Siegfried KASPER, MD
Professor and Chair
Department of Psychiatry and Psychotherapy
Medical University of Vienna
Interview with S. Kasper,Austria
Untreated or inadequately treated somatic diseases are indisputably linked to adverse outcome. This statement, tantamount to a medical principle, curiously tends sometimes not to be thought to apply likewise to psychiatric diseases in general, and depression in particular. This is compoundedby the fact that anumberofguidelinesnow recommend a “watchful waiting” approach in the presence of mild-to-moderate depression before pharmacotherapy is instituted. Ever since the introduction of the antidepressants, researchers have been striving to determine which agent(s) and which variables best contribute to a beneficial outcome in depressed patients. Thus attempts have been made to establish the predictive value of demographic, clinical, and biological variables in the outcome of short- and longterm antidepressant treatment. Most data have been obtained retrospectively and concern uncontrolled treatment modalities. These findings indicate that patients suffering from 3 or more episodes of depression, or 2 episodes and additional vulnerability factors are at high risk for relapse/recurrence. Knowledge of these risk factors may help prevent depression from running a chronic course. In addition to demographic, illness, and treatment variables there is still a need to evaluate biological measures (vulnerability factors), for their potential usefulness in helping clinicians determine which patients would stand to benefit from somatic or nonsomatic therapies, whether at the beginning, continuation, and/or maintenance treatment phases.
What are the main categories of predictors of response to antidepressant treatment?
Prospective data concerning clinical predictors of antidepressant treatment response are scarce. In most randomized controlled trials (RCTs), predictors are analyzed post-hoc and not defined at the beginning of the study, because the intent is to establish the efficacy of antidepressant treatment.1
Most available data are therefore derived from retrospective assessment of the course and outcome of depression.2 These predictors include demographic, illness, treatment, and biological variables. Table I (page 188) summarizes the potential predictors of response to antidepressant treatment.
How important are demographic variables in depression?
The influence of age of onset on outcome in antidepressant trials is controversial. In the long-term RCT study of Frank et al3 no such association was found. However, previous observations showed that both early onset of depression (under the age of 40 years) and late onset (after the age of 50 years) are associated with a higher risk of recurrence. These diverging results may be explained by the fact that late-onset data have been obtained in clinical populations,4,5 whereas the relationship between early onset of depression and higher risk of recurrence was established in a nonclinical population.6 The latter data were obtained as part of the National Institute of Mental Health (NIMH) Collaborative Study Program on the Psychobiology of Depression (see below). However, some data from clinical populations indicate that an early onset of depression (before age 20) is significantly associated with recurrence.7
Table I. Predictors of outcome of antidepressant treatment and
possible factors favoring chronicity of depression
Bauwens et al8 studied the predictive value of psychosocial vulnerability factors within the observation period of 12 months in a sample of unipolar and bipolar patients. Before entry into the study, these patients were stabilized for 6 months with reference to the continuation phase. Unipolar patients were then treated with tricyclics, and bipolar patients with lithium, both in an open-label fashion. The authors found that one of the most robust predictors was adjustment to work, both in the unipolar and in the bipolar groups. In bipolar patients, low degree of social and leisure activities and low degree of selfesteem predicted major and minor recurrences of depression. In unipolar patients, low degree of marital adjustment was associated with recurrences. No such association was reported with life events.
Keitner et al9 studied factors associated with 12-month outcome in 28 inpatients with a DSM-III (Diagnostic and Statistical Manual of Mental Diseases, 3rd Edition) diagnosis of major depression. The majority of patients who had recovered by 12 months (49% of total sample) had done so within an average duration of 4.9 months. The five most important factors related to recovery were shorter length of hospital stay, older age at onset of depression, better family functioning, fewer than two previous hospitalizations, and absence of comorbid illness.
What can you tell us about illness-related variables?
In terms of diagnosis it is important to distinguish between single and recurrent episodes. Angst et al10 and Lavori et al11 showed in natural observation studies that the probability of recurrence is a function of the number of previous episodes. In these studies, the recurrence rate was below 50%when patients experienced one episode. When patients had suffered a second episode, the likelihood of a recurrence was between 50% and 90%, and if patients had a history of 3 or more episodes, the recurrence rate was above 90%. In a placebocontrolled continuation treatment study (6 months) with ami- triptyline, Mindham et al12 found that in patients with no prior episode, the relapse rate was 46%in the placebo group, compared with 27% in the amitriptyline group. This was not different in patients with more than one previous episode (55% in the placebo group and 15% in the amitriptyline group). These data indicate that even one episode of depression is associated with a high likelihood of relapse/recurrence if not treated properly. Clinical variables such as age,13 comorbid personality disorders,14 pain,15 anxiety,16 or residual symptoms17 may influence treatment outcome with tricyclic antidepressants. Behavioral signs such as early changes in psychomotor retardation18 and anxiety,19,20 or a combination of both,21 may be clinically useful to predict long-term treatment response (Table II).
Table II. Predictors of chronicity of depression.
Wells et al22 studied the impact of the number of parental episodes on the recovery from depression in the index patient. Patients whose parents had experienced equal to or more than 2 episodes recovered significantly more slowly from their depression within a 2-year observation period. In contrast, nearly all patients whose parents had suffered either no or just one episode of depression recovered, while over 30% of patients remained depressed if their parents had suffered 2 or more episodes of depression. The influence of family risk factors on the course of affective illness has also been studied by Winokur et al,23 who showed that early-onset depression was associated with having female relatives with depression and male relatives with alcoholism and/or sociopathy. In patients with late-onset depression, male and female relatives had equal rates of depression and there was little alcoholism in the families. Based on the pattern of familial psychopathology described by Winokur et al,24 it was found that patients with a depressive spectrum disease (DSD), ie, family history of alcoholism or antisocial personality were much less likely to be symptom-free for extended periods.25
Thies-Flechtner et al26 and Rouillon et al27 showed that patients with a potential problem of suicidality were more likely to benefit from lithium or an antidepressant with a predominantly serotonergic mechanism of action rather than from carbamazepine or the noradrenergic compound maprotiline. Thies-Flechtner et al reported significantly lower rates of suicide in patients treated with lithium compared with carbamazepine during a 2½-year observation period. Rouillon et al,27 in their 1-year trial, found that treatment with maprotiline was significantly associated with more suicides and suicide attempts compared with placebo. This finding is insofar of importance, as a significant reduction in depression scores was reported with maprotiline, compared with placebo, in the total group. It therefore appears that a serotonergic mechanism of action is beneficial in the long-term treatment of patients with suicidal problems.
The results of a 6-year follow-up of the already mentioned NIMH Collaborative Program on the Psychobiology of Depression (596 patients) revealed a higher rate of relapse in patients with comorbidities.28 After 6 years there was a 34% rate of relapse in patients with major depression. However, if depression was comorbid with one of the following other disorders, the relapse rate was significantly higher: drug abuse (57%), phobia (53%), panic disorder (52%), alcohol dependence (44%). The likelihood of relapse was also higher when patients had a history of major depression and nonaffective disorder, compared with the group of patients with a history of major depression only. This was particularly prominent in the younger (<30 years) and older (>50 years) patients, but not in the middle-aged group (30 to 50 years).
Keller and Shapiro29 studied the comorbidity of major depression superimposed on chronic minor depression, termed double depression (dysthymia in DSM-III-R terminology). In the group of 133 patients who completed the 2-year follow-up in the NIMH Collaborative Program on the Psychobiology of Depression,30 they found that 78% of patients recovered within this time span if they just suffered from major depression alone. However, there was only a 39% recovery rate for patients with double depression (recovered from both major and minor depression), and a 58% recovery rate for patients with major, but not chronic minor, depression.Three percent did not recover from both forms of depression.
Another clinically relevant factor in the long-term treatment of depression (cycle length (time from the onset of one episode to the onset of the next) was reported as early as 1913 by Kraepelin31 and later confirmed by Zis,5 Angst,32 and Roy-Byrne et al.33 These authors found that cycle length has a tendency to shorten with each episode. Specifically, the interval of being well between the first and the second episode was longer than that between each subsequent set of episodes. Furthermore, there is convincing evidence that the greater the number of episodes, the greater the risk of occurrence of another episode (summarized by Goodwin et al34). As shown by Post35 and Post et al,36 this finding has implications for pharmacological treatment, since, according to the “kindling model,” patients with a higher number of episodes may benefit from carbamazepine rather than from antidepressants or lithium. A further important point, derived from epidemiological data,4 is the finding that the older the patients at the onset of illness, the likelier the early relapses if they were untreated. Age is, however, also reported to be negatively correlated with the likelihood of recurrences.6 Based on these findings it appears that number of prior episodes, psychopathology, and comorbidity are strong predictors of future episodes.
How do the different antidepressant drugs influence the response to antidepressant treatment?
New-generation antidepressants, like the selective serotonin reuptake inhibitors (SSRIs) or agomelatine,37 have a lower and more favorable side-effect profile.38 Recent acute and long-term studies indicate that they are as effective as tricyclic antidepressants, at least over the studied time period of 1 year.39 Since the occurrence of side effects is the major reason for patients to discontinue antidepressant medication, it seems likely that these antidepressants have a higher chance of being taken in the long term than antidepressants with an unfavorable side-effects profile, like the tri- or tetracyclic antidepressants. Kupfer et al40 sought whether early treatment intervention in recurrent depression shortened the length of the episode. In a group of 45 patients the authors found that early intervention shortened the overall length of a depressive episode by approximately 4 to 5 months.
The most robust clinical predictorof response toantidepressant treatment seems to be an early onset of clinical changes.41-43 Absence of any change (improvement less than 20%) during the first 2 weeks of treatment was highly predictive of later clinical nonresponse.44,45 Early onset of improvement has been shown repeatedly to be highly predictive of later outcome.44-46 Effective antidepressant treatment initiates clinical changes even during the first week of antidepressant treatment.47,48 Interestingly, use of half-dose antidepressant treatment resulted in a worse outcome than full-dose treatment.49
What is the status today of biological variables?
Despite all endeavors to predict clinical response to antidepressant treatments, there is a paucity of biological predictors of antidepressant treatment outcome and no biological method is sufficiently developed to be used in daily clinical practice.50
One of the earliest approaches was the study of electroencephalographic (EEG) sleep variables, as reported by Kupfer et al51 and Frank et al.52 Considerable objective data on sleep changes associated with affective illness were already obtained over 20 years ago, but fewer thereafter.53 The most reliable predictive parameters associated with sleep abnormalities in depression were sleep discontinuity disturbance, diminished slow-wave sleep (delta), shift from slow-wave activity from the first to the second nonrapid eye-movement (NREM) period, shortened rapid eye-movement (REM) latency, and alteration in temporal distribution of REM-sleep. Among these sleep variables, a specific measure termed delta sleep ratio (ratio of average delta wave counts in the first NREM period to average counts in the second NREM period) has been shown to be a strong predictor of short- and long-term outcome in depression. Kupfer et al51 reported that the delta sleep ratio predicted the survival time following discontinuation of interpersonal psychotherapy (IPT-M). Survival time was significantly longer (P<0.05) in patients with a delta ratio >1.1 (95.7±12.4 weeks) compared with the group with a delta ratio <1 (50.4± 13.6 weeks). These data suggest that a low delta ratio reflects the “need for pharmacotherapy” in maintenance treatment. Interestingly, there was no clinical characteristic that differentiated the group with a delta ratio of below 1.1, compared with a delta ratio of 1.1 or greater. Probably the most promising approaches concerning biological predictors relate to genetic investigations.54,55 Studies of candidate genes such as the serotonin transporter gene have shown that genetic variations influence the outcome of treatment with SSRIs, but the effects were relatively small56,57 and inconsistent with regard to various ethnic groups. Genetic variations of the norepinephrine transporter gene showed an influence on the outcome of treatment with norepinephrine reuptake inhibitors.58 Brain-derived neurotrophic factor (BDNF)59 and angiotensin-converting enzyme (ACE)60,61 gene polymorphisms seem to be associated with treatment effects with various antidepressants. Pharmacodynamic mechanisms associated with the genetic variants of the drug transporter Pglycoprotein or differential degradation capabilities associated with cytochrome P450 polymorphisms62 may also play a crucial role in the response to antidepressant treatments. Genome-wide association studies in large samples including multilocus analyses also confirmed the multifactorial influence of genetic and clinical variables on the outcome of antidepressant treatment.63,64 Nevertheless, the high expectations raised by pharmacogenetic investigations in terms of their possible contribution to personalized antidepressant treatment have had to be scaled down due to the relatively large number of associations that were discovered to have only a very small magnitude of effect.55,65 Genetic testing for antidepressant treatment prediction may meet our expectations in the near future, but to date, no genetic test has found its way into clinical practice.
The possibility of including imaging studies in genetic paradigms could probably enhance our understanding of biological mechanisms involved in the response to treatment modalities.66 Electrophysiological investigations using, eg, quantitative pharmacoelectroencephalographic biomarkers, auditory evoked potentials, as well as other methods,67-74 has failed to provide consistent results.
Endocrine biomarkers that have been in widespread use for decades, such as those related to the activity of the hypothalamic- pituitary-adrenal axis, studied with the dexamethasone suppression test or the combined dexamethasone/corticotropin releasing hormone (Dex/CRH) test,75-78 have not been conclusively proven to be associated with depressed states and relapse probability. These techniques have failed to find their way into clinical routine, in spite of the availability of simplified testing procedures,79 most probably due to conflicting results concerning a clear prediction of treatment outcomes. Hypothalamic-pituitary-thyroid axis testing showing associations with SSRI response80 are likewise not used in clinical routine. Interestingly, nocturnal change in thyroid-stimulating hormone and prolactin was associated with response to therapeutic sleep deprivation81 as a possible indicator of serotonin alteration. Along this line, a psychopathological response, ie, with depression to tryptophan depletion, predicted future depression episodes in seasonal affective disorder.82
Brain imaging techniques like regional cerebral blood flow measured with single photon emission computer tomography (SPECT) provide a predictor estimate for SSRI treatment response83 and regional brain activity in the midbrain measured with positron emission tomography (PET) is correlated with remission after 3 months of SSRI treatment.84 The availability of serotonin transporter was associated with positive outcome in a recent PET-study.85 But, again, these methods are not in clinical use.
Are there any specific predictors for chronicity of depression?
Several factors have been found to be associated with chronicity for depression (see also Table II). Chronicity is more likely to develop: if there has been a longer illness episode prior to treatment,86 in older individuals,87 in bipolar patients,88 with psychotic symptoms,89 with a family history of affective illness,90 and with premorbid neurotic traits and neuroticism.91 A worse outcome has been found to be associated with negative life events and absence of social support, whereas positive life events predicted better outcome.92 Biological markers for chronicity have not shown a consistent pattern. Thus, although persistent dexamethasone nonsuppression at the time of discharge has been shown to predict a greater risk of early relapse,93 this is not a consistent finding and has not been confirmed by other authors.94
Chronicity can also be related to partial remission. This often causes considerable disability and increases the burden on the family. In a follow-up study, Kupfer and Spiker95 found that approximately one third of inpatients treated with amitriptyline were partial responders. These residual symptoms have also been shown to predict higher relapse and recurrence rates in naturalistic follow-up studies, for instance.17,96
So what conclusion would you draw based on the current understanding of predictive factors in depression?
In integrating the potential methods for the prediction of antidepressant response and the associated concepts,97,98 it is important to note that depression is a long-term illness and that it is necessary to determine as rapidly as possible which patients should receive maintenance treatment in order to prevent the development of a chronic course of the illness. Some guidance can be found in the literature, like the survey of Angst et al conducted in over 400 patients and followed up for 20 years.32 According to this survey, patients experiencing two major episodes within 5 years have a 70% probability of developing two or more episodes during the subsequent 5 years. This has led to the recommendation that a patient should have at least two or three well-defined episodes requiring psychiatric intervention before being treated with maintenance drug therapy. Based on the now existing literature we suggest including other variables to determine which patients are likely to benefit from long-term antidepressant therapy. Table III summarizes the items that would be worth further investigation in future prospective trials.
Table III. Who is likely to benefit from maintenance pharmacotherapy?
To sumup, alongside demographic, illness, and treatment variables, there is still a need for evaluating biological measures (vulnerability factors), to determine which patients would be likely to benefit from somatic or nonsomatic therapies, either at the beginning of treatment or for the continuation and maintenance treatment phases. Better knowledge of these factors would also help to better understand what are the risk factors associated with chronicity of depression, and possibly lead to new treatment approaches. _
1. Kasper S. The rationale for long-term antidepressant therapy. Int Clin Psychopharmacol. 1993;8(4):225-235.
2. Coryell W, Winokur G. Course and Outcome. Handbook of Affective Disorders. Edinburgh, UK; Churchill Livingstone. 1992:89-108.
3. Frank E, Kupfer DJ, Perel JM, McEachran MS, Grochocenski VJ. Maintenance therapies on recurrent depression protocol; treatment outcome. Arch Gen Psychiat. 1990;47:1093-1099.
4. Grof P, Angst J, Haines T. The clinical course of depression: practical issues. In: Angst J, ed. Symposia Medica Hoechst: Classification and Prediction of Outcome of Depression. Vol 8. New York, NY: Schattauer; 1974:141-148.
5. Zis AP, Grof P, Goodwin FK. The natural course of affective disorders: implications for lithium prophylaxis. In: Cooper TB, Gershon S, Kline NS, Schou M, eds. Lithium: Controversies and Unresolved Issues. Amsterdam, Netherlands: Excerpta Medica; 1979:381-389.
6. CoryellW, Endicott J, Keller MB. Predictors of relapse into major depressive disorder in a nonclinical population. Am J Psychiatry. 1991;148:1353-1358.
7. Giles DE, Jarrett RB, Biggs MM, Guzick DS, Rush AJ. Clinical predictors of recurrence in depression. Am J Psychiatry. 1989;146:764-767.
8. Bauwens F, Mendlewicz J, Staner L, Pardoen D. Psychosocial vulnerabilty factors and long-term treatment of depression: Perspectives for future studies. Paper presented at: IX. World Congress of Psychiatry, 1994.
9. Keitner GI, Ryan CE, Miller IW, Norman WH. Recovery and major depression: Factors associated with twelve-month outcome. Am J Psychiatry. 1992;149: 93-99.
10. Angst J, Baastrup P, Grof P, Hippius H, Poldinger W, Weis P. The course of monopolar depression and bipolar psychoses. Psychiatr Neurol Neurochir. 1973;76(6):489-500.
11. Lavori PW, Keller MB, ScheftnerW, Fawcett J, Mueller TI, CoryellW. Recurrence after recovery in unipolar MDD: An observational follow-up study of clinical predictors and somatic treatment as a mediating factor. Int J Meth Psych Res. 1994;4:211-229.
12. Mindham RH, Howland C, Shepherd M. An evaluation of continuation therapy with tricyclic antidepressants in depressive illness. Psychol Med. 1973;3(1):5-17.
13. Mulder RT, Watkins WG, Joyce PR, Luty SE. Age may affect response to antidepressants with serotonergic and noradrenergic actions. J Affect Disord. 2003; 76:143-149.
14. Mulder RT, Joyce PR, Luty SE. The relationship of personality disorders to treatment outcome in depressed outpatients. J Clin Psychiatry. 2003;64(3):259- 264.
15. Schreiber S, Frishtick R, Volis I, Rubovitch V, Pick CG,Weizman R. The antinociceptive properties of reboxetine in acute pain. Eur Neuropsychopharmacol. 2009;19(10):735-739.
16. Fava M, Rush AJ, Alpert JE, et al. Difference in treatment outcome in outpatients with anxious versus nonanxious depression: a STAR*D report. Am J Psychiatry. 2008;165(3):342-351.
17. Paykel ES, Ramana R, Cooper Z, Hayhurst H, Kerr J, Barocka A. Residual symptoms after partial remission: an important outcome in depression. Psychol Med. 1995;25(6):1171-1180.
18. Parker G. Defining melancholia: the primacy of psychomotor disturbance. Acta Psychiatr Scand Suppl. 2007(433):21-30.
19. Katz MM, Bowden CL, Frazer A. Rethinking depression and the actions of antidepressants: uncovering the links between the neural and behavioral elements. J Affect Disord. 2010;120:16-23.
20. Katz MM, Houston JP, Brannan S, et al. A multivantaged behavioural method for measuring onset and sequence of the clinical actions of antidepressant. Int J Neuropsychopharmacol. 2004;7:471-479.
21. Rampello L, Chiechio S, Nicoletti G, et al. Prediction of the response to citalopram and reboxetine in post-stroke depressed patients. Psychopharmacology (Berl). 2004;173(1-2):73-78.
22. Wells KB, Burnam MA, Rogers W, Hays R, Camp P. The course of depression in adult outpatients. Results from the Medical Outcomes Study. Arch Gen Psychiatry. 1992;49(10):788-794.
23. Winokur G, Cadoret R, Dorzab J, Baker M. Depressive disease: a genetic study. Arch Gen Psychiatry. 1971;24(2):135-144.
24. Winokur G, Behar D, Van Valkenburg C, Lowry M. Is a familial definition of depression both feasible and valid? J Nerv Ment Dis. 1978;166:764-768.
25. Smith EM, North CS. Familial subtypes of depression: a longitudinal perspective. J Affect Disord. 1988;14(2):145-154.
26. Thies-Flechtner K, Seibert W, Walther A, Greil W, Müller-Oerlinghausen B. Suizide bei rezidivprophylaktisch behandelten Patienten mit affektiven Psychosen. In: Müller-Oerlinghausen B, Berghöfer A, eds. Ziele und Ergebnisse der medikamentösen Prophylaxe affektiver Psychosen. Stuttgart, Germany: Georg Thieme Verlag; 1994:61-64.
27. Rouillon F, Philips R, Serrurier E, Ansart E, Gérard MJ. Prophylactic efficacy of maprotiline on relapses of unipolar depression. Encéphale. 1989;15:527-534.
28. Coryell W, Keller M, Endicott J, Andreasen N, Clayton P, Hirschfeld R. Bipolar II illness: course and outcome over a five-year period. Psychol Med. 1989; 19(1):129-141.
29. Keller MB, Shapiro RW. “Double depression”: superimposition of acute depressive episodes on chronic depressive disorders. Am J Psychiatry. 1982;139(4): 438-442.
30. Keller MB, Lavori PW, Endicott J, Coryell W, Klerman GL. “Double depression”: two-year follow-up. Am J Psychiatry. 1983;140(6):689-694.
31. Kraepelin E. Psychiatrie. Ein Lehrbuch für Studierende und Ärzte. Vol 8. Leipzig, Germany: Joh. Ambrosius Barth; 1913.
32. Angst J. Course of affective disorders. In: van Praag H, Lader M, Rafaelsen OJ, Sachar EJ, eds. Handbook of Biological Psychiatry. Part IV. New York, NY: Marcel Dekker; 1981:225-242.
33. Roy-Byrne P, Post RM, Uhde TW, Porcu T, Davis D. The longitudinal course of recurrent affective illness: life chart data from research patients at the NIMH. Acta Psychiatr Scand Suppl. 1985;317:1-34.
34. Goodwin FK, Jamison KR. Course and Outcome. Manic-Depressive Illness. New York, NY: Oxford University Press; 1990:127-156.
35. Post RM. Transduction of psychosocial stress into the neurobiology of recurrent affective disorder. Am J Psychiat. 1992;149:999-1010.
36. Post RM, Ketter TA, Pazzaglia PJ, Geroge MS, Marangell L, Denicoff K. New developments in the use of anticonvulsants as mood stabilizers. Neuropsychobiology. 1993;27:132-137.
37. Kasper S, Hamon M. Agomelatine, a new antidepressant with an innovative mechanism of action—an overview on its preclinical and clinical development program. World J Biol Psychiatry. 2009;10:117-126.
38. Kasper S, Höflich G, Scholl HP, Möller HJ. Safety and antidepressant efficacy of selective serotonin re-uptake inhibitors. Hum Psychopharm Clin. 1994;9: 1-12.
39. Bauer M, Bschor T, Pfennig A, et al; World Federation of Societies of Biological Psychiatry (WFSBP) Guidelines for Biological Treatment of Unipolar Depressive Disorders in Primary Care. WFSBP Task Force on Unipolar Depressive Disorders. World J Biol Psychiatry. 2007;8:67-104.
40. Kupfer DJ, Perel JM, Frank E. Adequate treatment with imipramine in continuation treatment. J Clin Psychiatry. 1989;50(7):250-255.
41. Henkel V, Seemüller F, Obermeier M, et al. Does early improvement triggered by antidepressants predict response/remission? Analysis of data from a naturalistic study on a large sample of inpatients with major depression. J Affect Disord. 2009;115:439-449.
42. Kieser M, Szegedi A. Predicting stable treatment response in patients with major depression treated with hypericum extract WS 5570/5572. Pharmacopsychiatry. 2005;38(5):194-200.
43. Lutz W, Stulz N, Kock K. Patterns of early change and their relationship to outcome and follow-up among patients with major depressive disorders. J Affect Disord. 2009;118:60-68.
44.CNS Drugs. 1998;9:117-84.
45. Stassen HH, Angst J, Ini-Stula A. Delayed onset of action of antidepressant drugs? Survey of results of Zurich meta-analysis. Pharmacopsychiatry. 1996;29: 87-96.
46. Stassen HH, Angst J, Delini-Stula A. Onset of action under antidepressant treatment. Eur Psychiatry. 1997;12(4):163-165.
47. Stassen HH, Angst J, Delini-Stula A. Fluoxetine versus moclobemide: crosscomparison between the time courses of improvement. Pharmacopsychiatry. 1999;32(2):56-60.
48. Tayor MA, Fink M. Melancholia: The Diagnosis, Pathophysiology and Treatment of Depressive Illness. Cambridge, UK: Cambridge University Press; 2006.
49. Frank E, Kupfer DJ, Perel JM, et al. Comparison of full-dose versus half-dose pharmacotherapy in the maintenance treatment of recurrent depression. J Affect Disord. 1993;27(3):139-145.
50. Mossner R, Mikova O, Koutsilieri E, et al. Consensus paper of the WFSBP Task Force on Biological Markers: biological markers in depression. World J Biol Psychiatry. 2007;8(3):141-174.
51. Kupfer DJ, Frank E, McEachran AB, Grochocinski VJ. Delta sleep ratio. A biological correlate of early recurrence in unipolar affective disorder. Arch Gen Psychiatry. 1990;47(12):1100-1105.
52. Frank E, Kupfer DJ, Wagner EF, McEachran AB. Biological measures as predictors of response to pharmacotherapy and psychotherapy. Clin Neuropharmacol. 1992;15:578A-597A.
53. Reynolds CF, 3rd, Kupfer DJ. Sleep research in affective illness: state of the art circa 1987. Sleep. 1987;10(3):199-215.
54. Kirchheiner J, Nickchen K, Bauer M, et al. Pharmacogenetics of antidepresssants and antipsychotics: the contribution of allelic variations to the phenotype of drug response. Mol Psychiatry. 2004;9:442-473.
55. Schosser A, Kasper S. The role of pharmacogenetics in the treatment of depression and anxiety disorders. Int Clin Psychopharmacol. 2009;24(6):277-288.
56. Collier DA, Stober G, Li T, et al. A novel functional polymorphism within the promoter of the serotonin transporter gene: possible role in susceptibility to affective disorders. Mol Psychiatry. 1996;1(6):453-460.
57. Kirchheiner J, Grundemann D, Schomig E. Contribution of allelic variations in transporters to the phenotype of drug response. J Psychopharmacol. 2006; 20:27-32.
58. Yoshida K, Takahashi H, Higuchi H, et al. Prediction of antidepressant response to milnacipran by norepinephrine transporter gene polymorphisms. Am J Psychiatry. 2004;161(9):1575-1580.
59. Yoshida K, Higuchi H, Kamata M, et al. The G196A polymorphism of the brainderived neurotrophic factor gene and the antidepressant effect of milnacipran and fluvoxamine. J Psychopharmacol. 2007;21(6):650-656.
60. Baghai TC, Schule C, Zill P, et al. The angiotensin I converting enzyme insertion/ deletion polymorphism influences therapeutic outcome in major depressed women, but not in men. Neurosci Lett. 2004;363:38-42.
61. Baghai TC, Schule C, Zwanzger P, et al. Possible influence of the insertion/deletion polymorphism in the angiotensin I-converting enzyme gene on therapeutic outcome in affective disorders. Mol Psychiatry. 2001;6(3):258-259.
62. Mihaljevic-Peles A, Sagud M, Bozina N, Zivkovic M. Pharmacogenetics and antidepressant treatment in integrative psychiatry perspective. Psychiatr Danub. 2008;20:399-401.
63. Ising M, Lucae S, Binder EB, et al. A genomewide association study points to multiple loci that predict antidepressant drug treatment outcome in depression. Arch Gen Psychiatry. 2009;66(9):966-975.
64. Uher R, Huezo-Diaz P, Perroud N, et al. Genetic predictors of response to antidepressants in the GENDEP project. Pharmacogenomics J. 2009;9(4):225-233.
65. Arranz MJ, Kapur S. Pharmacogenetics in psychiatry: are we ready for widespread clinical use? Schizophr Bull. 2008;34(6):1130-1144.
66. Scharinger C, Rabl U, Sitte HH, Pezawas L. Imaging genetics of mood disorders. Neuroimage. 2010;53(3):610-621.
67. Bares M, Brunovsky M, Kopecek M, et al. Early reduction in prefrontal theta QEEG cordance value predicts response to venlafaxine treatment in patients with resistant depressive disorder. Eur Psychiatry. 2008;23(5):350-355.
68. Iosifescu DV. Prediction of response to antidepressants: is quantitative EEG (QEEG) an alternative? CNS Neurosci Ther. 2008;14(4):263-265.
69. Iosifescu DV, Greenwald S, Devlin P, et al. Frontal EEG predictors of treatment outcome in major depressive disorder. Eur Neuropsychopharmacol. 2009;19 (11):772-777.
70. Korb AS, Hunter AM, Cook IA, Leuchter AF. Rostral anterior cingulate cortex theta current density and response to antidepressants and placebo in major depression. Clin Neurophysiol. 2009;120(7):1313-1319.
71. Leuchter AF, Cook IA, Gilmer WS, et al. Effectiveness of a quantitative electroencephalographic biomarker for predicting differential response or remission with escitalopram and bupropion in major depressive disorder. Psychiatry Res. 2009;169(2):132-138.
72. Leuchter AF, Cook IA, Marangell LB, et al. Comparative effectiveness of biomarkers and clinical indicators for predicting outcomes of SSRI treatment in Major Depressive Disorder: results of the BRITE-MD study. Psychiatry Res. 2009; 169(2):124-131.
73. Mulert C, Juckel G, Augustin H, Hegerl U. Comparison between the analysis of the loudness dependency of the auditory N1/P2 component with LORETA and dipole source analysis in the prediction of treatment response to the selective serotonin reuptake inhibitor citalopram in major depression. Clin Neurophysiol. 2002;113:1566-1572.
74. Pogarell O, Juckel G, Norra C, et al. Prediction of clinical response to antidepressants in patients with depression: neurophysiology in clinical practice. Clin EEG Neurosci. 2007;38(2):74-77.
75. Bschor T, Baethge C, Adli M, Eichmann U, et al. Association between response to lithium augmentation and the combined DEX/CRH test in major depressive disorder. Psychiatr Res. 2003;37:135-143.
76. Ising M, Kunzel HE, Binder EB, Nickel T, Modell S, Holsboer F. The combined dexamethasone/CRH test as a potential surrogate marker in depression. Prog Neuropsychopharmacol Biol Psychiatry. 2005;29(6):1085-1093.
77. Schule C, Baghai TC, Eser D, et al. The combined dexamethasone/CRH Test (DEX/CRH test) and prediction of acute treatment response in major depression. PLoS One. 2009;4(1):e4324.
78. Zobel AW, Nickel T, Sonntag A, Uhr M, Holsboer F, Ising M. Cortisol response in the combined dexamethasone/CRH test as predictor of relapse in patients with remitted depression. a prospective study. J Psychiatr Res. 2001;35(2): 83-94.
79. Baghai TC, Schule C, Zwanzger P, et al. Evaluation of a salivary based combined dexamethasone/CRH test in patients with major depression. Psychoneuroendocrinology. 2002;27(3):385-399.
80. Brouwer JP, Appelhof BC, Peeters RP, et al. Thyrotropin, but not a polymorphism in type II deiodinase, predicts response to paroxetine in major depression. Eur J Endocrinol. 2006;154:819-825.
81. Kasper S, Sack DA, Wehr TA, Kick H, Voll G, Vieira A. Nocturnal TSH and prolactin secretion during sleep deprivation and prediction of antidepressant response in patients with major depression. Biol Psychiatry. 1988;24(6):631-641.
82. Neumeister A, Habeler A, Praschak-Rieder N, Willeit M, Kasper S. Tryptophan depletion: a predictor of future depressive episodes in seasonal affective disorder? Int Clin Psychopharmacol. 1999;14(5):313-315.
83. Brockmann H, Zobel A, Joe A, et al. The value of HMPAO SPECT in predicting treatment response to citalopram in patients with major depression. Psychiatry Res. 2009;173(2):107-112.
84. Milak MS, Parsey RV, Lee L, et al. Pretreatment regional brain glucose uptake in the midbrain on PET may predict remission from a major depressive episode after three months of treatment. Psychiatry Res. 2009;173(1):63-70.
85. Akimova E, Lanzenberger R, Savli M, et al. Serotonin transporter availability predicts clinical outcome in patients with major depressive disorder. [11 C] DASB PET Study. Int J Neuropsychopharmacol. 2010;13(1):70.
86. Keller MB, Klerman GL, Lavori PW, Coryell W, Endicott J, Taylor J. Long-term outcome of episodes ofmajor depression. Clinical and public health significance. JAMA. 1984;252(6):788-792.
87. Keller MB, Lavori PW, Rice J, Coryell W, Hirschfeld RM. The persistent risk of chronicity in recurrent episodes of nonbipolar major depressive disorder: a prospective follow-up. Am J Psychiatry. 1986;143(1):24-28.
88. Akiskal HS, King D, Rosenthal TL, Robinson D, Scott-Strauss A. Chronic depressions. Part 1. Clinical and familial characteristics in 137 probands. J Affect Disord. 1981;3(3):297-315.
89. Lee AS, Murray RM. The long-term outcome of Maudsley depressives. Br J Psychiatry. 1988;153:741-751.
90. Scott J, BarkerWA, Eccleston D. The Newcastle Chronic Depression Study. Patient characteristics and factors associated with chronicity. Br J Psychiatry. 1988;152:28-33.
91. Weissman MM, Klerman GL. The chronic depressive in the community: unrecognized and poorly treated. Compr Psychiatry. 1977;18(6):523-532.
92. Paykel ES, Cooper Z. Life events and social support. In: Paykel ES, ed. Handbook of Affective Disorders. Edinburgh, UK: Churchill Livingstone; 1992:149-170.
93. Holsboer F, Liebl R, Hofschuster E. Repeated dexamethasone suppression test during depressive illness. Normalisation of test result compared with clinical improvement. J Affect Disord. 1982;4(2):93-101.
94. Peselow ED, Baxter N, Rieve RR, Barouche F. The dexamethasone suppression test as a monitor of clinical recovery. Am J Psychiatry. 1987;144(1):30-35.
95. Kupfer DJ, Spiker DG. Refractory depression: prediction of non-response by clinical indicators. J Clin Psychiatry. 1981;42(8):307-312.
96. Faravelli C, Ambonetti A, Palanti S, Pazzagli A. Depressive relapses and incomplete recovery from index episode. Am J Psychiatry. 1986;143:888-891.
97. Kemp AH, Gordon E, Rush AJ, Williams LM. Improving the prediction of treatment response in depression: integration of clinical, cognitive, psychophysiological, neuroimaging, and genetic measures. CNS Spectr. 2008;13:1066-1086.
98. Mulert C, Juckel G, BrunnmeierM, et al. Prediction of treatment response inmajor depression: integration of concepts. J Affect Disord. 2007;98:215-225.
Keywords: depression; prediction; antidepressant; recurrence; chronicity