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August 04, 2020; 95 (5) Article

Cost-effectiveness analysis of multimodal prognostication in cardiac arrest with EEG monitoring

View ORCID ProfileEdilberto Amorim, Shirley S. Mo, Sebastian Palacios, View ORCID ProfileMohammad M. Ghassemi, Wei-Hung Weng, Sydney S. Cash, Matthew T. Bianchi, View ORCID ProfileM. Brandon Westover
First published July 13, 2020, DOI: https://doi.org/10.1212/WNL.0000000000009916
Edilberto Amorim
From Harvard Medical School (E.A., S.S.M., S.S.C., M.T.B., M.B.W.); Department of Neurology (E.A., S.S.C., M.T.B., M.B.W.), Massachusetts General Hospital, Boston; Department of Neurology (E.A.), University of California, San Francisco; and Computer Science and Artificial Intelligence Laboratory (E.A., S.P.) and Department of Electrical Engineering and Computer Science (M.M.G., W.-H.W.), Massachusetts Institute of Technology, Cambridge.
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Shirley S. Mo
From Harvard Medical School (E.A., S.S.M., S.S.C., M.T.B., M.B.W.); Department of Neurology (E.A., S.S.C., M.T.B., M.B.W.), Massachusetts General Hospital, Boston; Department of Neurology (E.A.), University of California, San Francisco; and Computer Science and Artificial Intelligence Laboratory (E.A., S.P.) and Department of Electrical Engineering and Computer Science (M.M.G., W.-H.W.), Massachusetts Institute of Technology, Cambridge.
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Sebastian Palacios
From Harvard Medical School (E.A., S.S.M., S.S.C., M.T.B., M.B.W.); Department of Neurology (E.A., S.S.C., M.T.B., M.B.W.), Massachusetts General Hospital, Boston; Department of Neurology (E.A.), University of California, San Francisco; and Computer Science and Artificial Intelligence Laboratory (E.A., S.P.) and Department of Electrical Engineering and Computer Science (M.M.G., W.-H.W.), Massachusetts Institute of Technology, Cambridge.
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Mohammad M. Ghassemi
From Harvard Medical School (E.A., S.S.M., S.S.C., M.T.B., M.B.W.); Department of Neurology (E.A., S.S.C., M.T.B., M.B.W.), Massachusetts General Hospital, Boston; Department of Neurology (E.A.), University of California, San Francisco; and Computer Science and Artificial Intelligence Laboratory (E.A., S.P.) and Department of Electrical Engineering and Computer Science (M.M.G., W.-H.W.), Massachusetts Institute of Technology, Cambridge.
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Wei-Hung Weng
From Harvard Medical School (E.A., S.S.M., S.S.C., M.T.B., M.B.W.); Department of Neurology (E.A., S.S.C., M.T.B., M.B.W.), Massachusetts General Hospital, Boston; Department of Neurology (E.A.), University of California, San Francisco; and Computer Science and Artificial Intelligence Laboratory (E.A., S.P.) and Department of Electrical Engineering and Computer Science (M.M.G., W.-H.W.), Massachusetts Institute of Technology, Cambridge.
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Sydney S. Cash
From Harvard Medical School (E.A., S.S.M., S.S.C., M.T.B., M.B.W.); Department of Neurology (E.A., S.S.C., M.T.B., M.B.W.), Massachusetts General Hospital, Boston; Department of Neurology (E.A.), University of California, San Francisco; and Computer Science and Artificial Intelligence Laboratory (E.A., S.P.) and Department of Electrical Engineering and Computer Science (M.M.G., W.-H.W.), Massachusetts Institute of Technology, Cambridge.
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Matthew T. Bianchi
From Harvard Medical School (E.A., S.S.M., S.S.C., M.T.B., M.B.W.); Department of Neurology (E.A., S.S.C., M.T.B., M.B.W.), Massachusetts General Hospital, Boston; Department of Neurology (E.A.), University of California, San Francisco; and Computer Science and Artificial Intelligence Laboratory (E.A., S.P.) and Department of Electrical Engineering and Computer Science (M.M.G., W.-H.W.), Massachusetts Institute of Technology, Cambridge.
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M. Brandon Westover
From Harvard Medical School (E.A., S.S.M., S.S.C., M.T.B., M.B.W.); Department of Neurology (E.A., S.S.C., M.T.B., M.B.W.), Massachusetts General Hospital, Boston; Department of Neurology (E.A.), University of California, San Francisco; and Computer Science and Artificial Intelligence Laboratory (E.A., S.P.) and Department of Electrical Engineering and Computer Science (M.M.G., W.-H.W.), Massachusetts Institute of Technology, Cambridge.
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Cost-effectiveness analysis of multimodal prognostication in cardiac arrest with EEG monitoring
Edilberto Amorim, Shirley S. Mo, Sebastian Palacios, Mohammad M. Ghassemi, Wei-Hung Weng, Sydney S. Cash, Matthew T. Bianchi, M. Brandon Westover
Neurology Aug 2020, 95 (5) e563-e575; DOI: 10.1212/WNL.0000000000009916

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Abstract

Objective To determine cost-effectiveness parameters for EEG monitoring in cardiac arrest prognostication.

Methods We conducted a cost-effectiveness analysis to estimate the cost per quality-adjusted life-year (QALY) gained by adding continuous EEG monitoring to standard cardiac arrest prognostication using the American Academy of Neurology Practice Parameter (AANPP) decision algorithm: neurologic examination, somatosensory evoked potentials, and neuron-specific enolase. We explored lifetime cost-effectiveness in a closed system that incorporates revenue back into the medical system (return) from payers who survive a cardiac arrest with good outcome and contribute to the health system during the remaining years of life. Good outcome was defined as a Cerebral Performance Category (CPC) score of 1–2 and poor outcome as CPC of 3–5.

Results An improvement in specificity for poor outcome prediction of 4.2% would be sufficient to make continuous EEG monitoring cost-effective (baseline AANPP specificity = 83.9%). In sensitivity analysis, the effect of increased sensitivity on the cost-effectiveness of EEG depends on the utility (u) assigned to a poor outcome. For patients who regard surviving with a poor outcome (CPC 3–4) worse than death (u = −0.34), an increased sensitivity for poor outcome prediction of 13.8% would make AANPP + EEG monitoring cost-effective (baseline AANPP sensitivity = 76.3%). In the closed system, an improvement in sensitivity of 1.8% together with an improvement in specificity of 3% was sufficient to make AANPP + EEG monitoring cost-effective, assuming lifetime return of 50% (USD $70,687).

Conclusion Incorporating continuous EEG monitoring into cardiac arrest prognostication is cost-effective if relatively small improvements in sensitivity and specificity are achieved.

Glossary

AANPP=
American Academy of Neurology Practice Parameter;
CPC=
Cerebral Performance Category;
GCS=
Glasgow Coma Scale;
HUI-3=
Health Utility Index Mark 3;
NSE=
neuron-specific enolase;
QALY=
quality-adjusted life year;
SSEP=
somatosensory evoked potentials;
WTP=
willingness to pay

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.

  • ↵* These authors contributed equally to this work.

  • Received April 5, 2019.
  • Accepted in final form January 10, 2020.
  • © 2020 American Academy of Neurology
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