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Ann Thorac Surg 2005;80:471-479
© 2005 The Society of Thoracic Surgeons

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Original article: Cardiovascular

Validation and Refinement of Mortality Risk Models for Heart Valve Surgery

Providence Health System, Portland, Oregon

Accepted for publication February 23, 2005.

^_* Address reprint requests to Dr Jin, Providence St. Vincent Hospital and Medical Center, 9205 SW Barnes Rd, LL 33, Portland, OR97225 (Email: ruyun.jin{at}providence.org).

BACKGROUND: The Northern New England Cardiovascular Disease Study Group (NNE) recently published risk models for hospital mortality after heart valve surgery. The Providence Health System Cardiovascular Study Group (PHS) has been collecting similar heart valve data for 8 years, providing an ideal opportunity to both validate the NNE risk models and attempt to produce an improved model, by using some different modeling techniques.

METHODS: From 1997 to 2004, 3,324 patients aged 30 to 95 years underwent aortic valve replacement (AVR), and 1,596 underwent mitral valve replacement or repair (MVRR) at one of nine PHS medical centers. We used area under the receiver operating characteristic curve (c-index) to measure model discrimination, and Hosmer-Lemeshow statistic (H-L) to measure calibration. We modified the NNE models by ungrouping continuous variables, seeking optimal transformations of continuous variables, and imputing missing values by multiple regression.

RESULTS: The prevalence and the lethality of risk factors were similar in PHS and NNE patients. The NNE models fit PHS patients well: c-index (95% confidence interval) = 0.75 (0.70 to 0.80) for AVR and 0.81 (0.76 to 0.86) for MVRR; H-L = 3.95 (p = 0.861) for AVR and 7.10 (p = 0.526) for MVRR. A single PHS model performed slightly better for both positions: c-index = 0.79 (0.75 to 0.83) for AVR and 0.84 (0.80 to 0.88) for MVRR; H-L = 2.75 (p = 0.949) for AVR and 12.21 (p = 0.142) for MVRR.

CONCLUSIONS: The NNE models for aortic and mitral valve surgery were successfully validated using PHS patients. Using some different statistical approaches to modeling, we produced a new, unified model for both positions.

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