scholarly journals A simple and readily available inflammation-based risk scoring system on admission predicts the need for mechanical ventilation in patients with COVID-19

Author(s):  
Luis M. Amezcua-Guerra ◽  
Karen Audelo ◽  
Juan Guzmán ◽  
Diana Santiago ◽  
Julieta González-Flores ◽  
...  
2021 ◽  
Vol 10 (16) ◽  
pp. 3657
Author(s):  
Julieta González-Flores ◽  
Carlos García-Ávila ◽  
Rashidi Springall ◽  
Malinalli Brianza-Padilla ◽  
Yaneli Juárez-Vicuña ◽  
...  

Background: Several easy-to-use risk scoring systems have been built to identify patients at risk of developing complications associated with COVID-19. However, information about the ability of each score to early predict major adverse outcomes during hospitalization of severe COVID-19 patients is still scarce. Methods: Eight risk scoring systems were rated upon arrival at the Emergency Department, and the occurrence of thrombosis, need for mechanical ventilation, death, and a composite that included all major adverse outcomes were assessed during the hospital stay. The clinical performance of each risk scoring system was evaluated to predict each major outcome. Finally, the diagnostic characteristics of the risk scoring system that showed the best performance for each major outcome were obtained. Results: One hundred and fifty-seven adult patients (55 ± 12 years, 66% men) were assessed at admission to the Emergency Department and included in the study. A total of 96 patients (61%) had at least one major outcome during hospitalization; 32 had thrombosis (20%), 80 required mechanical ventilation (50%), and 52 eventually died (33%). Of all the scores, Obesity and Diabetes (based on a history of comorbid conditions) showed the best performance for predicting mechanical ventilation (area under the ROC curve (AUC), 0.96; positive likelihood ratio (LR+), 23.7), death (AUC, 0.86; LR+, 4.6), and the composite outcome (AUC, 0.89; LR+, 15.6). Meanwhile, the inflammation-based risk scoring system (including leukocyte count, albumin, and C-reactive protein levels) was the best at predicting thrombosis (AUC, 0.63; LR+, 2.0). Conclusions: Both the Obesity and Diabetes score and the inflammation-based risk scoring system appeared to be efficient enough to be integrated into the evaluation of COVID-19 patients upon arrival at the Emergency Department.


2020 ◽  
Author(s):  
Haibei Xin ◽  
Guanxiong Zhang ◽  
Wei Zhou ◽  
Shanshan Li ◽  
Minfeng Zhang ◽  
...  

2020 ◽  
Vol 26 (10) ◽  
pp. S136-S137
Author(s):  
Syed Adeel Ahsan ◽  
Jasjit Bhinder ◽  
Syed Zaid ◽  
Parija Sharedalal ◽  
Chhaya Aggarwal-Gupta ◽  
...  

Author(s):  
Dylan J. Martini ◽  
Meredith R. Kline ◽  
Yuan Liu ◽  
Julie M. Shabto ◽  
Bradley C. Carthon ◽  
...  

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 853
Author(s):  
Jee-Yun Kim ◽  
Jeong Yee ◽  
Tae-Im Park ◽  
So-Youn Shin ◽  
Man-Ho Ha ◽  
...  

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.


Author(s):  
ShuJie Liao ◽  
Lei Jin ◽  
Wan‐Qiang Dai ◽  
Ge Huang ◽  
Wulin Pan ◽  
...  

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