Risk Scoring System of Mortality and Prediction Model of Hospital Stay for Critically Ill Patients Receiving Parenteral Nutrition
- 주제(키워드) intensive care unit , mortality , length of stay , scoring , prediction model
- 주제(기타) Health Care Sciences & Services
- 주제(기타) Health Policy & Services
- 설명문(일반) [Kim, Jee-Yun; Yee, Jeong; Gwak, Hye-Sun] Ewha Womans Univ, Coll Pharm & Grad Sch Pharmaceut Sci, 52 Ewhayeodae Gil, Seoul 03760, South Korea; [Kim, Jee-Yun; Park, Tae-Im] Catholic Kwandong Univ, Dept Pharm, Int St Marys Hosp, Incheon 22711, South Korea; [Shin, So-Youn] Catholic Kwandong Univ, Dept Infect Dis, Int St Marys Hosp, Incheon 22711, South Korea; [Ha, Man-Ho] Catholic Kwandong Univ, Dept Gen Surg, Int St Marys Hosp, Incheon 22711, South Korea
- 등재 SCIE, SSCI, SCOPUS
- 발행기관 MDPI
- 발행년도 2021
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000182321
- 본문언어 영어
- Published As http://dx.doi.org/10.3390/healthcare9070853
초록/요약
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 x (APACHE II) + 0.04 x (total bilirubin) - 0.09 x (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.
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