*Purpose: In 2017, we developed a simple algorithm for use prior to transplantation to determine risk of fatal outcome. Pre-transplant organ system failure risk factors analyzed were as follows: 1. Pulmonary (mechanical ventilation), 2. Hematology (platelet count of <30K); 3. CNS (Glasgow score GCS of 9 or <), 4. Renal (on dialysis), 5. Immune (WBC 1K or less or > 25K) and 6. Cardiovascular (2+ pressors). Overall, while patients with 0-2 risk factors observed a 94% 1 year survival, patients with high (35+) lab MELD scores with 3+ risk factors had 50% 1 year survival. This new study examined the association between the pre-transplant clinical risk score and program financial performance, specifically cost of transplant index admission (TIA), and also included more recent transplant and outcomes data.

*Methods: Organ system failure risk scores were calculated for 175 LT recipients. Each patient was assigned to one of three categories as follows:

• Group1: low (<35) MELD & low risk (0-2 risk score),
• Group2. high MELD (35+) &low risk (0-2 risk score),
• Group3. high MELD (35+) & high risk (3-5 risk score).

TIA cost data were collected from the Vizient Clinical Data Base/ Resource Manager (CDB/RM) tool. The CDB/RM tool also provided TIA length of stay (LOS) data. Median LOS and median direct cost were calculated. Cost proportion for each group was calculated in comparison with the overall patient population. Correlation between pre-transplant risk category and cost of TIA was examined. 2-sample t-test was used for statistical analyses for comparing TIA cost for low and high risk patient populations.

*Results:

Risk scores, survival, median LOS, and median direct cost proportion are shown below:

In comparison with the TIA cost for the overall patient population, Group1 observed 13% less cost, and Group2 observed 27% higher cost. Group3 patients, with the highest 1- year mortality, and longest median LOS, observed 98% increased costs for their TIA. Two-sample t-test for comparing 0-2 with 3-5 risk factors resulted with a p-value <0.05.

*Conclusions: Program quality improvement efforts to determine best candidates for liver transplantation have driven financial performance with lowered TIA costs. Linking clinical data to cost data have enabled us to study and achieve high-value in our program, providing patient-centric care with improved quality and outcomes, at lower costs.

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