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Duty to Warn: Antidepressant Black Box Suicidality Warning Is Empirically Justified.

ABSTRACT: The United States Food and Drug Administration issued a Black Box warning in October 2004 after placebo-controlled trials of antidepressant medications found an increased risk of suicidal thoughts and behaviors among children and adolescents taking antidepressant medications relative to placebo. Subsequently, some researchers have concluded that the Black Box warning caused severe unintended consequences; specifically, they have argued that the warning led to reduced use of antidepressants among youth, which led to more suicides. In this paper, we critically examine research regarding the Black Box warning's alleged deleterious consequences. One study claimed that controlled trials did not actually find an increased risk of suicidality among youth taking fluoxetine relative to those taking placebo, but its measure of suicidality is likely invalid. We found that ecological time series studies claiming that decreasing antidepressant prescriptions are linked to higher rates of suicide attempts or actual suicides among youth were methodologically weak. These studies exhibited shortcomings including: selective use of time points, use of only a short-term time series, lack of performing statistical analysis, not examining level of severity/impairment among participants, inability to control confounding variables, and/or use of questionable measures of suicide attempts. Further, while some time-series studies claim that increased antidepressant prescriptions are related to fewer youth suicides, more recent data suggests that increasing antidepressant prescriptions are related to more youth suicide attempts and more completed suicides among American children and adolescents. We also note that case-control studies show increased risk of suicide attempts and suicide among youth taking antidepressants, even after controlling for some relevant confounds. As clinical trials have the greatest ability to control relevant confounds, it is important to remember such trials demonstrated increased risk of suicidality adverse events among youth taking antidepressants. The Black Box warning is firmly rooted in solid data whereas attempts to claim the warning has caused harm are based on quite weak evidence.

SUBMITTER: Spielmans GI

PROVIDER: S-EPMC7031767 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Duty to Warn: Antidepressant Black Box Suicidality Warning Is Empirically Justified.

Spielmans Glen I GI Spence-Sing Tess T Parry Peter P

Frontiers in psychiatry 20200213

The United States Food and Drug Administration issued a Black Box warning in October 2004 after placebo-controlled trials of antidepressant medications found an increased risk of suicidal thoughts and behaviors among children and adolescents taking antidepressant medications relative to placebo. Subsequently, some researchers have concluded that the Black Box warning caused severe unintended consequences; specifically, they have argued that the warning led to reduced use of antidepressants among ...[more]

PMID: 32116839

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Project description:To link public data resources for predicting post-marketing drug safety label changes by analyzing the Convergent Focus Shift patterns among drug testing trials.We identified 256 top-selling prescription drugs between 2003 and 2013 and divided them into 83 BBW drugs (drugs with at least one black box warning label) and 173 ROBUST drugs (drugs without any black box warning label) based on their FDA black box warning (BBW) records. We retrieved 7499 clinical trials that each had at least one of these drugs for intervention from the ClinicalTrials.gov. We stratified all the trials by pre-marketing or post-marketing status, study phase, and study start date. For each trial, we retrieved drug and disease concepts from clinical trial summaries to model its study population using medParser and SNOMED-CT. Convergent Focus Shift (CFS) pattern was calculated and used to assess the temporal changes in study populations from pre-marketing to post-marketing trials for each drug. Then we selected 68 candidate drugs, 18 with BBW warning and 50 without, that each had at least nine pre-marketing trials and nine post-marketing trials for predictive modeling. A random forest predictive model was developed to predict BBW acquisition incidents based on CFS patterns among these drugs. Pre- and post-marketing trials of BBW and ROBUST drugs were compared to look for their differences in CFS patterns.Among the 18 BBW drugs, we consistently observed that the post-marketing trials focused more on recruiting patients with medical conditions previously unconsidered in the pre-marketing trials. In contrast, among the 50 ROBUST drugs, the post-marketing trials involved a variety of medications for testing their associations with target intervention(s). We found it feasible to predict BBW acquisitions using different CFS patterns between the two groups of drugs. Our random forest predictor achieved an AUC of 0.77. We also demonstrated the feasibility of the predictor for identifying long-term BBW acquisition events without compromising prediction accuracy.This study contributes a method for post-marketing pharmacovigilance using Convergent Focus Shift (CFS) patterns in clinical trial study populations mined from linked public data resources. These signals are otherwise unavailable from individual data resources. We demonstrated the added value of linked public data and the feasibility of integrating ClinicalTrials.gov summaries and drug safety labels for post-marketing surveillance. Future research is needed to ensure better accessibility and linkage of heterogeneous drug safety data for efficient pharmacovigilance.

Project description:Software systems typically have an input domain that can be subdivided into sub-domains, each of which generates similar or related outputs. Testing it on the boundaries between these sub-domains is critical to ensure high-quality software. Therefore, boundary value analysis and testing have been a fundamental part of the software testing toolbox for a long time and are typically taught early to software engineering students. Despite its many argued benefits, boundary value analysis for a given software specification or application is typically described in abstract terms. This allows for variation in how testers apply it and in the benefits they see. Additionally, its adoption has been limited since it requires a specification or model to be analysed. We propose an automated black-box boundary value detection method to support software testers in performing systematic boundary value analysis. This dynamic method can be utilized even without a specification or model. The proposed method is based on a metric referred to as the program derivative, which quantifies the level of boundariness of test inputs. By combining this metric with search algorithms, we can identify and rank pairs of inputs as good boundary candidates, i.e., inputs that are in close proximity to each other but with outputs that are far apart. We have implemented the AutoBVA approach and evaluated it on a curated dataset of example programs. Furthermore, we have applied the approach broadly to a sample of 613 functions from the base library of the Julia programming language. The approach could identify boundary candidates that highlight diverse boundary behaviours in over 70% of investigated systems under test. The results demonstrate that even a simple variant of the program derivative, combined with broad sampling and search over the input space, can identify interesting boundary candidates for a significant portion of the functions under investigation. In conclusion, we also discuss the future extension of the approach to encompass more complex systems under test cases and datatypes.

Project description:ObjectiveTo study serious harms associated with selective serotonin and serotonin-norepinephrine reuptake inhibitors.Design Systematic review and meta-analysis.Main outcome measuresMortality and suicidality. Secondary outcomes were aggressive behaviour and akathisia.Data sourcesClinical study reports for duloxetine, fluoxetine, paroxetine, sertraline, and venlafaxine obtained from the European and UK drug regulators, and summary trial reports for duloxetine and fluoxetine from Eli Lilly's website.Eligibility criteria for study selectionDouble blind placebo controlled trials that contained any patient narratives or individual patient listings of harms.Data extraction and analysisTwo researchers extracted data independently; the outcomes were meta-analysed by Peto's exact method (fixed effect model).ResultsWe included 70 trials (64,381 pages of clinical study reports) with 18,526 patients. These trials had limitations in the study design and discrepancies in reporting, which may have led to serious under-reporting of harms. For example, some outcomes appeared only in individual patient listings in appendices, which we had for only 32 trials, and we did not have case report forms for any of the trials. Differences in mortality (all deaths were in adults, odds ratio 1.28, 95% confidence interval 0.40 to 4.06), suicidality (1.21, 0.84 to 1.74), and akathisia (2.04, 0.93 to 4.48) were not significant, whereas patients taking antidepressants displayed more aggressive behaviour (1.93, 1.26 to 2.95). For adults, the odds ratios were 0.81 (0.51 to 1.28) for suicidality, 1.09 (0.55 to 2.14) for aggression, and 2.00 (0.79 to 5.04) for akathisia. The corresponding values for children and adolescents were 2.39 (1.31 to 4.33), 2.79 (1.62 to 4.81), and 2.15 (0.48 to 9.65). In the summary trial reports on Eli Lilly's website, almost all deaths were noted, but all suicidal ideation events were missing, and the information on the remaining outcomes was incomplete.ConclusionsBecause of the shortcomings identified and having only partial access to appendices with no access to case report forms, the harms could not be estimated accurately. In adults there was no significant increase in all four outcomes, but in children and adolescents the risk of suicidality and aggression doubled. To elucidate the harms reliably, access to anonymised individual patient data is needed.

Project description:We propose a BlackBox Counterfactual Explainer, designed to explain image classification models for medical applications. Classical approaches (e.g., , saliency maps) that assess feature importance do not explain how imaging features in important anatomical regions are relevant to the classification decision. Such reasoning is crucial for transparent decision-making in healthcare applications. Our framework explains the decision for a target class by gradually exaggerating the semantic effect of the class in a query image. We adopted a Generative Adversarial Network (GAN) to generate a progressive set of perturbations to a query image, such that the classification decision changes from its original class to its negation. Our proposed loss function preserves essential details (e.g., support devices) in the generated images. We used counterfactual explanations from our framework to audit a classifier trained on a chest X-ray dataset with multiple labels. Clinical evaluation of model explanations is a challenging task. We proposed clinically-relevant quantitative metrics such as cardiothoracic ratio and the score of a healthy costophrenic recess to evaluate our explanations. We used these metrics to quantify the counterfactual changes between the populations with negative and positive decisions for a diagnosis by the given classifier. We conducted a human-grounded experiment with diagnostic radiology residents to compare different styles of explanations (no explanation, saliency map, cycleGAN explanation, and our counterfactual explanation) by evaluating different aspects of explanations: (1) understandability, (2) classifier's decision justification, (3) visual quality, (d) identity preservation, and (5) overall helpfulness of an explanation to the users. Our results show that our counterfactual explanation was the only explanation method that significantly improved the users' understanding of the classifier's decision compared to the no-explanation baseline. Our metrics established a benchmark for evaluating model explanation methods in medical images. Our explanations revealed that the classifier relied on clinically relevant radiographic features for its diagnostic decisions, thus making its decision-making process more transparent to the end-user.

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Duty to Warn: Antidepressant Black Box Suicidality Warning Is Empirically Justified.

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Duty to Warn: Antidepressant Black Box Suicidality Warning Is Empirically Justified.

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