首页    期刊浏览 2024年07月16日 星期二
登录注册

文章基本信息

  • 标题:Risk Scoring System of Mortality and Prediction Model of Hospital Stay for Critically Ill Patients Receiving Parenteral Nutrition
  • 本地全文:下载
  • 作者:Jee-Yun Kim ; Jeong Yee ; Tae-Im Park
  • 期刊名称:Healthcare
  • 电子版ISSN:2227-9032
  • 出版年度:2021
  • 卷号:9
  • 期号:7
  • 页码:853
  • DOI:10.3390/healthcare9070853
  • 出版社:MDPI Publishing
  • 摘要: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.
  • 关键词:intensive care unit; mortality; length of stay; scoring; prediction model intensive care unit ; mortality ; length of stay ; scoring ; prediction model
国家哲学社会科学文献中心版权所有