Deliberate Self-Poisoning: Real-Time Characterization of Suicidal Habits and Toxidromes in the Food and Drug Administration Adverse Event Reporting System

Abstract

Introduction: Deliberate self-poisoning (DSP) using drugs is the preferred method of suicide at a global level. Its investigation is hampered by limited sample sizes and data reliability. We investigate the role of the US FDA Adverse Event Reporting System (FAERS), a consolidated pharmacovigilance database, in outlining DSP habits and toxidromes.

Methods: We retrieved cases of 'intentional overdose' and 'poisoning deliberate' from the FAERS (January 2004-December 2021). Using descriptive and disproportionality analyses, we estimated temporal trends, potential risk factors, toxidromes, case-fatality rates and lethal doses (LDs) for the most frequently reported drugs.

Results: We retrieved 42,103 DSP cases (17% fatal). Most cases were submitted in winter. Reports of DSP involved younger people, psychiatric conditions, and alcohol use, compared with non-DSP, and fatality was higher in men and older patients. Suspected drugs were mainly antidepressants, analgesics, and antipsychotics. Multiple drug intake was recorded in more than 50% of the reports, especially analgesics, psychotropics, and cardiovascular agents. The most frequently reported drugs were paracetamol, promethazine, amlodipine, quetiapine, and metformin. We estimated LD25 for paracetamol (150 g).

Conclusion: Worldwide coverage of the FAERS complements existing knowledge about DSP and may drive tailored prevention measures to timely address the DSP phenomenon and prevent intentional suicides.

Fig. 1

Reported case-fatality rate of drugs used to commit suicide. Drugs recorded as primary suspects in more than 1% of the DSP reports are shown on the y-axis, ordered by the number of DSP reports (in blue, right side of the pyramid plot). The reported case-fatality rate is visualized on the left side of the pyramid plot (in red). DSP deliberate self-poisoning

Fig. 2

Multiple drug intake. Snapshot of the interactive network showing more frequent exposure (> 1%) as nodes, and more frequent polydrug intoxications (> 100) as links. More frequent drugs are represented by larger nodes and more frequent combined exposures are represented by more visible links. The nodes were clustered by color using a multilevel algorithm. We identified a pain cluster (pink), a cardiovascular cluster (green), and two psychotropic clusters (violet and blue). Only nodes with a link were included in the final graph, and in the snapshot, only drugs used in combination with paracetamol were colored. The interactive network is available at: https://osf.io/n5q8x/.

Fig. 3

Toxidromes. The 10 most frequent suspected drugs in order of ATC code are shown on the x-axis, and events (at their MedDRA HLGT level) clustered by SOC are shown on the y-axis. A point was shown in the intersection if the drug was disproportionally reported with the event (on the entire FAERS), with a size proportional to the percentage of intentional overdose reports recording the event (i.e., a large dot identifies an event that occurs in a high percentage of the drug overdose report), and a white-red gradient based on the ratio between the reporting rate in intentional overdoses versus the therapeutic use (i.e., a red dot identifies an event that occurs much more often as a toxic effect than as an adverse effect). We included only events present in at least 5% of the drug overdose reports. Rows without disproportionalities are not shown. ATC Anatomical Therapeutic Classification, HLGT high-level group terms, MedDRA Medical Dictionary for Regulatory Activitives, SOC System Organ Class, FAERS US FDA Adverse Event Reporting System

Fig. 4

Lethal dose estimation for paracetamol. Observational data regarding doses (in grams) and fatality were fitted to a logistic model to estimate the lethal dose for different percentages. Reports were then grouped by dose (in logarithmic intervals—narrow near 0 and wider for higher doses—to account for the exposure distribution skewed to the right, i.e., many reports record lower doses, few record higher doses) and the case-fatality rate for each group was calculated. For each dose interval, we plotted a point over the logistic, with the x-coordinate the midpoint between dose limits, the y-coordinate the estimated case-fatality rate, and size of the logarithm of the number of reports. The pseudo-R² of the model is 0.07, suggesting a low goodness of fit