions. [online] London: Pharmaceutical Press 
<http://www.medicinescomplete.com/> (accessed on 20 Sep 2022). 
 
Blum MR, Sallevelt BTGM, Spinewine A, O'Mahony D, Moutzouri E, Feller M, Baumgartner C, Roumet 
M, Jungo KT, Schwab N, Bretagne L, Beglinger S, Aubert CE, Wilting I, Thevelin S, Murphy K, Huibers 
CJA, Drenth-van Maanen AC, Boland B, Crowley E, Eichenberger A, Meulendijk M, Jennings E, Adam L, 
Roos MJ, Gleeson L, Shen Z, Marien S, Meinders AJ, Baretella O, Netzer S, de Montmollin M, Fournier 
A, Mouzon A, O'Mahony C, Aujesky D, Mavridis D, Byrne S, Jansen PAF, Schwenkglenks M, Spruit M, 
Dalleur O, Knol W, Trelle S, Rodondi N. Optimizing Therapy to Prevent Avoidable Hospital Admissions 

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in Multimorbid Older Adults (OPERAM): cluster randomised controlled trial. BMJ. 2021 Jul 
13;374:n1585. 
 
Cardwell K. Reducing medication errors and transitions of care. Age Ageing. 2020;49(4):537-539. 
 
Cullinan, S., O’Mahony, D. & Byrne, S. Application of the structured history taking of medication use 
tool to optimise prescribing for older patients and reduce adverse events. Int J Clin Pharm 2016 Apr;38: 
374–379.  
 
Dalton K, O'Mahony D, O'Sullivan D, O'Connor MN, Byrne S. Prescriber implementation of 
STOPP/START recommendations for hospitalised older adults: a comparison of a pharmacist approach 
and a physician approach. Drugs Aging. 2019 Mar;36(3):279-288. 
 
Drenth-van Maanen AC, Spee J, van Hensbergen L, Jansen PA, Egberts TC, van Marum RJ. Structured 
history taking of medication use reveals iatrogenic harm due to discrepancies in medication histories 
in hospital and pharmacy records. J Am Geriatr Soc. 2011 Oct;59(10):1976-7. 
 
El Morabet N, Uitvlugt EB, van den Bemt BJF, van den Bemt PMLA, Janssen MJA, Karapinar-Çarkit F. 
Prevalence and preventability of drug-related hospital readmissions: a systematic review. J Am Geriatr 
Soc. 2018 Mar;66(3):602-608. 
 
EQ-5D website https://euroqol.org/eq-5d-instruments/eq-5d-5l-available-modes-of-
administration/proxy/   (accessed Dec 9, 2021). 
 
HIQA guideline https://www.hiqa.ie/sites/default/files/2017-01/A-Guide-to-Health-Technology-
Assessment.pdf   (accessed December 9, 2021). 
 
Hyttinen V, Jyrkkä J, Valtonen H. A Systematic Review of the impact of potentially inappropriate 
medication on health care utilization and costs among older adults. Med Care. 2016 Oct;54(10):950-
64. 
 
Kongkaew C, Noyce PR, Ashcroft DM. Hospital admissions associated with adverse drug reactions: a 
systematic review of prospective observational studies. Ann Pharmacother. 2008 Jul;42(7):1017-25. 
 
Kuhn-Thiel AM, Weiß C, Wehling M; FORTA authors/expert panel members. Consensus validation of 
the FORTA (Fit fOR The Aged) List: a clinical tool for increasing the appropriateness of 
pharmacotherapy in the elderly. Drugs Aging. 2014 Feb;31(2):131-40.  
 
Laugaland K, Aase K, Barach P. Interventions to improve patient safety in transitional care-a review of 
the evidence. Work. 2012;41 Suppl 1:2915-24. 
 
Lavan A, Eustace J, Dahly D, et al. Incident adverse drug reactions in geriatric inpatients: a multicentred 
observational study. Ther Adv Drug Saf. 2018;9(1):13-23. 
 
Leendertse AJ, Van Den Bemt PM, Poolman JB, Stoker LJ, Egberts AC, Postma MJ. Preventable hospital 
admissions related to medication (HARM): cost analysis of the HARM study. Value Health. 2011 
Jan;14(1):34-40. 
 
Mahoney FI, Barthel D. “Functional evaluation: the Barthel Index.” 
Maryland State Med Journal 1965;14:56-61. Used with permission. 

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McAuliffe LH, Zullo AR, Dapaah-Afriyie R, Berard-Collins C. Development and validation of a transitions 
of-care pharmacist tool to predict potentially avoidable 30-day readmissions. Am J Health Syst Pharm. 
2018 Feb 1;75(3):111-119. 
 
 
Moriarty F, Bennett K, Cahir C, Kenny RA, Fahey T. Potentially inappropriate prescribing according to 
STOPP and START and adverse outcomes in community-dwelling older people: a prospective cohort 
study. Br J Clin Pharmacol. 2016 Sep;82(3):849-57. 
 
O'Brien GL, O'Mahony D, Gillespie P, Mulcahy M, Walshe V, O'Connor MN, O'Sullivan D, Gallagher J, 
Byrne S. Cost-effectiveness analysis of a physician-implemented medication screening tool in older 
hospitalised patients in Ireland. Drugs Aging. 2018 Aug;35(8):751-762. 
 
O'Connor MN, O'Sullivan D, Gallagher PF, Eustace J, Byrne S, O'Mahony D. Prevention of hospital-
acquired adverse drug reactions in older people using Screening Tool of Older Persons' Prescriptions 
and Screening Tool to Alert to Right Treatment Criteria: a cluster randomized controlled trial. J Am 
Geriatr Soc. 2016 Aug;64(8):1558-66. 
 
O’Mahony D. Optimizing pharmacotherapy for older patients. In: Oxford Textbook of Geriatric 
Medicine, JP Michel, BL Beattie, FC Martin & JD Walston eds., Oxford University Press, 2018, pp 183-
88. 
 
O'Mahony D, O'Sullivan D, Byrne S, O'Connor MN, Ryan C, Gallagher P. STOPP/START criteria for 
potentially inappropriate prescribing in older people: version 2. Age Ageing. 2015 Mar;44(2):213-8. 
 
O'Mahony D, Gudmundsson A, Soiza RL, et al. Prevention of adverse drug reactions in hospitalized 
older patients with multi-morbidity and polypharmacy: the SENATOR* randomized controlled clinical 
trial. Age Ageing. 2020;49(4):605-614. 
 
O'Sullivan D, O'Mahony D, O'Connor MN, Gallagher P, Gallagher J, Cullinan S, O'Sullivan R, Eustace J, 
Byrne S. Prevention of adverse drug reactions in hospitalised older patients using a software-
supported structured pharmacist intervention: a cluster randomised controlled trial. Drugs Aging. 
2016 Jan;33(1):63-73. 
 
Oscanoa TJ, Lizaraso F, Carvajal A. Hospital admissions due to adverse drug reactions in the elderly. A 
meta-analysis. Eur J Clin Pharmacol. 2017 Jun;73(6):759-770. 
 
Ravn-Nielsen LV, Duckert ML, Lund ML, Henriksen JP, Nielsen ML, Eriksen CS, Buck TC, Pottegård A, 
Hansen MR, Hallas J. Effect of an in-hospital multifaceted clinical pharmacist intervention on the risk 
of readmission: a randomized clinical trial. JAMA Intern Med. 2018 Mar 1;178(3):375-382. 
 
Thomsen LA, Winterstein AG, Søndergaard B, Haugbølle LS, Melander A. Systematic review of the 
incidence and characteristics of preventable adverse drug events in ambulatory care. Ann 
Pharmacother. 2007 Sep;41(9):1411-26. 
 
Thompson K, Kulkarni J, Sergejew AA. Reliability and validity of a new Medication Adherence Rating 
Scale (MARS) for the psychoses. Schizophr Res 2000;42:241–7. 
 

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Weir DL, Lee TC, McDonald EG, et al. Both new and chronic potentially inappropriate medications 
continued at hospital discharge are associated with increased risk of adverse events. J Am Geriatr Soc. 
2020;68(6):1184-1192. 
 
Xing XX, Zhu C, Liang HY, Wang K, Chu YQ, Zhao LB, Jiang C, Wang YQ, Yan SY. Associations between 
potentially inappropriate medications and adverse health outcomes in the elderly: a systematic review 
and meta-analysis. Ann Pharmacother. 2019 Oct; 53(10): 1005-1019. 
 
 
 
 
 
 
14.0 Approval and Agreement to the Protocol 
 
The Principal Investigator agrees to perform the clinical study as written and to abide by this protocol 
except in case where deviation is necessary for urgent safety reasons.   
 
Principal Investigator  
 
Prof. Denis O’Mahony 
 
Signature:  
     
 
 
Date 13/06/2023 
 
 
 

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Appendix 1: Eq-5D-5L Questionnaire 
Under each heading, please tick the ONE box that best describes your health 
TODAY. 
MOBILITY 
I have no problems in walking about  
I have slight problems in walking about  
I have moderate problems in walking about  
I have severe problems in walking about  
I am unable to walk about  
SELF-CARE 
I have no problems washing or dressing myself  
I have slight problems washing or dressing myself  
I have moderate problems washing or dressing myself  
I have severe problems washing or dressing myself  
I am unable to wash or dress myself  
USUAL ACTIVITIES (e.g. work, study, housework, family or 
leisure activities) 
I have no problems doing my usual activities  
I have slight problems doing my usual activities  
I have moderate problems doing my usual activities  
I have severe problems doing my usual activities  
I am unable to do my usual activities  
PAIN / DISCOMFORT 
I have no pain or discomfort  
I have slight pain or discomfort  
I have moderate pain or discomfort  
I have severe pain or discomfort  

OPTIMATE Trial  
Principal Investigator: Prof. Denis O’Mahony 
 
OPTIMATE trial protocol 
V6.0         13 June 2023  
28 
I have extreme pain or discomfort  
ANXIETY / DEPRESSION 
I am not anxious or depressed  
I am slightly anxious or depressed  
I am moderately anxious or depressed  
I am severely anxious or depressed  
I am extremely anxious or depressed  
 

OPTIMATE Trial  
Principal Investigator: Prof. Denis O’Mahony 
 
OPTIMATE trial protocol 
V6.0         13 June 2023  
29