Prediction Model for Wait Times in Cardiac Transplantation

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Abstract

Wait times have increased for patients approved for heart transplants. We reviewed United Network for Organ Sharing (UNOS) data for 14,242 patients listed for isolated heart transplant (2009–2013) to develop a risk score model for timing left ventricular assist device (LVAD) implantation in bridge-to-transplant patients. We used a multivariable Cox proportional hazards regression model with subsequent bootstrap resampling for internal validation to develop a scoring system that combined risk factors, weighted by the corresponding regression coefficients, to define an individual’s risk score. Four risk factors were identified (body mass index, blood type, region, and urgency status) to be significantly and independently associated with wait time (p < 0.001), showing adequate model discrimination (C = 0.704) and calibration. Higher risk scores correlated with shorter wait times. Our model corresponded closely with observed transplant rates, predicting longer wait times for lower status, larger size, certain blood groups, and some UNOS regions. This tool has the potential to more accurately describe the wait-time duration for an individual patient, which may influence care decisions. The wait-time discrepancies (blood types/regions) reinforce the need to reevaluate the geographic-allocation policy. The proposed review of the UNOS heart allocation policy may make this model especially relevant.

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