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

Abstract

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.

Prediction model for in-hospital mortality in patients at high altitudes with ARDS due to COVID-19

“…Zhao et al There are other prediction models for ARDS, such as that of Zhang et al [21] in which a model with eight variables was presented with a good discrimination capacity and an AUC of 0.85, emphasizing that the mortality of the patients in the study was lower (21%) than that reported in other ARDS cohorts; this may be because the patients were enrolled in randomized clinical trials and received specific interventions, which reduced the guarantee of external validity. Recently, Ye et al [28] developed a prediction model with nine variables for patients with ARDS receiving IMV and found an AUC of 0.75, similar to that shown in our model. Without adjusting the P/F for altitude, overall mortality in this cohort was 43.7%, mortality for mild ARDS was 29.7%, 46.3% for moderate ARDS and 65% for severe ARDS.…”

Interpretable machine learning for predicting 28-day all-cause in-hospital mortality for hypertensive ischemic or hemorrhagic stroke patients in the ICU: a multi-center retrospective cohort study with internal and external cross-validation

“…The eICU-CRD database is a multi-center intensive care database that is made available to the public by Philips Healthcare in collaboration with the MIT Laboratory for Computational Physiology. It contains de-identified clinical data for over 200,000 patients who were admitted to the ICU from 2014 to 2015 ( 11 ). The de-identified health information of patients was collected, and informed consent was not required for this study.…”