&hoc. Res. Ther. Vol. 26. No. 6. pp. 531-534. 1988 Printed in Great Britain. All rights reserved

000%7967i88 53.00 + 0.00 Copyright C 1988 Pergamon Press plc

Predicting response to anxiety management in patients with generalised anxiety disorders*

GILLIAN BUTLER and PAVLOS ANASTASIADES

University of Oxford, Department of Psychiatry, Warneford Hospital, Headington, OIcjord OX3 7/X, England

(Received 16 February 1988)

Summary-This paper describes the analysis of predictive factors in a group of generally anxious patients treated with anxiety management. Evidence for three reliable predictors is presented. It is argued these reflect anxiety, demoralisation and depression. The same three variables contributed to prediction of outcome acter treatment and 6 months later, and their pre-treatment values classified 80% of the patients correctly into good or poor responders. Lower initial levels of anxiety and demoralisation together with higher depression rated by the assessor predicted a better outcome. Predictions were more accurate for patients with a mild degree of depression than for those who were not depressed.

Psychological treatment for persistent generalised anxiety disorder (GAD) has now a reasonable chance of success (Butler, Cullington, Hibbert, Klimes and Gelder, 1987a; Lindsey, Gamsu, McLaughlin, Hood and Espie, 1987; Durham and Turvey, 1987). However relatively few patients are symptom free at the end of treatment, and about l/4 in Butler er al.‘s study improved relatively little. The present study asks whether it is possible to predict response to anxiety management before treatment has started, and to define subgroups of patients who respond well or less well. If so, scarce therapy resources could be used more efficiently, and attention could be focused more productively on the relatively poor responders.

So far there have been no reports of predictive factors in GAD, so hypotheses were derived from findings in other populations, from theoretical considerations and from clinical experience. We reasoned that generally anxious patients with associated depression might respond less well to anxiety management than those who are not also depressed, because depression might exacerbate symptoms and make it harder for patients to comply with treatment, Patients who have recently experienced more negative life events, or who have a less extensive network of social support (Finlay-Jones and Brown, 1981) might also respond less well. Theoretically trait anxiety, an indicator of the long-standing or wide-ranging nature of problems, would be associated with a poorer response, or with a higher base level of residual anxiety after treatment. Lastly, associated panic disorder may emerge as a predictor, or moderator, of response to treatment.

In most analyses of predictive factors the best predictor is closely related to the outcome measure, for statistical as well as for substantive reasons. In other words anxiety at the time of first testing is likely to be the best indicator of subsequent anxiety. All of the meaures of anxiety used in the Butler et nl. trial were included in the regression analyses.

METHOD Only information necessary for understanding the analyses reported in this paper are provided here. Further details of

the clinical trial, the source of the data, can be found in Butler ef al. (1987a) and Butler, Gelder, Hibbert, Cullington and Klimes (1987b).

Outline of the original srudy The trial concerned 45 patients with GAD defined by the Research Diagnostic Criteria (RDC; Spitzer, Endicott and

Robins, 1978). Patients were included if anxiety had persisted for 6 months and reached 7 or more on the Leeds Anxiety Scale (Snaith, Bridge and Hamilton, 1976). Patients with panic disorder (Present State Examination, Wing, Cooper and Sartorius, 1974) as well as GAD were included only if GAD was the primary disorder (had started first or was the major source of distress). Patients were excluded if they met RDC for obsessive+ompulsive or major depressive disorder. Data from the 38 patients who completed both treatment and the 6 month follow-up assessments were included.

Patients were subdivided before treatment using the RDC criteria for minor depressive disorder: I7 fell in the depressed group; 21 in the non-depressed group. Mean scores on the Hamilton Rating Scale for depression were 13.47 and 9.95 respectively; t (36) = 2.34, P c 0.05. Both groups responded well to treatment; the depressed group tending to improve most, so that there was subsequently no difference between the groups in measures of either anxiety or depression.

Suitable patients were randomly assigned to immediate treatment with anxiety management or to a waiting list. The treatment is described in a booklet (available from the first author) that was given to patients before starting treatment. Changes during treatment were highly significant and clinically important. An almost identical degree of improvement was observed irrespective of whether the start of treatment was delayed, and this improvement persisted for the next 6 months. The groups did not differ at any stage on any of the measures of anxiety, and data from both were combined for the analyses reported here.

Smtislical analyses Stepwise multiple regression analysis was used to identify significant predictors of immediate response to treatment and

also of outcome 6 months later for the entire sample. Separate analyses were also performed for the depressed and

*A more detailed version of this paper is available from the first author.

531

532 CASE HISTORIES AND SHORTER COM.WUNICATIONS

Table I. IMeans and standard deviattons of the main measures before treatment. and of the outcome measure

Depressed Non-depressed Whole sample subgroup subgroup

(n = 38) fn = 171 (n =21)

Anxiety Leeds Scale LA Il.84 2.80 13.06 2.79 10.86 2.46 Hamilton HA 16.98 5.98 20.06 6.73 14.48 3.9 I O-g Assessor AA 5.61 0.94 5.88 0.86 5.38 0.97 l&8 Patient PA 4.92 I.71 5.12 1.76 4.76 1.70 STAI-trait 54.30 9.54 59.12 7.20 50.80 9.45

Depression -- Leeds Scale LD 8.26 2.77 8.94 1.52 7.71 3.4 I Hamilton HD Il.53 4.93 13.47 4.35 9.95 4.91 O-8 Assessor AD 2.47 I .56 3.76 0.97 I .43 I .08 O-8 Patient PD 2.74 I.88 3 59 2.00 2.05 1.50

G.H.Q. 26.00 15.28 32.53 14.65 20.71 13.95

Outcome measure Leeds AnxietyScale LA Post treatment 6 m Follow-UD

6.3 I 4.03 7.18 4.63 5.62 3.44 5.63 3.40 6 06 3.27 5.29 3.55

non-depressed subgroups. Stepwise discriminant analysis was used to find out whether division of patients into consistently good and poor responders (see below) could accurately be predicted on the basis of pre-treatment information.

Measures (see Table I) independent cariables. Mood ratings included 4 measures of anxiety and 4 of depression: the Leeds Scales (Snaith et al.,

1976) the Hamilton Rating Scales (Hamilton, 1959, 1967). and O-8 ratings made separately by the assessor and by the patient of the severity of anxiety and depression. Before treatment, trait anxiety was also measured with the State-Trait Anxiety

Inventory (STAI; Spielberger, Gorsuch and Lushene, 1970). Additional independent variables included age, the General Health Questionnaire (Goldberg and Hillier, 1979) associated panic disorder, the Life Events Checklist (Paykel, Myers, Dievett, Klerman, Lindenthal and Pepper, 1969) and the Index of Social Support (Surtees, 1980).

Ourcome measure. The Leeds Anxiety score (LA) was chosen as the outcome measure because it provides a cut-off between the scores of anxious patients and non-anxious people. This choice permits the same, clinically relevant, criterion variable to be used in both multiple regression and discriminant analyses, providing a check for consistency. Good responders were defined as those whose scores fell below 7 both immediately after treatment and also 6 months later. This outcome measure reflects absolute levels rather than change scores, or responsiveness. However in multiple regression it is likely that the first predictor will be the initial level of anxiety, and subsequent steps in the analysis take this into account, so that predictors emerging at later steps in the analysis reflect associations with degree of change.

RESULTS

These results are presented as preliminary findings because the set of predictor variables from which to select is large relative to the sample size. More weight will therefore be given to general patterns than to the size of the observed effects.

Prediction for lhe sample as a whole Three variables, measured before treatment, contributed significantly to the prediction of outcome after treatment, and

accounted for 47% of the variance; multiple I? = 0.47, F(3,34) = 10.04, P < 0.001. The 3 variables were: (i) LA, (ii) the patient’s O-8 rating of depression (PD), and (iii) either the assessor’s O-8 rating of depression (AD) or the Hamilton Anxiety score (HA). The contributions of these variables are shown in Table 2, in the order in which the predictors entered the stepwise regression analysis. Significance figures relate to the final regression equation.

The same 3 variables contributed significantly to prediction of outcome 6 months later; rr = 0.43, F(3,34) = 8.71, P < 0.01. The relative contribution of the variables to prediction on the two occasions is very similar (Table 2). For both LA and PD, the higher the score before treatment the higher the LA score after treatment. The opposite relationship was observed for AD (or HA), so that the combination of low LA and PD scores with high AD (or HA) scores predicted a better outcome.

Neither AD nor HA consistently predicted best on either occasion. For AD, r* = 0.48 after treatment and 0.44 six months later; for HA, r? = 0.42 and 0.48. The two measures correlated highly (I = 0.6, P c 0.001) and they both contributed negatively to outcome. In neither case was the simple correlation between pre-treatment levels and outcome significant, while the correlations between outcome and pre-treatment levels of both LA and PD were significant and positive on both occasions (LA = 0.55 and 0.53; PD = 0.41 and 0.34).

None of the remaining variables were related to outcome at either point in time. The simple correlation between them and outcome after treatment was only significant in the case of trait anxiety (r = 0.48, P < 0.001). and never rose above 0.25 (NS) for any of the other variables.

Prediction for rhe depressed and non-depressed subgroups Stepwise multiple regression analyses were separately applied to the depressed and non-depressed subgroups, as defined

u priori by the RDC. The immediate response to treatment was extremely accurately predicted for the depressed patients: r = 0.93, accounting for 86% of the variance; F(4,12) = 18.13, P < 0.001. Four variables contributed reliably to prediction of outcome: the first 3 were those observed in the whole sample analysis (LA, AD and PD) while the fourth was the patient’s rating of anxiety (see Table 2 for their relative contributions). The same 4 variables together did not predict well for the

CASE HISTORIES AND SHORTER COMMUNICATIONS 533

Table 2. Summary of multiple regression analyses showing the contribution of each variable to prediction of outcome

Post-treatment 6 month follow-up

Increase Unique IlXXCaSC

in I’ in rz Unique

contrib. St. reg. contrib. Variable %

St. reg. % COCK Variable % % COCK

Whole sample (n = 38) LA* 30 27 0.54’ LA 28 28 0.55”

PD IO I6 0.4% PD 6 I4 AD

0.44§ 7 7 -0.33: AD IO IO -0.37:

Depressed subgroup (n = 17) LA 31 4 0.26) LA 42 53 0.901

PD I9 38 1.13’ PD 5 I2 0.37t AD 22 2s -0.56’ HA I7 I7 -0S4f PA I4 I4 -0.66s

Non-depressed subgroup (n = ?I) LA 23 23 0.48: LA 19 23 0.614

PD I5 I5 0.615

*See Table I for abbreviations. t = P < 0.08; : = P < 0.05; 9 = P < 0.01; ‘i = P c 0.001. Unique contrib. = the amount by which r* would decrease if that particular variable were removed from the final regression

equation. St. reg. co&. = Standardised regression coefficient.

non-depressed group; I’ = 0.29 (NS). For these patients only LA contributed significantly to prediction of outcome; r’=0.23, F(1,19)=5.64, P ~0.05.

AS in the whole sample, for the depressed group the combination of LA, PD and HA predicted anxiety scores at 6-month follow-up; r’ = 0.65, F(3.13) = 7.89, P c 0.005. For the non-depressed group, rz = 0.34, F(2,IS) = 4.63, P < 0.05 (see Table 2). Prediction may be more accurate in depressed patients because two of the predictive variables measure depression, and might therefore have a smaller variance in the non-depressed group. However it is clear from Table I that the standard deviations of these measures were practically identical in the two subgroups. It would therefore seem more probable that anxiety itself is the only relevant predictor in patients who are anxious but not depressed, while factors relating to depression are more salient in patients also suffering from minor depressive disorder.

Discriminant analysis Twenty patients were identified as good responders and I8 as poor responders. Discriminant analysis indicated that 79%

of the patients could be correctly classified on the basis of the pre-treatment scores of LA, DP and HA; F(3,34) = 9.03, P c 0.001. This discriminative accuracy improved to 90% when O-8 ratings of anxiety made by the patient and the assessor were included; F(S.32) = 7.36, P < 0.001. Only one patient in the good outcome group, and 3 in the poor outcome group would be misclassified using these variables. The separation between the groups is illustrated in Fig. 1. Independent confirmation of the discrimination of patients into good and poor responders is available. When the follow-up was extended by about I yr (Butler et al., 1987a). 9 patients had required more treatment for anxiety, including anti-depressant and/or anxiolytic medication. Eight of these 9 patients fell in the poor outcome group.

DISCUSSION

A combination of 3 variables measured before treatment reliably predicted outcome in this sample of patients who had generally responded well to treatment, and in whom the subgroup who were also depressed improved as much or more than those who were not depressed. Outcome was relatively poor if initial levels of anxiety were higher, patients rated their depression as more disabling, and at the same time the assessor’s ratings on the O-8 ratings of depression or the Hamilton Anxiety scale were lower. The same 3 variables contributed to prediction after treatment and 6 months later, and pre-treatment scores on these variables classified 80% of patients correctly as good or poor responders. Predictions for patients who had a significant but minor degree of depression were more accurate than those for patients who were not depressed. The findings are extremely consistent in that the same 3 variables predict outcome on two occasions, and when using either multiple regression or discriminant analysis. None of the other factors investigated contributed to prediction in this sample.

Qj Poor outcome

8 [7 Good outcome

n

m I I

Discriminant score

Fig. I. The separation between patients with a good outcome and those with a poor outcome using discriminant analysis.

53-I CASE HISTORIES AND SHORTER COMMUNICATIONS

It is not surprising to find that the lower the initial level of anxiety the better the outcome. At the same time, subsidiary analyses of change scores indicated that patients with higher anxiety had improved most by 6 months after treatment. Interpretation of the other two predictor variables is open to several possibilities and presented as speculative.

The variable which contributed negatively to prediction is considered next. This was eirher AD or HA. and these variables were strikingly similar in their effects. Both measures require the assessor to make a single rating of depressed mood: AD on the O-8 scale: the Hamilton Anxiefy scale in Question 6. AD and HA correlated highly (r = 0.60). HA also correlated highly with Hamilton Depression scores (r = 0.74). but less with Leeds Anxiety scores (r = 0.57); it correlated with Leeds Depression scores (r = 0.33) while LA did not. Thus we suggest that this predictive factor reflects an aspect of HA that is shared with AD. and which plays its part in prediction after anxiety, measured by LA, has been taken into account. This is depression rated by the assessor. The negative relationship with outcome suggests that outcome was befrer if depression was higher for the same level of anxiety (N.B. Butler ef al., 1987a. reported that depressed patients tended to improve more than those who were not depressed). Moderate, secondary. depression may not prevent engagement in treatment, which reduced both the primary, general anxiety and secondary depression. Thus higher initial scores are associated with a better response.

The other predictor variable. PD. was the patient’s O-8 rating of depression (labelled according to how disturbing or disabling this is). This may reflect the degree to which the problem has “got the patient down”, and relate to Franks’concept of demoralisation. The more exaggerated the demoralisation for the same level of depression the worse the outcome. Patients may rate their depression as disabling either because it is in fact severe, or because they cannot cope well with it and so become demoralised. This interpretation is suppqrted by the fact that depression (AD or HA) operates as a suppression variable, since its contribution emerges as significant and negative only at the final step (Guilford, 1973). It suppresses the variance in PD that is not shared with the outcome variable, so that the contribution of PD to prediction increases when AD or HA is considered. Once the variation due to the ‘actual’ severity of depression (measured more objectively by the assessor) has been removed, then the specific variation in PD that is due to more subjective factors is clearly revealed, and it is this aspect of PD (i.e. demoralisation) that significantly predicts outcome. I! also predicted poorer response to treatment (further confirmed in a subsidiary analysis of change scores).

If it is assumed that people become demoralised when they fail to cope with their symptoms, then this factor could be similar to ‘learned resourcefulness’ (kosenbaum, 1980). which Simons, Lustman, Wetzel and Murphy (1985) found to be the only factor that significantly predicted response to cognitive therapy for depression. Demoralisation might also be associated with low self-esteem, which was found to be the most reliable predictor of response to cognitive-bchavioural treatment for bulimia nervosa (Fairburn. Kirk, O’Connor, Anastasiades and Cooper. 1987). In fact the 3 factors, demoralisation, learned resourcefulness and low self-esteem reflect an important, and similar, predictor of response to these treatments that has as yet been imperfectly defined in any of these studies, and which influences the individual’s coping potential.

Altogether, outcome was better if patients were less anxious to start with and relatively less demoralised for the same level of depression. Outcome was relatively poor if initial levels of anxiety were higher and patients felt more demoralised by their problems even if the assessor considered their depression to be less severe. The most informative implications of these findings concern those anxious patients who also suffer from minor depressive disorder. These patients responded well to anxiety management. Those who did not respond well were those who were demoralised. Thus demoralisation may interfere with progress more than a mild level of depression, especially if resources for coping are limited or under-used. We need therefore to develop procedures for improving patients’ coping resources and for overcoming demoralisation. as well as more precise measures of this factor.

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