Mortality Prediction Using SaO2/FiO2 Ratio Based on eICU Database Analysis

Purpose. PaO2 to FiO2 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. SaO2/FiO2 (S/F...

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Autores principales:	Sharad Patel, Gurkeerat Singh, Samson Zarbiv, Kia Ghiassi, Jean-Sebastien Rachoin
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Medical emergencies. Critical care. Intensive care. First aid RC86-88.9
Acceso en línea:	https://doaj.org/article/fcaea9d5aed740c6a8c4ae9aa0c8d334
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spelling	oai:doaj.org-article:fcaea9d5aed740c6a8c4ae9aa0c8d3342021-11-22T01:09:29ZMortality Prediction Using SaO2/FiO2 Ratio Based on eICU Database Analysis2090-131310.1155/2021/6672603https://doaj.org/article/fcaea9d5aed740c6a8c4ae9aa0c8d3342021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/6672603https://doaj.org/toc/2090-1313Purpose. PaO2 to FiO2 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. SaO2/FiO2 (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 SaO2/FiO2 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, SaO2, PaO2, FIO2, 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 feature importance analysis demonstrates that the S/F ratio ranks as the strongest predictor for mortality amongst the physiologic variables. The partial dependence plots illustrate that mortality rises significantly above S/F values of 200. Conclusion. S/F was a stronger predictor of mortality than P/F based upon feature importance evaluation of our data. Our study is hypothesis-generating and a prospective evaluation is warranted. Take-Home Points. S/F ratio is a noninvasive continuous method of measuring hypoxemia as compared to P/F ratio. Our study shows that the S/F ratio is a better predictor of mortality than the more widely used P/F ratio to monitor and manage hypoxemia.Sharad PatelGurkeerat SinghSamson ZarbivKia GhiassiJean-Sebastien RachoinHindawi LimitedarticleMedical emergencies. Critical care. Intensive care. First aidRC86-88.9ENCritical Care Research and Practice, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medical emergencies. Critical care. Intensive care. First aid RC86-88.9
spellingShingle	Medical emergencies. Critical care. Intensive care. First aid RC86-88.9 Sharad Patel Gurkeerat Singh Samson Zarbiv Kia Ghiassi Jean-Sebastien Rachoin Mortality Prediction Using SaO2/FiO2 Ratio Based on eICU Database Analysis
description	Purpose. PaO2 to FiO2 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. SaO2/FiO2 (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 SaO2/FiO2 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, SaO2, PaO2, FIO2, 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 feature importance analysis demonstrates that the S/F ratio ranks as the strongest predictor for mortality amongst the physiologic variables. The partial dependence plots illustrate that mortality rises significantly above S/F values of 200. Conclusion. S/F was a stronger predictor of mortality than P/F based upon feature importance evaluation of our data. Our study is hypothesis-generating and a prospective evaluation is warranted. Take-Home Points. S/F ratio is a noninvasive continuous method of measuring hypoxemia as compared to P/F ratio. Our study shows that the S/F ratio is a better predictor of mortality than the more widely used P/F ratio to monitor and manage hypoxemia.
format	article
author	Sharad Patel Gurkeerat Singh Samson Zarbiv Kia Ghiassi Jean-Sebastien Rachoin
author_facet	Sharad Patel Gurkeerat Singh Samson Zarbiv Kia Ghiassi Jean-Sebastien Rachoin
author_sort	Sharad Patel
title	Mortality Prediction Using SaO2/FiO2 Ratio Based on eICU Database Analysis
title_short	Mortality Prediction Using SaO2/FiO2 Ratio Based on eICU Database Analysis
title_full	Mortality Prediction Using SaO2/FiO2 Ratio Based on eICU Database Analysis
title_fullStr	Mortality Prediction Using SaO2/FiO2 Ratio Based on eICU Database Analysis
title_full_unstemmed	Mortality Prediction Using SaO2/FiO2 Ratio Based on eICU Database Analysis
title_sort	mortality prediction using sao2/fio2 ratio based on eicu database analysis
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/fcaea9d5aed740c6a8c4ae9aa0c8d334
work_keys_str_mv	AT sharadpatel mortalitypredictionusingsao2fio2ratiobasedoneicudatabaseanalysis AT gurkeeratsingh mortalitypredictionusingsao2fio2ratiobasedoneicudatabaseanalysis AT samsonzarbiv mortalitypredictionusingsao2fio2ratiobasedoneicudatabaseanalysis AT kiaghiassi mortalitypredictionusingsao2fio2ratiobasedoneicudatabaseanalysis AT jeansebastienrachoin mortalitypredictionusingsao2fio2ratiobasedoneicudatabaseanalysis
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Mortality Prediction Using SaO2/FiO2 Ratio Based on eICU Database Analysis

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