Predicting early discharge from hospital after liver transplantation (ERDALT) at a single center: a new model

Abstract

ERDALT was applicable in 34 of 289 patients (11.8%). Variables independently associated with ERDALT were MELD exception points OR 1.9 (P = 0.04), surgery time < 4 h OR 3.8 (P = 0.013), < 5 units of blood products consumption (BPC) OR 3.5 (P = 0.001) and early weaning from mechanical intubation OR 6.3 (P = 0.006). Points in the predictive scoring model were allocated as follows: MELD exception points (absence = 0 points, presence = 1 point), surgery time < 4 h (0-2 points), < 5 units of BPC (0-2 points), and early weaning (0-3 points). Final scores ranged from 0 to 8 points with a c-statistic of 0.83 (95% CI 0.77-0.90; P < 0.0001). Transplant costs were significantly lower in patients with ERDALT (median $23,078 vs. $28,986; P < 0.0001). Neither lower patient and graft survival, nor higher rates of short-term re-hospitalization and acute rejection events after discharge were observed in patients with ERDALT. In conclusion, the ERDALT score identifies patients suitable for early discharge with excellent outcomes after transplantation. This score may provide applicable models particularly for emerging economies.

When is the optimal time to discharge patients after liver transplantation with respect to short‐term outcomes? A systematic review of the literature and expert panel recommendations

“…Standardization and optimizing the timing for discharge from hospital might also reduce costs directly attributable to LT during hospitalization. A cost‐saving effect was shown in one single center study 17 but was less than expected in an analysis from the SRTR 20 . The reason might be the low granularity of data in the registry, as one could assume that shortened LOS would translate into significant cost savings.…”

“…The safety and efficacy of an ERAS‐protocol might be measured by parameters such as early readmission rate, morbidity, mortality and cost‐effectiveness. In the reviewed literature, early readmission rate was comparable between the population with early versus regular/late discharge from hospital with a trend toward lower readmission in the ERAS group 8,14,16–19 . In daily clinical practice, a definition of functional recovery after LT would be a requirement for a standardized timely discharge protocol.…”

“…In the reviewed literature, early readmission rate was comparable between the population with early versus regular/late discharge from hospital with a trend toward lower readmission in the ERAS group. 8 , 14 , 16 , 17 , 18 , 19 In daily clinical practice, a definition of functional recovery after LT would be a requirement for a standardized timely discharge protocol. As a first step, a checklist was established on confounders indicating the recipients´ postoperative course for a safe discharge policy.…”

Enhanced recovery after surgery in liver transplantation: Challenges and feasibility

“…Although ERAS with its multimodal approach pattern did not appear in the literature until recently, the concept of multimodal clinical pathways in LT was raised as early as 2011 by Pavlakis et al of the Beth Israel Deaconess Medical Center team[ 15 ], characterizing the transplantation domain as an “ideal forum for successful implementation of clinical pathways” and highlighting their importance and potential in reducing length of stay, morbidity, costs, as well as improving patient satisfaction. Piñero et al [ 16 ] introduced in 2015 the concept of the early discharge from hospital following LT focusing on healthcare costs and proposed an early discharge prediction model based on MELD points (exception MELD points were deemed a favorable prognostic factor), length of surgery (time < 4 h), transfusion of less than 5 units of packed red blood cells, and early respirator weaning. The author concluded that early discharge from the hospital following LT is feasible, without a negative impact on patient or graft survival, nor did it increase short-term rehospitalization.…”

Development and Internal Validation of a Model Predicting Safe, Early Discharge Among Patients Presenting to the Emergency Department with an Infection.

“…Several studies have focused on predicting early discharge among speci c patient populations [35,36,[53][54][55][56][57][58] and multiple have proposed prediction models. [36,[55][56][57][58] Only two, however, based their prediction model on ED patients. [57,58] The rst included 894 general ED patients and showed an AUC of 0.84 for the prediction of discharge within 48…”