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.

OBJECTIVE: We compared and contrasted guidelines on metformin treatment in patients with chronic kidney disease (CKD) around the world, with the aim of helping physicians to refine their analysis of the available evidence before deciding whether to continue or withdraw this drug.
METHODS: We performed a systematic research for metformin contraindications in: (i) official documents from the world\'s 20 most populated countries and the 20 most scientifically productive countries in the field of diabetology and (ii) publications referenced in electronic databases from 1990 onwards.
RESULTS: We identified three international guidelines, 31 national guidelines, and 20 proposals in the scientific literature. The criteria for metformin withdrawal were (i) mainly qualitative in the most populated countries; (ii) mainly quantitative in the most scientifically productive countries (with, in all cases, a suggested threshold for withdrawing metformin); and (iii) quantitative in all, but one of the literature proposals, with a threshold for withdrawal in most cases (n = 17) and/or adjustment of the metformin dose as a function of renal status (n = 8). There was a good degree of consensus on serum creatinine thresholds; whereas guidelines based on estimated glomerular filtration rate thresholds varied from 60 mL/minute/1.73 m(2) up to stage 5 CKD. Only one of the proposals has been tested in a prospective study.
CONCLUSIONS: In general, proposals for continuing or stopping metformin therapy in CKD involve a threshold (whether based on serum creatinine or estimated glomerular filtration rate) rather than the dose adjustment as a function of renal status (in stable patients) performed for other drugs excreted by the kidney.