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

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

doi:10.3390/jpm11111132

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

J Pers Med. 2021 Nov 2;11(11):1132. doi: 10.3390/jpm11111132.

Authors

Affiliations

¹ Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12121, Thailand.
² Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.
³ Division of Pulmonary and Pulmonary Critical Care Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand.
⁴ Division of Pulmonary Medicine, St. Agnes Hosipital, Baltimore, MD 21229, USA.
⁵ Unit of Nephrology and Dialysis, Department of Clinical and Experimental Medicine, University of Messina, 98125 Messina, Italy.
⁶ Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand.
⁷ Division of Infectious Disease, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.
⁸ Section of Cardiovascular Medicine, Department of Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA.
⁹ Department of Thoracic Medicine and Surgery, Temple University Hospital, Philadelphia, PA 19140, USA.
¹⁰ Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA.

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.

Keywords: artificial intelligence; clustering; critical care; critical care medicine; hyperlactatemia; individualized medicine; intensive care unit; lactate; lactic acid; lactic acidosis; machine learning; nephrology; personalized medicine; precision medicine.