Systematic Review
Predictors of Liver Transplant Patient Survival: A Critical Review Using a Holistic Framework
Progress in Transplantation 2017, Vol. 27(1) 98-106 ª 2016, NATCO. All rights reserved. Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/1526924816680099 journals.sagepub.com/home/pit
Lisiane Pruinelli, PhD, MS, RN1, Karen A. Monsen, PhD, RN, FAAN1, Cynthia R. Gross, PhD2, David M. Radosevich, PhD, RN3, Gyo¨rgy J. Simon, PhD4, and Bonnie L. Westra, PhD, RN, FAAN, FACMI5
Abstract Objective: Liver transplantation is a costly and risky procedure, representing 25 050 procedures worldwide in 2013, with 6729 procedures performed in the United States in 2014. Considering the scarcity of organs and uncertainty regarding prognosis, limited studies address the variety of risk factors before transplantation that might contribute to predicting patient’s survival and therefore developing better models that address a holistic view of transplant patients. This critical review aimed to identify predictors of liver transplant patient survival included in large-scale studies and assess the gap in risk factors from a holistic approach using the Wellbeing Model and the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement. Data Source: Search of the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Medline, and PubMed from the 1980s to July 2014. Study Selection: Original longitudinal large-scale studies, of 500 or more subjects, published in English, Spanish, or Portuguese, which described predictors of patient survival after deceased donor liver transplantation. Data Extraction: Predictors were extracted from 26 studies that met the inclusion criteria. Data Synthesis: Each article was reviewed and predictors were categorized using a holistic framework, the Wellbeing Model (health, community, environment, relationship, purpose, and security dimensions). Conclusions: The majority (69.7%) of the predictors represented the Wellbeing Model Health dimension. There were no predictors representing the Wellbeing Dimensions for purpose and relationship nor emotional, mental, and spiritual health. This review showed that there is rigorously conducted research of predictors of liver transplant survival; however, the reported significant results were inconsistent across studies, and further research is needed to examine liver transplantation from a whole-person perspective. Keywords liver transplant predictors, liver transplant recipient, body regions, liver transplantation, well-being, patient survival
Introduction Liver transplantation is the ultimate intervention for end-stage liver disease with no other alternative therapeutic approach. There were 25 050 procedures worldwide 1 in 2013, with 6729 performed in the United States in 2014.2 Patient survival is the most common outcome evaluated for transplant success and ranges from 79.5% to 84.6% during the first year and 65% to 79.1% at 5 years after transplantation.2 In the United States, the expenditure for transplantation was estimated at US$4.9 billion in 2014.3 Considering the scarcity of organs and the uncertainty regarding prognosis, transplantation is a costly and risky procedure; moreover, there are limited studies addressing the broad array of pretransplant risk factors for liver recipients that might contribute to predicting and, therefore, improving patient survival. In order to improve liver transplant survival, there is an urgent need to go beyond physiological predictors and identify other determinants of health and well-being that
may be involved with the transplantation process and patient survival. There are considerable research findings that address recipient characteristics, organ donor characteristics, management 1
School of Nursing, University of Minnesota, Minneapolis, MN, USA School of Nursing and College of Pharmacy, University of Minnesota, Minneapolis, MN, USA 3 Department of Surgery, University of Minnesota Medical School, Minneapolis, MN, USA 4 Department of Health Science Research, Mayo Clinic Rochester, Rochester, MN, USA 5 School of Nursing and Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA 2
Corresponding Author: Lisiane Pruinelli, School of Nursing, University of Minnesota, Minneapolis, MN. 1180 Gibbs Ave N3, Saint Paul, MN 55108, USA. Email:
[email protected]
Pruinelli et al
99 This new perspective is possible as a result of an increased adoption of patient care management tools for collecting and centralizing health data.2,13 Adopting the Wellbeing Model as a framework, the aim of this critical review was to identify recipient predictors of liver transplant patient survival included in large-scale studies and assess the gap in risk factors that address a holistic approach to health and well-being.
Materials and Methods Search Strategy
Figure 1. The 6 dimensions of the Wellbeing Model.
of the allocation and transportation of organs, transplant center–related factors, and sociocultural and economic factors.4-6 Although all these factors impact patient survival, predictors of transplantation survival have been mostly grounded in positivist theories that embrace the disease model.7,8 To further improve survival rates and understand risk factors before transplantation that may potentially be predictive of survival, additional risk factors are needed that address a holistic view of transplant patients and components of their health, for example, risk factors other than physiologic that may be more representative of the recipient as a whole. The World Health Organization defines health as ‘‘a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity.’’9 As a component of health, well-being is a concept that has recently been adopted to entail the whole-person perspective and share common elements across countries and cultures.10,11 As an example, the Wellbeing Model conceptualized by Kreitzer 12 theorizes health as interconnectedness and interdependence between individual, family, organization/system, and community. The model comprises 6 dimensions that affect well-being (Figure 1).10,12 The Wellbeing Model has been used as a framework to categorize liver transplant predictors of survival found in the included articles, detect gaps that address a holistic approach to health and well-being, and delineate additional risk factors for big data research.10,12 The majority of liver transplantation studies has been performed by single centers with small sample sizes and primarily with a focus on physiological measures. Further, large-scale studies with multicentric data can provide findings that are more likely to be generalizable. With the current trend for using large data sets for research and the amount of data available from electronic health records, liver transplant researchers may have a richer source of information to examine additional health data and incorporate into big data research.
A critical review, as described by Grant and Booth,14 was adopted for this study. A literature search was conducted of original research published from the 1980s through July 31, 2014. This time frame was used with the aim of including the most significant landmark studies in liver transplantation after this procedure was clinically accepted by the US National Institutes of Health as the definitive therapy for end-stage liver diseases.2 Multiple databases were searched including the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Medline, and PubMed. Longitudinal studies with large sample sizes were included to increase the generalizability of the transplant predictors of patient survival.15 For this review, a large sample size was defined as including 500 or more participants. As definition of large sample size may differ, for this study, the sample size of 500 was selected after considering the average number of liver transplants per center per year in United States and the amount of procedures necessary to represent approximately 10 years of procedures per center when a study was conducted in a single center. This strategy aimed to determine the predictors of liver transplant patient survival over time. A combined search strategy used the following MeSH terms: ‘‘liver transplant,’’ ‘‘predictors’’/’’risk factors,’’ ‘‘cohort studies,’’ and ‘‘patient survival.’’
Eligibility Criteria Quantitative longitudinal studies with a large sample size, either from multicenter or from single-center sites were included. Additional inclusion criteria were recipient liver disease risk factors published as impacting patient survival from liver transplantation and studies published in English, Portuguese, or Spanish since the researcher was able to understand these languages. Exclusion criteria were studies that used populations younger than 18 years old, liver interventions such as those for staging and liver-directed therapies, and combined organ transplantation.
Data Extraction and Synthesis Articles were analyzed for quality, data extracted, and synthesized. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) was used to assess the level of the reporting quality of the included articles.16 This statement contains a checklist of 22 items that covers critical parameters in observational studies. All included studies were ranked from
100 0 (lowest) to 22 (highest), based upon the STROBE quality items. Articles were analyzed using an adaptation of Garrard matrix method, where a matrix summarized the articles according to the most pertinent points, such as author, title, year, purpose, research design, sample, data source, and high-level results.17 All reported predictors of liver transplant patient survival were identified across studies and were assessed according to their reported level of statistical significance. All predictors, statistically and not statistically significant, included in the statistical models across studies were extracted, including those that went beyond the inclusion criteria for this literature review if they were addressed in studies, such as donated liver risk factors. Predictors were considered statistically significant (P .05), not statistically significant (P > .05), or not available (NA) when no result was reported. Predictors were then mapped to a Wellbeing Model dimension and validated by 2 investigators (B.L.W. and K.A.M.). Considering the unique factors related to liver transplantation and the adopted framework for this study, definitions of the dimensions were refined to assign predictors to mutually exclusive dimensions. The health dimension included recipient health determinants as well as demographic risk factors such as age, race, and gender. Donor-related predictors were categorized as the community dimension, since these factors are shared between a human interaction and society (ie, a body part is donated by a social community interaction) and are not in the domain of the health of the recipient. Risk factors related to the health system and providers’ use of resources to manage recipient’s health were classified in the environment dimension. The security dimension included risk factors related to basic human needs that go beyond the recipient’s health.
Results Of the 2675 articles initially identified, 843 remained for review after advanced limiters were applied. Of these, 777 were excluded on the basis of title and abstracts, with 66 remaining studies for full-text evaluation. In total, 26 articles were eligible for addressing the research aim and were included in this analysis. Figure 2 shows the results of the search strategy. From the included 26 articles,18-43 85 predictors of patient survival were identified in the studies.
Study Characteristics Articles were assessed for study characteristics, including year of publication, source of data, geographical location, sample size, and type of data analysis (Table 1). There was an increasing number of studies over time, predominately conducted in North America using national databases or data from a single center.18-43 This increase in the number of studies, specifically with larger sample size and using national databases, may be result of the increased number of transplants in the recent years and the adoption of information technology for data capture.
Progress in Transplantation 27(1) Most studies included
