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Sedatives and Hypnotics Drugs: Benzodiazepines01:19

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Benzodiazepines have both sedative and hypnotic properties. They include compounds such as diazepam (Valium) and alprazolam (Xanax). Structurally, their cores are similar, consisting of the fusion of a benzene ring and a diazepine ring, but they share a common mechanism of action in the central nervous system (CNS).
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CNS depressants include drugs from the category of barbiturates and benzodiazepines. They are valuable medications for managing anxiety disorders and insomnia. Barbiturates, once used to induce and maintain sleep, have been replaced mainly by benzodiazepines due to barbiturate's toxicity, tolerance, and overdose risks. They interact with GABAA receptors, leading to sedation at low doses and potentially coma and death at higher doses. Phenobarbital, a long-acting barbiturate, possesses...
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Anxiolytic Drugs: Benzodiazepines and Buspirone01:29

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Benzodiazepines are a class of anxiolytic drugs known for their rapid efficacy and high therapeutic-to-lethal dose ratio, but with a potential risk of drug dependence. These drugs are lipophilic, allowing for rapid absorption after oral administration, eventually reaching the central nervous system (CNS). Once in the CNS, benzodiazepines bind to the allosteric site of the GABAA receptor. This binding enhances the inhibitory effects of the neurotransmitter GABA. By doing so, they prevent...
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Sedatives and Hypnotics: Overview01:23

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Sedatives are drugs that alleviate anxiety, while hypnotics induce sleep. Both classes of medication suppress neuronal activity, leading to a calming effect for sedatives and facilitating sleep for hypnotics.
Sedative-hypnotics are categorized into barbiturates, benzodiazepines (BZDs), and non-benzodiazepines or Z-drugs. These drugs work by suppressing central nervous system activity, and this suppression is dose-dependent. Older sedative medications, like barbiturates, follow a linear curve in...
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Anxiolytic drugs are vital in managing anxiety disorders by effectively alleviating symptoms such as excessive fear, tachycardia, and tremors. There are several classes of anxiolytic medications, each with unique mechanisms of action and potential side effects.
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Sedatives and Hypnotics Drugs: Barbiturates01:20

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Sedatives and hypnotics encompass a drug class that acts on the central nervous system (CNS) to alleviate anxiety, promote relaxation and induce sleep.These drugs function by amplifying the actions of the neurotransmitter γ-aminobutyric acid (GABA), resulting in reduced neuronal activity. Barbiturates, a subset of sedatives and hypnotics first synthesized in the late 1800s, are categorized into ultra-short, short, intermediate, and long-acting groups based on their duration of effect. A...
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Predicting benzodiazepine prescriptions: A proof-of-concept machine learning approach.

Kerry L Kinney1,2, Yufeng Zheng2,3, Matthew C Morris1,2

  • 1Department of Psychiatry and Human Behavior, University of Mississippi Medical Center, Jackson, MS, United States.

Frontiers in Psychiatry
|March 27, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning models accurately predict benzodiazepine prescriptions using electronic health records. These algorithms can identify patients receiving benzodiazepines, aiding public health efforts to reduce medication risks.

Keywords:
benzodiazepinemachine learningprescriptionsrandom forestsupport vector machine

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Area of Science:

  • Pharmacovigilance
  • Health Informatics
  • Machine Learning in Healthcare

Background:

  • Benzodiazepines are widely prescribed psychotropic medications with potential for serious adverse effects.
  • Predictive modeling for benzodiazepine prescriptions is crucial for developing targeted prevention strategies.

Purpose of the Study:

  • To develop and evaluate machine learning algorithms for predicting benzodiazepine prescription receipt and quantity.
  • To utilize de-identified electronic health record (EHR) data for predictive model development.

Main Methods:

  • Support-vector machine (SVM) and random forest (RF) algorithms were applied to EHR data from outpatient psychiatry, family medicine, and geriatric medicine.
  • Models were trained on data from January 2020 to December 2021 (N = 204,723 encounters) and tested on data from January to March 2022 (N = 28,631 encounters).
  • Features included diagnoses (anxiety, sleep disorders), demographics, concurrent medications (opioids, antidepressants, antipsychotics), clinical variables, and insurance status.

Main Results:

  • Both SVM and RF models demonstrated high accuracy and Area Under the ROC Curve (AUC) for predicting benzodiazepine prescription receipt (yes/no).
  • SVM achieved accuracies of 0.868-0.883 and AUCs of 0.864-0.924; RF achieved accuracies of 0.860-0.887 and AUCs of 0.877-0.953.
  • High accuracy was also observed for predicting the number of benzodiazepine prescriptions (0, 1, 2+), with SVM (0.861-0.877) and RF (0.846-0.878).

Conclusions:

  • Machine learning models, specifically SVM and RF, can accurately predict benzodiazepine prescription status and quantity.
  • These predictive models hold potential for informing system-level interventions to mitigate the public health impact of benzodiazepine use.
  • Further replication is recommended to validate these findings for clinical implementation.