Incidence of Adverse Drug Reactions in Two Tertiary Hospitals: A Prospective Observational Study

Background: Monitoring and detecting adverse drug reactions (ADRs) in hospitals is crucial to improving drug safety and healthcare delivery quality. Nevertheless, there was not enough information on ADR incidence and its figure in Iran. Aim: this study aimed to determine the incidence of ADRs in hospitalized patients and investigate their characteristics in Iran.Methods: We conducted a three-month prospective study in two tertiary hospitals in 2019. All admitted patients were intensively monitored for all suspected ADRs through daily visiting hospital wards and soliciting information from physicians, nurses and interviewing suspicious patients. We calculated the incidence of ADRs, and 95% confidence intervals (95% CI). Poisson regression was used to evaluate risk factors for ADR incidence. Results: Among 13,613 admitted patients, we detected 115 ADRs in 114 patients. The incidence of ADR was 8.4 per 1000 admissions (95% CI: 7.0-10.1), and 13.9% of them were ADR-related hospital admissions. The risk of ADRs was significantly predicted by age, length of hospital stay, patients’ diagnostics, number of drug usage, and using respiratory system agents and anti-infectives for systemic use. The most common ADRs were skin and subcutaneous manifestations (52.2%), and 62.6% of ADRs were caused by anti-infectives (commonly vancomycin, ceftriaxone, and ciprofloxacin). Conclusion: This study indicated that ADRs occurrence during the hospital stay or resulting in hospital admissions are considerable. Given that ADR occurrence could be associated with increased morbidity, mortality, and economic burden, constant intensive monitoring is required to address the drug safety issue and promote actions to improve patient safety and reduce the health and economic burden.

A systematic review of validated suicide outcome classification in observational studies

Background Suicidal outcomes, including ideation, attempt, and completed suicide, are an important drug safety issue, though few epidemiological studies address the accuracy of suicidal outcome ascertainment. Our primary objective was to evaluate validated methods for suicidal outcome classification in electronic health care database studies. Methods We performed a systematic review of PubMed and EMBASE to identify studies that validated methods for suicidal outcome classification published 1 January 1990 to 15 March 2016. Abstracts and full texts were screened by two reviewers using prespecified criteria. Sensitivity, specificity, and predictive value for suicidal outcomes were extracted by two reviewers. Methods followed PRISMA-P guidelines, PROSPERO Protocol: 2016: CRD42016042794. Results We identified 2202 citations, of which 34 validated the accuracy of measuring suicidal outcomes using International Classification of Diseases (ICD) codes or algorithms, chart review or vital records. ICD E-codes (E950-9) for suicide attempt had 2–19% sensitivity, and 83–100% positive predictive value (PPV). ICD algorithms that included events with ‘uncertain’ intent had 4–70% PPV. The three best-performing algorithms had 74–92% PPV, with improved sensitivity compared with E-codes. Read code algorithms had 14–68% sensitivity and 0–56% PPV. Studies estimated 19–80% sensitivity for chart review, and 41–97% sensitivity and 100% PPV for vital records. Conclusions Pharmacoepidemiological studies measuring suicidal outcomes often use methodologies with poor sensitivity or predictive value or both, which may result in underestimation of associations between drugs and suicidal behaviour. Studies should validate outcomes or use a previously validated algorithm with high PPV and acceptable sensitivity in an appropriate population and data source.

PHARMACOVIGILANCE: DYNAMICS IN INDIAN PHARMA INDUSTRY

Pharmacovigilance refers to the process of identifying, detecting, and responding to drug safety issue and has witnessed dynamic advancements in pharmaceutical industries throughout the world. The main objective of pharmacovigilance is to extend the safety monitoring and to detect any ADRs that previously got unrecognized in evolution during clinical trial. ADRs monitoring is required for each medicine throughout its lifecycle which includes early stage of drug design, clinical trials, and post marketing surveillance. The emerging trend in pharmacovigilance is to link the pre marketing data with the data collected during post marketing phase that include safety information. India is a vast country with population of over 1.32 Billion with different social economics status, different patterns of disease prevalence it becomes more important to have a standardized and robust pharmacovigilance. Pharmacists, as doctor remark that their involvement may increase the reporting rate and have a greater role to play in the area of pharmacovigilance

Predicting adverse drug reaction outcomes with machine learning

Background: Adverse drug reactions are a drug safety issue affecting more than two million people in the U.S. annually. The Food and Drug Administration (FDA) maintains a comprehensive database of adverse drug reactions reported known as FAERS (FDA adverse event reporting system), providing a valuable resource for studying factors associated with ADRs. The goal of the project is to build predictive models to predict the outcome given patient characteristics and drug usage. The results can be valuable for health care practitioners by offering new knowledge on adverse drug reactions which can be used to improve decision making related to drug prescriptions.Methods: In this paper I present and discuss results from machine learning models used to predict outcomes of ADRs. Machine learning models are a popular set of models for prediction. They have gained attention recently and have been used in a variety of fields. They can be trained on existing data and retrained when new data become available. The trained models are then used to make predictions.Results: I find that the supervised learning models are work similarly within groups, with accuracy between 65% and 75% for predicting deaths and 70% to 75% for predicting hospitalizations. Across groups the models predict hospitalizations better than deaths.Conclusions: The predictive models I built achieve good accuracy. The results can potentially be improved when more data become available in the future.

Leishmaniasis in the era of tumor necrosis factor alpha antagonist therapy – a research agenda for Europe

A number of published case reports suggest an association of tumor necrosis factor (TNF) alpha antagonist use and manifest leishmaniasis. Despite increasing popularity of antagonising TNF alpha for the treatment of autoimmune disorders, systematic research on the risk of opportunistic leishmaniasis in patients receiving these drugs is lacking. This perspective identifies areas of uncertainty regarding the safety profile of TNF alpha antagonist drugs and their clinical use in patients at risk of leishmaniasis. Then, we reflect on how current pharmacovigilance activities in Europe could be enhanced to help reduce these uncertainties. Our aim is to stimulate a debate about this important drug safety issue with potential consequences for patients receiving TNF alpha antagonists living in or travelling to areas endemic for leishmaniasis.