Effectiveness, response, and dropout of dialectical behavior therapy for borderline personality disorder in an inpatient setting

Behaviour Research and Therapy 51 (2013) 411e416

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Effectiveness, response, and dropout of dialectical behavior therapy for borderline personality disorder in an inpatient setting Christoph Kröger a, *, Susanne Harbeck b, Michael Armbrust b, Sören Kliem a a b

Technical University Brunswick, Department of Psychology, Humboldtstraße 33, 38106 Brunswick, Germany Schön Klinik Bad Bramstedt, Birkenweg 10, 24576 Bad Bramstedt, Germany

a r t i c l e i n f o

a b s t r a c t

Article history: Received 11 December 2012 Received in revised form 23 April 2013 Accepted 26 April 2013

To examine the effectiveness of dialectical behavior therapy for inpatients with borderline personality disorder (BPD), small sample sizes and, predominantly, tests of statistical significance have been used so far. We studied 1423 consecutively admitted individuals with BPD, who were seeking a 3-month inpatient treatment. They completed the Borderline Symptom List (BSL) as the main outcome measure, and other self-rating measures at pre- and post-treatment. Therapy outcome was defined in three ways: effect size (ES), response based on the reliable change index, and remission compared to the general population symptom level. Non-parametric conditional inference trees were used to predict dropouts. In the pre-post comparison of the BSL, the ES was 0.54 (95% CI: 0.49e0.59). The response rate was 45%; 31% remained unchanged, and 11% deteriorated. Approximately 15% showed a symptom level equivalent to that of the general population. A further 10% of participants dropped out. A predictive impact on dropout was demonstrated by substance use disorders and a younger age at pre-treatment. In future research, follow-up assessments should be conducted to investigate the extent to which response and remission rates at post-treatment remain stable over time. A consistent definition of response appears to be essential for cross-study and cross-methodological comparisons. Ó 2013 Elsevier Ltd. All rights reserved.

Keywords: Borderline personality disorder Dialectical behavior therapy Effectiveness Dropout Clinical significance

Dialectical behavior therapy (DBT; Linehan, 1993a, 1993b) is currently the most frequently investigated psychosocial intervention for borderline personality disorder (BPD). The four core elements of DBT are individual therapy, which takes place once a week; weekly skills training within the group; telephone coaching by the individual therapist; and supervision for the therapeutic team (Linehan, 1993a, 1993b). The treatment concept was originally conceived on an outpatient basis, but has been adapted to the inpatient setting (Swenson, Sanderson, Duilt, & Linehan, 2001). The short- and long-term effectiveness of inpatient DBT was shown by various work groups (Bohus et al., 2004; Fassbinder et al., 2007; Höschel, 2006; Kleindienst et al., 2008; Kröger et al., 2006; Simpson et al., 2004). For inpatient DBT, moderate to large effect sizes emerged at the end of treatment with regard to self-reported, general, or depressive symptom severity (ES ¼ 0.56 to 0.84 and ES ¼ 0.59 to 1.90, respectively), and large effect sizes were found with regard to psychosocial functioning as rated by others (ES ¼ 0.80e1.33). However, the results of these studies are based on

* Corresponding author. Tel.: þ49 (0)531 391 2866; fax: þ49 (0)531 391 8195. E-mail address: [email protected] (C. Kröger). 0005-7967/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.brat.2013.04.008

relatively small samples (N ¼ 20 to N ¼ 50), which, moreover, were treated in university establishments. To date, mean value comparisons and effect sizes as a benchmark for assessing the effectiveness of a treatment are predominant in the publications on DBT, whereas the clinical significance enables an individual assessment of the change status (cf. Jacobson, Roberts, Berns & McGlinchey, 1999). Using the parameters of clinical significance, it can be determined whether, at post-treatment, a patient has reliably deteriorated or improved (response), or whether the symptom level has adjusted to that of a clinically unimpaired sample (remission). Only in one completer sample (N ¼ 31) was the clinical significance indicated in addition to the ES of 0.84 following a three-month inpatient DBT treatment: According to this, 42% of patients at post-treatment (Bohus et al., 2004) and 50% within 21 months after the end of therapy (Kleindienst et al., 2008) were remitted in terms of general symptom strain. One of the main aims of DBT is to lower dropout rates (Linehan, 1993a), even though no significant difference in the mean dropout rates between DBT (24.7%) and control conditions (27.3%) was found in a meta-analysis (Kliem, Kröger, & Kosfelder, 2010). In the face of these (partially) high dropout rates, ranging from 4.2% to

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61.1% (SD ¼ 15.6%), it seemed to be important to identify characteristics that are associated with discontinuation of treatment. To the best of our knowledge, four studies have examined differences compared to completers and predictive factors for inpatients who dropped out of DBT (Bohus et al., 2004; Kröger et al., 2006; Perroud, Uher, Dieben, Nicastro, & Huguelet, 2010; Rüsch et al., 2008). While no differences in any aspect were found between completers and dropouts in the Bohus et al. (2004) and Kröger et al. (2006) studies, dropouts in the Rüsch et al. (2008) study reported more trait anxiety, fewer lifetime suicide attempts, and higher experiential avoidance (without error correction for multiple testing). The latter two characteristics were both confirmed in a stepwise logistic regression as dependent variables for dropout. However, lower education was found to be the only predictive characteristic in the Perroud et al. (2010) study, which did not include those characteristics (i.e., lifetime suicide attempts and experiential avoidance) that were found in the Rüsch et al. (2008) study. These results were based on small sample sizes, ranging from 40 to 60 mostly female participants, with the exception of the Perroud et al. (2010) study, with 447 participants. In addition, low dropout rates were reported, ranging from 12% to 25.8%, with the exception of the Rüsch et al. (2008) study, with 46%. Therefore, sample sizes and dropout rates made it difficult to find any differences between completers and dropouts due to a lack of statistical power. The use of a regression analysis in the Rüsch et al. (2008) study implies a larger sample size than 60 participants, and requires a confirmation in a cross-validation analysis. Since individuals with specific cooccurring mental disorders were excluded (e.g., anorexia nervosa, substance use disorders, Bohus et al., 2004; Rüsch et al., 2008), these conditions could not be included in the analyses, even though they might also be suggested as risk factors for a discontinuation of treatment (Kröger et al., 2010; Linehan et al., 2002). Hence, results need to be confirmed and expanded in further analyses, which should be based on larger sample sizes with fewer exclusion criteria. The aim of the current study is, therefore, to use a large consecutive sample of patients admitted to a 3-month DBT program in order to draw on various parameters for assessing its effectiveness regarding disorder-specific symptom strain and further complaints. For this purpose, in particular, effect sizes should be calculated in comparison to the clinical significance through the RCI method (Jacobson & Truax, 1991). Moreover, a further aim is to identify predictors of discontinuation of therapy.

Method Participants The participants were admitted consecutively to a psychosomatic care hospital, which is certified according to DIN EN ISO 9001:2008, in the period from March 2006 to October 2011. For the diagnosis of mental disorders and personality disorders, the German versions of the Structured Clinical Interview for DSM-IV Axis I Disorders (SKID-I; Wittchen, Wunderlich, Gruschitz, & Zaudig, 1997) and for Axis II Disorders (SKID-II; Fydrich, Renneberg, Schmitz, & Wittchen, 1997) were used. All participants had to a) be over the age of 18 years, b) show no indications of mental retardation, dementia, or schizophrenia, c) show no acute symptoms of a severe organic disease that are associated with the development of the mental illness, and d) show no substance dependence with current intoxication, which would indicate an admission at a specialized unit for detoxification. Other mental disorders were not excluded. Each patient was informed about the course of the study in writing and was required to provide

consent to it. The treatment period amounted to a maximum of twelve weeks. The analysis included N ¼ 1423 patients with BPD, of whom n ¼ 1075 were women (75.5%). Table 1 shows the sociodemographic data and comorbid mental disorders. The mean age lay at 32.0 years (SD ¼ 10.27). Approximately 14% lived in a partnership. On average, each patient had 3.70 (SD ¼ 1.59) Axis I disorders and 0.90 (SD ¼ 0.6) Axis II disorders, in addition to BPD. The length of stay in the clinic amounted to an average of 63.9 days (SD ¼ 19.65). Hence, several participants were discharged with the support and permission of the therapists before the three months were over, because these patients had legal or other obligations (e.g., lawsuit, start of school) and changes in managing their daily life (e.g., admission at a therapeutic apartment-sharing community). The treatment was not ended in the standard manner by 148 (10.4%) patients (discontinuation with or without physician consent or transfer). Of these individuals, 93 did not fill out the postal survey questionnaires (missing values). Therapists and treatment The multidisciplinary teams consisted of 5 certified DBT therapists, 5 certified DBT co-therapists, and 5 DBT therapists in advanced training. Moreover, they also intermittently included physicians in training as specialists for psychosomatic medicine or for psychiatry and psychotherapy, and psychotherapists in training. Also, the teams consistently included social education workers and art and movement therapists who possessed basic knowledge of DBT through in-house and external training programs. The teams discussed the individual patients on a daily basis. Moreover, recurring structural or content-based questions were tackled at least twice yearly in a half-day workshop.

Table 1 Socio-demographic characteristics and co-occurring mental disorders (N ¼ 1423). Characteristics Marital status Single Married Divorced School education In school education No school-leaving qualification Special needs or lower-track school-leaving qualification Medium-track school-leaving qualification University-entrance-level school-leaving qualification Other Employment status Never employed In training Military service/civilian service (in lieu of military service)/ voluntary social year Housewife/househusband Pensioner Laborer Skilled worker/craftsperson Employee Civil servant Self-employed Other Prior treatments Outpatient psychiatric Outpatient psychotherapeutic Mental disorders Affective disorders Substance use disorders Anxiety disorders Somatoform disorders Eating disorders

n

%

1026 200 110

72.1 14.1 7.7

28 52 327 515 76 10

2.0 3.7 22.9 36.2 5.3 0.7

190 234 22

13.4 16.4 1.5

272 98 77 39 297 23 27 83

19.1 6.9 5.4 2.7 20.9 1.6 1.9 5.8

842 960

59.2 67.5

1363 264 588 97 527

95.8 18.6 41.3 6.9 37.1

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The teams were supervised by three team leaders who were state-recognized supervisors for behavioral therapy and certified DBT therapists. The team leaders were available three times per week for supervisory and structural matters. Once a year, a wholeday workshop with a licensed DBT supervisor took place. The inpatient concept of DBT was certified by the German Umbrella Association of DBT and is described in detail elsewhere (Armbrust & Jungbluth, 2009). At the beginning and end of treatment, a team leader and the patient establish and check treatment goals. The target hierarchy determines that skills be set up which enable an outpatient treatment. Hence, the treatment focus is on the behaviors that led to the inpatient admission, to lengthening the clinic stay, or to readmission. The therapy components were individual therapy (once a week; 50 min), the psycho-educative basic group (once a week; 50 min), the skills training in the area of distress tolerance, emotion regulation, and interpersonal effectiveness (twice a week; each 100 min), the mindfulness training (once a week; 60 min), and the practice groups managed by the patients themselves. Further components were movement therapy and art therapy (each twice a week; 100 min), which predominantly served the purpose of building up skills in the area of emotion and self-worth regulation. Furthermore, a “Patient Parliament” took place weekly, and a trialogue meeting (e.g., with patients and their relatives or friends) took place every six weeks (Link & Tilly, 2006). Measures The anamnesis questionnaire consisted of 52 questions about the patient and his or her problems. The following outcome measures were used: Borderline Symptom List (BSL; Bohus et al., 2007), Brief Symptom Inventory (BSI, Franke, 2000), Beck Depression Inventory (BDI; Hautzinger, Bailer, Worall, & Keller, 1995), and the Global Assessment of Functioning Scale (GAF; APA, 1994). Statistical analyses Effectiveness The problem of missing data was corrected using multiple imputations, as recommended by Schafer and Graham (2002). The independent variables age, gender, baseline measurements, and post-treatment measurements were included in the generation of 10 complete data sets by chained equations modeling (White, Royston, & Wood, 2011). Variables with missing data were completed following the imputation method of predictive mean matching. The R package “MICE” (Multivariate Imputation by Chained Equations; Buuren & Groothuis-Oudshoorn, 2011) was applied for this procedure. To complete the process, the 10 imputed data sets were used to compare the mean values before and after the treatment. In accordance with Rubin’s rule (1987), the calculation of the standard errors of the pooled coefficients considers the variations within each imputation (common uncertainty of estimation) as well as between imputations (uncertainty of imputation). Subsequently, pooled dependent t-tests with Bonferroni correction (two-sided) were carried out. In order to quantify the effect of the treatment, we estimated the pooled effect sizes (ES; Hedges & Olkin, 1985). In accordance with Cohen’s convention (1988), ES > 0.2 is regarded as a small, ES > 0.5 as a medium, and ES > 0.8 as a large effect size. Response Following Jacobson and Truax (1991), we calculated the RCI to determine the percentage share of patients who remained unchanged or reliably improved or deteriorated. The calculation was made according to the following formula:

Xpre  Xpost ; whereby Sdiff ¼ Sdiff pffiffiffiffiffiffiffiffiffiffiffiffiffiffi SE ¼ SD* 1  rtt RCI ¼

413

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  2 and 2* SE

with: Xpre ¼ BSL total score at beginning of therapy Xpost ¼ BSL total score at end of therapy SD ¼ Standard deviation of the clinical norm sample, rtt ¼ Reliability of the measurement instrument, which corresponds here to Cronbach’s alpha. In order to calculate the standard error (SE), we referred to a reference population of inpatients with BPD (Kröger et al., 2010: SD ¼ 0.66, a ¼ 0.94). An RCI above the 95% confidence limits 1.96 counts as evidence of a reliable change (p  0.05). A critical value of 0.45 must be exceeded by the difference between pre- and post-mean value of a patient for the patient to be counted as having responded. Remission To establish whether responders can also be seen as remitted, the cut-off point C was calculated, the use of which was favored because it takes into account the overlapping of the two distributions of normal and impaired population (Jacobson et al., 1999). The calculation was made according to the following formula:

  MDys *SDNorm þ MNorm *SDDys qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Cut  off point C ¼ SDDys *SDNorm For the impaired population, the data of the inpatient sample with BPD were also used (Kröger et al., 2010: M ¼ 1.89, SD ¼ 0.66). As a frame of reference with respect to the normal population, the data of a general population (Bohus et al., 2007: M ¼ 0.4; SD ¼ 0.22) were drawn on in order to operationalize remission as the achievement of the symptom level of a clinically unimpaired sample. To calculate the remission rates, a cut-off point of C ¼ 0.80 emerged. If a responded patient falls below the respective cut-off point C with his or her BSL post-value, he or she is seen as remitted. Dropout Non-parametric conditional inference trees (C-Trees; Hothorn, Hornik, & Zeileis, 2006; Strobl, Malley, & Tutz, 2009) based on the principle of recursive partitioning were applied to analyze associations between pre-treatment measurements and risk of dropout. An exact permutation test will assess the strength of the association between response and input variable (Strasser & Weber, 1999). Since permutation tests derive the p-values from sample-specific permutation distributions of the test statistics, only p-values are reported. In the following analysis, gender, age, social, educational, and employment status, treatment history, as well as the level of mean pre-treatment scores of outcome measures (BSL, BDI, GSI, and GAF, respectively), co-occurring mental disorders, and psychosocial stress factors were selected for testing the association with dropout (no ¼ 0; yes ¼ 1). The R package “party” (a laboratory for recursive partitioning; Hothorn, Hornik, Strobl, & Zeileis, 2011) was used for this analysis. Results Outcome Table 2 displays the means and standard deviation of the four outcome measures based on the 10 imputed data sets, using Rubin’s rule for the pooling procedure. The mean pre-treatment

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Table 2 Means (M), standard deviations (SD) and effect sizes (ES) at pre- and post-treatment. Pre

BSL total score BSL-Subscales

BDI GSI GAF

Self-perception Affect regulation Self-destruction Dysphoria Loneliness Intrusions Hostility

Post

M

SD

M

SD

1.83 1.56 2.18 1.71 3.05 1.65 0.95 1.47 28.98 1.72 46.99

0.70 0.87 0.87 1.01 0.49 0.86 0.70 0.90 14.49 0.69 8.85

1.44 1.11 1.74 1.34 2.69 1.14 0.77 1.28 20.79 1.25 54.33

0.76 0.86 0.93 1.08 0.66 0.87 0.71 0.84 13.65 0.73 9.07

t

p*

jESj

95% CI

20.22 19.68 17.39 14.05 20.47 20.02 10.94 7.37 25.29 26.82 38.38

<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001

0.54 0.52 0.46 0.37 0.54 0.53 0.29 0.20 0.67 0.71 1.02

0.49; 0.47; 0.41; 0.32; 0.49; 0.48; 0.24; 0.15; 0.62; 0.66; 0.94;

0.59 0.57 0.51 0.42 0.59 0.58 0.34 0.25 0.72 0.76 1.08

Note. BSL ¼ Borderline Symptom List; BDI ¼ Beck Depression Inventory; GSI ¼ Global Severity Index of the Brief Symptom Inventory; GAF ¼ Global Assessment of Functioning Scale; p* ¼ p-value, corrected for multiple testing.

BSL score of 1.83 (SD ¼ 0.70) was comparable to the level of borderline-specific symptoms of the BPD subsample that was reported in Bohus et al. (2007). The mean BDI score of 28.98 (SD ¼ 14.49) and the mean pre-treatment GSI score of 1.72 (SD ¼ 0.69) were comparable to other inpatient samples (see Bohus et al., 2004; Kröger et al., 2006: mean BDI scores ¼ 31.3, SD ¼ 9.4, and 31.96, SD ¼ 11.03; mean GSI scores ¼ 1.74, SD ¼ 0.48, and 1.80, SD ¼ 0.68, respectively), indicating a very high level of symptom strain and depression at pre-treatment. The GAF score of 46.99 (SD ¼ 8.85) was similar to the Bohus et al. study (2004; mean GAF score ¼ 48.5, SD ¼ 8.4), but higher than the Kröger et al. study (2006, mean GAF score ¼ 35.16, SD ¼ 10.11), indicating a moderate impairment in psychosocial adaptation. Effectiveness As shown in Table 2, the scores of the BSL, BDI, and GSI of the BSI decreased significantly over time, while the score of the GAF increased significantly. The ES was small to moderate in the selfrating measures (ES ¼ 0.20e0.71) and large in the GAF Scale (ES ¼ 1.02). Response and remission rates Forty-five percent of the participants were classified as responded. A symptom level corresponding to that of a general population was shown by 14.8%. About 11% deteriorated, and 30.6% remained unchanged despite the treatment. Fig. 1 illustrates the response and remission rates. Dropout Participants with co-occurring substance use disorders showed a significantly higher risk of discontinuing treatment. Moreover, patients who were additionally aged 20 years were particularly at risk. Fig. 2 illustrates the risk of dropout in the C-Tree. Discussion The ES of 0.54 for the reduction of the BSL total score lies in the range of a current meta-analysis (Kliem et al., 2010), reporting a global ES of 0.50 (95% CI [0.43, 0.57]) based on various outcome measures. In line with previous studies (Bohus et al., 2004; Höschel, 2006; Kröger et al., 2006; Simpson et al., 2004), the non-disorderspecific and depressive symptoms were reduced and the psychosocial functioning was increased. In accordance with the RCI, approximately a third of the treated individuals remained unchanged and about 11% deteriorated. A symptom level like that of a general population was reached by

approximately 15%. Compared to the Bohus et al. (2004) study, the remission rate (42%) thus emerged as clearly lower, although both inpatient staffs were certified by the German Umbrella Association of DBT. Again, it should be noted that the results of the Bohus et al. study were based on a small completer sample (N ¼ 31), excluding all substance use disorders and using the general symptom strain as the main outcome, but no borderline-specific measure. The dropout rate of approximately 10% was low in comparison to previous studies in the inpatient setting (from 12.0%, Kröger et al., 2006; to 25.8%, Bohus et al., 2004). Substance use disorders and younger age were found to be predictive of dropout. Hence, there might be evidence of an adaptation, including more validating and motivating strategies (Linehan et al., 2002) as well as multifamily skills training and interventions addressing family members and social environment (Katz, Cox, Gunasekara, & Miller, 2004; Woodberry & Popenoe, 2008). However, we did not include factors that have shown their predictive significance in other studies (i.e., trait anxiety, experiential avoidance; Rüsch et al., 2008). On a further critical note, the sociodemographic characteristics were only assessed through self-disclosure. To measure family burden as a possible predictor, an interaction diagnosis or an interview with family members, for instance, would be desirable (cf. Hooley & Hoffman, 1999). The major limitation of the study is that it uses an uncontrolled, nonrandomized design, thus making it impossible to determine whether any changes that were found were a result of treatment versus other factors (e.g., the passage of time, regression to the mean). Furthermore, the main outcome measure was only a selfrating instrument and not one of the recommended disorderspecific interviews (Zanarini et al. 2010). Under the conditions of a standard care hospital, the treatment providers’ adherence to the manual could not be evaluated by external assessors. The pharmacological treatment was not systematically recorded and could therefore not be controlled for as an additional predictor of dropouts. However, the effects of the pharmacological treatment on the target symptoms can be assumed to be low (e.g., Simpson et al., 2004). To the best of our knowledge, this is the first study that uses a larger, gender-mixed sample in order to evaluate the treatment success of inpatient DBT using group- and individual-specific parameters. In our view, including indices of statistical and clinical significance results in a realistic basis for treatment evaluation. To conduct comparisons across studies and procedures, uniform definitions of response and remission appear to be necessary. In the future, follow-up assessments should be implemented in order to examine the extent to which the rates at the end of treatment remain stable over time.

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415

Fig. 1. Clinically significant change in accordance with the RCI for the total score of the Borderline Symptom List. In the area between the dotted diagonal lines are patients in whom no change occurred (black). Above the upper dotted line are patients whose symptoms worsened (red). All patients below the lower dotted line showed a response to the treatment. Patients who responded to the treatment but were still burdened after the treatment are found between the dotted line and above the cut-off point (yellow). Below the green line (cut-off point C) are patients whose symptom level at the end of treatment corresponded to that of a healthy sample (Bohus et al., 2007). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

substance use disorder

yes

no age

20 years

> 20 years

subgroup 3 (n = 135)

subgroup 2 (n = 1167)

subgroup 1 (n = 121) 100 %

100 %

100 %

80 %

80 %

80 %

60 %

60 %

60 %

40 %

40 %

40 %

20 %

20 %

20 %

0%

0%

0%

Fig. 2. The risk of dropout in a non-parametric conditional inference tree. (The percentaged share of dropouts in each subgroup is marked in black.)

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