Ghorbani Mohammad, Ghaem Haleh, Rezaianzadeh Abbas, Shayan Zahra, Zand Farid, Nikandish Reza
Ph.D. of Epidemiology, Assistant Professor, Department of Public Health, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran.
Ph.D. of Epidemiology, Assistant Professor, Research Center for Health Sciences, Institute of Health, Department of Epidemiology, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran.
Electron Physician. 2018 Mar 25;10(3):6540-6547. doi: 10.19082/6540. eCollection 2018 Mar.
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.
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.
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.
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.
准确预测重症监护病房（ICU）患者的预后对于患者的临床管理非常重要。本研究旨在使用竞争风险模型确定影响ICU患者死亡和出院的独立因素。
本回顾性队列研究纳入了2013年至2015年期间在伊朗设拉子医科大学纳马齐医院急诊ICU住院的880例患者。数据由经过培训的护士使用研究者编制的检查表从患者病历中收集。对影响ICU患者死亡和出院发生的因素拟合竞争风险回归模型。使用STATA 13和R 3.3.3软件进行数据分析。
在这些患者中，682例（77.5%）从ICU出院，157例（17.8%）在ICU死亡。患者的平均年龄±标准差为48.90±19.52岁。在研究患者中，45.57%为女性，54.43%为男性。在竞争风险模型中，年龄（亚分布风险比（SHR）=1.02，95%置信区间：1.007 - 1.032）、最高心率（SHR = 1.009，95%置信区间：1.001 - 1.019）、最低钠水平（SHR = 1.035，95%置信区间：1.007 - 1.064）、pH值（SHR = 7.982，95%置信区间：1.259 - 50.61）和胆红素（SHR = 1.046，95%置信区间：1.015 - 1.078）增加了死亡风险，而最高钠水平（SHR = 0.946，95%置信区间：0.908 - 0.986）和最高红细胞压积（SHR = 0.938，95%置信区间：0.882 - 0.998）降低了死亡风险。
总之，本研究结果揭示了几个对ICU住院时间（LOS）有影响的变量。独立影响出院时间的变量是年龄、最高收缩压、最低红细胞压积、最高白细胞计数和尿量、最高红细胞压积和格拉斯哥昏迷评分。结果还表明，年龄、最高心率、最高钠水平、pH值、尿量和胆红素、最低钠水平和最高红细胞压积是死亡的预测因素。此外，我们的研究结果表明，在评估ICU住院患者死亡和出院发生的相关预测因素时，竞争风险模型比Cox模型更合适。因此，该模型可在管理人员和临床医生的决策以及提高ICU护理标准方面发挥重要作用。
