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.

Modeling in-Hospital Patient Survival During the First 28 Days After Intensive Care Unit Admission: a Prognostic Model for Clinical Trials in General Critically Ill Patients

OBJECTIVE: The objective of the study was to develop a model for estimating patient 28-day in-hospital mortality using 2 different statistical approaches. DESIGN: The study was designed to develop an outcome prediction model for 28-day in-hospital mortality using (a) logistic regression with random effects and (b) a multilevel Cox proportional hazards model. SETTING: The study involved 305 intensive care units (ICUs) from the basic Simplified Acute Physiology Score (SAPS) 3 cohort. PATIENTS AND PARTICIPANTS: Patients (n = 17138) were from the SAPS 3 database with follow-up data pertaining to the first 28 days in hospital after ICU admission. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: The database was divided randomly into 5 roughly equal-sized parts (at the ICU level). It was thus possible to run the model-building procedure 5 times, each time taking four fifths of the sample as a development set and the remaining fifth as the validation set. At 28 days after ICU admission, 19.98% of the patients were still in the hospital. Because of the different sampling space and outcome variables, both models presented a better fit in this sample than did the SAPS 3 admission score calibrated to vital status at hospital discharge, both on the general population and in major subgroups. CONCLUSIONS: Both statistical methods can be used to model the 28-day in-hospital mortality better than the SAPS 3 admission model. However, because the logistic regression approach is specifically designed to forecast 28-day mortality, and given the high uncertainty associated with the assumption of the proportionality of risks in the Cox model, the logistic regression approach proved to be superior.