A Holistic Clustering Methodology for Liver Transplantation Survival.

Autor:	Pruinelli L; 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., Simon GJ, Monsen KA, Pruett T, Gross CR, Radosevich DM, Westra BL
Jazyk:	angličtina
Zdroj:	Nursing research [Nurs Res] 2018 Jul/Aug; Vol. 67 (4), pp. 331-340.
DOI:	10.1097/NNR.0000000000000289
Abstrakt:	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.
Databáze:	MEDLINE
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