Predicting Outcome after Cardiopulmonary Arrest in Therapeutic Hypothermia Patients: Clinical, Electrophysiological and Imaging Prognosticators

INTRODUCTION: Predicting outcome in comatose survivors of cardiac arrest is based on data validated by guidelines that were established before the era of therapeutic hypothermia. We sought to evaluate the predictive value of clinical, electrophysiological and imaging data on patients submitted to therapeutic hypothermia. MATERIALS AND METHODS: A retrospective analysis of consecutive patients receiving therapeutic hypothermia during years 2010 and 2011 was made. Neurological examination, somatosensory evoked potentials, auditory evoked potentials, electroencephalography and brain magnetic resonance imaging were obtained during the first 72 hours. Glasgow Outcome Scale at 6 months, dichotomized into bad outcome (grades 1 and 2) and good outcome (grades 3, 4 and 5), was defined as the primary outcome. RESULTS: A total of 26 patients were studied. Absent pupillary light reflex, absent corneal and oculocephalic reflexes, absent N20 responses on evoked potentials and myoclonic status epilepticus showed no false-positives in predicting bad outcome. A malignant electroencephalographic pattern was also associated with a bad outcome (p = 0.05), with no false-positives. Two patients with a good outcome showed motor responses no better than extension (false-positive rate of 25%, p = 0.008) within 72 hours, both of them requiring prolonged sedation. Imaging findings of brain ischemia did not correlate with outcome. DISCUSSION: Absent pupillary, corneal and oculocephalic reflexes, absent N20 responses and a malignant electroencephalographic pattern all remain accurate predictors of poor outcome in cardiac arrest patients submitted to therapeutic hypothermia. CONCLUSION: Prolonged sedation beyond the hypothermia period may confound prediction strength of motor responses.

A Pilot Cost-Effectiveness Analysis of Treatments in Newly Diagnosed High-Grade Gliomas: The Example of 5-Aminolevulinic Acid Compared With White-Light Surgery

BACKGROUND: High-grade gliomas are aggressive, incurable tumors characterized by extensive diffuse invasion of the normal brain parenchyma. Novel therapies at best prolong survival; their costs are formidable and benefit is marginal. Economic restrictions thus require knowledge of the cost-effectiveness of treatments. Here, we show the cost-effectiveness of enhanced resections in malignant glioma surgery using a well-characterized tool for intraoperative tumor visualization, 5-aminolevulinic acid (5-ALA). OBJECTIVE: To evaluate the cost-effectiveness of 5-ALA fluorescence-guided neurosurgery compared with white-light surgery in adult patients with newly diagnosed high-grade glioma, adopting the perspective of the Portuguese National Health Service. METHODS: We used a Markov model (cohort simulation). Transition probabilities were estimated with the use of data from 1 randomized clinical trial and 1 noninterventional prospective study. Utility values and resource use were obtained from published literature and expert opinion. Unit costs were taken from official Portuguese reimbursement lists (2012 values). The health outcomes considered were quality-adjusted life-years, lifeyears, and progression-free life-years. Extensive 1-way and probabilistic sensitivity analyses were performed. RESULTS: The incremental cost-effectiveness ratios are below €10 000 in all evaluated outcomes, being around €9100 per quality-adjusted life-year gained, €6700 per life-year gained, and €8800 per progression-free life-year gained. The probability of 5-ALA fluorescence-guided surgery cost-effectiveness at a threshold of €20000 is 96.0% for quality-adjusted life-year, 99.6% for life-year, and 98.8% for progression-free life-year. CONCLUSION: 5-ALA fluorescence-guided surgery appears to be cost-effective in newly diagnosed high-grade gliomas compared with white-light surgery. This example demonstrates cost-effectiveness analyses for malignant glioma surgery to be feasible on the basis of existing data.