Dataset Information

Development and Validation of a Model to Predict Long-Term Survival After Liver Transplantation.

ABSTRACT: Patients are prioritized for liver transplantation (LT) under an "urgency-based" system using the Model for End-Stage Liver Disease score. This system focuses solely on waitlist mortality, without considerations of posttransplant morbidity, mortality, and health care use. We sought to develop and internally validate a continuous posttransplant risk score during 5-year and 10-year time horizons. This retrospective cohort study used national registry data of adult deceased donor LT (DDLT) recipients with ≥90 days of pretransplant waiting time from February 27, 2002 to December 31, 2018. We fit Cox regression models at 5 and 10 years to estimate beta coefficients for a risk score using manual variable selection and calculated the absolute predicted survival time. Among 21,103 adult DDLT recipients, 11 variables were selected for the final model. The area under the curves at 5 and 10 years were 0.63 (95% confidence interval [CI], 0.60-0.66) and 0.67 (95% CI, 0.64-0.70), respectively. The group with the highest ("best") scores had 5-year and 10-year survivals of 89.4% and 85.4%, respectively, compared with 45.9% and 22.2% for those with the lowest ("worst") scores. Our score was significantly better at predicting long-term survival compared with the existing scores. We developed and validated a risk score using nearly 17 years of data to prioritize patients with end-stage liver disease based on projected posttransplant survival. This score can serve as the building block by which the transplant field can change the entire approach to prioritizing patients to an approach that is based on considerations of maximizing benefits (ie, survival benefit-based allocation) rather than simply waitlist mortality.

SUBMITTER: Goldberg D

PROVIDER: S-EPMC8742146 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Development and Validation of a Model to Predict Long-Term Survival After Liver Transplantation.

Goldberg David D Mantero Alejandro A Newcomb Craig C Delgado Cindy C Forde Kimberly K Kaplan David D John Binu B Nuchovich Nadine N Dominguez Barbara B Emanuel Ezekiel E Reese Peter P PP

Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society 20210601 6

Patients are prioritized for liver transplantation (LT) under an "urgency-based" system using the Model for End-Stage Liver Disease score. This system focuses solely on waitlist mortality, without considerations of posttransplant morbidity, mortality, and health care use. We sought to develop and internally validate a continuous posttransplant risk score during 5-year and 10-year time horizons. This retrospective cohort study used national registry data of adult deceased donor LT (DDLT) recipien ...[more]

PMID: 33540489

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Project description:ObjectivesRisk stratification and prognosis prediction are critical for appropriate management of anti-neutrophil cytoplasmic antibody (ANCA) associated vasculitis (AAV). Herein, we aim to develop and internally validate a prediction model specifically for long-term survival of patients with AAV.MethodsWe thoroughly reviewed the medical charts of patients with AAV admitted to Peking Union Medical College Hospital from January 1999 to July 2019. The Least Absolute Shrinkage and Selection Operator method and the COX proportional hazard regression was used to develop the prediction model. The Harrell's concordance index (C-index), calibration curves and Brier scores were calculated to evaluate the model performance. The model was internally validated by bootstrap resampling methods.ResultsA total of 653 patients were included in the study, including 303 patients with microscopic polyangiitis, 245 patients with granulomatosis with polyangiitis and 105 patients with eosinophilic granulomatosis with polyangiitis, respectively. During a median follow-up of 33 months (interquartile range 15-60 months), 120 deaths occurred. Age at admission, chest and cardiovascular involvement, serum creatinine grade, hemoglobin levels at baseline and AAV sub-types were selected as predictive parameters in the final model. The optimism-corrected C-index and integrated Brier score of our prediction model were 0.728 and 0.109. The calibration plots showed fine agreement between observed and predicted probability of all-cause death. The decision curve analysis (DCA) showed that in a wide range of threshold probabilities, our prediction model had higher net benefits compared with the revised five factor score (rFFSand) and the birmingham vasculitis activity score (BVAS) system.ConclusionOur model performs well in predicting outcomes of AAV patients. Patients with moderate-to-high probability of death should be followed closely and personalized monitoring plan should be scheduled.

Project description:BackgroundPostoperative sepsis is one of the main causes of mortality after liver transplantation (LT). Our study aimed to develop and validate a predictive model for postoperative sepsis within 7 d in LT recipients using machine learning (ML) technology.MethodsData of 786 patients received LT from January 2015 to January 2020 was retrospectively extracted from the big data platform of Third Affiliated Hospital of Sun Yat-sen University. Seven ML models were developed to predict postoperative sepsis. The area under the receiver-operating curve (AUC), sensitivity, specificity, accuracy, and f1-score were evaluated as the model performances. The model with the best performance was validated in an independent dataset involving 118 adult LT cases from February 2020 to April 2021. The postoperative sepsis-associated outcomes were also explored in the study.ResultsAfter excluding 109 patients according to the exclusion criteria, 677 patients underwent LT were finally included in the analysis. Among them, 216 (31.9%) were diagnosed with sepsis after LT, which were related to more perioperative complications, increased postoperative hospital stay and mortality after LT (all p < .05). Our results revealed that a larger volume of red blood cell infusion, ascitic removal, blood loss and gastric drainage, less volume of crystalloid infusion and urine, longer anesthesia time, higher level of preoperative TBIL were the top 8 important variables contributing to the prediction of post-LT sepsis. The Random Forest Classifier (RF) model showed the best overall performance to predict sepsis after LT among the seven ML models developed in the study, with an AUC of 0.731, an accuracy of 71.6%, the sensitivity of 62.1%, and specificity of 76.1% in the internal validation set, and a comparable AUC of 0.755 in the external validation set.ConclusionsOur study enrolled eight pre- and intra-operative variables to develop an RF-based predictive model of post-LT sepsis to assist clinical decision-making procedure.

Dataset Information

Development and Validation of a Model to Predict Long-Term Survival After Liver Transplantation.

Publications

Development and Validation of a Model to Predict Long-Term Survival After Liver Transplantation.

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