Outcome Prediction by a Mathematical Model Based on Noninvasive Hemodynamic Monitoring

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Abstract

Background:

The aims are to apply a mathematical search and display model based on noninvasive hemodynamic monitoring, to predict outcome early in a consecutively monitored series of 661 severely injured patients

Methods:

A prospective observational study by a previously designed protocol in a Level I trauma service in a university-run inner city public hospital was conducted. The survival probabilities were calculated at the initial resuscitation on admission and at subsequent intervals during their hospitalization beginning shortly after admission to the emergency department. Cardiac function was evaluated by cardiac output (CI), heart rate (HR), and mean arterial blood pressure (MAP), pulmonary function by pulse oximetry (SapO2), and tissue perfusion function by transcutaneous oxygen indexed to FiO2, (PtcO2/FiO2), and carbon dioxide (PtcCO2) tension.

Results:

The survival probability (SP) averaged 89 ± 0.4% for survivors and 75.7 ± 1.6% (p < 0.001) for nonsurvivors in the first 24-hour period of resuscitation. The CI, MAP, SapO2, PtcO2, and PtcO2/FiO2 were significantly higher in survivors than in nonsurvivors in initial resuscitation, whereas HR and PtcCO2 were higher in nonsurvivors.

Conclusions:

During the initial resuscitation period, misclassifications were 102 of 661 or 15%. The SP provided early objective criteria to evaluate hospital outcome and to track changes throughout the hospital course based on a large database of patients with similar clinical-hemodynamic states.

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