Probable effects of polypharmacy and equivalent doses of psychotropic drugs on prevalence of adverse drug events among psychiatric inpatients in a general hospital in Japan.

OBJECTIVE: In psychiatry, polypharmacy or high psychotropic drug doses increase adverse drug event (ADE) prevalence. However, the full relationship between polypharmacy and ADEs is unclear, and few studies have evaluated dose equivalents for psychotropic drugs for ADEs. Thus, we conducted a retrospective analysis to clarify the effects of polypharmacy and chlorpromazine (CP)-, diazepam (DAP)-, and imipramine- equivalent doses on all ADEs in inpatients. METHODS: Psychiatric inpatients in a Japanese hospital from April 1, 2016 to March 31, 2018, were enrolled. ADE severity and causality were assessed. Multiple logistic regression analyses were performed to evaluate ADE risk factors. RESULTS: Among 462 patients analyzed, out of 471 patients enrolled, 145 (31.4%) experienced ADEs. The causality assessment determined that "possible" was 96.5%. The most common ADEs were nervous system disorders (35%). Multiple logistic regression analyses indicated an increase in ADE prevalence with the number of drugs used (≥5; p = 0.026); CP-equivalent dose (p = 0.048); and endocrine, nutritional, and metabolic disorders (p = 0.045). DAP-equivalent dose; infectious and parasitic diseases; and injury, poisoning, and consequences of other external causes decreased ADE prevalence (p = 0.047, 0.022, and 0.021, respectively). CONCLUSIONS: Avoiding polypharmacy in psychiatric inpatients and adjusting drug regimens to safe equivalent doses could reduce ADEs during hospitalization.

Verification of the "Upward Variation in the Reporting Odds Ratio Scores" to Detect the Signals of Drug-Drug Interactions.

The reporting odds ratio (ROR) is easy to calculate, and there have been several examples of its use because of its potential to speed up the detection of drug-drug interaction signals by using the "upward variation of ROR score". However, since the validity of the detection method is unknown, this study followed previous studies to investigate the detection trend. The statistics models (the Ω shrinkage measure and the "upward variation of ROR score") were compared using the verification dataset created from the Japanese Adverse Drug Event Report database (JADER). The drugs registered as "suspect drugs" in the verification dataset were considered as the drugs to be investigated, and the target adverse event in this study was Stevens-Johnson syndrome (SJS), as in previous studies. Of 3924 pairs that reported SJS, the number of positive signals detected by the Ω shrinkage measure and the "upward variation of ROR score" (Model 1, the Susuta Model, and Model 2) was 712, 2112, 1758, and 637, respectively. Furthermore, 1239 positive signals were detected when the Haldane-Anscombe 1/2 correction was applied to Model 2, the statistical model that showed the most conservative detection trend. This result indicated the instability of the positive signal detected in Model 2. The ROR scores based on the frequency-based statistics are easily inflated; thus, the use of the "upward variation of ROR scores" to search for drug-drug interaction signals increases the likelihood of false-positive signal detection. Consequently, the active use of the "upward variation of ROR scores" is not recommended, despite the existence of the Ω shrinkage measure, which shows a conservative detection trend.

Improved Detection Criteria for Detecting Drug-Drug Interaction Signals Using the Proportional Reporting Ratio.

There is a current demand for "safety signal" screening, not only for single drugs but also for drug-drug interactions. The detection of drug-drug interaction signals using the proportional reporting ratio (PRR) has been reported, such as through using the combination risk ratio (CRR). However, the CRR does not consider the overlap between the lower limit of the 95% confidence interval of the PRR of concomitant-use drugs and the upper limit of the 95% confidence interval of the PRR of single drugs. In this study, we proposed the concomitant signal score (CSS), with the improved detection criteria, to overcome the issues associated with the CRR. "Hypothetical" true data were generated through a combination of signals detected using three detection algorithms. The signal detection accuracy of the analytical model under investigation was verified using machine learning indicators. The CSS presented improved signal detection when the number of reports was ≥3, with respect to the following metrics: accuracy (CRR: 0.752 → CSS: 0.817), Youden's index (CRR: 0.555 → CSS: 0.661), and F-measure (CRR: 0.780 → CSS: 0.820). The proposed model significantly improved the accuracy of signal detection for drug-drug interactions using the PRR.