Quantitative versus standard pupillary light reflex for early prognostication in comatose cardiac arrest patients: an international prospective multicenter double-blinded study.

Oddo, Mauro; Sandroni, Claudio; Citerio, Giuseppe; Miroz, John-Paul; Horn, Janneke; Rundgren, Malin; Cariou, Alain; Payen, Jean-François et al. · Intensive Care Med · 2018

prospective_cohort · Level II

Where this comes from

Abstract

To assess the ability of quantitative pupillometry [using the Neurological Pupil index (NPi)] to predict an unfavorable neurological outcome after cardiac arrest (CA). We performed a prospective international multicenter study (10 centers) in adult comatose CA patients. Quantitative NPi and standard manual pupillary light reflex (sPLR)-blinded to clinicians and outcome assessors-were recorded in parallel from day 1 to 3 after CA. Primary study endpoint was to compare the value of NPi versus sPLR to predict 3-month Cerebral Performance Category (CPC), dichotomized as favorable (CPC 1-2: full recovery or moderate disability) versus unfavorable outcome (CPC 3-5: severe disability, vegetative state, or death). At any time between day 1 and 3, an NPi ≤ 2 (n = 456 patients) had a 51% (95% CI 49-53) negative predictive value and a 100% positive predictive value [PPV; 0% (0-2) false-positive rate], with a 100% (98-100) specificity and 32% (27-38) sensitivity for the prediction of unfavorable outcome. Compared with NPi, sPLR had significantly lower PPV and significantly lower specificity (p  < 0.001 at day 1 and 2; p  = 0.06 at day 3). The combination of NPi ≤ 2 with bilaterally absent somatosensory evoked potentials (SSEP; n = 188 patients) provided higher sensitivity [58% (49-67) vs. 48% (39-57) for SSEP alone], with comparable specificity [100% (94-100)]. Quantitative NPi had excellent ability to predict an unfavorable outcome from day 1 after CA, with no false positives, and significantly higher specificity than standard manual pupillary examination. The addition of NPi to SSEP increased sensitivity of outcome prediction, while maintaining 100% specificity.

Medical subject headings