Diagnostic accuracy of an artificial intelligence online engine in migraine: A multi‐center study
Objective This study assesses the concordance in migraine diagnosis between an online, self‐administered, Computer‐based, Diagnostic Engine (CDE) and semi‐structured interview (SSI) by a headache specialist, both using International Classification of Headache Disorders, 3rd edition (ICHD‐3) criteria...
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| Published in | Headache Vol. 62; no. 7; pp. 870 - 882 |
|---|---|
| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Wiley Subscription Services, Inc
01.07.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0017-8748 1526-4610 1526-4610 |
| DOI | 10.1111/head.14324 |
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| Abstract | Objective
This study assesses the concordance in migraine diagnosis between an online, self‐administered, Computer‐based, Diagnostic Engine (CDE) and semi‐structured interview (SSI) by a headache specialist, both using International Classification of Headache Disorders, 3rd edition (ICHD‐3) criteria.
Background
Delay in accurate diagnosis is a major barrier to headache care. Accurate computer‐based algorithms may help reduce the need for SSI‐based encounters to arrive at correct ICHD‐3 diagnosis.
Methods
Between March 2018 and August 2019, adult participants were recruited from three academic headache centers and the community via advertising to our cross‐sectional study. Participants completed two evaluations: phone interview conducted by headache specialists using the SSI and a web‐based expert questionnaire and analytics, CDE. Participants were randomly assigned to either the SSI followed by the web‐based questionnaire or the web‐based questionnaire followed by the SSI. Participants completed protocols a few minutes apart. The concordance in migraine/probable migraine (M/PM) diagnosis between SSI and CDE was measured using Cohen’s kappa statistics. The diagnostic accuracy of CDE was assessed using the SSI as reference standard.
Results
Of the 276 participants consented, 212 completed both SSI and CDE (study completion rate = 77%; median age = 32 years [interquartile range: 28–40], female:male ratio = 3:1). Concordance in M/PM diagnosis between SSI and CDE was: κ = 0.83 (95% confidence interval [CI]: 0.75–0.91). CDE diagnostic accuracy: sensitivity = 90.1% (118/131), 95% CI: 83.6%–94.6%; specificity = 95.8% (68/71), 95% CI: 88.1%–99.1%. Positive and negative predictive values = 97.0% (95% CI: 91.3%–99.0%) and 86.6% (95% CI: 79.3%–91.5%), respectively, using identified migraine prevalence of 60%. Assuming a general migraine population prevalence of 10%, positive and negative predictive values were 70.3% (95% CI: 43.9%–87.8%) and 98.9% (95% CI: 98.1%–99.3%), respectively.
Conclusion
The SSI and CDE have excellent concordance in diagnosing M/PM. Positive CDE helps rule in M/PM, through high specificity and positive likelihood ratio. A negative CDE helps rule out M/PM through high sensitivity and low negative likelihood ratio. CDE that mimics SSI logic is a valid tool for migraine diagnosis. |
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| AbstractList | This study assesses the concordance in migraine diagnosis between an online, self-administered, Computer-based, Diagnostic Engine (CDE) and semi-structured interview (SSI) by a headache specialist, both using International Classification of Headache Disorders, 3rd edition (ICHD-3) criteria.OBJECTIVEThis study assesses the concordance in migraine diagnosis between an online, self-administered, Computer-based, Diagnostic Engine (CDE) and semi-structured interview (SSI) by a headache specialist, both using International Classification of Headache Disorders, 3rd edition (ICHD-3) criteria.Delay in accurate diagnosis is a major barrier to headache care. Accurate computer-based algorithms may help reduce the need for SSI-based encounters to arrive at correct ICHD-3 diagnosis.BACKGROUNDDelay in accurate diagnosis is a major barrier to headache care. Accurate computer-based algorithms may help reduce the need for SSI-based encounters to arrive at correct ICHD-3 diagnosis.Between March 2018 and August 2019, adult participants were recruited from three academic headache centers and the community via advertising to our cross-sectional study. Participants completed two evaluations: phone interview conducted by headache specialists using the SSI and a web-based expert questionnaire and analytics, CDE. Participants were randomly assigned to either the SSI followed by the web-based questionnaire or the web-based questionnaire followed by the SSI. Participants completed protocols a few minutes apart. The concordance in migraine/probable migraine (M/PM) diagnosis between SSI and CDE was measured using Cohen's kappa statistics. The diagnostic accuracy of CDE was assessed using the SSI as reference standard.METHODSBetween March 2018 and August 2019, adult participants were recruited from three academic headache centers and the community via advertising to our cross-sectional study. Participants completed two evaluations: phone interview conducted by headache specialists using the SSI and a web-based expert questionnaire and analytics, CDE. Participants were randomly assigned to either the SSI followed by the web-based questionnaire or the web-based questionnaire followed by the SSI. Participants completed protocols a few minutes apart. The concordance in migraine/probable migraine (M/PM) diagnosis between SSI and CDE was measured using Cohen's kappa statistics. The diagnostic accuracy of CDE was assessed using the SSI as reference standard.Of the 276 participants consented, 212 completed both SSI and CDE (study completion rate = 77%; median age = 32 years [interquartile range: 28-40], female:male ratio = 3:1). Concordance in M/PM diagnosis between SSI and CDE was: κ = 0.83 (95% confidence interval [CI]: 0.75-0.91). CDE diagnostic accuracy: sensitivity = 90.1% (118/131), 95% CI: 83.6%-94.6%; specificity = 95.8% (68/71), 95% CI: 88.1%-99.1%. Positive and negative predictive values = 97.0% (95% CI: 91.3%-99.0%) and 86.6% (95% CI: 79.3%-91.5%), respectively, using identified migraine prevalence of 60%. Assuming a general migraine population prevalence of 10%, positive and negative predictive values were 70.3% (95% CI: 43.9%-87.8%) and 98.9% (95% CI: 98.1%-99.3%), respectively.RESULTSOf the 276 participants consented, 212 completed both SSI and CDE (study completion rate = 77%; median age = 32 years [interquartile range: 28-40], female:male ratio = 3:1). Concordance in M/PM diagnosis between SSI and CDE was: κ = 0.83 (95% confidence interval [CI]: 0.75-0.91). CDE diagnostic accuracy: sensitivity = 90.1% (118/131), 95% CI: 83.6%-94.6%; specificity = 95.8% (68/71), 95% CI: 88.1%-99.1%. Positive and negative predictive values = 97.0% (95% CI: 91.3%-99.0%) and 86.6% (95% CI: 79.3%-91.5%), respectively, using identified migraine prevalence of 60%. Assuming a general migraine population prevalence of 10%, positive and negative predictive values were 70.3% (95% CI: 43.9%-87.8%) and 98.9% (95% CI: 98.1%-99.3%), respectively.The SSI and CDE have excellent concordance in diagnosing M/PM. Positive CDE helps rule in M/PM, through high specificity and positive likelihood ratio. A negative CDE helps rule out M/PM through high sensitivity and low negative likelihood ratio. CDE that mimics SSI logic is a valid tool for migraine diagnosis.CONCLUSIONThe SSI and CDE have excellent concordance in diagnosing M/PM. Positive CDE helps rule in M/PM, through high specificity and positive likelihood ratio. A negative CDE helps rule out M/PM through high sensitivity and low negative likelihood ratio. CDE that mimics SSI logic is a valid tool for migraine diagnosis. This study assesses the concordance in migraine diagnosis between an online, self-administered, Computer-based, Diagnostic Engine (CDE) and semi-structured interview (SSI) by a headache specialist, both using International Classification of Headache Disorders, 3rd edition (ICHD-3) criteria. Delay in accurate diagnosis is a major barrier to headache care. Accurate computer-based algorithms may help reduce the need for SSI-based encounters to arrive at correct ICHD-3 diagnosis. Between March 2018 and August 2019, adult participants were recruited from three academic headache centers and the community via advertising to our cross-sectional study. Participants completed two evaluations: phone interview conducted by headache specialists using the SSI and a web-based expert questionnaire and analytics, CDE. Participants were randomly assigned to either the SSI followed by the web-based questionnaire or the web-based questionnaire followed by the SSI. Participants completed protocols a few minutes apart. The concordance in migraine/probable migraine (M/PM) diagnosis between SSI and CDE was measured using Cohen's kappa statistics. The diagnostic accuracy of CDE was assessed using the SSI as reference standard. Of the 276 participants consented, 212 completed both SSI and CDE (study completion rate = 77%; median age = 32 years [interquartile range: 28-40], female:male ratio = 3:1). Concordance in M/PM diagnosis between SSI and CDE was: κ = 0.83 (95% confidence interval [CI]: 0.75-0.91). CDE diagnostic accuracy: sensitivity = 90.1% (118/131), 95% CI: 83.6%-94.6%; specificity = 95.8% (68/71), 95% CI: 88.1%-99.1%. Positive and negative predictive values = 97.0% (95% CI: 91.3%-99.0%) and 86.6% (95% CI: 79.3%-91.5%), respectively, using identified migraine prevalence of 60%. Assuming a general migraine population prevalence of 10%, positive and negative predictive values were 70.3% (95% CI: 43.9%-87.8%) and 98.9% (95% CI: 98.1%-99.3%), respectively. The SSI and CDE have excellent concordance in diagnosing M/PM. Positive CDE helps rule in M/PM, through high specificity and positive likelihood ratio. A negative CDE helps rule out M/PM through high sensitivity and low negative likelihood ratio. CDE that mimics SSI logic is a valid tool for migraine diagnosis. ObjectiveThis study assesses the concordance in migraine diagnosis between an online, self‐administered, Computer‐based, Diagnostic Engine (CDE) and semi‐structured interview (SSI) by a headache specialist, both using International Classification of Headache Disorders, 3rd edition (ICHD‐3) criteria.BackgroundDelay in accurate diagnosis is a major barrier to headache care. Accurate computer‐based algorithms may help reduce the need for SSI‐based encounters to arrive at correct ICHD‐3 diagnosis.MethodsBetween March 2018 and August 2019, adult participants were recruited from three academic headache centers and the community via advertising to our cross‐sectional study. Participants completed two evaluations: phone interview conducted by headache specialists using the SSI and a web‐based expert questionnaire and analytics, CDE. Participants were randomly assigned to either the SSI followed by the web‐based questionnaire or the web‐based questionnaire followed by the SSI. Participants completed protocols a few minutes apart. The concordance in migraine/probable migraine (M/PM) diagnosis between SSI and CDE was measured using Cohen’s kappa statistics. The diagnostic accuracy of CDE was assessed using the SSI as reference standard.ResultsOf the 276 participants consented, 212 completed both SSI and CDE (study completion rate = 77%; median age = 32 years [interquartile range: 28–40], female:male ratio = 3:1). Concordance in M/PM diagnosis between SSI and CDE was: κ = 0.83 (95% confidence interval [CI]: 0.75–0.91). CDE diagnostic accuracy: sensitivity = 90.1% (118/131), 95% CI: 83.6%–94.6%; specificity = 95.8% (68/71), 95% CI: 88.1%–99.1%. Positive and negative predictive values = 97.0% (95% CI: 91.3%–99.0%) and 86.6% (95% CI: 79.3%–91.5%), respectively, using identified migraine prevalence of 60%. Assuming a general migraine population prevalence of 10%, positive and negative predictive values were 70.3% (95% CI: 43.9%–87.8%) and 98.9% (95% CI: 98.1%–99.3%), respectively.ConclusionThe SSI and CDE have excellent concordance in diagnosing M/PM. Positive CDE helps rule in M/PM, through high specificity and positive likelihood ratio. A negative CDE helps rule out M/PM through high sensitivity and low negative likelihood ratio. CDE that mimics SSI logic is a valid tool for migraine diagnosis. Objective This study assesses the concordance in migraine diagnosis between an online, self‐administered, Computer‐based, Diagnostic Engine (CDE) and semi‐structured interview (SSI) by a headache specialist, both using International Classification of Headache Disorders, 3rd edition (ICHD‐3) criteria. Background Delay in accurate diagnosis is a major barrier to headache care. Accurate computer‐based algorithms may help reduce the need for SSI‐based encounters to arrive at correct ICHD‐3 diagnosis. Methods Between March 2018 and August 2019, adult participants were recruited from three academic headache centers and the community via advertising to our cross‐sectional study. Participants completed two evaluations: phone interview conducted by headache specialists using the SSI and a web‐based expert questionnaire and analytics, CDE. Participants were randomly assigned to either the SSI followed by the web‐based questionnaire or the web‐based questionnaire followed by the SSI. Participants completed protocols a few minutes apart. The concordance in migraine/probable migraine (M/PM) diagnosis between SSI and CDE was measured using Cohen’s kappa statistics. The diagnostic accuracy of CDE was assessed using the SSI as reference standard. Results Of the 276 participants consented, 212 completed both SSI and CDE (study completion rate = 77%; median age = 32 years [interquartile range: 28–40], female:male ratio = 3:1). Concordance in M/PM diagnosis between SSI and CDE was: κ = 0.83 (95% confidence interval [CI]: 0.75–0.91). CDE diagnostic accuracy: sensitivity = 90.1% (118/131), 95% CI: 83.6%–94.6%; specificity = 95.8% (68/71), 95% CI: 88.1%–99.1%. Positive and negative predictive values = 97.0% (95% CI: 91.3%–99.0%) and 86.6% (95% CI: 79.3%–91.5%), respectively, using identified migraine prevalence of 60%. Assuming a general migraine population prevalence of 10%, positive and negative predictive values were 70.3% (95% CI: 43.9%–87.8%) and 98.9% (95% CI: 98.1%–99.3%), respectively. Conclusion The SSI and CDE have excellent concordance in diagnosing M/PM. Positive CDE helps rule in M/PM, through high specificity and positive likelihood ratio. A negative CDE helps rule out M/PM through high sensitivity and low negative likelihood ratio. CDE that mimics SSI logic is a valid tool for migraine diagnosis. |
| Author | Sanjanwala, Bharati M. Rapoport, Alan M. Cowan, Robert P. Blythe, Jim Ekpo, Elizabeth Rothrock, John Knievel, Kerry Woldeamanuel, Yohannes W. Peretz, Addie M. |
| AuthorAffiliation | 1 Division of Headache and Facial Pain, Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA 2 Neurology, University of California, Los Angeles, California, USA 4 Neurology, The George Washington University School of Medicine and Health Sciences, Washington, District of Columbia, USA 6 Neurology, University of California Davis, Davis, California, USA 3 Information Sciences Institute, University of Southern California, Los Angeles, California, USA 5 Neurology, Barrow Neurological Institute, Phoenix, Arizona, USA |
| AuthorAffiliation_xml | – name: 6 Neurology, University of California Davis, Davis, California, USA – name: 1 Division of Headache and Facial Pain, Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA – name: 2 Neurology, University of California, Los Angeles, California, USA – name: 4 Neurology, The George Washington University School of Medicine and Health Sciences, Washington, District of Columbia, USA – name: 5 Neurology, Barrow Neurological Institute, Phoenix, Arizona, USA – name: 3 Information Sciences Institute, University of Southern California, Los Angeles, California, USA |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35657603$$D View this record in MEDLINE/PubMed |
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| Copyright | 2022 The Authors. published by Wiley Periodicals LLC on behalf of American Headache Society 2022 The Authors. Headache: The Journal of Head and Face Pain published by Wiley Periodicals LLC on behalf of American Headache Society. 2022. This article is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Keywords | diagnostic accuracy study diagnosis online engine migraine semi-structured interview artificial intelligence |
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| Notes | Funding information The authors are thankful to The SunStar Foundation for financially sponsoring this study. YWW is a recipient of the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under award number 1K01NS124911‐01. The funding sources had no role in the design of this study, its execution, analyses, interpretation of the data, or decision to submit results. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 ObjectType-Undefined-3 AUTHOR CONTRIBUTIONS Study concept and design: Robert P. Cowan, Alan M. Rapoport, Jim Blythe, Yohannes W. Woldeamanuel. Acquisition of data: Robert P. Cowan, John Rothrock, Kerry Knievel, Addie M. Peretz, Elizabeth Ekpo, Bharati M. Sanjanwala, Yohannes W. Woldeamanuel. Analysis and interpretation of data: Robert P. Cowan, Jim Blythe, Yohannes W. Woldeamanuel. Drafting of the manuscript: Yohannes W. Woldeamanuel. Revising it for intellectual content: Robert P. Cowan, Alan M. Rapoport, Jim Blythe, John Rothrock, Kerry Knievel, Addie M. Peretz, Elizabeth Ekpo, Bharati M. Sanjanwala, Yohannes W. Woldeamanuel. Final approval of the completed manuscript: Robert P. Cowan, Alan M. Rapoport, Jim Blythe, John Rothrock, Kerry Knievel, Addie M. Peretz, Elizabeth Ekpo, Bharati M. Sanjanwala, Yohannes W. Woldeamanuel. |
| ORCID | 0000-0003-4879-6098 |
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| PublicationDate | July/August 2022 |
| PublicationDateYYYYMMDD | 2022-07-01 |
| PublicationDate_xml | – month: 07 year: 2022 text: July/August 2022 |
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| PublicationTitle | Headache |
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This study assesses the concordance in migraine diagnosis between an online, self‐administered, Computer‐based, Diagnostic Engine (CDE) and... This study assesses the concordance in migraine diagnosis between an online, self-administered, Computer-based, Diagnostic Engine (CDE) and semi-structured... ObjectiveThis study assesses the concordance in migraine diagnosis between an online, self‐administered, Computer‐based, Diagnostic Engine (CDE) and... |
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| SubjectTerms | Accuracy Algorithms Artificial intelligence Confidence intervals Diagnosis diagnostic accuracy study Headache Headaches Likelihood ratio Migraine online engine Questionnaires semi‐structured interview Sensitivity |
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| Title | Diagnostic accuracy of an artificial intelligence online engine in migraine: A multi‐center study |
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