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Performance Assessment of Software to Detect and Assist Prescribers with Antimicrobial Drug Interactions: Are all of them Created Equal?

ABSTRACT: BACKGROUND:Detecting and managing antimicrobial drug interactions (ADIs) is one of the facets of prudent antimicrobial prescribing. Our aim is to compare the capability of several electronic drug-drug interaction (DDI) checkers to detect and report ADIs. METHODS:Six electronic DDI checking platforms were evaluated: Drugs.com®, Medscape®, Epocrates®, Medimecum®, iDoctus®, and Guía IF®. Lexicomp® Drug Interactions was selected as the gold standard. Ten ADIs addressing different mechanisms were evaluated with every electronic DDI checker. For each ADI, we assessed five dimensions and calculated an overall performance score (maximum possible score: 10 points). The explored dimensions were sensitivity (capability to detect ADI), clinical effect (type and severity), mechanism of interaction, recommended action(s), and documentation (quality of evidence and availability of references). RESULTS:The electronic DDI checkers did not detect a significant proportion of the ADI assessed. The overall performance score ranged between 4.4 (Medimecum) and 8.8 (Drugs.com). Drugs.com was the highest ranked platform in four out of five dimensions (sensitivity, effect, mechanism, and recommended action). CONCLUSIONS:There is significant variability in the performance of the available platforms in detecting and assessing ADI. Although some ADI checkers have proven to be very accurate, others missed almost half of the explored interactions.

SUBMITTER: Morte-Romea E

PROVIDER: S-EPMC7167986 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Antibodies that block T cell inhibition via the immune checkpoints CTLA-4 and PD-1 have revolutionized cancer therapy during the last 15 years. T cells express additional inhibitory surface receptors that are considered to have potential as targets in cancer immunotherapy. Antibodies against LAG-3 and TIM-3 are currently clinically tested to evaluate their effectiveness in patients suffering from advanced solid tumors or hematologic malignancies. In addition, blockade of the inhibitory BTLA receptors on human T cells may have potential to unleash T cells to effectively combat cancer cells. Much research on these immune checkpoints has focused on mouse models. The analysis of animals that lack individual inhibitory receptors has shed some light on the role of these molecules in regulating T cells, but also immune responses in general. There are current intensive efforts to gauge the efficacy of antibodies targeting these molecules called immune checkpoint inhibitors alone or in different combinations in preclinical models of cancer. Differences between mouse and human immunology warrant studies on human immune cells to appreciate the potential of individual pathways in enhancing T cell responses. Results from clinical studies are not only highlighting the great benefit of immune checkpoint inhibitors for treating cancer but also yield precious information on their role in regulating T cells and other cells of the immune system. However, despite the clinical relevance of CTLA-4 and PD-1 and the high potential of the emerging immune checkpoints, there are still substantial gaps in our understanding of the biology of these molecules, which might prevent the full realization of their therapeutic potential. This review addresses PD-1, CTLA-4, BTLA, LAG-3, and TIM-3, which are considered major inhibitory immune checkpoints expressed on T cells. It provides summaries of our current conception of the role of these molecules in regulating T cell responses, and discussions about major ambiguities and gaps in our knowledge. We emphasize that each of these molecules harbors unique properties that set it apart from the others. Their distinct functional profiles should be taken into account in therapeutic strategies that aim to exploit these pathways to enhance immune responses to combat cancer.

Project description:Objective:The federal electronic health record (EHR) certification process was intended to ensure a baseline level of system quality and the ability to support meaningful use criteria. We sought to assess whether there was variation across EHR vendors in the degree to which hospitals using products from those vendors were able to achieve high levels of performance on meaningful use criteria. Materials and Methods:We created a cross-sectional national hospital sample from the Office of the National Coordinator for Health Information Technology EHR Products Used for Meaningful Use Attestation public use file and the Centers for Medicare & Medicaid Services Medicare EHR Incentive Program Eligible Hospitals public use file. We used regression models to assess the relationship between vendor and hospital performance on 6 Stage 2 Meaningful Use criteria, controlling for hospital characteristics. We also calculated how much variation in performance is explained by vendor choice. Results:We found significant associations between specific vendor and level of hospital performance for all 6 meaningful use criteria. Epic was associated with significantly higher performance on 5 of the 6 criteria; relationships for other vendors were mixed, with some associated with significantly worse performance on multiple criteria. EHR vendor choice accounted for between 7% and 34% of performance variation across the 6 criteria. Discussion:A nontrivial proportion of variation in hospital meaningful use performance is explained by vendor choice, and certain vendors are more often associated with better meaningful use performance than others. Our results suggest that policy-makers should improve the certification process by including more "real-world" scenario testing and provider feedback or ratings to reduce this variation. Hospitals can use these results to guide interactions with vendors. Conclusion:Vendor choice accounts for a meaningful proportion of variation in hospital meaningful use performance, and specific vendors are consistently associated with higher or lower performance across criteria.

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Performance Assessment of Software to Detect and Assist Prescribers with Antimicrobial Drug Interactions: Are all of them Created Equal?

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