Development and validation of a clinical risk model to predict the hospital mortality in ventilated patients with acute respiratory distress syndrome: a population-based study

被引:7
作者
Ye, Weiyan [1 ,2 ]
Li, Rujian [1 ,2 ]
Liang, Hanwen [2 ]
Huang, Yongbo [1 ,2 ]
Xu, Yonghao [1 ,2 ]
Li, Yuchong [1 ,2 ]
Ou, Limin [3 ]
Mao, Pu [1 ,2 ]
Liu, Xiaoqing [1 ,2 ]
Li, Yimin [1 ,2 ]
机构
[1] Guangzhou Med Univ, Affiliated Hosp 1, Dept Crit Care Med, Guangzhou, Peoples R China
[2] Guangzhou Med Univ, Affiliated Hosp 1, State Key Lab Resp Dis, Natl Clin Res Ctr Resp Dis,Guangzhou Inst Resp Hl, Guangzhou, Peoples R China
[3] Jinan Univ, Affiliated Hosp 1, Guangzhou, Peoples R China
基金
中国国家自然科学基金;
关键词
Acute respiratory distress syndrome; Database; Mortality; Prediction; Ventilation; ACUTE PHYSIOLOGY; SCORE; ARDS; BIOMARKER;
D O I
10.1186/s12890-022-02057-0
中图分类号
R56 [呼吸系及胸部疾病];
学科分类号
摘要
Background Large variability in mortality exists in patients of acute respiratory distress syndrome (ARDS), especially those with invasive ventilation. The aim of this study was to develop a model to predict risk of in-hospital death in ventilated ARDS patients. Methods Ventilated patients with ARDS from two public databases (MIMIC-III and eICU-CRD) were randomly divided as training cohort and internal validation cohort. Least absolute shrinkage and selection operator (LASSO) and then Logistic regression was used to construct a predictive model with demographic, clinical, laboratory, comorbidities and ventilation variables ascertained at first 24 h of ICU admission and invasive ventilation. Our model was externally validated using data from another database (MIMIC-IV). Results A total of 1075 adult patients from MIMIC-III and eICU were randomly divided into training cohort (70%, n = 752) and internal validation cohort (30%, n = 323). 521 patients were included from MIMIC-IV. From 176 potential predictors, 9 independent predictive factors were included in the final model. Five variables were ascertained within the first 24 h of ICU admission, including age (OR, 1.02; 95% CI: 1.01-1.03), mean of respiratory rate (OR, 1.04; 95% CI: 1.01-1.08), the maximum of INR (OR, 1.14; 95% CI: 1.03-1.31) and alveolo-arterial oxygen difference (OR, 1.002; 95% CI: 1.001-1.003) and the minimum of RDW (OR, 1.17; 95% CI: 1.09-1.27). And four variables were collected within the first 24 h of invasive ventilation: mean of temperature (OR, 0.70; 95% CI: 0.57-0.86), the maximum of lactate (OR, 1.15; 95% CI: 1.09-1.22), the minimum of blood urea nitrogen (OR, 1.02; 95% CI: 1.01-1.03) and white blood cell counts (OR, 1.03; 95% CI: 1.01-1.06). Our model achieved good discrimination (AUC: 0.77, 95% CI: 0.73-0.80) in training cohort but the performance declined in internal (AUC: 0.75, 95% CI: 0.69-0.80) and external validation cohort (0.70, 95% CI: 0.65-0.74) and showed modest calibration. Conclusions A risk score based on routinely collected variables at the start of admission to ICU and invasive ventilation can predict mortality of ventilated ARDS patients, with a moderate performance.
引用
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页数:11
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