Steve Balt, MD, MS

With over 2 dozen FDA-approved antidepressants on the market, it is reasonable to ask: which antidepressants are most effective? After decades of clinical experience and literally millions of prescriptions written over the years, it stands to reason that 1 or 2 agents have risen from the pack to outshine the rest.

Unfortunately, clinical experience shows this not to be the case. The general consensus is that despite their different mechanisms of action, all current antidepressants seem to have more or less the same effect. The functional equivalency of antidepressants is highlighted in practice guidelines and, understandably, serves as justification for restricted formulary access to more expensive agents.

 As a result, most psychiatrists choose antidepressants not on the basis of efficacy, but rather on the basis of insurance coverage, adverse-effect profiles, or particular clinical features of depression (eg, melancholic, atypical, anxious features), for which some differences in efficacy do exist.

[[{"type":"media","view_mode":"media_crop","fid":"25025","attributes":{"alt":"antidepressants","class":"media-image media-image-right","id":"media_crop_5130693316609","media_crop_h":"0","media_crop_image_style":"-1","media_crop_instance":"2226","media_crop_rotate":"0","media_crop_scale_h":"139","media_crop_scale_w":"200","media_crop_w":"0","media_crop_x":"0","media_crop_y":"0","style":"margin: 1px; float: right;","title":" ","typeof":"foaf:Image"}}]]The question of how well antidepressants work for the routine treatment of depression can be answered in terms of efficacy or effectiveness. An efficacy trial asks the question, Does the drug work under ideal circumstances? Although such trials are usually brief (6 to 8 weeks) and interventions are standardized and rarely flexible, they serve as the basis for the FDA’s approval of drugs.

“Effectiveness” concerns the success or failure of drugs in the real world. A true effectiveness study asks the question, Does the drug work under usual conditions? Effectiveness trials enroll a more heterogeneous population, often with comorbid mental illness, substance abuse, or other psychiatric diagnoses, and health care professionals are often free to offer concurrent therapies. As a result, effectiveness trials tend to have more generalizability, or external validity, to real patient populations.

Effectiveness trials help clinicians and policymakers select which medications work best for a given indication in real-world conditions. Surprisingly, despite decades of experience with antidepressants, information on their relative effective-ness is lacking, while health care costs continue to escalate. As a result, more emphasis is being placed on comparative effectiveness research, in which alternative treatments are compared under real-world conditions, and costs and adverse effects are measured in addition to clinical outcomes.

Effectiveness trials are often large, expensive, and time-consuming. They sometimes take the form of a practical clinical trial in which multiple clinically relevant treatment regimens are compared across a large population of subjects. One such landmark study is the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial. In this NIMH-funded study, more than 4000 depressed patients in outpatient psychiatry and primary care practices received citalopram for up to 12 weeks; those who did not improve advanced to later phases that offered augmentation with, or a switch to, a second antidepressant or psychotherapy.

Remission rates in step 1 were low (28%) and decreased further with additional steps. Cumulatively, two-thirds of patients 

 Direct comparison of the effectiveness of antidepressants was not possible because of the overall lack of randomization and poor statistical power.

 Despite the scope and initial aims of the study, no single antidepressant strategy or combination appeared more advantageous than any other.

Other effectiveness trials have yielded similar results. A 24-week trial that randomized patients to sertraline or citalopram found no significant difference between groups.

 Another 24-week trial of 234 patients randomized to receive sertraline or fluoxetine also found no significant difference.

 In a 2001 study, 573 patients were randomized to 1 of 3 SSRIs for 9 months; sertraline, paroxetine, and fluoxetine were equally effective.

 Patients admitted to a German psychiatric hospital for treatment of depression were followed up for 10 weeks, and response and remission rates were 68.9% and 51.9%, respectively, again with no difference among individual antidepressant agents.

 Effectiveness trials, therefore, seem to confirm the conventional wisdom that no single antidepressant works better than-or worse than-any other.

Efficacy trials rarely resemble real-world conditions and, as such, tend to overestimate how well drugs work. Nevertheless, the aggregation of data from multiple efficacy trials can provide a suggestion as to the relative effectiveness of antidepressants.

In 2005, the Agency for Healthcare Research and Quality commissioned an exhaustive review of antidepressants and their use in MDD.

 Close to 300 studies were reviewed, many of them randomized efficacy trials that compared one antidepressant with another. There was sufficient evidence to make 4 drug-drug comparisons: 3 found no significant difference between the two drugs, while another found a small reduction (1.25 points) in the Montgomery-Ãsberg Depression Rating Scale (MADRS) score in patients taking escitalopram, relative to citalopram. Findings indicate that there were no significant differences in effectiveness of antidepressants, although individual drugs did differ in terms of onset of action and ease of dosing. 

 found a similarly slight benefit of both sertraline and venlafaxine over fluoxetine, as well as confirmation of escitalopram’s slight superiority over citalopram.

In a review of 117 randomized trials involving 25,928 patients, Cipriani and colleagues

 identified slight differences between certain pairs of antidepressants. Known as “network analysis,” this technique permits comparisons of 2 drugs according to how well they perform against a common comparator. Specifically, the authors found that mirtazapine, escitalopram, venlafaxine, and sertraline had slightly greater odds of inducing a response than other antidepressants studied. They also compared relative acceptability of antidepressants (by assessing dropouts) and found the most benefit for escitalopram and sertraline, but differences were slight and of questionable clinical significance.

Because a meta-analysis is only as good as the data on which it is based, these meta-analyses must be considered in light of the very real problem of selective publication. This is the tendency for favorable results to be published, while negative or neutral results are not. In an analysis of 74 antidepressant trials registered with the FDA between 1987 and 2004, Turner and colleagues

 found that nearly half (36, or 48.6%) were negative, and the vast majority of these were either not published or were published in a way that made the drug seem favorable. Likewise, industry-sponsored studies are more likely to favor the manufacturer’s drug, often because of nuances in experimental design.

 While most researchers make every effort to include unpublished results in their meta-analyses, the “file-drawer” phenomenon of unpublished negative results may bias the conclusions of analyses that exclude the 

Data appear to confirm 2 stark truths about antidepressants. First, there seem to be no significant differences among them; although future research may uncover patient-specific biomarkers that favor one medication over another, none has yet done so. Second, and somewhat surprisingly, antidepressant effectiveness is quite low. Thus, in the absence of data that can predict the best antidepressant regimen for a patient, enhancing the effectiveness of an antidepressant seems to be the best strategy.

One strategy has nothing to do with antidepressants, but rather involves a reconsideration of what is being treated. Treatment-resistant depression may be better defined as “depression that is resistant to currently available treatments.” Many of these refractory cases may lie on the bipolar spectrum. Study results show that bipolar depression responds poorly to antidepressants, although what counts as “bipolarity” has been the subject of some controversy.

Depression may be multiple conditions, each deserving its own unique treatment approach. Findings suggest that much of the antidepressant response in mild to moderate depression may be due to placebo effect.

 Similarly, patients with a history of trauma may do better with psychotherapy than with medications, while patients with significant anxiety may not respond as well to antidepressants and their depression might resemble a “neurotic” subtype.

Another way to enhance antidepressant effectiveness is to combine antidepressants or use augmentation agents. While combination strategies have intuitive appeal and offer great flexibility, they are not always supported by the available literature. In the Combining Medications to Enhance Depression Outcomes(CO-MED) trial, 665 patients with depression were randomized to receive bupropion plus escitalopram, venlafaxine XR plus mirtazapine, or escitalopram alone. Outcomes at 12 weeks, and again at 7 months, were the same across groups.

 Similarly, while evidence exists for the efficacy of a wide range of augmentation strategies, other analyses have found relatively low effectiveness or excessive cost or adverse-effect burden of some of these approaches.

The more important question may be more about whom we are treating rather than what we treat with. Recent interest in “personalized medicine” seeks to improve depression treatment by using new tools to more accurately identify whom we are treating. It has been estimated that 42% of the variance in antidepressant response can be explained by genetic variation.

 This suggests that nearly half of a patient’s response to an antidepressant may be due to his or her genetic profile. In reality, however, the genetic contribution likely involves an impractically large number of variants, each having a very small effect, that together contribute to the very complex phenotype of antidepressant response. Indeed, 2 meta-analyses, using genome-wide analysis to identify polymorphisms to predict treatment response, found only a 1.2% contribution or no contribution at all.

Another pharmacogenomic approach is to characterize functional variations in patients’ cytochrome P-450 enzymes. Classification of patients as “poor” or “rapid” metabolizers, for instance, may help predict medication choice or dosage. Unfortunately, these approaches are limited. With few possible exceptions, no evidence exists that blood concentrations influence antidepressant outcomes, and there are multiple nongenetic factors that influence drug metabolism, such as diet, other medications, and adherence. Existing studies that show benefit of pharmacogenetic testing are limited because clinicians are unblinded or randomization procedures are poor, a troubling fact given the high rate of placebo response to antidepressant treatment.

Not surprisingly, we can take advantage of patient preferences to enhance treatment outcomes. When patients in a clinical trial receive a treatment they prefer, response rates are significantly higher than when they are randomized to a non-preferred intervention.

 Even when patients’ preferences do not have any bearing on outcome, matching treatments with patients’ preferences increases their willingness to initiate and adhere to a treatment plan.

Clinical trials are often criticized because the ongoing, regular con-tact between patients and clinicians (frequent office visits, abundant personalized attention, etc) may inflate placebo response rates. Indeed, regular contact with health care pro-fessionals has a therapeutic effect in itself, as do patient expectations. When patients in a clinical trial know they will get 1 of 2 active drugs, response rates are one-third higher than when they know they may be randomized to placebo.

Finally, the quality of the therapeutic alliance between prescriber and patient is sometimes a better predictor of patient outcome than which drugs are prescribed. One study found that “effective” prescribers obtained better outcomes with placebos than “less effective” prescribers with active antidepressants.

Asking “which” medication may be less important than the “meaning” of medication to both clinician and patient. The characteristics of communication between prescriber and patient, whether the patient perceives an internal or external locus of control over the outcome, and a host of other factors may be more important than which drug is prescribed.

The generally accepted view that all antidepressants are essentially equivalent in their effectiveness appears valid. Selection of the right antidepressant, therefore, may rely less on matching a patient to a specific medication, and more on a consideration of adverse-effect profiles or medication availability, or on a redefinition of the phenotype of depression altogether. The recent emphasis on personalized antidepressant prescribing seems warranted, but rather than taking a combination or pharmacogenomic approach to medication selection, clinicians should focus more on a personalized approach, establish realistic (but hopeful) expectations, and use patient preferences and beliefs to optimize outcomes.

Dr Balt is Diplomate of the American Board of Psychiatry and Neurology, Diplomate of the American Board of Addiction Medicine, Editor in Chief of The Carlat Psychiatry Report, and Supervising Psychiatrist at John Muir Behavioral Health in Concord, Calif. He reports no personal conflicts of interest concerning the subject matter of this article; however, his wife is employed by Otsuka America, Inc.

Practice Guideline for the Treatment of Patients With Major Depressive Disorder

. 3rd ed. Arlington, VA: American Psychiatric Association; 2010.

 Fava M, Rush AJ, Trivedi MH, et al. Background and rationale for the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study. 

 Rush AJ, Trivedi MH, Wisniewski SR, et al. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. 

 Baghai TC, Blier P, Baldwin DS, et al; Section of Pharmacopsychiatry, World Psychiatric Association. General and comparative efficacy and effectiveness of antidepressants in the acute treatment of depressive disorders: a report by the WPA section of pharmacopsychiatry. 

 Ekselius L, von Knorring L, Eberhard G. A double-blind multicenter trial comparing sertraline and citalopram in patients with major depression treated in general practice. 

 Sechter D, Troy S, Paternetti S, Boyer P. A double-blind comparison of sertraline and fluoxetine in the treatment of major depressive episode in outpatients. 

 Kroenke K, West SL, Swindle R, et al. Similar effectiveness of paroxetine, fluoxetine, and sertraline in primary care: a randomized trial. 

 SeemÃ¼ller F, Riedel M, Obermeier M, et al. Outcomes of 1014 naturalistically treated inpatients with major depressive episode. 

 Gartlehner G, Hansen RA, Thieda P, et al. 

Comparative Effectiveness of Second-Generation Antidepressants in the Pharmacologic Treatment of Adult Depression

. Rockville, MD: Agency for Healthcare Research and Quality, US Dept of Health and Human Services; 2007.

 Gartlehner G, Hansen RA, Morgan LC, et al. 

Second-Generation Antidepressants in the Pharmacologic Treatment of Adult Depression: An Update of the 2007 Comparative Effectiveness Review

. Rockville, MD: Agency for Healthcare Research and Quality, US Dept of Health and Human Services; 2011.

 Gartlehner G, Hansen RA, Morgan LC, et al. Comparative benefits and harms of second-generation antidepressants for treating major depressive disorder: an updated meta-analysis. 

 Cipriani A, Furukawa TA, Salanti G, et al. Comparative efficacy and acceptability of 12 new-generation antidepressants: a multiple-treatments meta-analysis. 

 Turner EH, Matthews AM, Linardatos E, et al. Selective publication of antidepressant trials and its influence on apparent efficacy. 

 Sinyor M, Schaffer A, Smart KA, et al. Sponsorship, antidepressant dose, and outcome in major depressive disorder: meta-analysis of randomized controlled trials. 

 Goldacre B, Heneghan C. Improving, and auditing, access to clinical trial results. 

 Sidor MM, Macqueen GM. Antidepressants for the acute treatment of bipolar depression: a systematic review and meta-analysis. 

 Goldney RD. From mania and melancholia to the bipolar disorders spectrum: a brief history of controversy. 

 Fournier JC, DeRubeis RJ, Hollon SD, et al. Antidepressant drug effects and depression severity: a patient-level meta-analysis. 

 Nemeroff CB, Heim CM, Thase ME, et al. Differential responses to psychotherapy versus pharmacotherapy in patients with chronic forms of major depression and childhood trauma [published correction appears in 

 Ghaemi SN. Why antidepressants are not antidepressants: STEP-BD, STAR*D, and the return of neurotic depression. 

 Rush AJ, Trivedi MH, Stewart JW, et al. Combining medications to enhance depression outcomes (CO-MED): acute and long-term outcomes of a single-blind randomized study. 

 Bschor T, Bauer M. Efficacy and mechanisms of action of lithium augmentation in refractory major depression. 

 Spielmans GI, Berman MI, Linardatos E, et al. Adjunctive atypical antipsychotic treatment for major depressive disorder: a meta-analysis of depression, quality of life, and safety outcomes. 

 Tansey KE, Guipponi M, Hu X, et al. Contribution of common genetic variants to antidepressant response. 

 GENDEP Investigators, MARS Investigators, STAR*D Investigators. Common genetic variation and antidepressant efficacy in major depressive disorder: a meta-analysis of three genome-wide pharmacogenetic studies. 

 Tansey KE, Guipponi M, Perroud N, et al. Genetic predictors of response to serotonergic and noradrenergic antidepressants in major depressive disorder: a genome-wide analysis of individual-level data and a meta-analysis. 

 Kocsis JH, Leon AC, Markowitz JC, et al. Patient preference as a moderator of outcome for chronic forms of major depressive disorder treated with nefazodone, cognitive behavioral analysis system of psychotherapy, or their combination. 

 Winter SE, Barber JP. Should treatment for depression be based more on patient preference? 

 Sneed JR, Rutherford BR, Rindskopf D, et al. Design makes a difference: a meta-analysis of antidepressant response rates in placebo-controlled versus comparator trials in late-life depression. 

 McKay KM, Imel ZE, Wampold BE. Psychiatrist effects in the psychopharmacological treatment of depression. 

Articles

With over 2 dozen FDA-approved antidepressants on the market, it is reasonable to ask: which antidepressants are most effective? 

Assessing and Enhancing the Effectiveness of Antidepressants

Medical professionals take great pride in their advanced knowledge, the culmination of years of hard work, higher education, and first-hand experience. Patients look to us for explanations of their problems. In return, we like to give explanations, based on our privileged understanding of the science. But sometimes we forget that alternate explanations may be just as important.

It starts from day one, actually. I recall those early years of medical school, when in a discussion with a family member, someone would inevitably ask, “I have this nagging pain in my right hip, what do you think it could be?” There were always dozens of possibilities, but I always felt obligated to give an answer. “Sounds like IT band syndrome. Try resting it for a while, maybe some ice; you can also try some NSAIDs for a few days.”

We do this in psychiatry all the time. We listen to patients’ complaints and problems. We probe their histories; we evaluate their predisposing factors and environmental triggers. Sometimes we administer a questionnaire to quantify their symptoms. And we end by giving a diagnosis: an explanation.

These explanations are rooted in our understanding of psychopathology and neuroscience, society and culture. But our understanding is less than perfect. In the absence of sophisticated molecular or biochemical tests to prove the presence of a particular gene mutation or chemical deficiency, our biological explanations cannot be proven unequivocally. And our psychological interpretations can take even more forms-depending on our theoretical orientation-many of which can be contradictory.

That doesn’t mean treatment can’t commence, or that it won’t work. Treatment does work, surprisingly often.

Sometimes, however, patients describe experiences that violate our expectations: perhaps a unique side effect of a drug, or a failure to make the anticipated gains in therapy, or a subjective phenomenon that the patient attributes to medication withdrawal, but which isn’t described in the literature. In cases like these, what do we do? As with anything else, we like to give some sort of explanation. It’s hard to say, “I don’t know.”

I recently met with a patient (not my own) who had, in her words, a prolonged withdrawal reaction after stopping a low dose of an SSRI antidepressant; she later experienced emotional and physical problems for months after discontinuing an SNRI, and is currently being maintained successfully on a microscopic (some might say “homeopathic”) dose of an anticonvulsant.

As I listened to her story, I was struck by how her experiences would have been entirely unexpected. Unexpected, that is, according to the conventional knowledge presented in our texts. As her doctor, it would have been easy to say, “Hmm, that’s interesting, but withdrawal symptoms are usually more limited than what you describe. Perhaps you were experiencing a recurrence of your depression. And your current dose of medication is extremely low; the benefit you experience is probably a placebo effect.”

Would that have been incorrect? Not according to conventional psychiatry. But with a response like that, I would have completely invalidated her own subjective interpretation-her own reality.

Much of what we see in practice is similar. When a patient experiences recurrent psychosis after discontinuing an antipsychotic, is this a withdrawal phenomenon? Or a relapse? When an antidepressant is tapered off and the patient reports fatigue, malaise, and anhedonia a month later, is this recurrent depression, or a delayed withdrawal effect? The answers we give reflect our biases, and not necessarily the patient’s own interpretation (or reality).

 introduced the concept of an “exploratory map” to understand the subjective experiences of mental health patients. After interviewing 34 subjects presenting to a community clinic, the researchers found that individuals often entertained numerous causes for their psychological problems. Most of these diverged from the more absolute “explanatory models” offered by modern medical science. The idea of the “exploratory map” has recently re-emerged in the literature on the recovery model in mental illness,

 as it preserves-and prioritizes-the patient’s subjective experience, rather than offering an explanatory (often biological) rationale which may mean very little to the patient.

When we work with patients whose experiences don’t align with our expectations, we must be careful not to dismiss those experiences simply because they clash with the overriding psychiatric paradigm. The patient’s exploration of his or her symptoms, as well as the causes and the meaning of those symptoms, are what truly matters. To preserve the therapeutic relationship-not to mention the patient’s hope and power-we should honor those interpretations, and engage in further exploration with the patient, instead of offering an answer that our experience says is “right” but which the patient may perceive as utterly wrong.

Williams B, Healy D. Perceptions of illness causation among new referrals to a community mental health team: "explanatory model" or "exploratory map"?  

Recovery in Mental Health: Reshaping Clinical and Scientific Responsibilities. 

 West Sussex, UK: John Wiley & Sons, Ltd.;2009.

When we work with patients whose experiences don't align with our expectations, we must be careful not to dismiss those experiences simply because they clash with the overriding psychiatric paradigm.  

Symptoms and What They Mean: Do Patients Want Explanation or Exploration?

In 2013, the American Psychiatric Association will publish the 

, the next edition of its diagnostic manual.  Public reaction has, thus far, 

.  Critics decry the lowering of diagnostic thresholds in existing criteria; the conception of new diagnoses seemingly “out of thin air”; the radical overhaul of entire sections (

); and the secrecy under which many of the earlier planning stages were held.

Much of the criticism, including that from its most vocal critic, 

 (lead author of the current edition, the 

), laments the expansion of diagnostic criteria.  They argue that this may increase the number of “mentally ill” individuals and/or pathologize “normal” behavior, and lead to the possibility that thousands-if not millions-of new patients will be exposed to medications which may cause more harm than good.

The American Psychological Association, the 

 have expressed their opposition publicly.  An 

 from the Society for Humanistic Psychology (part of the APA) has garnered nearly 9,000 signatures in fewer than 60 days.

I understand and sympathize with the critics, particularly against the 

‘s emphasis on “user acceptability” over validity (and, in the interest of full disclosure, 

).  But I wonder whether the greater outcry against the 

 may very well turn out to be a highly flawed document, but that’s all it will be: 

.  Whether it results in the “overdrugging and overdiagnosing” predicted by critics like Frances is 

 the primary responsibility of its authors, but of those who will 

 is where the outrage should be directed.

First of all, let’s just state the obvious:  it is 

 to write a comprehensive, scientifically valid catalog of all mental illnesses (particularly when some argue convincingly that mental illness is 

).  When we’re talking about conditions that have both biological and sociocultural origins (in fact, this has long been part of the distinction between 

 disease), it seems clear that a diagnostic manual will never capture the full spectrum of psychiatric disorders.  Even if we included semi-accurate biological markers in the diagnostic criteria-a Holy Grail we’re 

-mental illness will always, in the end, depend primarily upon the patient’s subjective experience.

’s before it, will be, almost by definition, 

.  It will be a descriptive tool, a taxonomy, a guidebook, featuring the authors’ 

 constitute a treatable condition.  For example, in real life there is no one thing called “major depressive disorder” as it appears in the 

).  Nonetheless, we use “MDD” to label all of our patients with these combinations of symptoms, because it’s the best fit.  But a good mental health professional doesn’t treat MDD, he or she treats the 

 MDD.  Calling it “MDD” is only necessary for insurance billing, for drug companies to get 

 for new pharmaceuticals, and for patients and docs to give a name to (and, if necessary, demystify) their condition.

In other words, the danger lies not in the label, but in how we 

 it.  In fact, one might even argue that a lousy label-or a label that is so nonspecific that it applies to a broad swath of the population, including some in the “normal” part of the spectrum (wherever 

 may be)-may actually be beneficial, because it will be so meaningless that it will require the clinician to think more deeply about what that label is trying to convey.

As an example, consider “chronic pain,” a label frequently applied to patients and written in their charts.  (Even though it’s often written as a diagnosis, it is really a symptom.)  “Chronic pain” simply implies that the patient experiences pain.  Nothing more.  It says nothing about the origin of the pain, what exacerbates or soothes it, how long the patient has experienced it, or whether it responds to NSAIDs, opioids, acupuncture, yoga, or rest.  When a new patient complains of “chronic pain” to a good pain specialist, the doctor doesn’t just write a script; he or she performs an examination, obtains a detailed history and collateral information, and treats in a manner that relieves discomfort yet minimizes side effects (and cost) to the patient.

Perhaps this is what we can do in psychiatry, even with the 

 like “Attenuated Psychosis Syndrome” or “Disruptive Mood Dysregulation Disorder.”  Each of these new “diagnoses” suggests something about the patient and his or her behavior or experience.  But neither one should predict a course of treatment.  In fact, a vague diagnosis should actually prompt the doctor to probe more deeply into a patient’s symptoms and determine their impact on the patient’s well-being and functional status (which may actually help improve disability evaluations, too).  On a population basis, the heterogeneity of patients given a diagnosis might stimulate further research (neurobiological, psychological, epidemiologic, maybe even anthropological) to determine more specific subtypes of illness.

Will the new diagnoses be “overused”?  Probably.  Will they lead to “overdrugging” of patients-the outcome that everyone fears?  I guess that’s possible.  But if so, the spotlight should be turned on those who 

 on the document that simply describes the symptoms.  This may turn out to be difficult: official treatment guidelines might come out with recommendations to medicate, insurance companies may require diagnoses (or medications) in order to cover psychiatric services, and drug companies might aggressively market their products for these new indications.  And there will always be doctors who cut corners, arrive at diagnoses too quickly and are eager to use dangerous medications.  But these targets, ultimately, are where the anti-

 is unnecessary, premature, and flawed.  Unfortunately, it simply 

 of mental illness at this point in time.  But is it a 

“dangerous public health experiment,”

 as Allen Frances has warned?  Only if we allow it to override our eyes and ears, our hearts and minds, and what our patients truly need and want from us.   In the end, it’s just a book.  What really matters is how we use it.

[Editor's Note: This article was originally posted on Dr Balt's blog at 

http://thoughtbroadcast.com/2011/12/15/is-the-criticism-of-dsm-5-misguided/

Critics of DSM-5 argue that the expansion of diagnostic criteria may increase the number of “mentally ill” individuals and/or pathologize “normal” behavior, and lead to the possibility that thousands-if not millions-of new patients will be exposed to medications which may cause more harm than good.

Author | Steve Balt, MD, MS

Articles

Assessing and Enhancing the Effectiveness of Antidepressants

Symptoms and What They Mean: Do Patients Want Explanation or Exploration?

Is the Criticism of DSM-5 Misguided?