The Use of Time-Interrelated Covariates to Predict Survival Following Aortic Valve Replacement

Gary L. Grunkemeier, Quentin Macmanus, David R. Thomas, John M. Luber, Louis E. Lambert, Yuen Fure Suen, Albert Starr

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


One hundred four patients survived isolated aortic valve replacement with the model 1200 prosthesis between 1965 and 1968, with a 12-year survival of 64%. Multiple regression survival analysis was employed in an attempt to determine which of 26 preoperative variables affected late survival and to devise a formula to predict survival for a given individual. The most important variables in the regression equation were right atrial mean pressure, etiology, and sex. The effect of the last two were found to vary with time over the 12-year postoperative period. An extension of the standard regression analysis technique was developed to incorporate time-related cofactors into the model. Based on the multiple regression model, 12-year survival was estimated to range from 92% to 14% for the best and worst combinations, respectively, of the three significant variables. The advantages of the regression method are outlined and the findings of other studies with regard to factors affecting survival after aortic valve replacement are summarized and discussed.

Original languageEnglish (US)
Pages (from-to)240-246
Number of pages7
JournalAnnals of Thoracic Surgery
Issue number3
StatePublished - 1980

ASJC Scopus subject areas

  • Surgery
  • Pulmonary and Respiratory Medicine
  • Cardiology and Cardiovascular Medicine


Dive into the research topics of 'The Use of Time-Interrelated Covariates to Predict Survival Following Aortic Valve Replacement'. Together they form a unique fingerprint.

Cite this