Appropriateness of Alerts and Physicians' Responses With a Medication-Related Clinical Decision Support System: Retrospective Observational Study.

BACKGROUND: Alert fatigue is unavoidable when many irrelevant alerts are generated in response to a small number of useful alerts. It is necessary to increase the effectiveness of the clinical decision support system (CDSS) by understanding physicians' responses. OBJECTIVE: This study aimed to understand the CDSS and physicians' behavior by evaluating the clinical appropriateness of alerts and the corresponding physicians' responses in a medication-related passive alert system. METHODS: Data on medication-related orders, alerts, and patients' electronic medical records were analyzed. The analyzed data were generated between August 2019 and June 2020 while the patient was in the emergency department. We evaluated the appropriateness of alerts and physicians' responses for a subset of 382 alert cases and classified them. RESULTS: Of the 382 alert cases, only 7.3% (n=28) of the alerts were clinically appropriate. Regarding the appropriateness of the physicians' responses about the alerts, 92.4% (n=353) were deemed appropriate. In the classification of alerts, only 3.4% (n=13) of alerts were successfully triggered, and 2.1% (n=8) were inappropriate in both alert clinical relevance and physician's response. In this study, the override rate was 92.9% (n=355). CONCLUSIONS: We evaluated the appropriateness of alerts and physicians' responses through a detailed medical record review of the medication-related passive alert system. An excessive number of unnecessary alerts are generated, because the algorithm operates as a rule base without reflecting the individual condition of the patient. It is important to maximize the value of the CDSS by comprehending physicians' responses.

Vapor Pressure and Toxicity Prediction for Novichok Agent Candidates Using Machine Learning Model: Preparation for Unascertained Nerve Agents after Chemical Weapons Convention Schedule 1 Update.

The recent terrorist attacks using Novichok agents and subsequent operations have necessitated an understanding of its physicochemical properties, such as vapor pressure and toxicity, as well as unascertained nerve agent structures. To prevent continued threats from new types of nerve agents, the organization for the prohibition of chemical weapons (OPCW) updated the chemical weapons convention (CWC) schedule 1 list. However, this information is vague and may encompass more than 10 000 possible chemical structures, which makes it almost impossible to synthesize and measure their properties and toxicity. To assist this effort, we successfully developed machine learning (ML) models to predict the vapor pressure to help with escape and removal operations. The model shows robust and high-accuracy performance with promising features for predicting vapor pressure when applied to Novichok materials and accurate predictions with reasonable errors. The ML classification model was successfully built for the swallow globally harmonized system class of organophosphorus compounds (OP) for toxicity predictions. The tuned ML model was used to predict the toxicity of Novichok agents, as described in the CWC list. Although its accuracy and linearity can be improved, this ML model is expected to be a firm basis for developing more accurate models for predicting the vapor pressure and toxicity of nerve agents in the future to help handle future terror attacks with unknown nerve agents.

Identification and Study of Biomarkers from Novichok-Inhibited Butyrylcholinesterase in Human Plasma.

To identify biomarkers of ethyl (1-(diethylamino)ethylidene)phosphoramidofluoridate (A234)- or methyl (1-(diethylamino)ethylidene)phosphoramidofluoridate (A232)-inhibited butyrylcholinesterase (BChE), we investigated nonapeptide adducts containing the active site serine, which plays a key role in enzyme activity, using LC-MS/HRMS. Biomarkers were acquired as expected, and they exhibited a significant amount of fragment ions from the inhibiting agent itself, in contrast to the MS2 spectra of conventional nerve agents. These biomarkers had a higher abundance of [M+2H]2+ ions than [M+H]+ ions, making doubly charged ions more suitable for trace analysis.