Mortality Prediction Among Critically Ill Patients During Intensive Care Unit Stays At Assuit University Hospital

Abstract

Background: - Mortality of ICU patients is a global parameter reported as an endpoint in numerous studies. However, causes and characteristics of patients’ death studied only in particular pathologies such as sepsis, cardiac arrest or ARDS. Therefore, Aim of this study was to predict mortality rates among critically ill patient admitted to the intensive care units at Assuit University Hospital over a period of one year, and to identify the predisposing factors that affect mortality rates in intensive care units. Design: Descriptive Exploratory research design used to conduct this study. Setting: This study carried in the Trauma, General and Postoperative Intensive Care Units at Assuit University Hospital. A sample of convenience of all adults' critically ill patients admitted over a period of one year from January to December 2016. Three main Tools was used, Tool I: Personal and medical data sheet, Tool II: predisposing factors for hospital death of the critically ill patient assessment tool, Tool III: hospital mortality prediction scales" (SAPS II). The main result revealed that mean SAPS II score of the studied sample was (41.61 ± 18.54), With the estimated mortality risk of (33.82 ± 29.28%) and the observed mortality of the total sample, 600 patients in the three intensive care units were (45.7%).Postoperative complication, shock mainly septic shock, respiratory failure and trauma were the most prominent contributor to the mortality. Conclusion: - Majority of the studied patients at Assuit University Hospital was a high risk of mortality with total mortality rate of 45.7 and about 21.3 of them were at sever mortality risk. Recommendations:-Using the mortality prediction model (SAPS II) is very useful that help for comparing actual and expected outcomes, optimize allocation of critical care resources and evaluate effect of both Nursing and Medical care efforts.

Keywords: SAPS II, mortality prediction.