NCT04208789

Artificial Inteligent for Diagnosing Drug-Resistant Tuberculosis

Artificial Neural Network as Diagnostic Tools For Rifampicin-Resistant Tuberculosis In Indonesia: A Predictive Model Study and Economic Evaluation

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 524 (actual)
• Sponsor: Hasanuddin University · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Not accepted

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)

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 variable/parameter 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 (70:30) 6. Model Selection 5. External Validation will be done to the appointed study center. Precision: (true positive + True Negative)/All 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

Conditions

• MDR Tuberculosis
• Resistance to Tuberculostatic Drugs

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	Rapid Molecular Drug-Resistant Tuberculosis Test	GeneXpert MTB/RIF assay is a nucleic acid amplification (NAA) test which simultaneously detects DNA of Mycobacterium tuberculosis complex (MTBC) and resistance to rifampin (RIF) (i.e. mutation of the rpoB gene) in less than two hours. This system integrates and automates sample processing, nucleic acid amplification, and detection of the target sequences. The primers in the XpertMTB/RIF assay amplify a portion of the rpoB gene containing the 81 base pair "core" region. The probes are able to differentiate between the conserved wild-type sequence and mutations in the core region that is associated with rifampicin resistance. The output of this procedure is detected, undetected, or indeterminate.
OTHER	Artificial Intelligent Model	The artificial intelligent model is a model that developed from several associated factors with machine learning and deep learning method in order to classify the possibility of drug-resistant tuberculosis. The Artificial neural network will be built using deep learning software.
DIAGNOSTIC_TEST	Drug Susceptibility Test	This procedure uses Löwenstein-Jensen (LJ) medium to determine whether the isolates of M. tuberculosis are susceptible to anti-TB agents. Media containing the critical concentration of the anti-TB agent is inoculated with a dilution of a culture suspension (usually a 10-2 dilution of a MacFarland 1 suspension) and control media without the anti-TB agent is inoculated with usually a 10-4 dilution of a MacFarland 1 suspension. Growth (i.e. a number of colonies) on the agent-containing media is compared to the growth on the agent-free control media. The ratio of the number of colonies on the medium containing the anti-TB agent to the number of colonies (corrected for the dilution factor) on the medium without the anti-TB agent is calculated, and the proportion is expressed as a percentage. Provisional results for susceptible isolates may be read after 3-4 weeks of incubation; definitive results may be read after 6 weeks of incubation. Resistance may be reported within 3-4 weeks.

Timeline

• Start date: 2020-06-15
• Primary completion: 2020-09-30
• Completion: 2020-10-02
• First posted: 2019-12-23
• Last updated: 2020-10-27

Locations

5 sites across 1 country: Indonesia