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Joint modeling of liver transplant candidates outperforms the model for end-stage liver disease: The effect of disease development over time on patient outcome.

ABSTRACT: Liver function is measured regularly in liver transplantation (LT) candidates. Currently, these previous disease development data are not used for survival prediction. By constructing and validating joint models (JMs), we aimed to predict the outcome based on all available data, using both disease severity and its rate of change over time. Adult LT candidates listed in Eurotransplant between 2007 and 2018 (n = 16 283) and UNOS between 2016 and 2019 (n = 30 533) were included. Patients with acute liver failure, exception points, or priority status were excluded. Longitudinal MELD(-Na) data were modeled using spline-based mixed effects. Waiting list survival was modeled with Cox proportional hazards models. The JMs combined the longitudinal and survival analysis. JM 90-day mortality prediction performance was compared to MELD(-Na) in the validation cohorts. MELD(-Na) score and its rate of change over time significantly influenced patient survival. The JMs significantly outperformed the MELD(-Na) score at baseline and during follow-up. At baseline, MELD-JM AUC and MELD AUC were 0.94 (0.92-0.95) and 0.87 (0.85-0.89), respectively. MELDNa-JM AUC was 0.91 (0.89-0.93) and MELD-Na AUC was 0.84 (0.81-0.87). The JMs were significantly (p < .001) more accurate than MELD(-Na). After 90 days, we ranked patients for LT based on their MELD-Na and MELDNa-JM survival rates, showing that MELDNa-JM-prioritized patients had three times higher waiting list mortality.

SUBMITTER: Goudsmit BFJ

PROVIDER: S-EPMC8597089 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background and aimsPrevious recommendations suggested living donor liver transplantation (LDLT) should not be considered for patients with Model for End-Stage Liver Disease (MELD) > 25 and hepatorenal syndrome (HRS).Approach and resultsPatients who were listed with MELD > 25 from 2008 to 2017 were analyzed with intention-to-treat (ITT) basis retrospectively. Patients who had a potential live donor were analyzed as ITT-LDLT, whereas those who had none belonged to ITT-deceased donor liver transplantation (DDLT) group. ITT-overall survival (OS) was analyzed from the time of listing. Three hundred twenty-five patients were listed (ITT-LDLT n = 212, ITT-DDLT n = 113). The risk of delist/death was lower in the ITT-LDLT group (43.4% vs. 19.8%, P < 0.001), whereas the transplant rate was higher in the ITT-LDLT group (78.3% vs. 52.2%, P < 0.001). The 5-year ITT-OS was superior in the ITT-LDLT group (72.6% vs. 49.5%, P < 0.001) for patients with MELD > 25 and patients with both MELD > 25 and HRS (56% vs. 33.8%, P < 0.001). Waitlist mortality was the highest early after listing, and the distinct alteration of slope at survival curve showed that the benefits of ITT-LDLT occurred within the first month after listing. Perioperative outcomes and 5-year patient survival were comparable for patients with MELD > 25 (88% vs. 85.4%, P = 0.279) and patients with both MELD > 25 and HRS (77% vs. 76.4%, P = 0.701) after LDLT and DDLT, respectively. The LDLT group has a higher rate of renal recovery by 1 month (77.4% vs. 59.1%, P = 0.003) and 3 months (86.1% vs, 74.5%, P = 0.029), whereas the long-term estimated glomerular filtration rate (eGFR) was similar between the 2 groups. ITT-LDLT reduced the hazard of mortality (hazard ratio = 0.387-0.552) across all MELD strata.ConclusionsThe ITT-LDLT reduced waitlist mortality and allowed an earlier access to transplant. LDLT in patients with high MELD/HRS was feasible, and they had similar perioperative outcomes and better renal recovery, whereas the long-term survival and eGFR were comparable with DDLT. LDLT should be considered for patients with high MELD/HRS, and the application of LDLT should not be restricted with a MELD cutoff.

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Joint modeling of liver transplant candidates outperforms the model for end-stage liver disease: The effect of disease development over time on patient outcome.

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