Implementation of a Decision Support System and Its Effect on Early Optimisation of Lipid-Lowering Therapies in Patients With Acute Coronary Syndrome: a Cluster Randomised Controlled Trial
Phase: NA
Conditions: Acute Coronary Syndrome, Myocardial Infarction
Sponsor: Imperial College London



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Verbal or written discussion of results prior to study completion and full reporting should only 
be undertaken with written consent from the Sponsor. 
 
Therefore all information obtained as a result of the study will be regarded as 
CONFIDENTIAL, at least until appropriate analysis and review by the investigator(s) are 
completed.  
 
The results may be published or presented by the investigator(s), but the Sponsor will be 
given the opportunity to review and comment on any such results for up to 1 month before 
any presentations or publications are produced.  
 
A Clinical Study Report summarising the study results will be prepared and submitted to the 
REC within 90 days of the end of study. 
 
 
 
 

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12. REFERENCES 
1. Mach F, Baigent C, Catapano AL, et al. 2019 ESC/EAS Guidelines for the 
management of dyslipidaemias: lipid modification to reduce cardiovascular risk. Eur 
Heart J. 2020 Jan 1;41(1):111–88. 
2. Bohula EA, Morrow DA, Giugliano RP, et al. Atherothrombotic Risk Stratification and 
Ezetimibe for Secondary Prevention. J Am Coll Cardiol. 2017 Feb 28;69(8):911–21. 
3. Ference BA, Ginsberg HN, Graham I, et al. Low-density lipoproteins cause 
atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and 
clinical studies. A consensus statement from the European Atherosclerosis Society 
Consensus Panel. Eur Heart J. 2017; 38(32):2459-2472. 
4. Khan I, Peterson ED, Cannon CP, et al. Time-Dependent Cardiovascular Treatment 
Benefit Model for Lipid-Lowering Therapies. J Am Heart Assoc. 2020; 9(15):e016506. 
5. Ray KK, Reeskamp LF, Laufs U, et al. Combination lipid-lowering therapy as first-line 
strategy in very high-risk patients. Eur Heart J. 2021 Oct 12:ehab718. 
6. Schubert J, Lindahl B, Melhus H, et al. Low-density lipoprotein cholesterol reduction 
and statin intensity in myocardial infarction patients and major adverse outcomes: a 
Swedish nationwide cohort study. Eur Heart J. 2021; 42(3):243–52. 
7. Ray KK, Molemans B, Schoonen WM, et al. EU-Wide Cross-Sectional Observational 
Study of Lipid-Modifying Therapy Use in Secondary and Primary Care: the DA VINCI 
study. Eur J Prev Cardiol. 2021; 28(11):1279-1289. 
8. Steen DL, Khan I, Ansell D, et al. Retrospective examination of lipid-lowering treatment 
patterns in a real-world high-risk cohort in the UK in 2014: comparison with the National 
Institute for Health and Care Excellence (NICE) 2014 lipid modification guidelines. BMJ 
Open. 2017; 7(2):e013255. 
9. Ferrières J, Gorcyca K, Iorga ŞR, et al. Lipid-lowering Therapy and Goal Achievement 
in High-risk Patients From French General Practice. Clin Ther. 2018; 40(9):1484-
1495.e22. 
10. Arca M, Ansell D, Averna M, et al. Statin utilization and lipid goal attainment in high or 
very-high cardiovascular risk patients: Insights from Italian general practice. 
Atherosclerosis. 2018; 271:120-127. 
11. Blaum C, Seiffert M, Goßling A, et al. The need for PCSK9 inhibitors and associated 
treatment costs according to the 2019 ESC dyslipidaemia guidelines vs. the risk-based 
allocation algorithm of the 2017 ESC consensus statement: a simulation study in a 
contemporary CAD cohort. Eur J Prev Cardiol. 2021; 28(1):47-56. 
12. März W, Dippel FW, Theobald K, et al. Utilization of lipid-modifying therapy and low-
density lipoprotein cholesterol goal attainment in patients at high and very-high 
cardiovascular risk: Real-world evidence from Germany. Atherosclerosis. 2018; 
268:99-107. 
13. Kuiper JG, Sanchez RJ, Houben E, et al. Use of Lipid-modifying Therapy and LDL-C 
Goal Attainment in a High-Cardiovascular-Risk Population in the Netherlands. Clin 
Ther. 2017;3 9(4):819-827.e1. 
14. Koskinas KC, Gencer B, Nanchen D, et al. Eligibility for PCSK9 inhibitors based on the 
2019 ESC/EAS and 2018 ACC/AHA guidelines. Eur J Prev Cardiol. 2021; 28(1):59-65. 
15. Allahyari A, Jernberg T, Hagström E, et al. Application of the 2019 ESC/EAS 
dyslipidaemia guidelines to nationwide data of patients with a recent myocardial 
infarction: a simulation study. Eur Heart J. 2020 Oct 21;41(40):3900–9. 
16. Mancia G, Rea F, Corrao G, et al. Two-Drug Combinations as First-Step 
Antihypertensive Treatment. Circ Res. 2019; 124(7):1113–1123. 
17. Williams B, Mancia G, Spiering W, et al. 2018 ESC/ESH Guidelines for the 
management of arterial hypertension. Eur Heart J. 2018; 39(33):3021-3104. 

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18. Khan I, Peterson ED, Cannon CP, et al. Time-Dependent Cardiovascular Treatment 
Benefit AbstractModel for Lipid-Lowering Therapies. J Am Heart Assoc. 2020 Aug 
4;9(15):e016506. doi: 10.1161/JAHA.120.016506. Epub 2020 Jul 28. PMID: 32720582; 
PMCID: PMC7792260. 
19. Dorresteijn JAN, Visseren FLJ, Wassink AMJ, et al Development and validation of a 
prediction rule for recurrent vascular events based on a cohort study of patients with 
arterial disease: the SMART risk score Heart 2013;99:866-872. 
20. McKay Alisa J, Gunn Laura H, Ference Brian A, et al Is the SMART risk prediction 
model ready for real-world implementation? A validation study in a routine care setting 
of approximately 380 000 individuals doi.org/10.1093/eurjpc/zwab093 
 
21. https://angular.io/guide/what-is-angular  
 
22. https://cordova.apache.org/  
 
23. Khan I, Peterson ED, Cannon CP, et al. Time-Dependent Cardiovascular Treatment 
Benefit AbstractModel for Lipid-Lowering Therapies. J Am Heart Assoc. 2020 Aug 
4;9(15):e016506. doi: 10.1161/JAHA.120.016506. Epub 2020 Jul 28. PMID: 32720582; 
PMCID: PMC7792260.  
24. Konstantinos C Koskinas, Baris Gencer, David Nanchen, Mattia Branca, David 
Carballo, Roland Klingenberg, Manuel R Blum, Sebastian Carballo, Olivier Muller, 
Christian M Matter, Thomas F Lüscher, Nicolas Rodondi, Dik Heg, Matthias Wilhelm, 
Lorenz Räber, François Mach, Stephan Windecker, Eligibility for PCSK9 inhibitors 
based on the 2019 ESC/EAS and 2018 ACC/AHA guidelines, European Journal of 
Preventive Cardiology, Volume 28, Issue 1, January 2021, Pages 59–65, 
https://doi.org/10.1177/2047487320940102 
25. Campbell M, Grimshaw J, Steen N. Sample Size Calculations for Cluster Randomised 
Trials. Journal of Health Services Research & Policy. 2000;5(1):12-16. 
doi:10.1177/135581960000500105 
26. Health Services Research Unit. Database of ICCs: Spreadsheet (Empirical estimates 
of ICCs from changing professional practice studies).Available 
from: https://www.abdn.ac.uk/search/results.php?within=abdn&query=Iccfinal 
(accessed 20 April 2023). 
27. Gao F, Earnest A, Matchar DB, Campbell MJ, Machin D. Sample size calculations for 
the design of cluster randomised trials: A summary of methodology. Contemp Clin 
Trials. 2015 May;42:41-50. doi: 10.1016/j.cct.2015.02.011. Epub 2015 Mar 9. PMID: 
25766887. 
28. "PASS 2022 Power Analysis and Sample Size Software (2022). NCSS, LLC. Kaysville, 
Utah, USA, ncss.com/software/pass." 
29. Campbell MK, Piaggio G, Elbourne DR, Altman DG; for the CONSORT Group. Consort 
2010 statement: extension to cluster randomised trials. BMJ. 2012 Sep 4;345:e5661. 
PMID: 22951546 
30. Bellamy SL, Gibbard R, Hancock L, Howley P, Kennedy B, Klar N, Lipsitz S, Ryan L: 
Analysis of dichotomous outcome data for community intervention studies. Stat 
Methods Med Res. 2000, 9: 135-159. 10.1191/096228000672549488 
31. Campbell MJ, Donner A, Klar N. Developments in cluster randomised trials and 
Statistics in Medicine. Stat Med. 2007 Jan 15;26(1):2-19. doi: 10.1002/sim.2731. PMID: 
17136746. 

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32. Ritz J, Spiegelman D. Equivalence of conditional and marginal regression models for 
clustered and longitudinal data. Statistical Methods in Medical Research. 2004; 13(4): 
309-23 
33. Parker RA, & Weir CJ; Multiple secondary outcome analyses: precise interpretation is 
important. Trials. 2022; 23: 27. doi: 10.1186/s13063-021-05975-2 
34. Rubin M; When to adjust alpha during multiple testing: a consideration of disjunction, 
conjunction, and individual testing. Synthese. 2021;6:1–32  
35. Allahyari A, Jernberg T, Hagström E, et al. Application of the 2019 ESC/EAS 
dyslipidaemia guidelines to nationwide data of patients with a recent myocardial 
infarction: a simulation study. Eur Heart J. 2020 Oct 21;41(40):3900–9. 
36. Cannon CP, Khan I, Klimchak AC, et al. Simulation of Lipid-Lowering Therapy 
Intensification in a Population With Atherosclerotic Cardiovascular Disease. JAMA 
Cardiol. 2017 Sep 1;2(9):959-966. 
37. Khan I, Peterson ED, Cannon CP, et al. Time-Dependent Cardiovascular Treatment 
Benefit Model for Lipid-Lowering Therapies. J Am Heart Assoc. 2020; 9(15):e016506. 
13. REVISION HISTORY  
 
Version  
Date 
Summary of changes 
1.0 
04/OCT/2022 
First version  
2.0 
17/FEB/2023 
Minor updates to software details 
Several minor administrative updates   
3.0 
17/JAN/2024 
Addition of EU Representative 
Update to Endpoints 
Update to References (no. 24 + 26) 
Funder legal name change/ other administrative 
& staffing updates   
 
 

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SIGNATURE PAGE 1 (CHIEF INVESTIGATOR) 
 
The signature below constitutes approval of this protocol by the signatory, on behalf of the 
Protocol Development Group, and provides the necessary assurances that this study will be 
conducted according to all stipulations of the protocol including all statements regarding 
confidentiality. 
 
 
Study Title:  
 
Implementation of a Decision Support System and its effect on 
early optimisation of Lipid-Lowering Therapies in patients with Acute Coronary Syndrome: 
a cluster Randomised Controlled Trial 
 
 
Protocol Number:  
 
 
 
 
Signed: 
 
___________________________________________ 
 
 
 
 
Professor Kausik K Ray 
 
 
 
 
 
Date:  
 
_____________________ 
  
Kausik Ray
Digitally signed by Kausik Ray 
Date: 2024.01.23 13:32:24 Z

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SIGNATURE PAGE 2 (SPONSOR) 
 
The signatures below constitute approval of this protocol by the signatory.  
 
 
Study Title:  
 
Implementation of a Decision Support System and its effect on 
early optimisation of Lipid-Lowering Therapies in patients with Acute Coronary Syndrome: 
a cluster Randomised Controlled Trial 
 
 
Protocol Number:  
 
 
 
 
 
Signed: 
 
___________________________________________ 
 
 
 
 
Becky Ward  
 
 
 
Research Governance and Integrity Manager 
 
 
 
Research Governance and Integrity Team 
 
 
 
 
 
 
 
Date:  
 
_____________________ 
Becky 
Ward
Digitally signed 
by Becky Ward 
Date: 2024.01.23 
11:05:13 Z

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SIGNATURE PAGE 3 (STATISTICIAN) 
 
The signatures below constitute approval of this protocol by the signatory.  
 
 
Study Title:  
 
Implementation of a Decision Support System and its effect on 
early optimisation of Lipid-Lowering Therapies in patients with Acute Coronary Syndrome: 
a cluster Randomised Controlled Trial 
 
 
Protocol Number:  
 
 
 
 
 
Signed: 
 
___________________________________________ 
 
 
 
 
Professor Victoria Cornelius 
 
 
 
Imperial Clinical Trials Unit 
 
 
Date:  
 
_____________________ 
 
 
Professor Victoria 
Cornelius
Digitally signed by 
Professor Victoria Cornelius 
Date: 2024.01.23 14:19:26 Z

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SIGNATURE PAGE 4 (PRINCIPAL INVESTIGATOR) 
 
The signature of the below constitutes agreement of this protocol by the signatory and 
provides the necessary assurance that this study will be conducted at his/her investigational 
site according to all stipulations of the protocol including all statements regarding 
confidentiality. 
 
 
Study Title:  
 
Implementation of a Decision Support System and its effect on 
early optimisation of Lipid-Lowering Therapies in patients with Acute Coronary Syndrome: 
a cluster Randomised Controlled Trial 
 
 
Protocol Number:  
 
 
 
Address of Institution:  
____________________________________________ 
 
 
 
 
 
 
 
 
 
 
____________________________________________ 
 
 
 
 
 
 
____________________________________________ 
 
 
 
Signed: 
 
 
____________________________________________ 
 
 
Print Name and Title: 
____________________________________________ 
 
 
Date:  
 
 
_____________________ 
 

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14.APPENDICES 
14.1 Sample GP Letter  
 
 
Study Letter:  Implementation of a Decision Support System and its effect on early 
optimisation of Lipid-Lowering Therapies in patients with Acute Coronary Syndrome: a 
cluster Randomised Controlled Trial 
 
(insert date) 
Dr. (insert GP name)  
(insert GP address and contact details) 
 
Dear Dr. (insert GP name),  
Participant Name:  
DOB:  
Address: 
RE: OptimiZation Of lipid lowering therapies using a Decision support system In patients 
with Acute Coronary syndrome (ZODIAC). REC Ref: 22/SS/0106/ IRAS Ref: 317589 
I am writing to inform you that your patient named above has consented to participate in the 
above-named study, sponsored by Imperial College London.   
Imperial College London has developed a Decision Support System (DSS) which is a web 
app available online intended for clinicians to estimate the clinical benefit and risk over time 
of Lipid Lowering Treatment regimens, whether single or combination therapies. Your 
patient has been invited to join as they are an adult (up to 80 years of age) with ACS who 
has recently been hospitalised (within 72 hours).  
The aim of this study is to assess whether the availability of the DSS compared to routine 
care alone results in an increased initiation or intensification of combination LLTs over a 16-
week period after an ACS event. This is a cluster randomised trial with a total of 48 hospital 
sites across the UK, Spain and Italy– 24 DSS sites and 24 non DSS sites. DSS sites will 
receive a standardised period of training; following the DSS use it is still up to the clinician 
to choose & prescribe a suitable therapy for the patient.  
The participant will be followed up after the study Baseline Visit (i.e. post-ACS event) over 
a 4 month period as per routine care and study data will be captured accordingly. Aside from 
participant consent, there are no other participant study procedures.   
This study has been approved by the relevant Ethics Committees and Regulatory authorities 
and will be conducted in accordance with Good Clinical Practice and appropriate regulatory 
requirements.  
Please find enclosed a participant information sheet which provides more details about the 
study and what it involves for your patient. If you would like further information about the 
study, please do not hesitate to contact me.  
Thank you in advance for your co-operation.  
 
Kind Regards,  
 
 
 
 
XXX 
 
 

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Figure 2. Detailed Workflow for Estimation of the Impact of Lipid-Lowering Therapy 
Intensification via Monte-Carlo Simulation Based Approach 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Sample a patient 
from the study 
population
Treat the patient to 
reduce LDL-C
Summary
Measures:
Event rates over 
time, ARR, NNT
Estimate patient-
specific baseline 
risk via SMART risk 
score
Simulate outcomes 
over time
Estimate treated 
risk over time
Treatment Benefit 
Model from RCT 
Data
Treatment Strategy 1
Clone:
Treatment Strategy 2

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Table 1. Illustration for Reporting Results of Simulation: Estimated Utilization of 
Lipid-Lowering Therapy 
 
 
A:  
Baseline 
(Observed 
SOC in 
the 
Database) 
B:  HIS 
Only at 
Index 
ACS 
C1: HIS at 
Index ACS + 
Goal-Based 
Intensification* 
C2: HIS + EZE at 
Index ACS 
C4: HIS + EZE 
+ PCSK9i at 
Index ACS 
1 
MIS Only 
 
 
NA 
NA 
NA 
2 
HIS Only 
 
 
 
 
 
3 
MIS + EZE 
 
 
NA 
NA 
NA 
4 
HIS + EZE 
 
 
 
 
 
5 
MIS + PCSK9i 
 
 
NA 
NA 
NA 
6 
HIS + PCSK9i 
 
 
NA 
NA 
NA 
7 
MIS + EZE + 
PCSK9i 
 
 
NA 
NA 
NA 
8 
HIS + EZE + 
PCSK9i 
 
 
 
NA 
 
9 
EZE Only 
 
 
NA 
NA 
NA 
10 
PCSK9i Only 
 
 
NA 
NA 
NA 
11 
EZE + PCSK9i 
 
 
NA 
NA 
NA 
12 
No LLT 
 
 
NA 
NA 
NA 
 
Note: For all scenarios (with the exception of baseline and HIS only at Index ACS) HIS only will be 
utilized as the comparator strategy. Thus, “NA” has been listed as all patients will be on a minimum 
of HIS only during follow-up. Further, the sequential application of treatments ensures that patients 
cannot be treated with ezetimibe without already being treated with HIS, or PCSK9i without already 
being treated with both HIS and ezetimibe. 
* Ezetimibe added at 6 months and PCSK9i added at 12 months depending on LDL-C goal 
achievement (both LDL-C <1.4 mmol/L (55 mg/dL) and ≥50% reduction in LDL-C) 
 
 
 
 
 

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Table 2. Illustration for Reporting Results of Simulation: Estimated LDL-C Goal 
Achievement 
 
 
A:  Baseline 
(Observed 
SOC in the 
Database) 
B: HIS 
Only at 
Index 
ACS 
C1: HIS at Index 
ACS + Goal-
Based 
Intensification* 
C2: HIS + 
EZE at Index 
ACS 
C3: HIS + EZE + 
PCSK9i at Index 
ACS 
At 0-months 
LDL-C < 55 mg/dL 
 
 
 
 
 
55 ≤ LDL-C < 70 mg/dL 
 
 
 
 
 
70 ≤ LDL-C  
 
 
 
 
 
At 6-months 
LDL-C < 55 mg/dL 
 
 
 
 
 
55 ≤ LDL-C < 70 mg/dL 
 
 
 
 
 
70 ≤ LDL-C  
 
 
 
 
 
At 12-months 
LDL-C < 55 mg/dL 
 
 
 
 
 
55 ≤ LDL-C < 70 mg/dL 
 
 
 
 
 
70 ≤ LDL-C  
 
 
 
 
 
 
 
 
* Ezetimibe added at 6 months and PCSK9i added at 12 months depending on LDL-C goal 
achievement (both LDL-C <1.4 mmol/L (55 mg/dL) and ≥50% reduction in LDL-C) 
 
 
 
 
 
 
 
 
 
 

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Table 3. Illustration for Reporting Results of Simulation: Estimated LDL-C Goal 
Achievement 
 
A:  Baseline 
(Observed 
SOC in the 
Database) 
B: HIS Only 
at Index ACS 
C1: HIS at Index 
ACS + Goal-Based 
Intensification* 
C2: HIS + EZE 
at Index ACS 
C3: HIS + 
EZE + 
PCSK9i at 
Index ACS 
At 0-months 
LDL-C < 55 mg/dL + 
LDL-C Reduction ≥ 50% 
 
 
 
 
 
LDL-C < 55 mg/dL + 
LDL-C Reduction < 50% 
 
 
 
 
 
LDL-C ≥ 55 mg/dL + 
LDL-C Reduction ≥ 50% 
 
 
 
 
 
LDL-C ≥ 55 mg/dL + 
LDL-C Reduction < 50% 
 
 
 
 
 
At 6-months 
LDL-C < 55 mg/dL + 
LDL-C Reduction ≥ 50% 
 
 
 
 
 
LDL-C < 55 mg/dL + 
LDL-C Reduction < 50% 
 
 
 
 
 
LDL-C ≥ 55 mg/dL + 
LDL-C Reduction ≥ 50% 
 
 
 
 
 
LDL-C ≥ 55 mg/dL + 
LDL-C Reduction < 50% 
 
 
 
 
 
At 12-months 
LDL-C < 55 mg/dL + 
LDL-C Reduction ≥ 50% 
 
 
 
 
 
LDL-C < 55 mg/dL + 
LDL-C Reduction < 50% 
 
 
 
 
 
LDL-C ≥ 55 mg/dL + 
LDL-C Reduction ≥ 50% 
 
 
 
 
 
LDL-C ≥ 55 mg/dL + 
LDL-C Reduction < 50%