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An informatics consult approach for generating clinical evidence for treatment decisions

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 situati...

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Autores principales: Lai, Alvina G., Chang, Wai Hoong, Parisinos, Constantinos A., Katsoulis, Michail, Blackburn, Ruth M., Shah, Anoop D., Nguyen, Vincent, Denaxas, Spiros, Davey Smith, George, Gaunt, Tom R., Nirantharakumar, Krishnarajah, Cox, Murray P., Forde, Donall, Asselbergs, Folkert W., Harris, Steve, Richardson, Sylvia, Sofat, Reecha, Dobson, Richard J. B., Hingorani, Aroon, Patel, Riyaz, Sterne, Jonathan, Banerjee, Amitava, Denniston, Alastair K., Ball, Simon, Sebire, Neil J., Shah, Nigam H., Foster, Graham R., Williams, Bryan, Hemingway, Harry
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8506488/
https://www.ncbi.nlm.nih.gov/pubmed/34641870
http://dx.doi.org/10.1186/s12911-021-01638-z
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author Lai, Alvina G.
Chang, Wai Hoong
Parisinos, Constantinos A.
Katsoulis, Michail
Blackburn, Ruth M.
Shah, Anoop D.
Nguyen, Vincent
Denaxas, Spiros
Davey Smith, George
Gaunt, Tom R.
Nirantharakumar, Krishnarajah
Cox, Murray P.
Forde, Donall
Asselbergs, Folkert W.
Harris, Steve
Richardson, Sylvia
Sofat, Reecha
Dobson, Richard J. B.
Hingorani, Aroon
Patel, Riyaz
Sterne, Jonathan
Banerjee, Amitava
Denniston, Alastair K.
Ball, Simon
Sebire, Neil J.
Shah, Nigam H.
Foster, Graham R.
Williams, Bryan
Hemingway, Harry
author_facet Lai, Alvina G.
Chang, Wai Hoong
Parisinos, Constantinos A.
Katsoulis, Michail
Blackburn, Ruth M.
Shah, Anoop D.
Nguyen, Vincent
Denaxas, Spiros
Davey Smith, George
Gaunt, Tom R.
Nirantharakumar, Krishnarajah
Cox, Murray P.
Forde, Donall
Asselbergs, Folkert W.
Harris, Steve
Richardson, Sylvia
Sofat, Reecha
Dobson, Richard J. B.
Hingorani, Aroon
Patel, Riyaz
Sterne, Jonathan
Banerjee, Amitava
Denniston, Alastair K.
Ball, Simon
Sebire, Neil J.
Shah, Nigam H.
Foster, Graham R.
Williams, Bryan
Hemingway, Harry
author_sort Lai, Alvina G.
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12911-021-01638-z.
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spelling pubmed-85064882021-10-12 An informatics consult approach for generating clinical evidence for treatment decisions Lai, Alvina G. Chang, Wai Hoong Parisinos, Constantinos A. Katsoulis, Michail Blackburn, Ruth M. Shah, Anoop D. Nguyen, Vincent Denaxas, Spiros Davey Smith, George Gaunt, Tom R. Nirantharakumar, Krishnarajah Cox, Murray P. Forde, Donall Asselbergs, Folkert W. Harris, Steve Richardson, Sylvia Sofat, Reecha Dobson, Richard J. B. Hingorani, Aroon Patel, Riyaz Sterne, Jonathan Banerjee, Amitava Denniston, Alastair K. Ball, Simon Sebire, Neil J. Shah, Nigam H. Foster, Graham R. Williams, Bryan Hemingway, Harry BMC Med Inform Decis Mak Research Article 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12911-021-01638-z. BioMed Central 2021-10-12 /pmc/articles/PMC8506488/ /pubmed/34641870 http://dx.doi.org/10.1186/s12911-021-01638-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Lai, Alvina G.
Chang, Wai Hoong
Parisinos, Constantinos A.
Katsoulis, Michail
Blackburn, Ruth M.
Shah, Anoop D.
Nguyen, Vincent
Denaxas, Spiros
Davey Smith, George
Gaunt, Tom R.
Nirantharakumar, Krishnarajah
Cox, Murray P.
Forde, Donall
Asselbergs, Folkert W.
Harris, Steve
Richardson, Sylvia
Sofat, Reecha
Dobson, Richard J. B.
Hingorani, Aroon
Patel, Riyaz
Sterne, Jonathan
Banerjee, Amitava
Denniston, Alastair K.
Ball, Simon
Sebire, Neil J.
Shah, Nigam H.
Foster, Graham R.
Williams, Bryan
Hemingway, Harry
An informatics consult approach for generating clinical evidence for treatment decisions
title An informatics consult approach for generating clinical evidence for treatment decisions
title_full An informatics consult approach for generating clinical evidence for treatment decisions
title_fullStr An informatics consult approach for generating clinical evidence for treatment decisions
title_full_unstemmed An informatics consult approach for generating clinical evidence for treatment decisions
title_short An informatics consult approach for generating clinical evidence for treatment decisions
title_sort informatics consult approach for generating clinical evidence for treatment decisions
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8506488/
https://www.ncbi.nlm.nih.gov/pubmed/34641870
http://dx.doi.org/10.1186/s12911-021-01638-z
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