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September 28, 2021; 97 (13) Research Article

Association of Silent Cerebrovascular Disease Identified Using Natural Language Processing and Future Ischemic Stroke

David M. Kent, Lester Y. Leung, Yichen Zhou, Patrick H. Luetmer, David F. Kallmes, Jason Nelson, Sunyang Fu, Chengyi Zheng, Hongfang Liu, Wansu Chen
First published August 10, 2021, DOI: https://doi.org/10.1212/WNL.0000000000012602
David M. Kent
From the Predictive Analytics and Comparative Effectiveness Center (D.M.K., J.N.) and Department of Neurology (L.Y.L.), Tufts Medical Center, Boston, MA; Department of Research and Evaluation (Y.Z., C.Z., W.C.), Kaiser Permanente Southern California, Pasadena; and Department of Radiology (P.H.L., D.F.K.) and Division of Digital Health Services, Department of Health Sciences Research (S.F., H.L.), Mayo Clinic, Rochester, MN.
MD, MS
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Lester Y. Leung
From the Predictive Analytics and Comparative Effectiveness Center (D.M.K., J.N.) and Department of Neurology (L.Y.L.), Tufts Medical Center, Boston, MA; Department of Research and Evaluation (Y.Z., C.Z., W.C.), Kaiser Permanente Southern California, Pasadena; and Department of Radiology (P.H.L., D.F.K.) and Division of Digital Health Services, Department of Health Sciences Research (S.F., H.L.), Mayo Clinic, Rochester, MN.
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Yichen Zhou
From the Predictive Analytics and Comparative Effectiveness Center (D.M.K., J.N.) and Department of Neurology (L.Y.L.), Tufts Medical Center, Boston, MA; Department of Research and Evaluation (Y.Z., C.Z., W.C.), Kaiser Permanente Southern California, Pasadena; and Department of Radiology (P.H.L., D.F.K.) and Division of Digital Health Services, Department of Health Sciences Research (S.F., H.L.), Mayo Clinic, Rochester, MN.
MS
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Patrick H. Luetmer
From the Predictive Analytics and Comparative Effectiveness Center (D.M.K., J.N.) and Department of Neurology (L.Y.L.), Tufts Medical Center, Boston, MA; Department of Research and Evaluation (Y.Z., C.Z., W.C.), Kaiser Permanente Southern California, Pasadena; and Department of Radiology (P.H.L., D.F.K.) and Division of Digital Health Services, Department of Health Sciences Research (S.F., H.L.), Mayo Clinic, Rochester, MN.
MD
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David F. Kallmes
From the Predictive Analytics and Comparative Effectiveness Center (D.M.K., J.N.) and Department of Neurology (L.Y.L.), Tufts Medical Center, Boston, MA; Department of Research and Evaluation (Y.Z., C.Z., W.C.), Kaiser Permanente Southern California, Pasadena; and Department of Radiology (P.H.L., D.F.K.) and Division of Digital Health Services, Department of Health Sciences Research (S.F., H.L.), Mayo Clinic, Rochester, MN.
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Jason Nelson
From the Predictive Analytics and Comparative Effectiveness Center (D.M.K., J.N.) and Department of Neurology (L.Y.L.), Tufts Medical Center, Boston, MA; Department of Research and Evaluation (Y.Z., C.Z., W.C.), Kaiser Permanente Southern California, Pasadena; and Department of Radiology (P.H.L., D.F.K.) and Division of Digital Health Services, Department of Health Sciences Research (S.F., H.L.), Mayo Clinic, Rochester, MN.
MS
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Sunyang Fu
From the Predictive Analytics and Comparative Effectiveness Center (D.M.K., J.N.) and Department of Neurology (L.Y.L.), Tufts Medical Center, Boston, MA; Department of Research and Evaluation (Y.Z., C.Z., W.C.), Kaiser Permanente Southern California, Pasadena; and Department of Radiology (P.H.L., D.F.K.) and Division of Digital Health Services, Department of Health Sciences Research (S.F., H.L.), Mayo Clinic, Rochester, MN.
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Chengyi Zheng
From the Predictive Analytics and Comparative Effectiveness Center (D.M.K., J.N.) and Department of Neurology (L.Y.L.), Tufts Medical Center, Boston, MA; Department of Research and Evaluation (Y.Z., C.Z., W.C.), Kaiser Permanente Southern California, Pasadena; and Department of Radiology (P.H.L., D.F.K.) and Division of Digital Health Services, Department of Health Sciences Research (S.F., H.L.), Mayo Clinic, Rochester, MN.
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Hongfang Liu
From the Predictive Analytics and Comparative Effectiveness Center (D.M.K., J.N.) and Department of Neurology (L.Y.L.), Tufts Medical Center, Boston, MA; Department of Research and Evaluation (Y.Z., C.Z., W.C.), Kaiser Permanente Southern California, Pasadena; and Department of Radiology (P.H.L., D.F.K.) and Division of Digital Health Services, Department of Health Sciences Research (S.F., H.L.), Mayo Clinic, Rochester, MN.
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Wansu Chen
From the Predictive Analytics and Comparative Effectiveness Center (D.M.K., J.N.) and Department of Neurology (L.Y.L.), Tufts Medical Center, Boston, MA; Department of Research and Evaluation (Y.Z., C.Z., W.C.), Kaiser Permanente Southern California, Pasadena; and Department of Radiology (P.H.L., D.F.K.) and Division of Digital Health Services, Department of Health Sciences Research (S.F., H.L.), Mayo Clinic, Rochester, MN.
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Association of Silent Cerebrovascular Disease Identified Using Natural Language Processing and Future Ischemic Stroke
David M. Kent, Lester Y. Leung, Yichen Zhou, Patrick H. Luetmer, David F. Kallmes, Jason Nelson, Sunyang Fu, Chengyi Zheng, Hongfang Liu, Wansu Chen
Neurology Sep 2021, 97 (13) e1313-e1321; DOI: 10.1212/WNL.0000000000012602

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This article has a correction. Please see:

  • Association of Silent Cerebrovascular Disease Identified Using Natural Language Processing and Future Ischemic Stroke - July 25, 2023
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Abstract

Background and Objectives Silent cerebrovascular disease (SCD), comprising silent brain infarction (SBI) and white matter disease (WMD), is commonly found incidentally on neuroimaging scans obtained in routine clinical care. Their prognostic significance is not known. We aimed to estimate the incidence of and risk increase in future stroke in patients with incidentally discovered SCD.

Methods Patients in the Kaiser Permanente Southern California (KPSC) health system aged ≥50 years, without prior ischemic stroke, transient ischemic attack (TIA), or dementia/Alzheimer disease receiving a head CT or MRI between 2009 and 2019 were included. SBI and WMD were identified by natural language processing (NLP) from the neuroimage report.

Results Among 262,875 individuals receiving neuroimaging, NLP identified 13,154 (5.0%) with SBI and 78,330 (29.8%) with WMD. The incidence of future stroke was 35.5 (95% confidence interval [CI] 34.0, 37.1) per 1,000 patient-years for patients with SBI: 21.1 (95% CI 20.6, 21.6) for patients with WMD and 7.6 (95% CI 7.4, 7.8) for patients without SCD. The crude hazard ratio (HR) associated with SBI was 3.34 (95% CI 3.19 to 3.50) and for WMD 2.57 (95% CI 2.49 to 2.66). With MRI-discovered SBI, the adjusted HR was 3.02 (95% CI 2.58 to 3.54) for those <65 years of age and 2.15 (95% CI 1.91 to 2.42) for those ≥65. With CT scan, the adjusted HR was 2.55 (95% CI 2.24 to 2.91) for those <65 and 1.81 (95% CI 1.71 to 1.92) for those ≥65. The adjusted HR associated with a finding of WMD was 1.76 (95% CI 1.70 to 1.83) and was not modified by age or imaging modality.

Discussion Incidentally discovered SBI and WMD are common and associated with increased risk of subsequent symptomatic stroke, representing an important opportunity for stroke prevention.

Glossary

CI=
confidence interval;
HR=
hazard ratio;
EHR=
electronic health record;
ICD-9=
International Classification of Diseases–9;
KPSC=
Kaiser Permanente Southern California;
NLP=
natural language processing;
PPV=
positive predictive value;
SBI=
silent brain infarction;
SCD=
silent cerebrovascular disease;
TIA=
transient ischemic attack;
WMD=
white matter disease

Footnotes

  • Go to Neurology.org/N for full disclosures. Funding information and disclosures deemed relevant by the authors, if any, are provided at the end of the article.

  • See the Highlighted Changes supplement, showing the changes made in this updated version: http://links.lww.com/WNL/C611.

  • Received March 5, 2021.
  • Accepted in final form July 20, 2021.
  • © 2021 American Academy of Neurology
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