Machine Learning Consensus Clustering Approach for Patients with Lactic Acidosis in Intensive Care Units

Background: Lactic acidosis is a heterogeneous condition with multiple underlying causes and associated outcomes. The use of multi-dimensional patient data to subtype lactic acidosis can personalize patient care. Machine learning consensus clustering may identify lactic acidosis subgroups with uniqu...

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Autores principales:	Pattharawin Pattharanitima, Charat Thongprayoon, Tananchai Petnak, Narat Srivali, Guido Gembillo, Wisit Kaewput, Supavit Chesdachai, Saraschandra Vallabhajosyula, Oisin A. O’Corragain, Michael A. Mao, Vesna D. Garovic, Fawad Qureshi, John J. Dillon, Wisit Cheungpasitporn
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	lactic acid lactic acidosis lactate hyperlactatemia clustering intensive care unit Medicine R
Acceso en línea:	https://doaj.org/article/86c29d5838dd4925a72d8a5eabca7a70
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spelling	oai:doaj.org-article:86c29d5838dd4925a72d8a5eabca7a702021-11-25T18:07:26ZMachine Learning Consensus Clustering Approach for Patients with Lactic Acidosis in Intensive Care Units10.3390/jpm111111322075-4426https://doaj.org/article/86c29d5838dd4925a72d8a5eabca7a702021-11-01T00:00:00Zhttps://www.mdpi.com/2075-4426/11/11/1132https://doaj.org/toc/2075-4426Background: Lactic acidosis is a heterogeneous condition with multiple underlying causes and associated outcomes. The use of multi-dimensional patient data to subtype lactic acidosis can personalize patient care. Machine learning consensus clustering may identify lactic acidosis subgroups with unique clinical profiles and outcomes. Methods: We used the Medical Information Mart for Intensive Care III database to abstract electronic medical record data from patients admitted to intensive care units (ICU) in a tertiary care hospital in the United States. We included patients who developed lactic acidosis (defined as serum lactate ≥ 4 mmol/L) within 48 h of ICU admission. We performed consensus clustering analysis based on patient characteristics, comorbidities, vital signs, organ supports, and laboratory data to identify clinically distinct lactic acidosis subgroups. We calculated standardized mean differences to show key subgroup features. We compared outcomes among subgroups. Results: We identified 1919 patients with lactic acidosis. The algorithm revealed three best unique lactic acidosis subgroups based on patient variables. Cluster 1 (<i>n</i> = 554) was characterized by old age, elective admission to cardiac surgery ICU, vasopressor use, mechanical ventilation use, and higher pH and serum bicarbonate. Cluster 2 (<i>n</i> = 815) was characterized by young age, admission to trauma/surgical ICU with higher blood pressure, lower comorbidity burden, lower severity index, and less vasopressor use. Cluster 3 (<i>n</i> = 550) was characterized by admission to medical ICU, history of liver disease and coagulopathy, acute kidney injury, lower blood pressure, higher comorbidity burden, higher severity index, higher serum lactate, and lower pH and serum bicarbonate. Cluster 3 had the worst outcomes, while cluster 1 had the most favorable outcomes in terms of persistent lactic acidosis and mortality. Conclusions: Consensus clustering analysis synthesized the pattern of clinical and laboratory data to reveal clinically distinct lactic acidosis subgroups with different outcomes.Pattharawin PattharanitimaCharat ThongprayoonTananchai PetnakNarat SrivaliGuido GembilloWisit KaewputSupavit ChesdachaiSaraschandra VallabhajosyulaOisin A. O’CorragainMichael A. MaoVesna D. GarovicFawad QureshiJohn J. DillonWisit CheungpasitpornMDPI AGarticlelactic acidlactic acidosislactatehyperlactatemiaclusteringintensive care unitMedicineRENJournal of Personalized Medicine, Vol 11, Iss 1132, p 1132 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	lactic acid lactic acidosis lactate hyperlactatemia clustering intensive care unit Medicine R
spellingShingle	lactic acid lactic acidosis lactate hyperlactatemia clustering intensive care unit Medicine R Pattharawin Pattharanitima Charat Thongprayoon Tananchai Petnak Narat Srivali Guido Gembillo Wisit Kaewput Supavit Chesdachai Saraschandra Vallabhajosyula Oisin A. O’Corragain Michael A. Mao Vesna D. Garovic Fawad Qureshi John J. Dillon Wisit Cheungpasitporn Machine Learning Consensus Clustering Approach for Patients with Lactic Acidosis in Intensive Care Units
description	Background: Lactic acidosis is a heterogeneous condition with multiple underlying causes and associated outcomes. The use of multi-dimensional patient data to subtype lactic acidosis can personalize patient care. Machine learning consensus clustering may identify lactic acidosis subgroups with unique clinical profiles and outcomes. Methods: We used the Medical Information Mart for Intensive Care III database to abstract electronic medical record data from patients admitted to intensive care units (ICU) in a tertiary care hospital in the United States. We included patients who developed lactic acidosis (defined as serum lactate ≥ 4 mmol/L) within 48 h of ICU admission. We performed consensus clustering analysis based on patient characteristics, comorbidities, vital signs, organ supports, and laboratory data to identify clinically distinct lactic acidosis subgroups. We calculated standardized mean differences to show key subgroup features. We compared outcomes among subgroups. Results: We identified 1919 patients with lactic acidosis. The algorithm revealed three best unique lactic acidosis subgroups based on patient variables. Cluster 1 (<i>n</i> = 554) was characterized by old age, elective admission to cardiac surgery ICU, vasopressor use, mechanical ventilation use, and higher pH and serum bicarbonate. Cluster 2 (<i>n</i> = 815) was characterized by young age, admission to trauma/surgical ICU with higher blood pressure, lower comorbidity burden, lower severity index, and less vasopressor use. Cluster 3 (<i>n</i> = 550) was characterized by admission to medical ICU, history of liver disease and coagulopathy, acute kidney injury, lower blood pressure, higher comorbidity burden, higher severity index, higher serum lactate, and lower pH and serum bicarbonate. Cluster 3 had the worst outcomes, while cluster 1 had the most favorable outcomes in terms of persistent lactic acidosis and mortality. Conclusions: Consensus clustering analysis synthesized the pattern of clinical and laboratory data to reveal clinically distinct lactic acidosis subgroups with different outcomes.
format	article
author	Pattharawin Pattharanitima Charat Thongprayoon Tananchai Petnak Narat Srivali Guido Gembillo Wisit Kaewput Supavit Chesdachai Saraschandra Vallabhajosyula Oisin A. O’Corragain Michael A. Mao Vesna D. Garovic Fawad Qureshi John J. Dillon Wisit Cheungpasitporn
author_facet	Pattharawin Pattharanitima Charat Thongprayoon Tananchai Petnak Narat Srivali Guido Gembillo Wisit Kaewput Supavit Chesdachai Saraschandra Vallabhajosyula Oisin A. O’Corragain Michael A. Mao Vesna D. Garovic Fawad Qureshi John J. Dillon Wisit Cheungpasitporn
author_sort	Pattharawin Pattharanitima
title	Machine Learning Consensus Clustering Approach for Patients with Lactic Acidosis in Intensive Care Units
title_short	Machine Learning Consensus Clustering Approach for Patients with Lactic Acidosis in Intensive Care Units
title_full	Machine Learning Consensus Clustering Approach for Patients with Lactic Acidosis in Intensive Care Units
title_fullStr	Machine Learning Consensus Clustering Approach for Patients with Lactic Acidosis in Intensive Care Units
title_full_unstemmed	Machine Learning Consensus Clustering Approach for Patients with Lactic Acidosis in Intensive Care Units
title_sort	machine learning consensus clustering approach for patients with lactic acidosis in intensive care units
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/86c29d5838dd4925a72d8a5eabca7a70
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Machine Learning Consensus Clustering Approach for Patients with Lactic Acidosis in Intensive Care Units

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