Machine Learning Prediction Models for Mortality in Intensive Care Unit Patients with Lactic Acidosis

Background: Lactic acidosis is the most common cause of anion gap metabolic acidosis in the intensive care unit (ICU), associated with poor outcomes including mortality. We sought to compare machine learning (ML) approaches versus logistic regression analysis for prediction of mortality in lactic ac...

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Autores principales:	Pattharawin Pattharanitima, Charat Thongprayoon, Wisit Kaewput, Fawad Qureshi, Fahad Qureshi, Tananchai Petnak, Narat Srivali, Guido Gembillo, Oisin A. O’Corragain, Supavit Chesdachai, Saraschandra Vallabhajosyula, Pramod K. Guru, Michael A. Mao, Vesna D. Garovic, John J. Dillon, Wisit Cheungpasitporn
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	lactic acid lactic acidosis lactate mortality intensive care unit machine learning Medicine R
Acceso en línea:	https://doaj.org/article/a3e7d1176aa740038116d80ea6a4475f
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spelling	oai:doaj.org-article:a3e7d1176aa740038116d80ea6a4475f2021-11-11T17:38:57ZMachine Learning Prediction Models for Mortality in Intensive Care Unit Patients with Lactic Acidosis10.3390/jcm102150212077-0383https://doaj.org/article/a3e7d1176aa740038116d80ea6a4475f2021-10-01T00:00:00Zhttps://www.mdpi.com/2077-0383/10/21/5021https://doaj.org/toc/2077-0383Background: Lactic acidosis is the most common cause of anion gap metabolic acidosis in the intensive care unit (ICU), associated with poor outcomes including mortality. We sought to compare machine learning (ML) approaches versus logistic regression analysis for prediction of mortality in lactic acidosis patients admitted to the ICU. Methods: We used the Medical Information Mart for Intensive Care (MIMIC-III) database to identify ICU adult patients with lactic acidosis (serum lactate ≥4 mmol/L). The outcome of interest was hospital mortality. We developed prediction models using four ML approaches consisting of random forest (RF), decision tree (DT), extreme gradient boosting (XGBoost), artificial neural network (ANN), and statistical modeling with forward stepwise logistic regression using the testing dataset. We then assessed model performance using area under the receiver operating characteristic curve (AUROC), accuracy, precision, error rate, Matthews correlation coefficient (MCC), F1 score, and assessed model calibration using the Brier score, in the independent testing dataset. Results: Of 1919 lactic acidosis ICU patients, 1535 and 384 were included in the training and testing dataset, respectively. Hospital mortality was 30%. RF had the highest AUROC at 0.83, followed by logistic regression 0.81, XGBoost 0.81, ANN 0.79, and DT 0.71. In addition, RF also had the highest accuracy (0.79), MCC (0.45), F1 score (0.56), and lowest error rate (21.4%). The RF model was the most well-calibrated. The Brier score for RF, DT, XGBoost, ANN, and multivariable logistic regression was 0.15, 0.19, 0.18, 0.19, and 0.16, respectively. The RF model outperformed multivariable logistic regression model, SOFA score (AUROC 0.74), SAP II score (AUROC 0.77), and Charlson score (AUROC 0.69). Conclusion: The ML prediction model using RF algorithm provided the highest predictive performance for hospital mortality among ICU patient with lactic acidosis.Pattharawin PattharanitimaCharat ThongprayoonWisit KaewputFawad QureshiFahad QureshiTananchai PetnakNarat SrivaliGuido GembilloOisin A. O’CorragainSupavit ChesdachaiSaraschandra VallabhajosyulaPramod K. GuruMichael A. MaoVesna D. GarovicJohn J. DillonWisit CheungpasitpornMDPI AGarticlelactic acidlactic acidosislactatemortalityintensive care unitmachine learningMedicineRENJournal of Clinical Medicine, Vol 10, Iss 5021, p 5021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	lactic acid lactic acidosis lactate mortality intensive care unit machine learning Medicine R
spellingShingle	lactic acid lactic acidosis lactate mortality intensive care unit machine learning Medicine R Pattharawin Pattharanitima Charat Thongprayoon Wisit Kaewput Fawad Qureshi Fahad Qureshi Tananchai Petnak Narat Srivali Guido Gembillo Oisin A. O’Corragain Supavit Chesdachai Saraschandra Vallabhajosyula Pramod K. Guru Michael A. Mao Vesna D. Garovic John J. Dillon Wisit Cheungpasitporn Machine Learning Prediction Models for Mortality in Intensive Care Unit Patients with Lactic Acidosis
description	Background: Lactic acidosis is the most common cause of anion gap metabolic acidosis in the intensive care unit (ICU), associated with poor outcomes including mortality. We sought to compare machine learning (ML) approaches versus logistic regression analysis for prediction of mortality in lactic acidosis patients admitted to the ICU. Methods: We used the Medical Information Mart for Intensive Care (MIMIC-III) database to identify ICU adult patients with lactic acidosis (serum lactate ≥4 mmol/L). The outcome of interest was hospital mortality. We developed prediction models using four ML approaches consisting of random forest (RF), decision tree (DT), extreme gradient boosting (XGBoost), artificial neural network (ANN), and statistical modeling with forward stepwise logistic regression using the testing dataset. We then assessed model performance using area under the receiver operating characteristic curve (AUROC), accuracy, precision, error rate, Matthews correlation coefficient (MCC), F1 score, and assessed model calibration using the Brier score, in the independent testing dataset. Results: Of 1919 lactic acidosis ICU patients, 1535 and 384 were included in the training and testing dataset, respectively. Hospital mortality was 30%. RF had the highest AUROC at 0.83, followed by logistic regression 0.81, XGBoost 0.81, ANN 0.79, and DT 0.71. In addition, RF also had the highest accuracy (0.79), MCC (0.45), F1 score (0.56), and lowest error rate (21.4%). The RF model was the most well-calibrated. The Brier score for RF, DT, XGBoost, ANN, and multivariable logistic regression was 0.15, 0.19, 0.18, 0.19, and 0.16, respectively. The RF model outperformed multivariable logistic regression model, SOFA score (AUROC 0.74), SAP II score (AUROC 0.77), and Charlson score (AUROC 0.69). Conclusion: The ML prediction model using RF algorithm provided the highest predictive performance for hospital mortality among ICU patient with lactic acidosis.
format	article
author	Pattharawin Pattharanitima Charat Thongprayoon Wisit Kaewput Fawad Qureshi Fahad Qureshi Tananchai Petnak Narat Srivali Guido Gembillo Oisin A. O’Corragain Supavit Chesdachai Saraschandra Vallabhajosyula Pramod K. Guru Michael A. Mao Vesna D. Garovic John J. Dillon Wisit Cheungpasitporn
author_facet	Pattharawin Pattharanitima Charat Thongprayoon Wisit Kaewput Fawad Qureshi Fahad Qureshi Tananchai Petnak Narat Srivali Guido Gembillo Oisin A. O’Corragain Supavit Chesdachai Saraschandra Vallabhajosyula Pramod K. Guru Michael A. Mao Vesna D. Garovic John J. Dillon Wisit Cheungpasitporn
author_sort	Pattharawin Pattharanitima
title	Machine Learning Prediction Models for Mortality in Intensive Care Unit Patients with Lactic Acidosis
title_short	Machine Learning Prediction Models for Mortality in Intensive Care Unit Patients with Lactic Acidosis
title_full	Machine Learning Prediction Models for Mortality in Intensive Care Unit Patients with Lactic Acidosis
title_fullStr	Machine Learning Prediction Models for Mortality in Intensive Care Unit Patients with Lactic Acidosis
title_full_unstemmed	Machine Learning Prediction Models for Mortality in Intensive Care Unit Patients with Lactic Acidosis
title_sort	machine learning prediction models for mortality in intensive care unit patients with lactic acidosis
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a3e7d1176aa740038116d80ea6a4475f
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Machine Learning Prediction Models for Mortality in Intensive Care Unit Patients with Lactic Acidosis

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