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Record W2530283546 · doi:10.1111/acps.12641

Real‐world effectiveness of antipsychotics

2016· letter· en· W2530283546 on OpenAlexaboutno aff
Jari Tiihonen

Bibliographic record

VenueActa Psychiatrica Scandinavica · 2016
Typeletter
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePsychiatryAntipsychoticObservational studySchizophrenia (object-oriented programming)Adverse effectPopulationQuality of life (healthcare)Medical prescriptionComorbidityRandomized controlled trialPsychological interventionInternal medicine

Abstract

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There are two fundamental questions concerning the antipsychotic treatment of schizophrenia. First, is the overall effect of treatment positive; that is, does efficacy outweigh the adverse effects? Second, are there any clinically meaningful differences between specific antipsychotic agents? Although a large body of randomized controlled studies (RCTs) has shown that antipsychotics are highly effective in reducing symptoms and improving quality of life during short-term interventions, it has been suspected that the use of antipsychotics in long-term treatment may lead to brain atrophy 1 or a lower rate of recovery 2. Furthermore, it has been suggested that adverse effects such as weight gain would contribute significantly to excess mortality seen among patients with schizophrenia. There are several reasons why RCTs have not been able to solve this overall risk-benefit question. For example, the patients included in RCTs represent a small atypical minority of the patient population, as up to 80–90% of patients are excluded because of mental or physical comorbidity, suicidal or antisocial behaviour, or substance abuse 3. Another reason is that thousands of patients and follow-up periods of several years are required to achieve enough statistical power to study relatively infrequent phenomena such as suicide or death, or the incidence of severe physical illness. Observational studies can overcome these obstacles by using nation-wide electronic databases of hospitalization, mortality, and filled prescriptions. In this issue of the Journal, Vanasse et al. 4 report results on the comparative effectiveness of antipsychotic drugs in schizophrenia. The authors used administrative databases from Quebec province in Canada, which included more than 18 000 patients who started to use an antipsychotic from 1998 to 2005. Their results showed that using any antipsychotic drug was associated with a lower risk of mental and physical health events (i.e., suicide, any death, hospitalization, or an emergency hospital visit due to a mental or physical disorder), when compared to no use of an antipsychotic. This result is in line with previous large cohort studies that have included mainly chronic patients 5-10. It is rather reassuring that all seven large cohort studies published thus far indicate that the use of antipsychotics is associated with a lower risk of death or severe health problem when compared with no use. This suggests that antipsychotics do more good than harm. The other main finding by Vanasse et al. is that there are clinically meaningful differences in the overall effectiveness of antipsychotics. Clozapine was associated with best outcome, even when mortality and physical health events were included in the primary outcome measure. This is in agreement with four previous studies that suggest that clozapine use is associated with a lower risk of death, when compared to other treatments 7, 11-13. Also, olanzapine performed better than first-generation antipsychotics, but quetiapine was associated with a worse outcome. These findings were also reported previously in prevalent-user cohorts 6, 7. Thus, results from different cohorts, from different countries, show rather consistent results. Although observational studies have important advantages, such as non-selected representative study populations, long follow-up periods, and high statistical power, they also have some shortcomings. The most important limitation is selection bias. For example, old patients are more likely to receive first-generation drugs, while younger patients receive more novel medications. If the age difference is not adjusted, the results on mortality will be severely distorted. Even if the most important covariates such as sex, age, age at illness onset, duration of illness, number of previous hospitalizations, physical illness, and history of suicidal behaviour were adjusted, there always remains residual confounding. One way to overcome this problem is to use within-individual analysis, in which each individual is his or her own control. In this approach, the exposure periods of each individual are compared with the non-exposure periods of the same individual. Therefore, the only factors which need to be adjusted are those that change as a function of time. These factors include the calendar age of the patient, temporal order of exposure periods, and concomitant other medication. For example, the use of antidepressants and benzodiazepines is common among patients with schizophrenia, and these concomitant medications may have a substantial impact on mortality, for example 9. It is somewhat surprising that no studies on the real-world effectiveness of antipsychotics have used within-individual analysis this far, expect the one by Walker et al. 13. Even so, many of the main results of previous observational studies are unlikely to be explained by selection bias. For example, patients using clozapine are typically the most suicidal and severely ill patients, with the highest number of previous re-hospitalizations, and with the highest intrinsic risk of relapse. Therefore, we may assume that the superior outcome associated with clozapine is diluted, rather than enhanced, due to a selection bias and residual confounding. Another issue is the accuracy of the exposure definition. Thus far, this methodological procedure has received little attention. The most widely used methods for defining exposure periods have been fairly simplistic such as fixed time windows (e.g., 90 or 180 days from purchase order), or an assumption that all patients use one defined daily dose (DDD) per day. However, such procedures may lead to inaccurate definitions of drug exposure, and more valid methods giving better estimates are needed. For example, a novel PRE2DUP method constitutes drug use periods from the purchase histories of each individual, making it possible to achieve substantially more accurate exposure periods than traditional methods 14. What are the practical clinical implications of the study by Vanasse et al.? The results from this new-user cohort confirm the previous results from prevalent-user cohorts, indicating that clozapine and olanzapine are associated with a better outcome and quetiapine with worse outcome than first-generation antipsychotics 6, 7. The study also confirms previous results from prevalent and new-user cohorts 5-10, which conclude that antipsychotic use is associated with a lower risk of death when compared with no use. The effect sizes of the observed differences were moderately large, indicating that they are clinically meaningful. Since the study was able to compare both apples (i.e., the risk of mental health events reflecting efficacy) and oranges (i.e., the risk of physical health events reflecting tolerability) at the same time, the results provide a rather comprehensive view for the overall effectiveness of these treatments and the patients' wellbeing. Therefore, one can easily agree with the main conclusion of the study, which states that international public health and drug agencies should start surveillance of real-world antipsychotic treatment outcomes, to provide essential information for schizophrenia treatment guidelines.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.838
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.319
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations18
Published2016
Admission routes1
Has abstractyes

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