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

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

Official Title
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Iatrogenic Addiction to Sedation in Shanghai

Conditions
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Hypnotics and Sedatives, Addiction

Participation Criteria
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Eligibility Criteria
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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.
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| outpatient<br>Patients visiting a psychiatric hospital | |

What is the study measuring?
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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 |