Kim Jee-Yun, Yee Jeong, Park Tae-Im, Shin So-Youn, Ha Man-Ho, Gwak Hye-Sun
College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul 03760, Korea.
Department of Pharmacy, Catholic Kwandong University International St. Mary's Hospital, Incheon 22711, Korea.
Healthcare (Basel). 2021 Jul 6;9(7):853. doi: 10.3390/healthcare9070853.
Predicting the clinical progression of intensive care unit (ICU) patients is crucial for survival and prognosis. Therefore, this retrospective study aimed to develop the risk scoring system of mortality and the prediction model of ICU length of stay (LOS) among patients admitted to the ICU. Data from ICU patients aged at least 18 years who received parenteral nutrition support for ≥50% of the daily calorie requirement from February 2014 to January 2018 were collected. In-hospital mortality and log-transformed LOS were analyzed by logistic regression and linear regression, respectively. For calculating risk scores, each coefficient was obtained based on regression model. Of 445 patients, 97 patients died in the ICU; the observed mortality rate was 21.8%. Using logistic regression analysis, APACHE II score (15-29: 1 point, 30 or higher: 2 points), qSOFA score ≥ 2 (2 points), serum albumin level < 3.4 g/dL (1 point), and infectious or respiratory disease (1 point) were incorporated into risk scoring system for mortality; patients with 0, 1, 2-4, and 5-6 points had approximately 10%, 20%, 40%, and 65% risk of death. For LOS, linear regression analysis showed the following prediction equation: log(LOS) = 0.01 × (APACHE II) + 0.04 × (total bilirubin) - 0.09 × (admission diagnosis of gastrointestinal disease or injury, poisoning, or other external cause) + 0.970. Our study provides the mortality risk score and LOS prediction equation. It could help clinicians to identify those at risk and optimize ICU management.
预测重症监护病房(ICU)患者的临床进展对于生存和预后至关重要。因此,这项回顾性研究旨在建立ICU住院患者的死亡风险评分系统和ICU住院时间(LOS)预测模型。收集了2014年2月至2018年1月期间年龄至少18岁、接受肠外营养支持且每日热量需求≥50%的ICU患者的数据。分别通过逻辑回归和线性回归分析住院死亡率和对数转换后的LOS。为了计算风险评分,基于回归模型获得每个系数。在445例患者中,97例在ICU死亡;观察到的死亡率为21.8%。通过逻辑回归分析,APACHE II评分(15 - 29分:1分,30分及以上:2分)、qSOFA评分≥2(2分)、血清白蛋白水平<3.4 g/dL(1分)以及感染性或呼吸道疾病(1分)被纳入死亡风险评分系统;得0分、1分、2 - 4分和5 - 6分的患者死亡风险分别约为10%、20%、40%和65%。对于LOS,线性回归分析得出以下预测方程:log(LOS) = 0.01×(APACHE II) + 0.04×(总胆红素) - 0.09×(胃肠道疾病或损伤、中毒或其他外部原因的入院诊断) + 0.970。我们的研究提供了死亡风险评分和LOS预测方程。它可以帮助临床医生识别有风险的患者并优化ICU管理。