This is a retrospective study drawing on data from the Brigham and Women's Hospital Home Hospital Program's Database. Sociodemographic and clinical data from a training cohort were used to train a machine learning algorithm to predict blood potassium throughout a patient's admission. This algorithm was then validated in a validation cohort.
Study Type
OBSERVATIONAL
Apply a machine learning algorithm to estimate a patient's potassium.
Brigham and Women's Hospital
Boston, Massachusetts, United States
Brigham and Women's Faulkner Hospital
Boston, Massachusetts, United States
Serum potassium concentration
Serum potassium, measured in millimol per liter
Time frame: From date of admission to date of discharge, through study completion on average 7 days.
Hyperkalemia
Serum potassium greater than 5.1 millimol per liter
Time frame: From date of admission to date of discharge, through study completion on average 7 days.
Hypokalemia
Serum potassium less than 3.4 millimol per liter
Time frame: From date of admission to date of discharge, through study completion on average 7 days.
Normokalemia
Serum potassium between 3.4 and 5.1 millimol per liter
Time frame: From date of admission to date of discharge, through study completion on average 7 days.
Serum potassium less than versus greater than or equal to 4 millimol per liter
Serum potassium falling either less than versus greater than or equal to 4 millimol per liter
Time frame: From date of admission to date of discharge, through study completion on average 7 days.
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.