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Automated Quantitative Pupillometry for the Prognostication of Coma After Cardiac Arrest

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Abstract

Background

Sedation and therapeutic hypothermia (TH) delay neurological responses and might reduce the accuracy of clinical examination to predict outcome after cardiac arrest (CA). We examined the accuracy of quantitative pupillary light reactivity (PLR), using an automated infrared pupillometry, to predict outcome of post-CA coma in comparison to standard PLR, EEG, and somato-sensory evoked potentials (SSEP).

Methods

We prospectively studied over a 1-year period (June 2012–June 2013) 50 consecutive comatose CA patients treated with TH (33 °C, 24 h). Quantitative PLR (expressed as the % of pupillary response to a calibrated light stimulus) and standard PLR were measured at day 1 (TH and sedation; on average 16 h after CA) and day 2 (normothermia, off sedation: on average 46 h after CA). Neurological outcome was assessed at 90 days with Cerebral Performance Categories (CPC), dichotomized as good (CPC 1–2) versus poor (CPC 3–5). Predictive performance was analyzed using area under the ROC curves (AUC).

Results

Patients with good outcome [n = 23 (46 %)] had higher quantitative PLR than those with poor outcome [n = 27; 16 (range 9–23) vs. 10 (1–30) % at day 1, and 20 (13–39) vs. 11 (1–55) % at day 2, both p < 0.001]. Best cut-off for outcome prediction of quantitative PLR was <13 %. The AUC to predict poor outcome was higher for quantitative than for standard PLR at both time points (day 1, 0.79 vs. 0.56, p = 0.005; day 2, 0.81 vs. 0.64, p = 0.006). Prognostic accuracy of quantitative PLR was comparable to that of EEG and SSEP (0.81 vs. 0.80 and 0.73, respectively, both p > 0.20).

Conclusions

Quantitative PLR is more accurate than standard PLR in predicting outcome of post-anoxic coma, irrespective of temperature and sedation, and has comparable prognostic accuracy than EEG and SSEP.

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Acknowledgments

The authors thank Helena Marti-Soler, Ph.D., for independent review of statistical analysis, and Christine Stähli, RN and Elsa Juan, MPsych, for her help in data acquisition. This work was supported by grants from the Swiss National Science Foundation (to MO and AOR), the European Critical Care Research Network (to MO), and the Gueules Cassées Foundation (to PB).

Conflict of interest

The authors have no conflict of interest to declare. The manufacturer of automated pupillometry (IDMED) was not involved in the study.

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Authors and Affiliations

  1. Neuroscience Critical Care Research Group, Department of Intensive Care Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), University Hospital and Faculty of Biology and Medicine, University of Lausanne, Rue du Bugnon 46, 1011, Lausanne, Switzerland

    Tamarah Suys, Pierre Bouzat, Nathalie Sala & Mauro Oddo

  2. Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois (CHUV), University Hospital and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland

    Andrea O. Rossetti

  3. Department of Internal Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), University Hospital and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland

    Pedro Marques-Vidal

  4. Department of Anesthesiology and Intensive Care Medicine, Grenoble University Hospital and Joseph Fourier University, Grenoble, France

    Pierre Bouzat & Jean-François Payen

Authors
  1. Tamarah Suys
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  2. Pierre Bouzat
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  3. Pedro Marques-Vidal
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  4. Nathalie Sala
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  5. Jean-François Payen
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  6. Andrea O. Rossetti
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  7. Mauro Oddo
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Corresponding author

Correspondence to Mauro Oddo.

Additional information

Tamarah Suys and Pierre Bouzat contributed equally.

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Suys, T., Bouzat, P., Marques-Vidal, P. et al. Automated Quantitative Pupillometry for the Prognostication of Coma After Cardiac Arrest. Neurocrit Care 21, 300–308 (2014). https://doi.org/10.1007/s12028-014-9981-z

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