Abstract
PurposeTo distinguish clinical factors that have time-varying (as opposed to constant) impact upon patient and graft survival among pediatric liver transplant recipients.MethodsUsing national data from 2002 through 2013, we examined potential clinical and demographic covariates using Gray’s piecewise constant time-varying coefficients (TVC) models. For both patient and graft survival, we estimated univariable and multivariable Gray’s TVC, retaining significant covariates based on backward selection. We then estimated the same specification using traditional Cox proportional hazards (PH) models and compared our findings.ResultsFor patient survival, covariates included recipient diagnosis, age, race/ethnicity, ventilator support, encephalopathy, creatinine levels, use of living donor, and donor age. Only the effects of recipient diagnosis and donor age were constant; effects of other covariates varied over time. We retained identical covariates in the graft survival model but found several differences in their impact.ConclusionThe flexibility afforded by Gray’s TVC estimation methods identify several covariates that do not satisfy constant proportionality assumptions of the Cox PH model. Incorporating better survival estimates is critical for improving risk prediction tools used by the transplant community to inform organ allocation decisions.
Highlights
Liver allocation and transplantation policies in the US must balance the problems of donor organ scarcity with mandates for fair and effective allocation
Incorporating better survival estimates is critical for improving risk prediction tools used by the transplant community to inform organ allocation decisions
Diagnoses associated with Pediatric Acute Liver Failure (PALF) differ significantly from those seen in adults; in particular, specific diagnoses are not established in up to 50% of pediatric cases [4]
Summary
Liver allocation and transplantation policies in the US must balance the problems of donor organ scarcity with mandates for fair and effective allocation. Simulation models for adults have been critical in providing timely, evidencebased data to the transplant community They inform policy development and evaluate the potential impact of changes before policy adoption or implementation, by comparing survival and quality of life (with and without transplantation) under different recommendations and practices [3]. Serial assessments of clinical parameters (international normalized ratio (INR), total bilirubin, and clinical encephalopathy) differentiated subject outcomes using a growth mixture model [10]. These data inform natural history of PALF and could be used in developing predictive models for liver transplant decisions
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