Psychosomatics 49:332-340, July-August 2008
doi: 10.1176/appi.psy.49.4.332
© 2008 Academy of Psychosomatic Medicine
Clusters of Alcohol Use Disorders Diagnostic Criteria and Predictors of Alcohol Use After Liver Transplantation for Alcoholic Liver Disease
Andrea DiMartini, M.D.,
Mary Amanda Dew, Ph.D.,
Mary Grace Fitzgerald, R.N., M.S.N., and
Paulo Fontes, M.D.
Received April 21, 2006; revised August 3, 2006; accepted August 9, 2006. From The University of Pittsburgh Medical Center and Starzl Transplant Institute, Pittsburgh, PA. Send correspondence and reprint requests to Andrea DiMartini, M.D., Western Psychiatric Institute and Clinic, 3811 O’Hara St., Pittsburgh, PA 15213. e-mail: dimartiniaf{at}upmc.edu
© 2008 The Academy of Psychosomatic Medicine
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ABSTRACT
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BACKGROUND: Establishing the correct alcohol use disorder diagnosis is clinically relevant because several reports of post-transplant alcohol use suggest that a pre-transplant diagnosis of alcohol dependence (rather than abuse) predicts relapse to alcohol use. Numerous combinations of specific symptoms are possible to achieve diagnostic significance. OBJECTIVE: The authors hypothesized that there would be distinct clusters of liver transplant recipients who showed specific combinations of alcohol-related symptoms and that these clusters would be predictive of alcohol-abuse outcome after transplant. METHOD: A group of 120 ALD liver transplant recipients received the Structured Clinical Interview for DSM–IV (SCID) module for alcohol abuse/dependence, and a cluster analysis was performed. RESULTS: Within the clusters of those with alcohol dependence, cluster assignment did not predict those more likely to drink. However, those assigned to the alcohol abuse cluster were significantly less likely to drink than those with alcohol dependence. CONCLUSION: Results therefore suggest that the prognosis regarding continued abstinence posttransplant is much more positive for individuals with a diagnosis of abuse than for those who meet criteria for alcohol dependence.
Key Words: Alcoholism • Liver Transplantation • Outcome Studies
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INTRODUCTION
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Alcoholic liver disease (ALD) is a common indication for liver transplantation, yet there is little written in the transplant literature about comorbid alcohol use disorders in transplant recipients with alcoholic liver disease. Although these patients are often assumed to have an "alcohol abuse problem," few investigations have dealt with the issue of diagnosing their specific alcohol use disorder.1 Interestingly, not all ALD liver transplant recipients are alcohol-dependent. Approximately 70%–75% will meet DSM–IV criteria for alcohol dependence;2,3 20%–25% will meet DSM–IV criteria for alcohol abuse; and 4%–5% will not meet criteria for either disorder.3
Establishing the correct alcohol use disorder diagnosis is clinically relevant because several reports of post-transplant alcohol use suggest that a pre-transplant diagnosis of alcohol dependence (rather than abuse) predicts relapse to alcohol use.2,4 In one study, those with a diagnosis of alcohol dependence were 2.6 times more likely to drink post-transplant than those with alcohol abuse, and only those with alcohol dependence indulged in binge drinking (defined as 6 or more drinks in a single episode).5
Although establishing the correct alcohol use diagnosis has clinical utility, in practice, there is significant heterogeneity possible in the types of symptoms shown by any given patient who meets criteria for the diagnosis. DSM–IV diagnoses of alcohol use disorders are polythetic, based on whether a sheer count of specific criteria exceeds a numeric threshold (the presence of at least 1 of 4 potential criteria for alcohol abuse, or at least 3 of 7 potential criteria for alcohol dependence). Therefore, numerous combinations of specific symptoms are possible to achieve diagnostic significance. In fact, there are 23 theoretical subtypes of alcohol abuse and 99 theoretical subtypes of alcohol dependence.6 Considering the potential variations within alcohol use disorder diagnoses, we hypothesized that there would be distinct clusters of liver transplant recipients who showed specific combinations of alcohol-related symptoms. We also hypothesized that individuals with certain combinations of these symptoms would have a more severe form of addiction, which would, in turn, predict poorer post-transplant alcohol use outcomes.
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METHOD
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Subjects
A group of 120 ALD liver transplant recipients who are participating in a longitudinal study of post-transplant alcohol use have received the Structured Clinical Interview for DSM–IV (SCID) module for alcohol abuse/dependence; 23 met criteria for abuse; 90 met criteria for dependence; and 7 had neither abuse nor dependence. The present analysis focuses on those 113 ALD liver transplant recipients who, by SCID interview, met criteria for a pre-transplant alcohol use disorder.
These participants were drawn from a larger group of all liver transplant recipients at the Starzl Transplant Institute (STI) who were transplanted for either a primary or secondary diagnosis of ALD from May 1998 to September 2002. All participants were 3 or more months posttransplant and no longer in the hospital at enrollment. Participants were voluntarily enrolled after agreeing to participate and signing Informed Consent. During the period of study recruitment, 194 transplant recipients were eligible. Of these, 151 participated (32 [16%] died before enrollment, and 11 [ 5%] refused to participate). Participants were followed for 3.2 (standard deviation [SD] 1.5) years; range: 0.5 to 6.4 years.
Alcohol and Medical Diagnoses
The pre-transplant diagnosis of ALD was determined by consensus from interviews and examinations by our transplant surgeons, hepatologists, and psychiatry team (psychiatric nurse clinical-specialist MGF and psychiatrist AD) during the evaluation for transplant. Patients with ALD had a history of excessive alcohol use, defined as  20 g of ethanol per day for women and 60 g ethanol per day for men.7 The majority (88%) had consumed this amount for 10 years or longer.
In the transplant literature, alcohol diagnoses are most often made by clinical interview, usually by a psychiatrist or other mental health professional, without the aid of a standardized interview. However, we performed structured clinical interviews to identify not only the exact alcohol use disorder for each patient, but also whether each specific criterion of the alcohol use disorder was endorsed. Of the 151 participants, 120 have undergone the structured psychiatric interview, conducted by one research staff member trained to reliability in performing the alcohol use modules of the Structured Clinical Interview for DSM–IV (SCID).8 The accuracy and validity of this research member’s interviews were reviewed by experts in SCID training.
Procedures for Identifying and Defining Alcohol Use Outcomes
Interviews and Questionnaires
After transplant, alcohol use was identified by means of three prospective measures: First, every 3 months for the first posttransplant year and every 6 months thereafter, patients completed the Alcohol Time-Line Follow-Back questionnaire (ATLFB).9 The ATLFB is a calendar instrument that captures a daily profile of alcohol use (onset, quantity, frequency, and duration of alcohol use) for the intervals between follow-up interviews. The ATLFB has good psychometric characteristics and allows the dimensions of drinking to be examined separately. It has high test–retest reliability and validity and has been tested on clinical and general-population samples.10 Participants completed the ATLFB questionnaires during a return clinic visit, by telephone interview with the research staff, or by mail. The patients were informed that the ATLFB information would be strictly confidential, would not become part of their medical record, and would not be revealed to any member of the transplant team (including the transplant psychiatrist AD or psychiatric nurse clinical-specialist MGF). The research staff were not blinded to the patient’s diagnosis or history. Completion rates for ATLFB were high at all time-points (75%–98%). Also, participants who missed one time-point provided data on the missed time-period at the next study interval.
Second, over the same time intervals, a caregiver who knew the patient best and typically lived with the patient (usually a spouse or family member) filled out a quantity/frequency questionnaire, which specifically asked about the patient’s alcohol use since the transplant. The caregiver questionnaire was patterned after the National Institute on Alcohol Abuse and Alcoholism (NIAAA) Quantity/Frequency measure;11 it asks about the number of drinking days and the amount consumed.
Third, during routine posttransplant clinic appointments, clinical interviews were performed by the transplant psychiatrist (AD), who was blinded to the data obtained by the research staff (i.e., the ATLFB and caregiver reports). Responses to questions about alcohol use from the psychiatrist’s interview were corroborated with information given by the patient to the transplant coordinators and surgeons. Because this clinic interview was conducted in the transplant clinic in conjunction with the transplant team, information provided by the patient to the transplant psychiatrist about alcohol use was revealed to the transplant team and documented in the medical record. For data collection purposes, this information was recorded as quantity/frequency of alcohol use, with specific dates and amounts of use on a monthly calendar form. Patients are seen in the transplant clinic as medically indicated. However, when possible, most patients are seen twice weekly for the first month after hospital discharge, then monthly until 3 months posttransplant, and every 3 to 6 months thereafter.
Blood-Alcohol Levels
As part of our routine clinical care, random blood-alcohol levels were obtained on the patients. At our hospital lab, blood-alcohol levels (BAL) are performed by gas chromatography, with positive levels identified at values 0.01 g/dl. Using the BAL and the patient’s weight, information on the quantity of alcohol consumed to reach that specific BAL can be estimated. From the equation Q=Vd x Css, where Q=loading dose (in grams of ethanol), Vd: volume of distribution (in liters)=0.54 liter/kg x patient weight in kg, and Css=concentration at steady state (in grams/liter), we can predict the loading dose of ethanol required to achieve a specific BAL. From the loading doses in grams of alcohol, a BAL value can be converted into standard drinks (assuming 10 g of ethanol/standard drink). BAL data were used to identify specific alcohol-use outcomes (i.e., the time to first drink, the time to 6 drinks on a single occasion).
Definition of Posttransplant Alcohol Use Outcomes
Drinking occurs in a wide variety of patterns, defined by quantity, frequency, and duration. We chose two alcohol-use outcomes to define the drinking events for this study: 1) time to first drink (onset of use); and 2) time to 6 drinks in a day for men and 4 drinks in a day for women (binge use). On the basis of standard research practice, we chose different drink amounts for men and women to define binge use.12 However, all participants who binged drank 6 or more drinks for the episode that defined their binge. The alcohol outcomes were calculated with information from each of the four ascertainment measures (clinical interview, ATLFB, caregiver report, BAL) from date of discharge from the transplant hospitalization until the outcome was achieved. The time-to-first-alcohol-use was defined as the time to first positive report on any of the interview/questionnaires or the first positive BAL. Time-to-binge-use was defined as the time until the first interview/questionnaire report of this quantity in a day or a BAL that was calculated to be compatible with this quantity. For participants who did not reach the specific alcohol outcomes, we chose date of last follow-up on either the interview or questionnaires, whichever came first.
Statistical Analysis
In order to address the first research hypothesis, hierarchical agglomerative cluster analysis13,14 was used to identify distinct patterns of symptom endorsement among the 113 patients who met criteria for either abuse or dependence. Cluster analysis is an exploratory, descriptive technique that uses algorithms to sort individuals into groups such that group members are very similar to each other but different from members of other groups. In the present analysis, the determination began by considering each person as the only member of his or her own group. The analysis then uses an algorithm—here, the unweighted pair-group method, with arithmetic averages and squared euclidean distance coefficients—to begin to link individuals together into clusters. This process of linking individuals is known as agglomeration. At each stage of the analysis, an agglomeration coefficient is generated. The optimal stopping-point in the analysis comes at the point just before the agglomeration coefficient shows a much greater increase in size than the sizes of the increase on previous steps of the analysis.13 Cross-validation was used to determine whether the cluster solution was likely to be stable and, hence, generalizable. In cross-validation, the original sample is divided, and the cluster solution obtained in one subsample is compared with the cluster solution obtained in the other subsample. The goal is to obtain the same number of types of clusters in each subsample.
The second study aim was to identify key correlates of cluster-group membership; that is, pretransplant demographic and health-related correlates of distinctive patterns of symptom endorsement in the sample. Univariate analyses, followed by discriminant-function analysis, was used to accomplish this goal.
The discriminant-function analysis determined which correlates best distinguished among the groups. The analysis generates loadings on each discriminant function extracted (a discriminant function is an underlying dimension on which the groups differ). The larger a correlate’s loading on a discriminant function, the more important it is in relation to group membership. The analysis also classifies individuals on the basis of the set of correlates. The greater the proportion of individuals correctly classified, the more important the correlates are for understanding group differences. Finally, analogous to cluster analysis, cross-validation is used to determine the stability and generalizability of the results. Before the discriminant-function analysis, the potential correlates were examined and found to meet all analytic assumptions adequately.15 With the present sample size, we had a power of 0.88 to detect a moderate-sized association between any given correlate and group membership The third study aim was to determine whether membership in the distinct cluster groups predicted risk for return to alcohol consumption after liver transplant. Kaplan-Meier analysis was used to address this question. Under this analysis, the log-rank test is used to evaluate the statistical significance of any identified differences between groups. SPSS for Windows, Version 12 was used for the analyses.
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RESULTS
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Identification of Groups With Distinct Patterns of Alcohol-Symptom Endorsement
On the basis of degree of change in the amalgamation coefficient as the cluster agglomeration proceeded, a nine-cluster solution was found to be optimal. However, most individuals (N=104 of 113) fell into one of four clusters. These four cluster groups are shown in Figure 1, which plots the proportions of each who endorsed the DSM–IV symptom items pertaining to alcohol use. Thus, the first cluster included 30 individuals (26.5% of the sample) who, compared with all other cluster groups, were most likely to have the full range of symptoms (i.e., the proportion endorsing each symptom was larger than the proportions in other groups), and the figure shows that they were the most likely to endorse having had legal problems related to alcohol, continued use despite persistent or recurrent psychological or physical problems, inability to reduce the amount of alcohol consumed, and withdrawal symptoms. All individuals in this cluster met criteria for a diagnosis of alcohol dependence.
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FIGURE 1. Proportion (%) in Four Cluster Groups Who Endorsed Each DSM–IV Alcohol Criterion Symptom and Their Rates of DSM–IV Alcohol Abuse and Alcohol Dependence Diagnoses
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The second cluster included 21 individuals (18.6% of the sample), who also all met criteria for alcohol dependence, but showed a different pattern of symptoms. As compared with all other clusters, they were most likely to have symptoms of tolerance. The third cluster included 20 individuals (17.7% of the sample), who were different from those in the other clusters because none reported symptoms of withdrawal, despite evidence of tolerance. However, they were highly likely to report that alcohol had led them to be unable to perform social roles. Finally, the fourth cluster, of 33 individuals, included a large proportion of those who met criteria for abuse, but not dependence. In general, individuals in this cluster were the least likely to endorse any of the symptoms shown in Figure 1.
It should be noted that Figure 1 excludes two symptoms that were endorsed by the majority of individuals in all four clusters: use of alcohol in situations that could involve physical hazards (endorsed by 100%, 100%, 95%, and 94% of those in Clusters 1 through 4, respectively:  2=3.02; p=0.41), and a great deal of time spent in getting alcohol (endorsed by 100%, 100%, 100%, and 94% of the four clusters, respectively: 2=4.39; p=0.33). By contrast, the four clusters differed significantly in their distributions of all of the symptoms illustrated in Figure 1 (see note to Figure 1 for 2 values). The remaining respondents (N=9; 8% of the sample) were dispersed across five small clusters containing one or two persons each, and were therefore too small to evaluate further.
The stability and replicability of the cluster solution (i.e., the emergence of four dominant clusters, with a small minority of individuals who could not be classified into any of these) was evaluated by use of a cross-validation procedure, in which a random sample of 50% of respondents was withheld from the cluster solution. For the remaining 50%, a new cluster analysis replicated the pattern in the entire sample (i.e., the degree of change in the amalgamation coefficient as cluster agglomeration proceeded was virtually identical to that for the full sample, and 89% of respondents in the subsample were classified into the same clusters as in the full sample). For the other 50% in the cross-validation subsample, the solution again replicated: 91% were classified into the cluster in which they fell in the full-sample analysis. These classification rates suggest excellent stability, and hence generalizability, of the cluster solution.15
Pretransplant Correlates of Alcohol Symptom-Pattern Group Membership
The first four columns of Table 1 display the distribution of each potential correlate among respondents in the alcohol symptom-pattern groups, followed by the univariate test for each correlate. As shown in the fifth column of the table, there were significant group differences in the demographic variables of age and occupation and in the health history variables of grams of ethanol consumed per day, participation in rehabilitation before transplant, whether respondents met criteria for another, non-alcohol substance use diagnosis before transplant, and whether respondents had hepatitis C. These univariate tests, however, do not evaluate whether the groups of respondents could be reliably distinguished from each other across the complete array of interrelated correlates. Discriminant-function analysis was used to accomplish this goal.
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TABLE 1. Demographic and Health History Characteristics in Four Groups Defined by Pattern of Symptoms of Alcohol Use Disorder in Individuals Transplanted for Alcoholic Liver Disease (N=104)
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The small sample sizes in the groups necessitated a conservative approach to the multivariate analysis. We included in the discriminant analysis only those potential correlates that had at least modest effect sizes in the univariate analyses (f for continuous variables and phi ( ) for dichotomous variables >0.20.16 Therefore, the discriminant analysis compared the four alcohol symptom-pattern groups on a subset of nine potential correlates. These variables are listed in Table 2.
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TABLE 2. Relationship of Correlates to Alcohol-Symptom Patterns in Individuals Receiving Transplants for Alcoholic Liver Disease Discriminant Function Loading
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The discriminant analysis extracted two dimensions along which the alcohol symptom-pattern groups varied. The first separated respondents in Group 1 (the most highly symptomatic group of all; centroid=1.15) from Group 4 (the least symptomatic group; centroid = –0.85), with Groups 2 and 3 lying in between (centroids: –0.28 and 0.03, respectively). In contrast, the second dimension distinguished Groups 2 and 3 (the two groups with primarily social/role problems and tolerance and/or withdrawal symptoms; centroids of 0.63 and 0.72, respectively) from both Groups 1 and 4 (centroids: –0.38 and –0.59, respectively). Each of these two dimensions differentiating the groups is characterized by distinct sets of correlates, as described below. Each dimension accounted for a significant portion of the correlates’ discriminating power (before removal of either function: 2[27]=75.01; p<0.001; N=95); after removal of the first function: 2[16]=32.63; p=0.008; N=95); after removal of second function: 2[7]=6.13; p=0.524; N=95).
The correlates’ loadings on the two discriminant functions are shown in Table 2. The most important correlates (with loadings of absolute value >0.30) for whether respondents were in the most-symptomatic (Group 1) versus least-symptomatic group (Group 4) were the following: having less education, consuming more grams of ethanol on average, attending alcohol rehabilitation, and having a history of non-alcohol substance abuse/dependence. Along the second dimension, respondents in Groups 2 and 3 (dependence, but mostly social, and tolerance/withdrawal symptoms) were most likely to have worked in blue-collar occupations and were younger.
The set of predictors in the analysis classified 61% of respondents correctly. Group-specific classification accuracy was 77%, 50%, 50%, and 62% for Groups 1, 2, 3, and 4, respectively. This was substantially better than chance (accuracy at chance would have been 27%, 21%, 21%, and 31%, respectively). Although the discriminant functions did not classify all respondents accurately, the canonical correlations of 0.62 for the first function (i.e., the correlation of the function with group membership) and 0.51 for the second function indicate that the set of correlates were indeed important for the ultimate assignment to the pattern-of-distress groups.
Classification stability was examined by cross-validation.15 In an additional analysis, a random sample of 25% of respondents was withheld from the calculation of the classification functions. For the 75% of respondents from which the functions were derived, 60% were correctly classified. For the remaining 25% of respondents, 54% were correctly classified. These results indicate that the classification had very good consistency and, hence, generalizability.
Alcohol Symptom-Pattern as a Predictor of Return to Drinking Posttransplant
We examined the relationship of alcohol symptom-pattern group membership to time-to-first drink posttransplant and time-to-first episode of heavy drinking (6 drinks on a single occasion). For both outcomes, Group 4 (composed predominantly of individuals with milder alcohol abuse symptoms) had a significantly longer time-to-drinking and fewer individuals who drank than the remaining three groups, whereas the remaining three groups did not differ significantly from each other. These findings are illustrated in Figure 2, which plots the curves for time-to-first episode of heavy drinking (log-rank test comparing Group 4 with other groups: log-rank values: 3.72, p=0.054; 3.26, p=0.032; 6.20, p=0.012 for comparison with Groups 1, 2, and 3, respectively). Although the curve for Group 3 appears to be substantially different from that of the other groups, with all in that cluster having drunk heavily by 1,500 days, this did not reach statistical significance, possibly because of the small numbers reaching that time-point.
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CONCLUSIONS
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The heterogeneity of psychiatric disorders has been a nosologic issue since the initial development of the DSM classifications. Although alcohol use disorders are robust and valid diagnostic categories, the polythetic design of the DSM means that there are many distinct combinations of symptoms that will result in a diagnosis. Considering all of the possible combinations, in a non-transplant sample, Grant6 found that, of the 99 possible subtypes of alcohol dependence that could emerge within the DSM approach, 70% of those with alcohol dependence could be characterized by only six subtypes of dependence. Of the 23 theoretical subtypes of alcohol abuse, 90% of those with alcohol abuse could be represented by three subtypes. This and other investigations on the taxonomy of alcohol use disorders have prompted experts in the field to consider moving from a categorical classification system to a dimensional one that uses symptom clusters,17 and there is growing sentiment for DSM–V to incorporate a dimensional approach to alcohol and other substance-use psychopathology.18
We were similarly interested in looking beyond a categorical classification of alcohol use disorders in our sample. We had hypothesized that distinct clusters would exist that would identify those with a more severe form of these disorders and predict those more likely to drink again. In our sample, we identified three specific clusters of alcohol dependence and one of alcohol abuse. Only nine participants could not be assigned to a specific cluster.
These clusters were further distinguished by pre-transplant demographic and addiction-history variables. Those in Cluster 1 were distinguished from the other clusters not only by showing higher rates of almost all types of alcohol use symptoms, but also in that they were more likely to be less educated, have a larger daily alcohol consumption pre-transplant, were more likely to have attended addiction rehabilitation, and have an additional diagnosis of a non-alcohol substance use disorder. This cluster may represent the type II alcoholic defined by Cloninger (male, early age at onset, more severe course, antisocial behavior).19 Although we did not identify Cloninger’s specific alcohol classifications, one previous report found that the type II alcoholics in their transplant cohort were more likely to have used other illicit substances in addition to alcohol before their transplant.20 Patients with these characteristics may be increasingly represented in liver transplant populations as more patients with hepatitis C infections resulting from injected drug use go on to develop end-stage liver disease and proceed to transplantation. Although we investigated the use of alcohol in our cohort, patients with other substance use comorbidities should also be clinically monitored for use of these substances after liver transplantation.
Despite the significant variation in the variety and number of alcohol symptoms endorsed by our subtypes of alcohol dependence, within the clusters of those with alcohol dependence, cluster assignment did not predict those more likely to drink. However, those assigned to the alcohol abuse cluster were significantly less likely to drink (both any at all and binge-drink) than those with alcohol dependence. This replicates our earlier work and highlights the importance of establishing the correct pre-transplant alcohol use disorder diagnosis because not all liver transplant candidates will meet diagnostic criteria for alcohol dependence.
Although a limitation of our study may have been the alcohol outcomes we chose to investigate (any alcohol and binge use), we specifically looked at thresholds of alcohol consumption commonly used in alcohol research.12 Although our sample may be considered different from cohorts at other liver transplant programs, the demographics of our group and the percentage of ALD recipients with alcohol dependence is similar to statistics reported from other large national programs.21–24
Our results suggest that the prognosis regarding continued abstinence posttransplant is much more positive for individuals with a diagnosis of abuse than for those who meet criteria for alcohol dependence. Nevertheless, all ALD liver transplant recipients should be closely monitored for alcohol consumption, maintaining an open, nonjudgmental approach and offering assistance when any alcohol use is identified.
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ACKNOWLEDGMENTS
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This research is funded by grants K23 AA0257 from the National Institute of Alcohol Abuse and Alcoholism and R01 DK066266 from the National Institute of Digestive Disorders and Kidney Diseases, Rockville, MD.
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REFERENCES
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- DiMartini A, Weinrieb R, Lane T, et al: Defining the alcoholic liver transplant population: implications for future research. Liver Transpl 2001; 7:428–431[CrossRef][Medline]
- Beresford TP: Predictive factors for alcoholic relapse in the selection of alcohol-dependent persons for hepatic transplant. Liver Transpl 1997; 3:280–291[CrossRef]
- DiMartini A, Dew MA, Javed L, et al: The pretransplant psychiatric and medical comorbidity of alcoholic liver-disease patients who received liver transplant. Psychosomatics 2004; 45:517–523[Abstract/Free Full Text]
- Perney P, Bismuth M, Sigaud H, et al: Are preoperative patterns of alcohol consumption predictive of relapse after liver transplantation for alcoholic liver disease? Transplant Int 2005; 18:1292–1297[CrossRef][Medline]
- DiMartini A, Day N, Dew MA, et al: Alcohol consumption patterns and predictors of use following liver transplantation for alcoholic liver disease. Liver Transpl (in press)
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- Sobell LC, Sobell MB: Time-Line Follow-Back: A Calendar Method for Assessing Alcohol and Drug Use (User’s Guide). Addiction Research Foundation, 1996
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- Goldman MS, Boyd GM, Faden V (eds.): National Advisory Council on Alcohol Abuse and Alcoholism Task Force on College Drinking. College Drinking, What It Is, and What To Do About It: A Review of the State of the Science. J Stud Alcohol 2002; (suppl 14)
- Romesburg HC: Cluster Analysis for Researchers. Belmont, CA, Lifetime Learning Publications, 1984
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- Cohen J: Statistical Power Analysis for the Behavioral Sciences, 2nd Edition. New York, Academic Press, 1988
- Hasin DS, Schuckit MA, Martin CS, et al: The validity of DSM-IV Alcohol Dependence: what do we know and what do we need to know? Alcohol Clin Exp Res 2003; 27:244–252[Medline]
- Krueger RF, Markon KE, Patrick CJ, et al: Externalizing psychopathology in adulthood: a dimensional-spectrum conceptualization and its implications for DSM-V. J Abnorm Psychol 2005; 114:537–550[CrossRef][Medline]
- Cloninger CR: Neurogenetic adaptive mechanisms in alcoholism. Science 1987; 236:410–416[Abstract/Free Full Text]
- Coffman K, Hoffman A, Sher IL, et al: Treatment of the postoperative alcoholic liver transplant recipient with other addictions. Liver Transpl 1997; 3:322–327[CrossRef]
- Belle SH, Beringer KC, Deter KM: Liver transplantation for alcoholic liver disease in the United States, 1988 to 1995. Liver Transpl 1997; 3:212–219[CrossRef]
- Beresford TP: Alcoholism prognosis scale for major organ transplant candidates, in Psychiatric Aspects of Organ Transplant. Edited by Craven J, Rodin GM. Oxford, UK, Oxford Univ. Press, 1992, pp 31-32
- Gish RG, Lee AH, Keefe EB, et al: Liver transplantation for patients with alcoholism and end-stage liver disease. Am J Gastroenterol 1993; 88:1337–1342[Medline]
- Lucey MR, Merion RM, Henley KS, et al: Selection for and outcome of liver transplantation in alcoholic liver disease. Gastroenterology 1992; 102:1736–1741[Medline]
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ABSTRACT
INTRODUCTION
