Saturday, November 08, 2014

Meta-analysis on neurologic prognosis post arrest

Recently published in Critical Care Medicine:

Data Synthesis: Of 2,737 citations, 20 studies (n = 1,845) met inclusion criteria. Meta-analysis showed that three tests accurately predicted poor neurologic outcome with low false-positive rates: bilateral absence of pupillary reflexes more than 24 hours after a return of spontaneous circulation (false-positive rate, 0.02; 95% CI, 0.01–0.06; summary positive likelihood ratio, 10.45; 95% CI, 3.37–32.43), bilateral absence of corneal reflexes more than 24 hours (false-positive rate, 0.04; 95% CI, 0.01–0.09; positive likelihood ratio, 6.8; 95% CI, 2.52–18.38), and bilateral absence of somatosensory-evoked potentials between days 1 and 7 (false-positive rate, 0.03; 95% CI, 0.01–0.07; positive likelihood ratio, 12.79; 95% CI, 5.35–30.62). False-positive rates were higher for a Glasgow Coma Scale motor score showing extensor posturing or worse (false-positive rate, 0.09; 95% CI, 0.06–0.13; positive likelihood ratio, 7.11; 95% CI, 5.01–10.08), unfavorable electroencephalogram patterns (false-positive rate, 0.07; 95% CI, 0.04–0.12; positive likelihood ratio, 8.85; 95% CI, 4.87–16.08), myoclonic status epilepticus (false-positive rate, 0.05; 95% CI, 0.02–0.11; positive likelihood ratio, 5.58; 95% CI, 2.56–12.16), and elevated neuron-specific enolase (false-positive rate, 0.12; 95% CI, 0.06–0.23; positive likelihood ratio, 4.14; 95% CI, 1.82–9.42). The specificity of available tests improved when these were performed beyond 72 hours. Data on neuroimaging, biomarkers, or combination testing were limited and inconclusive.

Conclusion: Simple bedside tests and somatosensory-evoked potentials predict poor neurologic outcome for survivors of cardiac arrest treated with targeted temperature management, and specificity improves when performed beyond 72 hours. Clinicians should use caution with these predictors as they carry the inherent risk of becoming self-fulfilling.

1 comment:

Clinton said...

As much as I get excited about hard but useful statistics like this... I'm looking for a numbers-lite interpretation of this study.

What are the positive and negative predictive value of these tests? This statistic would be much more useful to me as a clinician than a "false-positive rate" and to be frank, it frustrates me when authors don't take the one extra step of making the results something that is readily interpreted for the sake of the clinician at the bedside.