The prediction of the in-hospital mortality of acutely ill medical patients by electrocardiogram (ECG) dispersion mapping compared with established risk factors and predictive scores--a pilot study.

Abstract

ECG dispersion mapping (ECG-DM) is a novel technique that analyzes low amplitude ECG oscillations and reports them as the myocardial micro-alternation index (MMI). This study compared the ability of ECG-DM to predict in-hospital mortality with traditional risk factors such as age, vital signs and co-morbid diagnoses, as well as three predictive scores: the Simple Clinical Score (SCS)--based on clinical and ECG findings, and two Medical Admission Risk System scores--one based on vital signs and laboratory data (MARS), and one only on laboratory data (LD).

A convenient sample of 455 acutely ill medical patients (mean age 69.7±14.0 years) had their vital signs, mental and functional status recorded and a 12 lead ECG, routine laboratory investigations and ECG-DM performed immediately after admission to hospital. Each patient's in-hospital course and diagnoses at death or discharge were reviewed.

Of the vital signs only oxygen saturation and respiratory rate were statistically significant predictors of death. The continuous variables that predicted death the best were: MARS, SCS, LD, white cell count and MMI. The categorical variables that predicted in-hospital mortality with highest Chi-square were: a diagnosis of stroke, SCS>=12, LD>0.10, MARS>0.09 and MMI>36%.

ECG-DM may be a clinically useful predictor of in-hospital mortality. ECG-DM is inexpensive, only takes a few seconds to perform and requires no skill to interpret.