title: Developing strategies for predicting hyperkalemia in potassium-increasing drug-drug interactions.
abstract: To compare different strategies predicting hyperkalemia (serum potassium level ≥5.5 mEq/l) in hospitalized patients for whom medications triggering potassium-increasing drug-drug interactions (DDIs) were ordered.
  
  
  
  We investigated 5 strategies that combined prediction triggered at onset of DDI versus continuous monitoring and taking into account an increasing number of patient parameters. The considered patient parameters were identified using generalized additive models, and the thresholds of the prediction strategies were calculated by applying Youden's J statistic to receiver operation characteristic curves. Half of the data served as the calibration set, half as the validation set.
  
  
  
  We identified 132 incidences of hyperkalemia induced by 8413 potentially severe potassium-increasing DDIs among 76 467 patients. The positive predictive value (PPV) of those strategies predicting hyperkalemia at the onset of DDI ranged from 1.79% (undifferentiated anticipation of hyperkalemia due to the DDI) to 3.02% (additionally considering the baseline serum potassium) and 3.10% (including further patient parameters). Continuous monitoring significantly increased the PPV to 8.25% (considering the current serum potassium) and 9.34% (additional patient parameters).
  
  
  
  Continuous monitoring of the risk for hyperkalemia based on current potassium level shows a better predictive power than predictions triggered at the onset of DDI. This contrasts with efforts to improve DDI alerts by taking into account more patient parameters at the time of ordering.
authors.affiliation: Research Center for Medical Informatics, Directorate of Research and Education, University Hospital Zurich and University of Zurich, Zurich, Switzerland emmanuel.eschmann@usz.ch.
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Eschmann
authors.affiliation: Research Center for Medical Informatics, Directorate of Research and Education, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
authors.forename: Patrick Emanuel
authors.initials: PE
authors.lastname: Beeler
authors.affiliation: Division of Internal Medicine, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
authors.forename: Markus
authors.initials: M
authors.lastname: Schneemann
authors.affiliation: Research Center for Medical Informatics, Directorate of Research and Education, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
authors.forename: Jürg
authors.initials: J
authors.lastname: Blaser
mesh_terms: D004347:Drug Interactions; D004360:Drug Therapy, Computer-Assisted; D005260:Female; D006801:Humans; D006947:Hyperkalemia / Q000139:chemically induced / Q000175:diagnosis*; D008297:Male; D050316:Medical Order Entry Systems; D008962:Models, Theoretical; D011188:Potassium / Q000097:blood
chemical_list: D011188:Potassium
keywords: clinical decision support; drug-drug interactions; hyperkalemia; monitoring; potassium