Ignite Creation Date:
2024-05-06 @ 2:04 PM
Last Modification Date:
2024-10-26 @ 1:24 PM
Study NCT ID:
NCT04208789
Status:
COMPLETED
Last Update Posted:
2020-10-27
First Post:
2019-12-06
Brief Title:
Artificial Inteligent for Diagnosing Drug-Resistant Tuberculosis
Sponsor:
Hasanuddin University
Organization:
Hasanuddin University
Study Overview
Official Title:
Artificial Neural Network as Diagnostic Tools For Rifampicin-Resistant Tuberculosis In Indonesia A Predictive Model Study and Economic Evaluation
Status:
COMPLETED
Status Verified Date:
2020-10
Last Known Status:
None
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:
Title Artificial Neural Network as Diagnostic Tools For Rifampicin-Resistant Tuberculosis In Indonesia A Predictive Model Study and Economic Evaluation
Background Drug-resistant tuberculosis has become a global threat particularly in Indonesia The need to increase detection followed by appropriate treatment is a concern in dealing with these cases The rapid molecular test specifically for detecting rifampicin-resistant is now being utilized in health care service particularly at primary care level with some challenges including the lack of quality control including how to obtained and treat the specimen properly prior to the examination which then affect the reliability of the results Drug-Susceptibility Test DST is still the gold standard in diagnosing drug-resistant tuberculosis but this procedure is time-consuming and costly The artificial intelligent including data exploration and modeling is a promising method to classify potential drug-resistant cases based on the association of several factors
Objective
1 To develop a model using an artificial intelligence approach that is able to classify the possibility of rifampicin-resistant tuberculosis
2 To assess the diagnostic ability and the accuracy of the model in comparison to existing rapid test and the gold standard
3 To evaluate the cost-effectiveness evaluation of Artificial Neural Network model in Web-Based Application in comparison with the standard diagnostic tools
Methodology
1 A cross-sectional study involving all suspected drug-resistant tuberculosis cases that being referred to the study center to undergo rapid molecular test and DST test over the past 5 years
2 A comprehensive retrospective medical records assessment and tuberculosis individual report will be performed to obtain a variable of interest
3 Questionnaire assessment for confirmation of insufficient information
4 Model Building through machine learning and deep learning procedure
5 Model Validation and testing using training data set and data from the different study center
Hypothesis
Artificial Intelligent Model will yield a similar or superior result of diagnostic ability compare the Rapid Molecular Test according to the Drug-Susceptibility Test Superiority Trial
Background Drug-resistant tuberculosis has become a global threat particularly in Indonesia The need to increase detection followed by appropriate treatment is a concern in dealing with these cases The rapid molecular test specifically for detecting rifampicin-resistant is now being utilized in health care service particularly at primary care level with some challenges including the lack of quality control including how to obtained and treat the specimen properly prior to the examination which then affect the reliability of the results Drug-Susceptibility Test DST is still the gold standard in diagnosing drug-resistant tuberculosis but this procedure is time-consuming and costly The artificial intelligent including data exploration and modeling is a promising method to classify potential drug-resistant cases based on the association of several factors
Objective
1 To develop a model using an artificial intelligence approach that is able to classify the possibility of rifampicin-resistant tuberculosis
2 To assess the diagnostic ability and the accuracy of the model in comparison to existing rapid test and the gold standard
3 To evaluate the cost-effectiveness evaluation of Artificial Neural Network model in Web-Based Application in comparison with the standard diagnostic tools
Methodology
1 A cross-sectional study involving all suspected drug-resistant tuberculosis cases that being referred to the study center to undergo rapid molecular test and DST test over the past 5 years
2 A comprehensive retrospective medical records assessment and tuberculosis individual report will be performed to obtain a variable of interest
3 Questionnaire assessment for confirmation of insufficient information
4 Model Building through machine learning and deep learning procedure
5 Model Validation and testing using training data set and data from the different study center
Hypothesis
Artificial Intelligent Model will yield a similar or superior result of diagnostic ability compare the Rapid Molecular Test according to the Drug-Susceptibility Test Superiority Trial
Detailed Description:
PROCEDURE
1 Under the permission granted by the study centers the team will obtain the medical records of all eligible cases within the past 5 years
2 The investigators then collect the information of interest variableparameter which obtained by history taking and further examinations and also medical Billing and Hospital pay per service For participants with Health Insurance the direct spending for treatment will be based on INA-CBGs case-based group payment This data then will be recorded in an electronic database
Parameter for model development
Host-based
1 Presence of Diabetes Mellitus Including years of being diagnosed HbA1c Before DST examination and treatment medication either insulin or oral anti-diabetic
2 Presence of HIV Including years of being diagnosed CD4 level Before DST examination and treatment and anti-retroviral medication
3 Tobacco cessation Brinkman Index
4 Alcohol consumption
5 History of Immunosuppressant use steroid
6 Presence of other diseases cancer stroke cardiovascular disease
7 History of drug abuse
8 History of adverse drug reaction during tuberculosis treatment
9 Adherence of previous tuberculosis therapy
10 Presence of COPD
11 Body Mass Index
Environment
1 History of Contact with Tuberculosis Patients
2 Healthy Index of Living Environment Household crowds
Agent
1 Level of Bacterial Smear Before DST
2 Extension of Lesion in Chest X-Ray
3 Presence of Cavitation
Sociodemographic Factors
1 Age
2 Gender
3 Education
4 Income Level
5 Health Insurance
6 Marital Status
7 Employment Status
3 For incomplete information a confirmation to the health center that was referring the cases will be done using the Tuberculosis Registration or questionnaire
4 The model building will be done using an Artificial Intelligent Model in R A selected model is an Artificial Neural Network either using Radial Base Function or multi-layer perceptron Several important procedures including
1 Determine Significant Parameter
2 Dealing with Insufficient and Imbalanced data class over or under-sampling
3 Normalization Batch Min-Max
4 Layer and design
5 Training and test distribution 7030
6 Model Selection
5 External Validation will be done to the appointed study center Precision true positive True NegativeAll cases
6 The Incremental Cost-Effectiveness Ratio Simulation will be done comparing the best model versus the gold standard and GeneXpert yielding a saving per unit of effectiveness
1 Under the permission granted by the study centers the team will obtain the medical records of all eligible cases within the past 5 years
2 The investigators then collect the information of interest variableparameter which obtained by history taking and further examinations and also medical Billing and Hospital pay per service For participants with Health Insurance the direct spending for treatment will be based on INA-CBGs case-based group payment This data then will be recorded in an electronic database
Parameter for model development
Host-based
1 Presence of Diabetes Mellitus Including years of being diagnosed HbA1c Before DST examination and treatment medication either insulin or oral anti-diabetic
2 Presence of HIV Including years of being diagnosed CD4 level Before DST examination and treatment and anti-retroviral medication
3 Tobacco cessation Brinkman Index
4 Alcohol consumption
5 History of Immunosuppressant use steroid
6 Presence of other diseases cancer stroke cardiovascular disease
7 History of drug abuse
8 History of adverse drug reaction during tuberculosis treatment
9 Adherence of previous tuberculosis therapy
10 Presence of COPD
11 Body Mass Index
Environment
1 History of Contact with Tuberculosis Patients
2 Healthy Index of Living Environment Household crowds
Agent
1 Level of Bacterial Smear Before DST
2 Extension of Lesion in Chest X-Ray
3 Presence of Cavitation
Sociodemographic Factors
1 Age
2 Gender
3 Education
4 Income Level
5 Health Insurance
6 Marital Status
7 Employment Status
3 For incomplete information a confirmation to the health center that was referring the cases will be done using the Tuberculosis Registration or questionnaire
4 The model building will be done using an Artificial Intelligent Model in R A selected model is an Artificial Neural Network either using Radial Base Function or multi-layer perceptron Several important procedures including
1 Determine Significant Parameter
2 Dealing with Insufficient and Imbalanced data class over or under-sampling
3 Normalization Batch Min-Max
4 Layer and design
5 Training and test distribution 7030
6 Model Selection
5 External Validation will be done to the appointed study center Precision true positive True NegativeAll cases
6 The Incremental Cost-Effectiveness Ratio Simulation will be done comparing the best model versus the gold standard and GeneXpert yielding a saving per unit of effectiveness
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