Admission Laboratory Results to Enhance Prediction Models of Postdischarge Outcomes in Cardiac Care

Michael Pine, Donald E. Fry, Edward L. Hannan, James M. Naessens, Kay Whitman, Agnes Reband, Feng Qian, Joseph Schindler, Mark Sonneborn, Jaclyn Roland, Linda Hyde, Barbara A. Dennison

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Predictive modeling for postdischarge outcomes of inpatient care has been suboptimal. This study evaluated whether admission numerical laboratory data added to administrative models from New York and Minnesota hospitals would enhance the prediction accuracy for 90-day postdischarge deaths without readmission (PD-90) and 90-day readmissions (RA-90) following inpatient care for cardiac patients. Risk-adjustment models for the prediction of PD-90 and RA-90 were designed for acute myocardial infarction, percutaneous cardiac intervention, coronary artery bypass grafting, and congestive heart failure. Models were derived from hospital claims data and were then enhanced with admission laboratory predictive results. Case-level discrimination, goodness of fit, and calibration were used to compare administrative models (ADM) and laboratory predictive models (LAB). LAB models for the prediction of PD-90 were modestly enhanced over ADM, but negligible benefit was seen for RA-90. A consistent predictor of PD-90 and RA-90 was prolonged length of stay outliers from the index hospitalization.

Original languageEnglish (US)
Pages (from-to)163-171
Number of pages9
JournalAmerican Journal of Medical Quality
Issue number2
StatePublished - Mar 1 2017


  • cardiovascular disease
  • clinically enhanced claims data
  • postdischarge patient outcomes
  • quality of care/patient safety (measurement)
  • risk adjustment

ASJC Scopus subject areas

  • Health Policy


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