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Circulation. 1997 Jun 17;95(12):2660-7.

Development and prospective validation of a clinical index to predict survival in ambulatory patients referred for cardiac transplant evaluation.

Author information

  • 1Division of Circulatory Physiology, College of Physicians and Surgeons, Columbia University, New York, NY, USA. keith@umich.edu

Abstract

BACKGROUND:

Risk stratification of patients with end-stage congestive heart failure is a critical component of the transplant candidate selection process. Accurate identification of individuals most likely to survive without a transplant would facilitate more efficient use of scarce donor organs.

METHODS AND RESULTS:

Multivariable proportional hazards survival models were developed with the use of data on 80 clinical characteristics from 268 ambulatory patients with advanced heart failure (derivation sample). Invasive and noninvasive models (with and without catheterization-derived data) were constructed. A prognostic score was determined for each patient from each model. Stratum-specific likelihood ratios were used to develop three prognostic-score risk groups. The models were prospectively validated on 199 similar patients (validation sample) by calculation of the area under the receiver operating characteristic curve for 1-year event-free survival, the censored c-index for event-free survival, and comparison of event-free survival curves for prognostic-score risk strata. Outcome events were defined as urgent transplant or death without transplant. The noninvasive model performed well in both samples, and increased performance was not attained by the addition of catheterization-derived variables. Prognostic-score risk groups derived from the noninvasive model in the derivation sample effectively stratified the risk of an outcome event in both samples (1-year event-free survival for derivation and validation samples, respectively: low risk, 93% and 88%; medium risk, 72% and 60%; high risk, 43% and 35%).

CONCLUSIONS:

Selection of candidates for cardiac transplantation may be improved by use of this noninvasive risk-stratification model.

Comment in

PMID:
9193435
[PubMed - indexed for MEDLINE]
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