Lisiane Pruinelli1, György J Simon, Karen A Monsen, Timothy Pruett, Cynthia R Gross, David M Radosevich, Bonnie L Westra. 1. Lisiane Pruinelli, PhD, MS, RN, is Assistant Professor, University of Minnesota School of Nursing, Minneapolis. György J. Simon, PhD, is Assistant Professor, University of Minnesota Institute for Health Informatics and School of Medicine, Minneapolis. Karen A. Monsen, PhD, RN, FAAN, is Associate Professor, University of Minnesota School of Nursing, Minneapolis. Timothy Pruett, MD, is Professor and Chief, Division of Transplantation, University of Minnesota Department of Surgery, Minneapolis. Cynthia R. Gross, PhD, is Professor Emerita, University of Minnesota Department of Experimental and Clinical Pharmacology and School of Nursing, Minneapolis. David M. Radosevich, PhD, RN, is Adjunct Assistant Professor, University of Minnesota School of Public Health, Minneapolis. Bonnie L. Westra, PhD, RN, FAAN, FACMI, is Associate Professor, University of Minnesota School of Nursing and Institute for Health Informatics, Minneapolis.
Abstract
BACKGROUND: Liver transplants account for a high number of procedures with major investments from all stakeholders involved; however, limited studies address liver transplant population heterogeneity pretransplant predictive of posttransplant survival. OBJECTIVE: The aim of the study was to identify novel and meaningful patient clusters predictive of mortality that explains the heterogeneity of liver transplant population, taking a holistic approach. METHODS: A retrospective cohort study of 344 adult patients who underwent liver transplantation between 2008 through 2014. Predictors were summarized severity scores for comorbidities and other suboptimal health states grouped into 11 body systems, the primary reason for transplantation, demographics/environmental factors, and Model for End Liver Disease score. Logistic regression was used to compute the severity scores, hierarchical clustering with weighted Euclidean distance for clustering, Lasso-penalized regression for characterizing the clusters, and Kaplan-Meier analysis to compare survival across the clusters. RESULTS: Cluster 1 included patients with more severe circulatory problems. Cluster 2 represented older patients with more severe primary disease, whereas Cluster 3 contained healthiest patients. Clusters 4 and 5 represented patients with musculoskeletal (e.g., pain) and endocrine problems (e.g., malnutrition), respectively. There was a statistically significant difference for mortality between clusters (p < .001). CONCLUSIONS: This study developed a novel methodology to address heterogeneous and high-dimensional liver transplant population characteristics in a single study predictive of survival. A holistic approach for data modeling and additional psychosocial risk factors has the potential to address holistically nursing challenges on liver transplant care and research.
BACKGROUND: Liver transplants account for a high number of procedures with major investments from all stakeholders involved; however, limited studies address liver transplant population heterogeneity pretransplant predictive of posttransplant survival. OBJECTIVE: The aim of the study was to identify novel and meaningful patient clusters predictive of mortality that explains the heterogeneity of liver transplant population, taking a holistic approach. METHODS: A retrospective cohort study of 344 adult patients who underwent liver transplantation between 2008 through 2014. Predictors were summarized severity scores for comorbidities and other suboptimal health states grouped into 11 body systems, the primary reason for transplantation, demographics/environmental factors, and Model for End Liver Disease score. Logistic regression was used to compute the severity scores, hierarchical clustering with weighted Euclidean distance for clustering, Lasso-penalized regression for characterizing the clusters, and Kaplan-Meier analysis to compare survival across the clusters. RESULTS: Cluster 1 included patients with more severe circulatory problems. Cluster 2 represented older patients with more severe primary disease, whereas Cluster 3 contained healthiest patients. Clusters 4 and 5 represented patients with musculoskeletal (e.g., pain) and endocrine problems (e.g., malnutrition), respectively. There was a statistically significant difference for mortality between clusters (p < .001). CONCLUSIONS: This study developed a novel methodology to address heterogeneous and high-dimensional liver transplant population characteristics in a single study predictive of survival. A holistic approach for data modeling and additional psychosocial risk factors has the potential to address holistically nursing challenges on liver transplant care and research.
Authors: Rolland C Dickson; Surakit Pungpapong; Andrew P Keaveny; C Burcin Taner; Marwan Ghabril; Jaime Aranda-Michel; Raj Satyanarayana; Hugo Bonatti; David J Kramer; Justin H Nguyen Journal: Clin Transplant Date: 2011-03-23 Impact factor: 2.863
Authors: L Negreanu; C Buşegeanu; D Trandafir; P Dragomir; Maria Udeanu; Carmen Fierbinţeanu-Braticevici; D Andronescu Journal: Rom J Intern Med Date: 2005
Authors: Sanjoy Dey; Jacob Cooner; Connie W Delaney; Joanna Fakhoury; Vipin Kumar; Gyorgy Simon; Michael Steinbach; Jeremy Weed; Bonnie L Westra Journal: Nurs Res Date: 2015 Jul-Aug Impact factor: 2.381
Authors: Jordan Elizabeth Derck; Angela E Thelen; David C Cron; Jeffrey F Friedman; Ashley D Gerebics; Michael J Englesbe; Christopher J Sonnenday Journal: Transplantation Date: 2015-02 Impact factor: 4.939
Authors: Ana L Gleisner; Alvaro Muñoz; Ajacio Brandao; Claudio Marroni; Maria Lucia Zanotelli; Guido Gracco Cantisani; Leila Beltrami Moreira; Michael A Choti; Timothy M Pawlik Journal: Surgery Date: 2009-12-03 Impact factor: 3.982
Authors: Judith G Regensteiner; William R Hiatt; Joseph R Coll; Michael H Criqui; Diane Treat-Jacobson; Mary M McDermott; Alan T Hirsch Journal: Vasc Med Date: 2008-02 Impact factor: 3.239
Authors: Supriya S Patel; Amanda K Arrington; Shaun McKenzie; Brian Mailey; Michelle Ding; Wendy Lee; Avo Artinyan; Nicholas Nissen; Steven D Colquhoun; Joseph Kim Journal: Int J Hepatol Date: 2012-08-22
Authors: Young Shin Park; Jean F Wyman; Barbara J McMorris; Lisiane Pruinelli; Ying Song; Merrie J Kaas; Scott E Sherman; Steven Fu Journal: Prev Med Rep Date: 2021-09-03