Ignite Creation Date:
2024-05-06 @ 3:03 PM
Last Modification Date:
2024-10-26 @ 1:42 PM
Study NCT ID:
NCT04504162
Status:
UNKNOWN
Last Update Posted:
2020-08-07
First Post:
2020-08-05
Brief Title:
Monitoring and Risk Prediction of Iatrogenic Sedative Hypnotics Addiction in a Shanghai Psychiatric Hospital
Sponsor:
Shanghai Mental Health Center
Organization:
Shanghai Mental Health Center
Study Overview
Official Title:
Iatrogenic Addiction to Sedation in Shanghai
Status:
UNKNOWN
Status Verified Date:
2020-07
Last Known Status:
NOT_YET_RECRUITING
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
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:
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 patients60 mg diazepam equivalent milligrams and the number of consecutive prescription days120 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 3Establish 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
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 patients60 mg diazepam equivalent milligrams and the number of consecutive prescription days120 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 3Establish 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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None