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P-5-18 Heart rate and MHPG as predictors of nonresponse to drug therapy in panic disorder
P-5 Stress, panic, anxiety and personality disorders (p "< 0.06). Comorbid anxtety or depresstve dtsorders, duratton of tllness, treatment category and intensity of treatment at relapse, and childhood history of anxiety disorders were not associated with time to relapse. Pharmacotherapy at onset of remission was examined in 3 categories: benzodiazepines alone (BZD}, antidepressants + benzodiazepines (AD + BZD}, and a group receiving no medications Category of treatment at onset of remission {but not -elapse) was a significant predictor of relapse Patients treated with benzodiazepines alone (n - 37) were at an increased risk of relapse compared to patients maintained on no medication (n - 31; c < 0.05). and, at a level of a trend, patients treated with antidepressants with or without a benzodiazepine adjunct (n - 35; p < 0 0 9 ) However, treatment intensLty at remission was significantly lower in the group receiving BZD's alone than those receiving AD + BZD's (<0.0005). The perc6~.tage of patients receiving behavioral therapy was not significantly different between treatment groups: BZD (30%), AD + BZD (37%), no medications (52%) Conclusions: Our findings suggest that disorder severity and treatment modality may affect the propensity of patients with panic disorder to remain in remission Our findings encourage further attention to the longer-term course of patients and the~mpact of maintenance treatments on this course
References Pollack. M H Otto M W, RosenbaUmr J F Sachs, G S . Asher. R and Meltzer-Brody, S (1990) Longitudinal course of panic disorder Findings from the Massachusetts Gen eral Hospital Naturalistic Study Journal of Chnical Psychiatw 51 12-16 Pollack, M H. and Otto M W (1994) Long-term pharmacological treatment of panic disorder. Psychiatric Annals 24. 291 298 Pollack. M H , Otto, M W, Sachs, G S , Leon A Shear M K , KaspL S P and Rosen baum, J E {1994} Anxiety psychopathology predictive of outcome in patients with panic disorder treated with imipramine, alprazolam and placebo Journal of Affectwe Disorders 30,273-281 r
Heart rate and MHPG as predictors of nonresponse to drug therapy in panic disorder B.R. Slaap, I M vanVliet. H G . M . Westenberg, J A den Boer Rudolf Magnus Institute for Neurosciences, Department of Psychiatry, RO Box 85500, 3508 GA Utrecht, The Nether/ands About 20 to 40% of panic disorder (PD) pagents treated w~th antidepres sants do not respond to treatment. Since it usually takes about 4 weeks before any treatment effect becomes evident it would be useful to be able to predict nonresponse In a previous study {Slaap et el 1995} we reported on psychological differences between responders and nonrespon ders assessed with psychometric measurements In the present study we investigated whether heart rate, blood pressure, plasma MHPG, cortisol and melatonin could predict nonresponse to drug therapy in PD patients treated with antidepressants For the present study the data of two prev ously published studies were pooled. In these studies brofaromnnewas compared with either fluvoxamine (van Viler et at 1994) or placebo ivan Vliet et at 1993). Both studies had a double blind design and lasted 12 w e e k s The psychometric scales and the biochemical measurements were similar in both studies. By pooling the data we obtained a data base of 60 panic disorder patients Twenty nine patients were treated with brofaromine, 15 patients were treated with fluvoxamine and 16 patients received placebo The 44 patients who received active drug treatment were of interest in this study and were analysed as one g r o u p We used a strict definition of nonresponse to find patients who did not respond at all after 12 weeks of treatment. An ANOVA was used to detect variables on which nonresponders differed from responders. These variables were then entered in a logistical regression analysis to see ~f they could predict nonresponse Of a sample of 44 panic disoreer patients 15 patients (32.6%) were c o n sidered nonresponders These nonresponders had a higher heart rate at baseline, as compared to responders Blood pressure was not differentbetween the groups. Nonresponders also had a higher plasma MHPG level Plasma concentrations of cortisol and melaton~n did not differ at baseline between responders and non-responders. Baseline predictors of nonresponse were a higher heart rate and a higher plasma MHPG level This study demonstrates that a higher baseline heart rate and a htgner plasma MHPG level are predictive of nonresponse to drug therapy in panic disorder Blood pressure and plasma levels of cortisol or melatonin were not predictive of nonresponse.
References Slaap. BR, van Vliet, I M, Westenberg, H G IV, ar'd den Boer J A (1995) Ptqobicsyrqp toms as predictors of nonresponse to drug therapy tn pannc disorder pattents (a ore
359 i!mmary report) Journal of Affective Disorders 33.31-38 van Vhet, I M Westenberg. H.GM, and Den Boar, JA. {1993) MAQ-inhibitors in Panic Disorder; clinical effects of treatment with brofaromine, a double blind placebo controlled study Psychopharmacology 112, 483-489 van Vliet. I M, Westenberg, H.G.M, Sleep, B R and Den Boer. J A (1994)A comparison of brofaromine and fluvoxamine in panic disorder, a double blind comparative study Submitted for publication
Diagnostic problems of somatoform disorders: a study comparing ICD-IO and DSM-liI-R J S~roni 1, G Tacchini 1, M. Isaac 2, A. Janca 2. 1A Institute of PsychiatG,
University of Milano, Via E 8forza 35, 20122 Milano, Italy, 2 Division of Mentat Health, World Health Organization, Geneva, Switzerland ICD-1O and DSM-III-R differ significantly in their diagnostic definitions of somatoform disorders: these differences are: (i) at a syndromic level, since ICD-10 includes some diagnoses like Neurasthenia and Somatoform Autonomic Dysfunction which are not present in DSM-ilI-R; (ii) at a symptom ~evel, since the number of required symptoms is not identical in the t w o systems; (i~i) at the onset level, since DSM-ilI-R poses some age constraints not present m ICD-10, and (iv) at the level of the exclusion rules, which differ in the two systems. We analyzed a sample of 2,002 psychiatric patients w h o were interviewed with the Somatoform Disorders Schedule (SDS), a fully structured irstrument developed by the World Health Organization (WHO). SDS allows d~agnosing according to both ICD-10 and DSM-III-R simultaneously and applies standardized threshold criteria for both systems in the evaluation of each s y m p t o m The diagnoses of each subject, one according to ICD-10 and one with ~)SM-liI-R, were obtained through the SDS computer programme. All subjects were theq cumulated and the comparability of the two diagnostic systems was computed by means of Cohen's Kappa and Intraciass Corre;ation Coefficient The dispersion of the cases in each diagnostic system was also taken into account as an index of their internal consistencies. The comparison of these latter data was then regarded as indicative of the reciprocal, comparative d~agnostic validities of ICD-10 and DSM-III-R
From DSM-III.R to DSM-IV: a study of the threshold problem of somatoform disorders ~i Sironl I, M. isaac 2, A Janca 2, G. Tacchini 1. 1 Institute of Psychiatry
University of Mffano, Via E Sforza 35, 20122 Mffano, Italy," 2 Division of Mental Health, World Health Organization, Geneva, Switzerland Somatization disorder is defined by 13 somatic symptoms in DSMqlI-R, 8 in DSM-IV, and 6 in ]CD-10; other differences are related to onset age and exclusion critena. An indirect result of these differences is the variable definitions of the residual categories of the undifferentiated and the not otherwise specified somatoform disorders, which on the contrary are widely prevalent in the general population and primary c a r e Furthermore. somatic complaints are to some extent culturally variable, and affected by variables like the socio-economical status, the education level etc, A certain amount of somatic symptoms is therefore considered "normal" for a given culture and a single individual A milestone In this field is the work of J. Escobar. who described a Somatic Sympton Index (SSI} and found that a threshold value of SSI can be described in the different populations: it was 4 and 6 symptoms for men and women respectively in Puerto Rico, and these figures ensured the same positive predictive value of the better defined somatization disorder, which Jn DSMqlI-R requires 13 s y m p t o m s We studied the threshold problem in a sample of 2,002 subjects assessed by means of t~e Somatoform Disorders Schedule (SDS), a fully structured interview which gives a diagnosis according to both ICD-10 and DSMIV simultaneously, with standardized symptom definitions, thus exploiting a direct comparability between different diagnostic systems. Symptoms density and groupings were used to verify different threshold levels, their predictive values and consequences.
References Pollack. M H Otto M W, RosenbaUmr J F Sachs, G S . Asher. R and Meltzer-Brody, S (1990) Longitudinal course of panic disorder Findings from the Massachusetts Gen eral Hospital Naturalistic Study Journal of Chnical Psychiatw 51 12-16 Pollack, M H. and Otto M W (1994) Long-term pharmacological treatment of panic disorder. Psychiatric Annals 24. 291 298 Pollack. M H , Otto, M W, Sachs, G S , Leon A Shear M K , KaspL S P and Rosen baum, J E {1994} Anxiety psychopathology predictive of outcome in patients with panic disorder treated with imipramine, alprazolam and placebo Journal of Affectwe Disorders 30,273-281 r
Heart rate and MHPG as predictors of nonresponse to drug therapy in panic disorder B.R. Slaap, I M vanVliet. H G . M . Westenberg, J A den Boer Rudolf Magnus Institute for Neurosciences, Department of Psychiatry, RO Box 85500, 3508 GA Utrecht, The Nether/ands About 20 to 40% of panic disorder (PD) pagents treated w~th antidepres sants do not respond to treatment. Since it usually takes about 4 weeks before any treatment effect becomes evident it would be useful to be able to predict nonresponse In a previous study {Slaap et el 1995} we reported on psychological differences between responders and nonrespon ders assessed with psychometric measurements In the present study we investigated whether heart rate, blood pressure, plasma MHPG, cortisol and melatonin could predict nonresponse to drug therapy in PD patients treated with antidepressants For the present study the data of two prev ously published studies were pooled. In these studies brofaromnnewas compared with either fluvoxamine (van Viler et at 1994) or placebo ivan Vliet et at 1993). Both studies had a double blind design and lasted 12 w e e k s The psychometric scales and the biochemical measurements were similar in both studies. By pooling the data we obtained a data base of 60 panic disorder patients Twenty nine patients were treated with brofaromine, 15 patients were treated with fluvoxamine and 16 patients received placebo The 44 patients who received active drug treatment were of interest in this study and were analysed as one g r o u p We used a strict definition of nonresponse to find patients who did not respond at all after 12 weeks of treatment. An ANOVA was used to detect variables on which nonresponders differed from responders. These variables were then entered in a logistical regression analysis to see ~f they could predict nonresponse Of a sample of 44 panic disoreer patients 15 patients (32.6%) were c o n sidered nonresponders These nonresponders had a higher heart rate at baseline, as compared to responders Blood pressure was not differentbetween the groups. Nonresponders also had a higher plasma MHPG level Plasma concentrations of cortisol and melaton~n did not differ at baseline between responders and non-responders. Baseline predictors of nonresponse were a higher heart rate and a higher plasma MHPG level This study demonstrates that a higher baseline heart rate and a htgner plasma MHPG level are predictive of nonresponse to drug therapy in panic disorder Blood pressure and plasma levels of cortisol or melatonin were not predictive of nonresponse.
References Slaap. BR, van Vliet, I M, Westenberg, H G IV, ar'd den Boer J A (1995) Ptqobicsyrqp toms as predictors of nonresponse to drug therapy tn pannc disorder pattents (a ore
359 i!mmary report) Journal of Affective Disorders 33.31-38 van Vhet, I M Westenberg. H.GM, and Den Boar, JA. {1993) MAQ-inhibitors in Panic Disorder; clinical effects of treatment with brofaromine, a double blind placebo controlled study Psychopharmacology 112, 483-489 van Vliet. I M, Westenberg, H.G.M, Sleep, B R and Den Boer. J A (1994)A comparison of brofaromine and fluvoxamine in panic disorder, a double blind comparative study Submitted for publication
Diagnostic problems of somatoform disorders: a study comparing ICD-IO and DSM-liI-R J S~roni 1, G Tacchini 1, M. Isaac 2, A. Janca 2. 1A Institute of PsychiatG,
University of Milano, Via E 8forza 35, 20122 Milano, Italy, 2 Division of Mentat Health, World Health Organization, Geneva, Switzerland ICD-1O and DSM-III-R differ significantly in their diagnostic definitions of somatoform disorders: these differences are: (i) at a syndromic level, since ICD-10 includes some diagnoses like Neurasthenia and Somatoform Autonomic Dysfunction which are not present in DSM-ilI-R; (ii) at a symptom ~evel, since the number of required symptoms is not identical in the t w o systems; (i~i) at the onset level, since DSM-ilI-R poses some age constraints not present m ICD-10, and (iv) at the level of the exclusion rules, which differ in the two systems. We analyzed a sample of 2,002 psychiatric patients w h o were interviewed with the Somatoform Disorders Schedule (SDS), a fully structured irstrument developed by the World Health Organization (WHO). SDS allows d~agnosing according to both ICD-10 and DSM-III-R simultaneously and applies standardized threshold criteria for both systems in the evaluation of each s y m p t o m The diagnoses of each subject, one according to ICD-10 and one with ~)SM-liI-R, were obtained through the SDS computer programme. All subjects were theq cumulated and the comparability of the two diagnostic systems was computed by means of Cohen's Kappa and Intraciass Corre;ation Coefficient The dispersion of the cases in each diagnostic system was also taken into account as an index of their internal consistencies. The comparison of these latter data was then regarded as indicative of the reciprocal, comparative d~agnostic validities of ICD-10 and DSM-III-R
From DSM-III.R to DSM-IV: a study of the threshold problem of somatoform disorders ~i Sironl I, M. isaac 2, A Janca 2, G. Tacchini 1. 1 Institute of Psychiatry
University of Mffano, Via E Sforza 35, 20122 Mffano, Italy," 2 Division of Mental Health, World Health Organization, Geneva, Switzerland Somatization disorder is defined by 13 somatic symptoms in DSMqlI-R, 8 in DSM-IV, and 6 in ]CD-10; other differences are related to onset age and exclusion critena. An indirect result of these differences is the variable definitions of the residual categories of the undifferentiated and the not otherwise specified somatoform disorders, which on the contrary are widely prevalent in the general population and primary c a r e Furthermore. somatic complaints are to some extent culturally variable, and affected by variables like the socio-economical status, the education level etc, A certain amount of somatic symptoms is therefore considered "normal" for a given culture and a single individual A milestone In this field is the work of J. Escobar. who described a Somatic Sympton Index (SSI} and found that a threshold value of SSI can be described in the different populations: it was 4 and 6 symptoms for men and women respectively in Puerto Rico, and these figures ensured the same positive predictive value of the better defined somatization disorder, which Jn DSMqlI-R requires 13 s y m p t o m s We studied the threshold problem in a sample of 2,002 subjects assessed by means of t~e Somatoform Disorders Schedule (SDS), a fully structured interview which gives a diagnosis according to both ICD-10 and DSMIV simultaneously, with standardized symptom definitions, thus exploiting a direct comparability between different diagnostic systems. Symptoms density and groupings were used to verify different threshold levels, their predictive values and consequences.