The utility of big data in spontaneous adverse drug reactions (ADRs) reporting systems has improved the pharmacovigilance process. However, identifying culprit drugs in ADRs remains challenging, although it is one of the foremost steps to managing ADRs. Aiming to estimate the likelihood of prescribed drugs being culprit drugs for given ADRs, we devised a Bayesian estimation model based on the Japanese Adverse Drug Events Reports database. After developing the model, a validation study was conducted with 67 ADR reports with a gross of 1,387 drugs (67 culprit drugs and 1,320 concomitant drugs) prescribed and recorded at Yamaguchi University Hospital. As a result, the model estimated a culprit drug of ADRs with acceptable accuracy (area under the receiver operating characteristic curve 0.93 (95% confidence interval 0.88-0.97)). The estimation results provided by the model to healthcare practitioners can be used as one clue to determine the culprit drugs for various ADRs, which will improve the management of ADRs by shortening the treatment turnaround time and increasing the precision of diagnosis, leading to minimizing the adverse effects on patients.An adverse drug reaction (ADR) is an unfavorable and unavoidable effect of a drug. 1 ADRs are fundamental problems in medical care worldwide, increasing hospital admissions and morbidity/ mortality. The in-hospital mortality due to ADRs is 0.4-2.7%. 2 ADRs account for ~ 3.5% of hospital admissions. 3,4 Efficient pharmacovigilance, a pharmaceutical science relating to the detection, risk assessment, and prevention of ADRs, is needed to improve drug safety. 5 In the pharmacovigilance process, ADR management includes reducing the dose or withdrawing a culprit drug. When an ADR occurs in patients, identifying the culprit drug is one of the foremost steps to minimize the adverse effects.However, identifying a culprit drug from several candidate drugs remains challenging to this date due to multiple candidate drugs, time-constraints, and sometimes diagnostic urgency. [6][7][8] A previous study demonstrated that 31% of healthcare practitioners reported, "It is often too difficult to identify the causative drug." 7 Although several conventional methods (e.g., Naranjo, Jones, and Karch-Lasagna algorithm) have been used for the identification of the culprit drugs in ADRs, 9-11 these methods did not show acceptable accuracy in medical practice (accuracy 45%). 12 Furthermore, identifying a culprit drug may be more difficult in cases of polypharmacy (usually defined as five or more medications daily) due to many potential candidates. 13,14 In the era of big data, a data-driven approach, such as data mining for early detection of pharmacovigilance signals and artificial intelligence technology for drug safety based on automatic reporting

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We encountered a case of microbial contamination of infusion fluid detected based on the discoloration of an inline filter (membrane filter) in the main route. A cause of this contamination may be the use of an extension tube connected to the main route for more than a month. In the infusion fluid above the inline filter (on the high calorie end), 2.4 × 10 5 colony forming units (cfu)/ml of Candida parapsilosis was detected. "Adenosine triphosphate (ATP) + adenosine monophosphate (AMP)" was determined in addition. We conclude that microbial contamination of this infusion fluid can be rapidly estimated by measuring the amounts of "ATP + AMP".

Cisplatin is classified as a drug with high emetic risk; thus, the use of aprepitant or fosaprepitant in addition to a 5-hydroxytryptamine-3 (5-HT 3 ) receptor antagonist and dexamethasone is recommended for antiemetic therapy. Further, hydration is required to prevent renal dysfunction, and the use of magnesium has been proposed as a part of the hydration procedure. When fosaprepitant is chosen for antiemetic therapy because the patient has dysphagia, and magnesium is added to the hydration procedure, there may be an incompatibility between the two drugs that reduces the antiemetic effect. In our hospital, in a former regimen, these two drugs were administered concurrently as premedication for regimens containing cisplatin. We varied the conditions so that in a revised regimen the two drugs did not come into contact due to pharmaceutical support, and we conducted a retrospective study to determine the difference in the antiemetic effect. The observation period was 2 years (from October 2015 to September 2017) for the former regimen group (n 89) and 2 years (from October 2017 to September 2019) for the revised regimen group (n 177). Comparison of the former and revised regimen groups revealed sex (p 0.012); anticancer drug dosage (p 0.006); and variation of premedication condition (p 0.043) as factors affected by the revised regimen. Optimization of the premedication regimen was a form of necessary pharmaceutical support to maintain the patient's QOL.

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