Abstract

Background and aim: Accurate prediction of prognosis of patients admitted to intensive care units (ICUs) is very important for the clinical management of the patients. The present study aims to identify independent factors affecting death and discharge in ICUs using competing risk modeling. Methods: This retrospective cohort study was conducted on enrolling 880 patients admitted to emergency ICU in Namazi hospital, Shiraz University of Medical Sciences, Shiraz, Iran during 2013-2015. The data was collected from patients' medical records using a researcher-made checklist by a trained nurse. Competing risk regression models were fitted for the factors affecting the occurrence of death and discharge in ICU. Data analysis was conducted using STATA 13 and R 3.3.3 software. Results: Among these patients, 682 (77.5) were discharged and 157 (17.8) died in the ICU. The patients' mean +/- SD age was 48.90+/-19.52 yr. Among the study patients, 45.57 were female and 54.43 were male. In the competing risk model, age (Sub-distribution Hazard Ratio (SHR)) =1.02, 95 CI: 1.007-1.032), maximum heart rate (SHR=1.009, 95 CI: 1.001-1.019), minimum sodium level (SHR=1.035, 95 CI: 1.007-1.064), PH (SHR=7.982, 95 CI: 1.259-50.61), and bilirubin (SHR=1.046, 95 CI: 1.015-1.078) increased the risk of death, while maximum sodium level (SHR=0.946, 95 CI: 0.908-0.986) and maximum HCT (SHR=0.938, 95 CI: 0.882-0.998) reduced the risk of death. Conclusion: In conclusion, the results of this study revealed several variables that were effective in ICU length of stay (LOS). The variables that independently influenced time-to-discharge were age, maximum systolic blood pressure, minimum HCT, maximum WBC, and urine output, maximum HCT and Glasgow coma score. The results also showed that age, maximum heart rate, maximum sodium level, PH, urine output, and bilirubin, minimum sodium level and maximum HCT were the predictors of death. Furthermore, our findings indicated that the competing risk model was more appropriate than the Cox model in evaluating the predictive factors associated with the occurrence of death and discharge in patients hospitalized in ICUs. Hence, this model could play an important role in managers' and clinicians' decision-making and improvement of the standard of care in ICUs.