Rapid Prediction of Trauma Patient Survival by Analysis of Heart Rate Complexity: Impact of Reducing Data Set Size.

摘要

Heart rate complexity (HRC) is an emerging new vital sign for critically ill and injured patients. Traditionally, 800-beat data sets have been used to calculate HRC variables, thus limiting their practical use in an emergency. We sought to investigate whether data set reductions diminish the use of HRC to predict mortality in prehospital trauma patients. Ectopy-free, 800-beat sections of electrocardiogram (EKG) were collected from 31 prehospital trauma patients during their helicopter transport to a level 1 trauma center. Twenty patients survived (survivors) and 11 died (nonsurvivors) after admission. HRC was assessed via approximate entropy (ApEn), sample entropy (SampEn), and similarity of distributions (SOD). The amplitude of high-frequency oscillations was measured via the method of complex demodulation. This analysis was repeated in data sets of 800, 600, 400, 200, and 100 beats. For 800 beats, ApEn and SampEn were lower in nonsurvivors than in survivors, and SOD was higher. With data set reduction, ApEn in survivors and nonsurvivors gradually approached each other but remained different until the 200-beat dataset. Sample entropy did not change with data shortening and remained lower in nonsurvivors in all data sets. Similarity of distributions was nearly constant within groups for all data sets and discriminated survivors from nonsurvivors in 800- and 100-beat data sets. High-frequency amplitude distinguished survivors from nonsurvivors in 400-, 200-, and 100-beat data sets. Logistic regression was performed for the 800-, 200-, and 100-beat data sets, retaining SampEn as a predictor of mortality (area under the receiver-operating-characteristic curves, 0.821-0.895). HRC decreased in nonsurvivors versus survivors. This finding was confirmed for data sets as short as 100 beats by computationally different metrics.