Monitoring and Risk Prediction of Iatrogenic Sedative Hypnotics Addiction in a Shanghai Psychiatric Hospital

Iatrogenic Addiction to Sedation in Shanghai

This study will establish a sedative and hypnotics iatrogenic addiction risk monitoring network composed of 4 psychiatric hospitals in Shanghai through standardized data construction of outpatient prescription data and personnel training. Develop a sedative-hypnotic addiction risk prediction tool based on patient prescription data, and use independent in-operation outpatient prescription data for verification, and carry out clinical application promotion.

Study Overview

• Status: Unknown
• Conditions: Addiction; Hypnotics and Sedatives

Detailed Description

This study is a longitudinal analysis of the outpatient prescription data of psychiatric hospitals. It includes two aspects: 1) Develop evaluation methods for the risk of sedative-hypnotic addiction in psychiatric hospitals; 2) Construct a predictive model for the risk of iatrogenic addiction to sedative-hypnotics.

Step 1. Export all sedative hypnotic prescription information from the outpatient medical record system of Shanghai Psychiatric Hospital.

The data range is from January 1, 2019 to December 31, 2020. The data items that need to be exported include: patient identification information, gender, age, diagnosis, prescription drug name, drug use method, total dose, time , and the physician number of the prescription. Generate a unique patient number based on identification information (such as ID number), and merge all prescription information and electronic medical records of the same patient during the study period.

Step 2. Identify patients at risk of addiction to sedative hypnotics. This study defines the risk of addiction to sedatives and hypnotics when the outpatients appear "double off-label prescriptions". The standard of "double off-label prescriptions" is: the highest daily average dose of prescriptions obtained by patients>60 mg diazepam equivalent milligrams, and the number of consecutive prescription days>120 days. Firstly, mark whether the patient has a prescription that exceeds the specification range (over indication, over daily dose range, over treatment course) during the study period. Secondly, the analysis data set is further converted and labeled, including: all benzodiazepine doses are converted into diazepam equivalents according to the "Benzazepine Dose Conversion Table". Calculate the "average daily prescription dose" for each patient: add up the prescriptions of benzodiazepines to get the total prescription, and divide by the number of days to get the average daily prescription dose. Finally, calculate the monthly or annual cases or proportion of "double off-label prescriptions" patients who are at risk of addiction to sedatives and hypnotics.

Step 3.Establish a risk prediction model for iatrogenic addiction to sedative and hypnotics in psychiatric hospitals.

Use correlation analysis or machine learning methods to explore the formation trajectory of the "double off-label" pattern of sedative hypnotic prescriptions, and build a predictive model that can predict the formation of the "double off-label" pattern. Use a subset of prescription data to identify patients with status of "double off-label", then evaluate and review them to confirm the addiction status of sedatives and hypnotics. Use the validation subset to verify and improve the addiction risk prediction model based on the training data set.

• Study Type: Observational
• Enrollment (Anticipated): 100000

Contacts and Locations

• Study Contact: Name: Haifeng Jiang, Dr.; Phone Number: (86)-021-64906315; Email: dragonjhf@hotmail.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Any patient visiting the outpatient clinic of Shanghai mental health center during the research period will be included in the analysis.

Description

This study is a data analysis based on outpatient visit records. The research object is the prescription data of outpatient visits during the research period, that is, the prescription information of any patient visiting the outpatient clinic during the research period will be included in the analysis.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort
outpatient; Patients visiting a psychiatric hospital

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Outpatient's prescription	The research object is the outpatient prescription information of each independent year (2019, 2020) of each hospital (4 hospitals), including: patient identification information, gender, age, diagnosis, prescription drug name, drug use method, total dose, time , And the physician number of the prescription. Generate a unique patient number based on identification information (such as ID number), and merge all prescription information and electronic medical records of the same patient during the study period. And mark whether the patient has a prescription that exceeds the specification range (over indication, over daily dose range, over treatment course) during the study period.	Data from January 1, 2019 to December 31, 2020

Collaborators and Investigators

• Sponsor: Shanghai Mental Health Center

Study record dates

Study Major Dates

• Study Start (Anticipated): August 1, 2020
• Primary Completion (Anticipated): July 31, 2021
• Study Completion (Anticipated): July 31, 2022

Study Registration Dates

• First Submitted: August 5, 2020
• First Submitted That Met QC Criteria: August 5, 2020
• First Posted (Actual): August 7, 2020

Study Record Updates

• Last Update Posted (Actual): August 7, 2020
• Last Update Submitted That Met QC Criteria: August 5, 2020
• Last Verified: July 1, 2020

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Compulsive Behavior; Impulsive Behavior; Behavior, Addictive
• Other Study ID Numbers: HFJiang-005

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED
• IPD Plan Description: Will share results within the scope of Chinese laws and regulations.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No