ICU Pad Project: application of modern computer technology in pediatric postoperative cardiac intensive care. Pilot study

After cardiac surgery procedures, all vital functions of the patient must be monitored carefully with particular focus on hemodynamic parameters. Optimizing the function of the circulatory system is the primary task of an intensive care physician as low cardiac output syndrome is one of the fundamental challenges of providing medical care to both children and adults after cardiac procedures. After a cardiac procedure, the cardiac output and, thereby, patient condition and prognosis, can be influenced by many factors, including arterial blood pressure, circulating fluid volume, arrhythmia as well as myocardial dysfunction resulting from open heart surgery using extracorporeal circulation [1, 2]. The evolution of hemodynamic parameters shortly after the procedure is usually difficult to predict. The situation may be additionally complicated by the presence of concomitant diseases or perioperative complications, specific properties of the procedure (palliative vs. total repair) and the heart defect, and the age of the patient (e.g., compensatory mechanisms are exhausted quicker in neonates and infants).
The described clinical scenario requires making correct therapeutic decisions that consider all the variables characterizing the patient’s condition before the procedure (including, among others, the diagnosis) and in the recent past (e.g., during the procedure and the following hours) as well as variables registered on a current basis by systems designed to monitor vital parameters (both physical and biochemical). Information obtained from clinical examinations, nurse activities and observations, life support systems (mechanical ventilators, renal replacement therapy, mechanical circulatory support) as well as information associated with pharmacotherapy (e.g., the amount of fluids administered to the patient, dosage of pressor agents) must also be considered [3].
Making therapeutic decisions in the situation presented above involves analyzing significant amounts of data from various sources (Fig. 1). This makes tracking the changes of individual parameters on an ongoing basis particularly difficult. Moreover, the variety of information sources makes data analysis less effective due to potential time loss and errors associated with the necessity of alternately gaining access to different systems. Additionally, the variety of information sources precludes uniform data presentation and introduction of mechanisms for detecting variables that fall...


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