Research Report European Addiction Research
Eur Addict Res 2015;21:19–30 DOI: 10.1159/000358194
Received: June 17, 2013 Accepted: December 23, 2013 Published online: October 24, 2014
Predictors of Posttreatment Drinking Outcomes in Patients with Alcohol Dependence Gerardo Flórez a, e Pilar A. Saiz c, e Paz García-Portilla c, e Francisco J. De Cos d Sonia Dapía b Sandra Álvarez a Luis Nogueiras a Julio Bobes c, e a
Addiction Treatment Unit, Department of Psychiatry CHUO, Galician Health System, and b Licentia, Ourense, and Departments of c Psychiatry and d Mining Exploitation and Prospecting, University of Oviedo, and e Centro de Investigación Biomédica en Red de Salud Mental, Oviedo, Spain
Key Words Alcohol dependence · Predictive factors · Outcome · Treatment · Follow-up · Genetic algorithms · Multivariate adaptive regression splines
Abstract Aim: This cohort study examined how predictors of alcohol dependence treatment outcomes work together over time by comparing pretreatment and posttreatment predictors. Methods: A sample of 274 alcohol-dependent patients was recruited and assessed at baseline, 6 months after treatment initiation (end of the active intervention phase), and 18 months after treatment initiation (end of the 12-month research follow-up phase). At each assessment point, the participants completed a battery of standardized tests [European Addiction Severity Index (EuropASI), Obsessive Compulsive Drinking Scale (OCDS), Alcohol Timeline Followback (TLFB), Fagerström, and International Personality Disorder Examination (IPDE)] that measured symptom severity and consequences; biological markers of alcohol consumption were also tested at each assessment point. A sequential strategy with univariate and multivariate analyses was used to identify how pretreatment and posttreatment predictors
© 2014 S. Karger AG, Basel 1022–6877/14/0211–0019$39.50/0 E-Mail [email protected] www.karger.com/ear
influence outcomes up to 1 year after treatment. Results: Pretreatment variables had less predictive power than posttreatment ones. OCDS scores and biological markers of alcohol consumption were the most significant variables for the prediction of posttreatment outcomes. Prior pharmacotherapy treatment and relapse prevention interventions were also associated with posttreatment outcomes. Conclusions: The findings highlight the positive impact of pharmacotherapy during the first 6 months after treatment initiation and of relapse prevention during the first year after treatment and how posttreatment predictors are more important than pretreatment predictors. © 2014 S. Karger AG, Basel
Introduction
Many studies have been conducted to determine whether treatment for alcohol dependence is effective, whether different treatments are more or less effective, and whether certain patients are more likely to benefit from a specific treatment (i.e. treatment matching). Multiple factors have been positively and negatively associated with resumption of alcohol consumption and outGerardo Flórez Unidad de Conductas Adictivas, Hospital Santa María Nai CHUO, Ramón Puga 52–56 ES–32005 Ourense (Spain) E-Mail gerardof @ mundo-r.com
comes in treated and untreated alcohol-dependent patients [1]. Knowledge of the influence of pre- and posttreatment factors on outcomes is a key aspect of treatment planning.
Pretreatment Predictors
In previous studies, poorer outcomes, understood as greater relapse and lower abstinence rates, have been predicted by: a more severe alcohol dependence [2–4], more psychiatric symptoms [5], a higher prior level of alcohol consumption [3, 4], a younger age at the onset of alcohol dependence [1], more lifetime drinking problems [1], more previous treatments [6, 7], comorbid drug use [8], and a family history of alcoholism [6, 7]. Better outcomes, understood as lower relapse and greater abstinence rates, have been predicted by: being employed [3], having family support [4, 5, 9, 10], female gender [3], older age [3], and more years of schooling [11].
Posttreatment Predictors
In previous studies, poorer outcomes have been predicted by: depressive symptoms [7, 12], a lack of self-efficacy [13], poor coping skills [13], a more frequent consumption of alcohol when remitted [13–15], and more reliance on alcohol to reduce tension [16]. Better outcomes have been predicted by: more participation in treatment or a longer duration of care [10, 11, 17], use of medications like naltrexone or psychotherapy like cognitive behavioral interventions [18], better social adjustment [17], and more reliance on approach coping and less reliance on avoidance coping [16, 17].
Outcome Assessment
Most studies have focused only on ultimate variables (distal dimensions that are supposed to improve as a result of treatment) [19]. Alcohol consumption, abstinence, or reduction of the quantity of alcohol consumed have been the primary outcome variables [14, 19–21]; other outcome variables, such as physiological markers of drinking, the severity of the drinking problem, dependence symptoms, global improvement, cravings, quality of life, and psychosocial functioning, have been used as secondary variables [19–22]. Alcohol consumption is often assessed using self-reports. This method has gener20
Eur Addict Res 2015;21:19–30 DOI: 10.1159/000358194
ally proven to be accurate if patients are alcohol free at the time of the assessment, although some experts see corroboration of the respondent’s self-reports as desirable [23, 24]. Combining self-reporting with biological indicators is also recommended [25]. In this study, we explored the relationship between pretreatment predictors, posttreatment predictors, and outcome variables, extending the existing research. Our hypothesis was that variables related to treatment would be best for predicting outcomes in treated alcohol-dependent patients.
Methods Subjects This cohort study was conducted at an outpatient addiction treatment clinic that receives alcohol-related referrals from the local public health system. Patients received detoxification (mainly on an outpatient basis as only 7 of them underwent inpatient detoxification) and dishabituation interventions for 6 months (active intervention phase) and were then followed for 12 more months (research follow-up phase). Participants were individuals who sought treatment for alcohol use disorders. They were assessed using the European Addiction Severity Index (EuropASI) [26] and the Alcohol Timeline Followback (TLFB) [27], and a breath alcohol level of zero and a negative urine drug screen were required before signing of the consent form or completion of any measures. All patients who came to the clinic seeking treatment for alcohol-related problems from January 2007 to May 2009 were screened. Of the 890 patients screened, 277 fulfilled the inclusion and exclusion criteria and 274 were enrolled into this study, as 3 refused to give full written informed consent. The inclusion and exclusion criteria are summarized in table 1. This study was approved by the local ethics committee. Written informed consent was obtained from all subjects enrolled into this study after the procedures had been fully explained. This study was subject to and in compliance with the Spanish national legislation. It was conducted according to the provisions of the World Medical Association Declaration of Helsinki and received institutional approval. Treatment conditions After detoxification, pharmacotherapy and psychotherapy interventions were started and continued during the active intervention phase. Both interventions have been previously described [28, 29]. The following dosing regime was used: 91 patients were prescribed one 50-mg naltrexone tablet once daily, 91 patients were prescribed topiramate starting at a dosage of one 50-mg tablet once daily and increased by 50 mg every 7 days until a daily dose of 200 mg was reached, and finally 92 patients were prescribed one 100mg amisulpride tablet once daily. Medications were discontinued 6 months after they were started, and patients were offered only relapse prevention appointments every 2 weeks during the following 12 months (research follow-up phase). Adherence to treatment, including subjects who came to the appointments and dropouts, were carefully recorded while the interventions were taking place.
Flórez /Saiz /García-Portilla /De Cos / Dapía /Álvarez /Nogueiras /Bobes
Table 1. Inclusion and exclusion criteria
Inclusion criteria – ICD-10 criteria for alcohol dependence1 (25% excluded, 4.07%) – Ethanol intake, during the 6 months before detoxification, of at least 210 g/week for men and 140 g/week for women2 – Expression of a desire to stop drinking alcohol (46 excluded, 7.5%) Exclusion criteria – Less than 18 or more than 65 years of age (78 excluded, 12.72%) – A current diagnosis of dependence or abuse of other substances except nicotine (128 excluded, 20.88%) – A current psychiatric diagnosis other than personality disorders (150 excluded, 24.46%) – Any clinically significant medical condition that in the opinion of the researchers would adversely affect safety or study participation (101 excluded, 16.47%) – Inability to give full informed consent (0 excluded) – Not speaking Spanish (3 excluded, 0.52%) – A clinical history of mental retardation (5 excluded, 0.52%) – Pregnancy or breast-feeding (0 excluded) – Not having a significant other, i.e. a stable family or social situation, to provide accurate daily alcohol-related information to the researchers (77 excluded, 12.56%)
were dropouts from research follow-up. When needed, dropouts were contacted by telephone or through their general practitioner so that an appointment could be arranged for collection of data only. Outcome variables, according to previous research [19–21], and predictors of outcomes are summarized in table 2. Basically, they were organized into baseline outcome variables, 6-month outcome variables, 18-month outcome variables, and predictors of outcomes.
Procedures Patients completed a baseline assessment that included the following instruments, all validated and published for use in the Spanish population: (1) EuropASI [28], (2) TLFB [29], (3) Obsessive Compulsive Drinking Scale (OCDS) [30], (4) Fagerström test for nicotine dependence [31], and (5) International Personality Disorder Examination (IPDE) [32]. The baseline assessment also included blood tests to measure the following biological markers of alcohol consumption [33]: γ-glutamyltransferase (GGT), serum aspartate aminotransferase (AST), serum alanine aminotransferase (ALT), the mean corpuscular volume (MCV), and the AST/ ALT ratio. When the active intervention phase ended, 6 months after the start of treatment, patients were assessed again using the following instruments: EuropASI, TLFB, OCDS, and Fagerström. Assessment also included repeated blood tests. Dropouts (27; 9.85%), defined as patients who stopped coming to appointments for at least 1 month, were also considered an outcome variable. Thus, these were dropouts from active intervention. When the research follow-up ended, 18 months after the start of treatment and 12 months after the end of the active intervention phase, patients were assessed again using the same instruments and blood tests. At that time, dropouts (118; 43.96%; 17 of them were also dropouts at the active intervention phase), defined as patients who stopped coming to appointments for at least 2 months, were again considered an outcome variable. Thus, these
Analytical Strategy Data was collected by researchers blinded to the treatment procedures. The data quality, including double data entry, was supervised by a database coordinator at Licentia Ltd. (a private provider of health-related technology). Individual patient plots were checked for unusual values and completeness. We ascertained that all patients who finished this study had an answer for all of the variables required so that we did not have any missing data. A sequential strategy was chosen for this study. An independent univariate comparison was performed to exclude those predictors of outcomes that were uncorrelated to the outcome variables (p ≥ 0.001). The Pearson product-moment correlation coefficient was calculated for all of the predictors, and all of those in which the null hypothesis was not rejected (no correlation with any of the outcome variables) were discarded. The following variables were excluded: all sociodemographic variables except age and gender, all alcohol-related variables, and IPDE scores. This variable preselection was done in order to improve the subsequent multivariate data processing as the presence of features that contain irrelevant information may cause problems in subsequent steps of the process. In the case of genetic algorithms (GA), this preselection considerably reduced the computational cost of the GA technique. The following two multivariate analysis techniques were applied to the remaining variables. Dimensional Reduction by Means of Genetic Algorithms. Due to the large number of variables involved (a total of 69), it was necessary to use a mathematical technique able to perform a dimensional reduction of the number of variables that affected each of our output variables. Although the variable selection problem can always be solved by exhaustively examining all possible combinations of variables [34], there is a class of algorithms that attempt to mimic natural selection to arrive at a variable combination producing optimal results for a particular problem. These are known as GA and were introduced by Holland [35] in 1975. In the present research, we used a package called subselect [36] from the statistical software R [37]. Simply stated, given a data set, the GA seeks a k-variable subset that is optimal with respect to a given criterion. An initial population of k-variable subsets is randomly selected from the full set of p variables. The size of this initial population is specified by a function argument called popsize. In each iteration, a number of couples equal to the total population divided by 2 are formed. Each couple generates a child that inherits the variables common to both parents and completes the list of variables with a random selection of the variables that belonged to one of them. In our case, after this mix, each offspring underwent a mutation with a probability that was defined as 10%. Mutation means that those variables that were not part of their parents can be randomly included in the variable subset of some offspring. Finally, all of the population members are ranked and the best of them will make up
Posttreatment Outcomes in Alcohol Dependence
Eur Addict Res 2015;21:19–30 DOI: 10.1159/000358194
ICD-10 = International Classification of Diseases, 10th revision. 1 Diagnosed by an experienced psychiatrist (ICD-10 criteria are mandatory in the Spanish National Health System). 2 Limits for low-risk ethanol consumption set by the World Health Organization.
21
Table 2. Outcome variables and predictors of outcomes
Baseline outcome variables
Six-month outcome variables
Eighteen-month outcome variables
Predictors of outcomes (18 months)
Age Gender Marital status Living situation Educational level Occupational status Monthly income Alcohol dependence age of onset Duration of alcohol dependence Prior treatments Positive family history of alcohol dependence Alcohol use before treatment1 IPDE scores OCDS scores EuropASI scores Fagerström score GGT AST ALT AST/ALT ratio MCV
OCDS scores EuropASI scores Fagerström score GGT AST ALT AST/ALT ratio MCV Abstinence Abstinence plus moderate drinking Number of heavy drinking days1, 2 Total drinking days1 Days to the first drink1 Drinks per drinking day (1 drink = 10 g ethanol)1 Dropouts Psychopharmacological treatment3
Dropouts
Good clinical response: abstinence plus moderate drinking without problems4 PSNHDD2 GGT
1
Previous 90 days: number of heavy drinking days, total drinking days, percentage of days abstinent, and drinks per drinking day. More than 60 g ethanol/day. 3 Naltrexone or topiramate or amisulpride. 4 The reported drinking was less than 40 g ethanol/day for men and less than 30 for women, with no more than 2 days of heavier drinking reported. No problems were reported. 2
the next generation, which is used as the current population in the subsequent iteration. The process continues until either the maximum number of generations that was specified is reached or the convergence criterion is reached. The use of GA [38] in previous research has proven that this is a very convenient technique for problems where a reduction in the number of variables involved (dimensional reduction) is required. Other researchers have already formulated a medical disease prediction as a pattern classification problem based on a set of clinical and laboratory parameters [39]. These are the main reasons why we applied GA in the present research. Training and Validation of a Multivariate Adaptive Regression Splines Model. Multivariate adaptive regression splines (MARS) is a multivariate nonparametric regression technique introduced by Friedman [40, 41] in 1991. The main purpose of the MARS model is to predict the values of continuous dependent variables from a set of independent explanatory variables. MARS does not require any a priori assumptions about the underlying functional relationship between dependent and independent variables [42]. Models were trained using as input variables the reduced subsets obtained in step 1 for each of the output variables. Once the MARS models had been trained, it was possible to evaluate the importance of the
22
Eur Addict Res 2015;21:19–30 DOI: 10.1159/000358194
explanatory variables used to construct these models. As documented in the literature [43], establishing predictor importance is a complex problem that generally requires the use of more than 1 criterion. To obtain reliable results, in the present research 3 common criteria [43] were used: NSubsets (counts the number of model subsets in which each variable is included), generalized crossvalidation (GCV; i.e. the mean squared residual error divided by a penalty dependent on model complexity), and the residual sum of squares (RSS; measured on the training data). This second step of the algorithm is in line with the principle of parsimony as it allowed us to build a model of each output variable using fewer input variables. A MARS model was used in the present research due to multiple advantages, i.e. it is more flexible than linear regression models, it is simpler to understand and interpret than other common artificial intelligence models, it often requires little or no data preparation (the hinge functions automatically partition the input data, so the effect of outliers is contained; in this respect, MARS is similar to recursive partitioning, which also partitions the data into disjoint regions, though using a different method), it is able to perform automatic variable selection, and it tends to have a good biasvariance trade-off. The models are flexible enough to model non-
Flórez /Saiz /García-Portilla /De Cos / Dapía /Álvarez /Nogueiras /Bobes
linearity and variable interactions (thus MARS models have a fairly low bias), yet the constrained form of MARS basis functions prevents too much flexibility (thus MARS models have a fairly low variance). The MARS model of a dependent variable → y with M basis functions (terms) can be written as follows [42]: M G ˆyG ˆf xG c cm Bm x , 0 M m 1
→ y is
where the dependent variable predicted by the MARS model, c0 is a constant, Bm (x→) is the m-th basis function, which may be a single spline basis function, and cm is the coefficient of the m-th basis functions. Note that the definition of function h(x) is as follows: h(x) = x ↔ x > 0 and h(x) = 0 ↔ ≤ 0. The adjusted R2 value of the linear model employed is calculated using the R2 value of the model, taking into account the number of cases (n) and variables (p) used for building the model according to the following equation: n 1 2 Adj. R 2 1 1 Rsq.
ⴢ n p 1. In the present research, the adjusted R2 value was used instead of simply the R2 due to the large number of variables involved. As is well known, using an infinite number of independent variables to explain the change in a dependent variable would result in an R2 value of 1. To compare all of the models (GA and MARS) in view of the number of variables, the adjusted R2 value is used as the main performance indicator.
Results
Table 3. Sociodemographics and alcohol-related pretreatment
variables Age, years Male gender Married Living with family High school graduates Regularly employed Monthly income
Eur Addict Res 2015;21:19–30 DOI: 10.1159/000358194
Received: June 17, 2013 Accepted: December 23, 2013 Published online: October 24, 2014
Predictors of Posttreatment Drinking Outcomes in Patients with Alcohol Dependence Gerardo Flórez a, e Pilar A. Saiz c, e Paz García-Portilla c, e Francisco J. De Cos d Sonia Dapía b Sandra Álvarez a Luis Nogueiras a Julio Bobes c, e a
Addiction Treatment Unit, Department of Psychiatry CHUO, Galician Health System, and b Licentia, Ourense, and Departments of c Psychiatry and d Mining Exploitation and Prospecting, University of Oviedo, and e Centro de Investigación Biomédica en Red de Salud Mental, Oviedo, Spain
Key Words Alcohol dependence · Predictive factors · Outcome · Treatment · Follow-up · Genetic algorithms · Multivariate adaptive regression splines
Abstract Aim: This cohort study examined how predictors of alcohol dependence treatment outcomes work together over time by comparing pretreatment and posttreatment predictors. Methods: A sample of 274 alcohol-dependent patients was recruited and assessed at baseline, 6 months after treatment initiation (end of the active intervention phase), and 18 months after treatment initiation (end of the 12-month research follow-up phase). At each assessment point, the participants completed a battery of standardized tests [European Addiction Severity Index (EuropASI), Obsessive Compulsive Drinking Scale (OCDS), Alcohol Timeline Followback (TLFB), Fagerström, and International Personality Disorder Examination (IPDE)] that measured symptom severity and consequences; biological markers of alcohol consumption were also tested at each assessment point. A sequential strategy with univariate and multivariate analyses was used to identify how pretreatment and posttreatment predictors
© 2014 S. Karger AG, Basel 1022–6877/14/0211–0019$39.50/0 E-Mail [email protected] www.karger.com/ear
influence outcomes up to 1 year after treatment. Results: Pretreatment variables had less predictive power than posttreatment ones. OCDS scores and biological markers of alcohol consumption were the most significant variables for the prediction of posttreatment outcomes. Prior pharmacotherapy treatment and relapse prevention interventions were also associated with posttreatment outcomes. Conclusions: The findings highlight the positive impact of pharmacotherapy during the first 6 months after treatment initiation and of relapse prevention during the first year after treatment and how posttreatment predictors are more important than pretreatment predictors. © 2014 S. Karger AG, Basel
Introduction
Many studies have been conducted to determine whether treatment for alcohol dependence is effective, whether different treatments are more or less effective, and whether certain patients are more likely to benefit from a specific treatment (i.e. treatment matching). Multiple factors have been positively and negatively associated with resumption of alcohol consumption and outGerardo Flórez Unidad de Conductas Adictivas, Hospital Santa María Nai CHUO, Ramón Puga 52–56 ES–32005 Ourense (Spain) E-Mail gerardof @ mundo-r.com
comes in treated and untreated alcohol-dependent patients [1]. Knowledge of the influence of pre- and posttreatment factors on outcomes is a key aspect of treatment planning.
Pretreatment Predictors
In previous studies, poorer outcomes, understood as greater relapse and lower abstinence rates, have been predicted by: a more severe alcohol dependence [2–4], more psychiatric symptoms [5], a higher prior level of alcohol consumption [3, 4], a younger age at the onset of alcohol dependence [1], more lifetime drinking problems [1], more previous treatments [6, 7], comorbid drug use [8], and a family history of alcoholism [6, 7]. Better outcomes, understood as lower relapse and greater abstinence rates, have been predicted by: being employed [3], having family support [4, 5, 9, 10], female gender [3], older age [3], and more years of schooling [11].
Posttreatment Predictors
In previous studies, poorer outcomes have been predicted by: depressive symptoms [7, 12], a lack of self-efficacy [13], poor coping skills [13], a more frequent consumption of alcohol when remitted [13–15], and more reliance on alcohol to reduce tension [16]. Better outcomes have been predicted by: more participation in treatment or a longer duration of care [10, 11, 17], use of medications like naltrexone or psychotherapy like cognitive behavioral interventions [18], better social adjustment [17], and more reliance on approach coping and less reliance on avoidance coping [16, 17].
Outcome Assessment
Most studies have focused only on ultimate variables (distal dimensions that are supposed to improve as a result of treatment) [19]. Alcohol consumption, abstinence, or reduction of the quantity of alcohol consumed have been the primary outcome variables [14, 19–21]; other outcome variables, such as physiological markers of drinking, the severity of the drinking problem, dependence symptoms, global improvement, cravings, quality of life, and psychosocial functioning, have been used as secondary variables [19–22]. Alcohol consumption is often assessed using self-reports. This method has gener20
Eur Addict Res 2015;21:19–30 DOI: 10.1159/000358194
ally proven to be accurate if patients are alcohol free at the time of the assessment, although some experts see corroboration of the respondent’s self-reports as desirable [23, 24]. Combining self-reporting with biological indicators is also recommended [25]. In this study, we explored the relationship between pretreatment predictors, posttreatment predictors, and outcome variables, extending the existing research. Our hypothesis was that variables related to treatment would be best for predicting outcomes in treated alcohol-dependent patients.
Methods Subjects This cohort study was conducted at an outpatient addiction treatment clinic that receives alcohol-related referrals from the local public health system. Patients received detoxification (mainly on an outpatient basis as only 7 of them underwent inpatient detoxification) and dishabituation interventions for 6 months (active intervention phase) and were then followed for 12 more months (research follow-up phase). Participants were individuals who sought treatment for alcohol use disorders. They were assessed using the European Addiction Severity Index (EuropASI) [26] and the Alcohol Timeline Followback (TLFB) [27], and a breath alcohol level of zero and a negative urine drug screen were required before signing of the consent form or completion of any measures. All patients who came to the clinic seeking treatment for alcohol-related problems from January 2007 to May 2009 were screened. Of the 890 patients screened, 277 fulfilled the inclusion and exclusion criteria and 274 were enrolled into this study, as 3 refused to give full written informed consent. The inclusion and exclusion criteria are summarized in table 1. This study was approved by the local ethics committee. Written informed consent was obtained from all subjects enrolled into this study after the procedures had been fully explained. This study was subject to and in compliance with the Spanish national legislation. It was conducted according to the provisions of the World Medical Association Declaration of Helsinki and received institutional approval. Treatment conditions After detoxification, pharmacotherapy and psychotherapy interventions were started and continued during the active intervention phase. Both interventions have been previously described [28, 29]. The following dosing regime was used: 91 patients were prescribed one 50-mg naltrexone tablet once daily, 91 patients were prescribed topiramate starting at a dosage of one 50-mg tablet once daily and increased by 50 mg every 7 days until a daily dose of 200 mg was reached, and finally 92 patients were prescribed one 100mg amisulpride tablet once daily. Medications were discontinued 6 months after they were started, and patients were offered only relapse prevention appointments every 2 weeks during the following 12 months (research follow-up phase). Adherence to treatment, including subjects who came to the appointments and dropouts, were carefully recorded while the interventions were taking place.
Flórez /Saiz /García-Portilla /De Cos / Dapía /Álvarez /Nogueiras /Bobes
Table 1. Inclusion and exclusion criteria
Inclusion criteria – ICD-10 criteria for alcohol dependence1 (25% excluded, 4.07%) – Ethanol intake, during the 6 months before detoxification, of at least 210 g/week for men and 140 g/week for women2 – Expression of a desire to stop drinking alcohol (46 excluded, 7.5%) Exclusion criteria – Less than 18 or more than 65 years of age (78 excluded, 12.72%) – A current diagnosis of dependence or abuse of other substances except nicotine (128 excluded, 20.88%) – A current psychiatric diagnosis other than personality disorders (150 excluded, 24.46%) – Any clinically significant medical condition that in the opinion of the researchers would adversely affect safety or study participation (101 excluded, 16.47%) – Inability to give full informed consent (0 excluded) – Not speaking Spanish (3 excluded, 0.52%) – A clinical history of mental retardation (5 excluded, 0.52%) – Pregnancy or breast-feeding (0 excluded) – Not having a significant other, i.e. a stable family or social situation, to provide accurate daily alcohol-related information to the researchers (77 excluded, 12.56%)
were dropouts from research follow-up. When needed, dropouts were contacted by telephone or through their general practitioner so that an appointment could be arranged for collection of data only. Outcome variables, according to previous research [19–21], and predictors of outcomes are summarized in table 2. Basically, they were organized into baseline outcome variables, 6-month outcome variables, 18-month outcome variables, and predictors of outcomes.
Procedures Patients completed a baseline assessment that included the following instruments, all validated and published for use in the Spanish population: (1) EuropASI [28], (2) TLFB [29], (3) Obsessive Compulsive Drinking Scale (OCDS) [30], (4) Fagerström test for nicotine dependence [31], and (5) International Personality Disorder Examination (IPDE) [32]. The baseline assessment also included blood tests to measure the following biological markers of alcohol consumption [33]: γ-glutamyltransferase (GGT), serum aspartate aminotransferase (AST), serum alanine aminotransferase (ALT), the mean corpuscular volume (MCV), and the AST/ ALT ratio. When the active intervention phase ended, 6 months after the start of treatment, patients were assessed again using the following instruments: EuropASI, TLFB, OCDS, and Fagerström. Assessment also included repeated blood tests. Dropouts (27; 9.85%), defined as patients who stopped coming to appointments for at least 1 month, were also considered an outcome variable. Thus, these were dropouts from active intervention. When the research follow-up ended, 18 months after the start of treatment and 12 months after the end of the active intervention phase, patients were assessed again using the same instruments and blood tests. At that time, dropouts (118; 43.96%; 17 of them were also dropouts at the active intervention phase), defined as patients who stopped coming to appointments for at least 2 months, were again considered an outcome variable. Thus, these
Analytical Strategy Data was collected by researchers blinded to the treatment procedures. The data quality, including double data entry, was supervised by a database coordinator at Licentia Ltd. (a private provider of health-related technology). Individual patient plots were checked for unusual values and completeness. We ascertained that all patients who finished this study had an answer for all of the variables required so that we did not have any missing data. A sequential strategy was chosen for this study. An independent univariate comparison was performed to exclude those predictors of outcomes that were uncorrelated to the outcome variables (p ≥ 0.001). The Pearson product-moment correlation coefficient was calculated for all of the predictors, and all of those in which the null hypothesis was not rejected (no correlation with any of the outcome variables) were discarded. The following variables were excluded: all sociodemographic variables except age and gender, all alcohol-related variables, and IPDE scores. This variable preselection was done in order to improve the subsequent multivariate data processing as the presence of features that contain irrelevant information may cause problems in subsequent steps of the process. In the case of genetic algorithms (GA), this preselection considerably reduced the computational cost of the GA technique. The following two multivariate analysis techniques were applied to the remaining variables. Dimensional Reduction by Means of Genetic Algorithms. Due to the large number of variables involved (a total of 69), it was necessary to use a mathematical technique able to perform a dimensional reduction of the number of variables that affected each of our output variables. Although the variable selection problem can always be solved by exhaustively examining all possible combinations of variables [34], there is a class of algorithms that attempt to mimic natural selection to arrive at a variable combination producing optimal results for a particular problem. These are known as GA and were introduced by Holland [35] in 1975. In the present research, we used a package called subselect [36] from the statistical software R [37]. Simply stated, given a data set, the GA seeks a k-variable subset that is optimal with respect to a given criterion. An initial population of k-variable subsets is randomly selected from the full set of p variables. The size of this initial population is specified by a function argument called popsize. In each iteration, a number of couples equal to the total population divided by 2 are formed. Each couple generates a child that inherits the variables common to both parents and completes the list of variables with a random selection of the variables that belonged to one of them. In our case, after this mix, each offspring underwent a mutation with a probability that was defined as 10%. Mutation means that those variables that were not part of their parents can be randomly included in the variable subset of some offspring. Finally, all of the population members are ranked and the best of them will make up
Posttreatment Outcomes in Alcohol Dependence
Eur Addict Res 2015;21:19–30 DOI: 10.1159/000358194
ICD-10 = International Classification of Diseases, 10th revision. 1 Diagnosed by an experienced psychiatrist (ICD-10 criteria are mandatory in the Spanish National Health System). 2 Limits for low-risk ethanol consumption set by the World Health Organization.
21
Table 2. Outcome variables and predictors of outcomes
Baseline outcome variables
Six-month outcome variables
Eighteen-month outcome variables
Predictors of outcomes (18 months)
Age Gender Marital status Living situation Educational level Occupational status Monthly income Alcohol dependence age of onset Duration of alcohol dependence Prior treatments Positive family history of alcohol dependence Alcohol use before treatment1 IPDE scores OCDS scores EuropASI scores Fagerström score GGT AST ALT AST/ALT ratio MCV
OCDS scores EuropASI scores Fagerström score GGT AST ALT AST/ALT ratio MCV Abstinence Abstinence plus moderate drinking Number of heavy drinking days1, 2 Total drinking days1 Days to the first drink1 Drinks per drinking day (1 drink = 10 g ethanol)1 Dropouts Psychopharmacological treatment3
Dropouts
Good clinical response: abstinence plus moderate drinking without problems4 PSNHDD2 GGT
1
Previous 90 days: number of heavy drinking days, total drinking days, percentage of days abstinent, and drinks per drinking day. More than 60 g ethanol/day. 3 Naltrexone or topiramate or amisulpride. 4 The reported drinking was less than 40 g ethanol/day for men and less than 30 for women, with no more than 2 days of heavier drinking reported. No problems were reported. 2
the next generation, which is used as the current population in the subsequent iteration. The process continues until either the maximum number of generations that was specified is reached or the convergence criterion is reached. The use of GA [38] in previous research has proven that this is a very convenient technique for problems where a reduction in the number of variables involved (dimensional reduction) is required. Other researchers have already formulated a medical disease prediction as a pattern classification problem based on a set of clinical and laboratory parameters [39]. These are the main reasons why we applied GA in the present research. Training and Validation of a Multivariate Adaptive Regression Splines Model. Multivariate adaptive regression splines (MARS) is a multivariate nonparametric regression technique introduced by Friedman [40, 41] in 1991. The main purpose of the MARS model is to predict the values of continuous dependent variables from a set of independent explanatory variables. MARS does not require any a priori assumptions about the underlying functional relationship between dependent and independent variables [42]. Models were trained using as input variables the reduced subsets obtained in step 1 for each of the output variables. Once the MARS models had been trained, it was possible to evaluate the importance of the
22
Eur Addict Res 2015;21:19–30 DOI: 10.1159/000358194
explanatory variables used to construct these models. As documented in the literature [43], establishing predictor importance is a complex problem that generally requires the use of more than 1 criterion. To obtain reliable results, in the present research 3 common criteria [43] were used: NSubsets (counts the number of model subsets in which each variable is included), generalized crossvalidation (GCV; i.e. the mean squared residual error divided by a penalty dependent on model complexity), and the residual sum of squares (RSS; measured on the training data). This second step of the algorithm is in line with the principle of parsimony as it allowed us to build a model of each output variable using fewer input variables. A MARS model was used in the present research due to multiple advantages, i.e. it is more flexible than linear regression models, it is simpler to understand and interpret than other common artificial intelligence models, it often requires little or no data preparation (the hinge functions automatically partition the input data, so the effect of outliers is contained; in this respect, MARS is similar to recursive partitioning, which also partitions the data into disjoint regions, though using a different method), it is able to perform automatic variable selection, and it tends to have a good biasvariance trade-off. The models are flexible enough to model non-
Flórez /Saiz /García-Portilla /De Cos / Dapía /Álvarez /Nogueiras /Bobes
linearity and variable interactions (thus MARS models have a fairly low bias), yet the constrained form of MARS basis functions prevents too much flexibility (thus MARS models have a fairly low variance). The MARS model of a dependent variable → y with M basis functions (terms) can be written as follows [42]: M G ˆyG ˆf xG c cm Bm x , 0 M m 1
→ y is
where the dependent variable predicted by the MARS model, c0 is a constant, Bm (x→) is the m-th basis function, which may be a single spline basis function, and cm is the coefficient of the m-th basis functions. Note that the definition of function h(x) is as follows: h(x) = x ↔ x > 0 and h(x) = 0 ↔ ≤ 0. The adjusted R2 value of the linear model employed is calculated using the R2 value of the model, taking into account the number of cases (n) and variables (p) used for building the model according to the following equation: n 1 2 Adj. R 2 1 1 Rsq.
ⴢ n p 1. In the present research, the adjusted R2 value was used instead of simply the R2 due to the large number of variables involved. As is well known, using an infinite number of independent variables to explain the change in a dependent variable would result in an R2 value of 1. To compare all of the models (GA and MARS) in view of the number of variables, the adjusted R2 value is used as the main performance indicator.
Results
Table 3. Sociodemographics and alcohol-related pretreatment
variables Age, years Male gender Married Living with family High school graduates Regularly employed Monthly income
