OBJECTIVE: Diabetic ketoacidosis (DKA) is a common emergency department presentation of both new-onset and established diabetes mellitus (DM). β-Hydroxybutyrate (BOHB) provides a direct measure of the pathophysiologic derangement in DKA as compared with the nonspecific measurements of blood pH and bicarbonate. Our objective was to characterize the relationship between BOHB and DKA. METHODS: This is a cross-sectional retrospective study of pediatric patients with DM presenting to an urban pediatric emergency department between January 1, 2016, and September 30, 2018. Analyses were performed on each patient's initial, simultaneous BOHB and pH. Diagnostic test characteristics of BOHB were calculated, and logistic regression was performed to investigate the effects of age and other key clinical factors. RESULTS: Among 594 patients with DM, with median age of 12.3 years (interquartile range, 8.7-15.9 years), 176 (29.6%) presented with DKA. The inclusion of age, transfer status, and new-onset in the statistical model did not improve the prediction of DKA beyond BOHB alone. β-Hydroxybutyrate demonstrated strong discrimination for DKA, with an area under the curve of 0.95 (95% confidence interval, 0.93-0.97). A BOHB value of 5.3 mmol/L predicted DKA with optimal accuracy (90.6% of patients were correctly classified). The sensitivity, specificity, and positive and negative predictive values of this cut point were 76.7% (95% confidence interval, 69.8%-82.7%), 96.4% (94.2%-98.0%), 90.0% (84.0%-94.3%), and 90.8% (87.7%-93.3%), respectively. CONCLUSIONS: β-Hydroxybutyrate accurately predicts DKA in children and adolescents. More importantly, because plasma BOHB is the ideal biochemical marker of DKA, BOHB may provide a more optimal definition of DKA for management decisions and treatment targets.
OBJECTIVE: Diabetic ketoacidosis (DKA) is a common emergency department presentation of both new-onset and established diabetes mellitus (DM). β-Hydroxybutyrate (BOHB) provides a direct measure of the pathophysiologic derangement in DKA as compared with the nonspecific measurements of blood pH and bicarbonate. Our objective was to characterize the relationship between BOHB and DKA. METHODS: This is a cross-sectional retrospective study of pediatric patients with DM presenting to an urban pediatric emergency department between January 1, 2016, and September 30, 2018. Analyses were performed on each patient's initial, simultaneous BOHB and pH. Diagnostic test characteristics of BOHB were calculated, and logistic regression was performed to investigate the effects of age and other key clinical factors. RESULTS: Among 594 patients with DM, with median age of 12.3 years (interquartile range, 8.7-15.9 years), 176 (29.6%) presented with DKA. The inclusion of age, transfer status, and new-onset in the statistical model did not improve the prediction of DKA beyond BOHB alone. β-Hydroxybutyrate demonstrated strong discrimination for DKA, with an area under the curve of 0.95 (95% confidence interval, 0.93-0.97). A BOHB value of 5.3 mmol/L predicted DKA with optimal accuracy (90.6% of patients were correctly classified). The sensitivity, specificity, and positive and negative predictive values of this cut point were 76.7% (95% confidence interval, 69.8%-82.7%), 96.4% (94.2%-98.0%), 90.0% (84.0%-94.3%), and 90.8% (87.7%-93.3%), respectively. CONCLUSIONS: β-Hydroxybutyrate accurately predicts DKA in children and adolescents. More importantly, because plasma BOHB is the ideal biochemical marker of DKA, BOHB may provide a more optimal definition of DKA for management decisions and treatment targets.
Authors: R Paul Wadwa; H Peter Chase; Dan Raghinaru; Bruce A Buckingham; Irene Hramiak; David M Maahs; Laurel Messer; Trang Ly; Tandy Aye; Paula Clinton; Craig Kollman; Roy W Beck; John Lum Journal: Pediatr Diabetes Date: 2016-07-12 Impact factor: 4.866
Authors: Nicole C Foster; Roy W Beck; Kellee M Miller; Mark A Clements; Michael R Rickels; Linda A DiMeglio; David M Maahs; William V Tamborlane; Richard Bergenstal; Elizabeth Smith; Beth A Olson; Satish K Garg Journal: Diabetes Technol Ther Date: 2019-01-18 Impact factor: 6.118
Authors: David M Maahs; Julia M Hermann; Naomi Holman; Nicole C Foster; Thomas M Kapellen; Jeremy Allgrove; Desmond A Schatz; Sabine E Hofer; Fiona Campbell; Claudia Steigleder-Schweiger; Roy W Beck; Justin T Warner; Reinhard W Holl Journal: Diabetes Care Date: 2015-08-17 Impact factor: 19.112
Authors: Mae Sheikh-Ali; Brad S Karon; Ananda Basu; Yogish C Kudva; Lisa A Muller; Jia Xu; W Frederick Schwenk; John M Miles Journal: Diabetes Care Date: 2008-01-09 Impact factor: 19.112