Mortality Prediction Using SaO[sub.2]/FiO[sub.2] Ratio Based on eICU Database Analysis

Purpose. PaO[sub.2] to FiO[sub.2] ratio (P/F) is used to assess the degree of hypoxemia adjusted for oxygen requirements. The Berlin definition of Acute Respiratory Distress Syndrome (ARDS) includes P/F as a diagnostic criterion. P/F is invasive and cost-prohibitive for resource-limited settings. Sa...

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Bibliographic Details
Published in:Critical care research and practice 2021-11, Vol.2021
Main Authors: Patel, Sharad, Singh, Gurkeerat, Zarbiv, Samson, Ghiassi, Kia, Rachoin, Jean-Sebastien
Format: Article
Language:English
Subjects:
Acute respiratory distress syndrome
Health aspects
Information management
Machine learning
Mortality
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United Kingdom
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Summary:Purpose. PaO[sub.2] to FiO[sub.2] ratio (P/F) is used to assess the degree of hypoxemia adjusted for oxygen requirements. The Berlin definition of Acute Respiratory Distress Syndrome (ARDS) includes P/F as a diagnostic criterion. P/F is invasive and cost-prohibitive for resource-limited settings. SaO[sub.2]/FiO[sub.2] (S/F) ratio has the advantages of being easy to calculate, noninvasive, continuous, cost-effective, and reliable, as well as lower infection exposure potential for staff, and avoids iatrogenic anemia. Previous work suggests that the SaO[sub.2]/FiO[sub.2] ratio (S/F) correlates with P/F and can be used as a surrogate in ARDS. Quantitative correlation between S/F and P/F has been verified, but the data for the relative predictive ability for ICU mortality remains in question. We hypothesize that S/F is noninferior to P/F as a predictive feature for ICU mortality. Using a machine-learning approach, we hope to demonstrate the relative mortality predictive capacities of S/F and P/F. Methods. We extracted data from the eICU Collaborative Research Database. The features age, gender, SaO[sub.2], PaO[sub.2], FIO[sub.2], admission diagnosis, Apache IV, mechanical ventilation (MV), and ICU mortality were extracted. Mortality was the dependent variable for our prediction models. Exploratory data analysis was performed in Python. Missing data was imputed with Sklearn Iterative Imputer. Random assignment of all the encounters, 80% to the training (n=26690) and 20% to testing (n=6741), was stratified by positive and negative classes to ensure a balanced distribution. We scaled the data using the Sklearn Standard Scaler. Categorical values were encoded using Target Encoding. We used a gradient boosting decision tree algorithm variant called XGBoost as our model. Model hyperparameters were tuned using the Sklearn RandomizedSearchCV with tenfold cross-validation. We used AUC as our metric for model performance. Feature importance was assessed using SHAP, ELI5 (permutation importance), and a built-in XGBoost feature importance method. We constructed partial dependence plots to illustrate the relationship between mortality probability and S/F values. Results. The XGBoost hyperparameter optimized model had an AUC score of .85 on the test set. The hyperparameters selected to train the final models were as follows: colsample_bytree of 0.8, gamma of 1, max_depth of 3, subsample of 1, min_child_weight of 10, and scale_pos_weight of 3. The SHAP, ELI5, and XGBoost feat
ISSN:2090-1305
2090-1313
DOI:10.1155/2021/6672603