Review

. 2021 Oct 12;21(1):281.

doi: 10.1186/s12911-021-01638-z.

An informatics consult approach for generating clinical evidence for treatment decisions

Alvina G Lai^{1

2}, Wai Hoong Chang^{3

4}, Constantinos A Parisinos³, Michail Katsoulis³, Ruth M Blackburn^{3

4}, Anoop D Shah^{3

5

6}, Vincent Nguyen³, Spiros Denaxas^{3

4

5

7}, George Davey Smith^{8

9}, Tom R Gaunt^{8

9}, Krishnarajah Nirantharakumar^{4

10}, Murray P Cox¹¹, Donall Forde¹², Folkert W Asselbergs^{3

4

5

13

14}, Steve Harris⁶, Sylvia Richardson¹⁵, Reecha Sofat^{3

5}, Richard J B Dobson^{3

4

16}, Aroon Hingorani^{4

14}, Riyaz Patel¹⁴, Jonathan Sterne⁹, Amitava Banerjee^{3

17}, Alastair K Denniston^{4

18}, Simon Ball^{4

18}, Neil J Sebire^{19

20}, Nigam H Shah²¹, Graham R Foster²², Bryan Williams^{5

14

6}, Harry Hemingway^{3

4}

Affiliations

¹ Institute of Health Informatics, University College London, London, UK. alvina.lai@ucl.ac.uk.
² Health Data Research UK, London, UK. alvina.lai@ucl.ac.uk.
³ Institute of Health Informatics, University College London, London, UK.
⁴ Health Data Research UK, London, UK.
⁵ University College London Hospitals NIHR Biomedical Research Centre, London, UK.
⁶ University College London Hospitals NHS Trust, London, UK.
⁷ The Alan Turing Institute, London, UK.
⁸ Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
⁹ Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
¹⁰ Institute of Applies Health Research, University of Birmingham, Birmingham, UK.
¹¹ Statistics and Bioinformatics Group, School of Fundamental Sciences, Massey University, Palmerston North, New Zealand.
¹² Public Health Wales, University Hospital of Wales, Cardiff, UK.
¹³ Department of Cardiology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
¹⁴ Institute of Cardiovascular Science, University College London, London, UK.
¹⁵ Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK.
¹⁶ Department of Biostatistics and Health Informatics, King's College London, London, UK.
¹⁷ Barts Health NHS Trust, The Royal London Hospital, Whitechapel Rd, London, UK.
¹⁸ University Hospitals Birmingham NHSFT, Birmingham, UK.
¹⁹ UCL Great Ormond Street Institute of Child Health, London, UK.
²⁰ NIHR Great Ormond Street Hospital Biomedical Research Centre, London, UK.
²¹ Department of Medicine, School of Medicine, Stanford University, Stanford, CA, USA.
²² Barts Liver Centre, Blizard Institute, Queen Mary University of London, London, UK.

PMID: 34641870
PMCID: PMC8506488
DOI: 10.1186/s12911-021-01638-z

Review

An informatics consult approach for generating clinical evidence for treatment decisions

Alvina G Lai et al. BMC Med Inform Decis Mak. 2021.

. 2021 Oct 12;21(1):281.

doi: 10.1186/s12911-021-01638-z.

Authors

Affiliations

¹ Institute of Health Informatics, University College London, London, UK. alvina.lai@ucl.ac.uk.
² Health Data Research UK, London, UK. alvina.lai@ucl.ac.uk.
³ Institute of Health Informatics, University College London, London, UK.
⁴ Health Data Research UK, London, UK.
⁵ University College London Hospitals NIHR Biomedical Research Centre, London, UK.
⁶ University College London Hospitals NHS Trust, London, UK.
⁷ The Alan Turing Institute, London, UK.
⁸ Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
⁹ Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
¹⁰ Institute of Applies Health Research, University of Birmingham, Birmingham, UK.
¹¹ Statistics and Bioinformatics Group, School of Fundamental Sciences, Massey University, Palmerston North, New Zealand.
¹² Public Health Wales, University Hospital of Wales, Cardiff, UK.
¹³ Department of Cardiology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
¹⁴ Institute of Cardiovascular Science, University College London, London, UK.
¹⁵ Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK.
¹⁶ Department of Biostatistics and Health Informatics, King's College London, London, UK.
¹⁷ Barts Health NHS Trust, The Royal London Hospital, Whitechapel Rd, London, UK.
¹⁸ University Hospitals Birmingham NHSFT, Birmingham, UK.
¹⁹ UCL Great Ormond Street Institute of Child Health, London, UK.
²⁰ NIHR Great Ormond Street Hospital Biomedical Research Centre, London, UK.
²¹ Department of Medicine, School of Medicine, Stanford University, Stanford, CA, USA.
²² Barts Liver Centre, Blizard Institute, Queen Mary University of London, London, UK.

PMID: 34641870
PMCID: PMC8506488
DOI: 10.1186/s12911-021-01638-z

Abstract

Background: An Informatics Consult has been proposed in which clinicians request novel evidence from large scale health data resources, tailored to the treatment of a specific patient. However, the availability of such consultations is lacking. We seek to provide an Informatics Consult for a situation where a treatment indication and contraindication coexist in the same patient, i.e., anti-coagulation use for stroke prevention in a patient with both atrial fibrillation (AF) and liver cirrhosis.

Methods: We examined four sources of evidence for the effect of warfarin on stroke risk or all-cause mortality from: (1) randomised controlled trials (RCTs), (2) meta-analysis of prior observational studies, (3) trial emulation (using population electronic health records (N = 3,854,710) and (4) genetic evidence (Mendelian randomisation). We developed prototype forms to request an Informatics Consult and return of results in electronic health record systems.

Results: We found 0 RCT reports and 0 trials recruiting for patients with AF and cirrhosis. We found broad concordance across the three new sources of evidence we generated. Meta-analysis of prior observational studies showed that warfarin use was associated with lower stroke risk (hazard ratio [HR] = 0.71, CI 0.39-1.29). In a target trial emulation, warfarin was associated with lower all-cause mortality (HR = 0.61, CI 0.49-0.76) and ischaemic stroke (HR = 0.27, CI 0.08-0.91). Mendelian randomisation served as a drug target validation where we found that lower levels of vitamin K1 (warfarin is a vitamin K1 antagonist) are associated with lower stroke risk. A pilot survey with an independent sample of 34 clinicians revealed that 85% of clinicians found information on prognosis useful and that 79% thought that they should have access to the Informatics Consult as a service within their healthcare systems. We identified candidate steps for automation to scale evidence generation and to accelerate the return of results.

Conclusion: We performed a proof-of-concept Informatics Consult for evidence generation, which may inform treatment decisions in situations where there is dearth of randomised trials. Patients are surprised to know that their clinicians are currently not able to learn in clinic from data on 'patients like me'. We identify the key challenges in offering such an Informatics Consult as a service.

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Conflict of interest statement

AB has received research funding from AstraZeneca for work unrelated to this research. GRF receives funding from companies that manufacture drugs for hepatitis C virus (AbbVie, Gilead, MSD) and consults for GSK, Arbutus and Shionogi in areas unrelated to this research. TRG and GDS have received research funding from GlaxoSmithKline and Biogen for work unrelated to this research.

Figures

**Fig. 1**
Informatics Consult Electronic health record request form prototype

**Fig. 2**
Informatics Consult Electronic health record report prototype

**Fig. 3**
Trial evidence and currently recruiting trials of anticoagulation in patients with atrial fibrillation and cirrhosis to reduce stroke risk. A Clinical question and summary of trial evidence. B Previously completed and currently recruiting randomised trials evaluating anticoagulants and stroke outcomes have exclusion criteria related to cirrhosis

**Fig. 4**
New synthesis of prior observational evidence. Meta-analysis of the association between warfarin use and the risk of ischaemic stroke in observational studies including approaches for automation. A Characteristics of observational studies included in the meta-analysis. B Forest plot depicting the hazard ratios calculated with the DerSimonian and Laird random-effects models. HR = hazard ratio; CI = confidence interval; SE = standard error

**Fig. 5**
New observational evidence through target trial emulation (intention-to-treat analysis) where eligibility and treatment assignment were aligned with time zero of follow-up, as is done in randomised controlled trials. A CONSORT diagram showing the selection of eligible individuals for the target trial emulation of anticoagulation therapy in patients with atrial fibrillation and cirrhosis. B Kaplan–Meier plots of the propensity-matched cohort for all-cause mortality and ischaemic stroke. Flow diagram depicts analysis design. P values from logrank tests were indicated. Hazard ratios from Cox proportional hazards regression analyses were indicated. Numbers in parentheses indicate the 95% confidence intervals

**Fig. 6**
Genetic evidence. Two-sample Mendelian randomisation on circulating vitamin K1 levels and risk of stroke. *Indicate significant results

See this image and copyright information in PMC

References

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An informatics consult approach for generating clinical evidence for treatment decisions

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An informatics consult approach for generating clinical evidence for treatment decisions

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