Publication: Machine learning consensus clustering approach for patients with lactic acidosis in intensive care units
Issued Date
2021-11-01
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ISSN
20754426
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2-s2.0-85118895309
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Mahidol University
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SCOPUS
Bibliographic Citation
Journal of Personalized Medicine. Vol.11, No.11 (2021)
Suggested Citation
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. Journal of Personalized Medicine. Vol.11, No.11 (2021). doi:10.3390/jpm11111132 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/77709
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Title
Machine learning consensus clustering approach for patients with lactic acidosis in intensive care units
Abstract
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 (n = 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 (n = 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 (n = 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.
