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Artificial Inteligent for Diagnosing Drug-Resistant Tuberculosis

Sponsor
Hasanuddin University (Other)
Overall Status
Completed
CT.gov ID
NCT04208789
Collaborator
Chulalongkorn University (Other)
524
Enrollment
5
Locations
3.6
Actual Duration (Months)
104.8
Patients Per Site
29.3
Patients Per Site Per Month

Study Details

Study Description

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)

Condition or Disease Intervention/Treatment Phase
  • Diagnostic Test: Rapid Molecular Drug-Resistant Tuberculosis Test
  • Other: Artificial Intelligent Model
  • Diagnostic Test: Drug Susceptibility Test

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

  8. For incomplete information, a confirmation to the health center that was referring the cases will be done using the Tuberculosis Registration or questionnaire.

  9. 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 :

  10. Determine Significant Parameter

  11. Dealing with Insufficient and Imbalanced data class (over or under-sampling)

  12. Normalization (Batch, Min-Max)

  13. Layer and design

  14. Training and test distribution (70:30)

  15. Model Selection

  16. External Validation will be done to the appointed study center. Precision: (true positive + True Negative)/All cases

  17. 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 Design

Study Type:
Observational
Actual Enrollment :
524 participants
Observational Model:
Case-Control
Time Perspective:
Retrospective
Official Title:
Artificial Neural Network as Diagnostic Tools For Rifampicin-Resistant Tuberculosis In Indonesia: A Predictive Model Study and Economic Evaluation
Actual Study Start Date :
Jun 15, 2020
Actual Primary Completion Date :
Sep 30, 2020
Actual Study Completion Date :
Oct 2, 2020

Arms and Interventions

Arm Intervention/Treatment
Positive Rifampicin-Resistant Tuberculosis

All suspected cases that yielded Positive Rifampicin-Resistant Tuberculosis under the Gold-Standard Test (Culture on Lowenstein-Jensen Medium)

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 Names:
  • GeneXpert MTB/RIF

    • 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.
    Other Names:
  • Artificial Neural Network

    • 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.
    Other Names:
  • Lowenstein-Jensen Medium Drug Susceptibility Test

    		•
    Negative Rifampicin-Resistant Tuberculosis

    All suspected cases that yielded Negative Rifampicin-Resistant Tuberculosis under the Gold-Standard Test (Culture on Lowenstein-Jensen Medium)

    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 Names:
  • GeneXpert MTB/RIF

    • 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.
    Other Names:
  • Artificial Neural Network

    • 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.
    Other Names:
  • Lowenstein-Jensen Medium Drug Susceptibility Test

    		•

    Outcome Measures

    Primary Outcome Measures

    1. Accuracy of Artificial Intelligent Model to Drug Susceptibility Test Results [through study completion, an average of 1 year]

      The accuracy is the number of correct cases (the results obtained by the model is the same as obtained by culture) predicted by the model per total cases.

    Secondary Outcome Measures

    1. Accuracy of Rapid Molecular Drug Resistant Tuberculosis test to Drug Susceptibility Test Results [through study completion, an average of 1 year]

      The accuracy is the number of correct cases (the results obtained by the GeneXpert MTB/RIF is the same as obtained by culture) predicted by the model per total cases.

    Other Outcome Measures

    1. Diagnostic Ability of Artificial Intelligent Model to Drug Susceptibility Test Results [through study completion, an average of 1 year]

      Sensitivity, Specificity, Negative Predictive Value and Positive Predictive value of Artificial Intelligent Model to Drug Susceptibility Test Results

    Eligibility Criteria

    Criteria

    Ages Eligible for Study:
    N/A and Older
    Sexes Eligible for Study:
    All
    Accepts Healthy Volunteers:
    No
    Inclusion criteria:

    1. Default cases under WHO criteria

    2. Failure cases under WHO criteria

    3. Physician-referred cases for presumptive drug-resistant TB as follows :

    With or without immunocompromised condition, With or without any adverse reaction of anti TB drug, With or without any comorbidities (such as diabetes mellitus, heart disease)

    Exclusion Criteria:

    1. Incomplete Information on Rapid Molecular Test Results, and Culture Results

    2. Participants or family are unable/unwilling to provide additional information obtained through questionnaire

    Contacts and Locations

    Locations

    Site City State Country Postal Code
    1 Kanudjoso Djatiwibowo General Hospital Balikpapan East Kalimantan Indonesia 76115
    2 Tarakan General Hospital Tarakan North Kalimantan Indonesia 77113
    3 Labuang Baji General Hospital Makasar South Sulawesi Indonesia 90132
    4 Balai Besar Kesehatan Paru Masyarakat Makasar South Sulawesi Indonesia
    5 Wahidin Sudirohusodo General Hospital Makassar South Sulawesi Indonesia 76124

    Sponsors and Collaborators

    • Hasanuddin University
    • Chulalongkorn University

    Investigators

    • Study Director: Sathirakorn Pongpanich, Prof, Chulalongkorn University
    • Principal Investigator: Wandee Sirichokchatchawan, Ph.D, Chulalongkorn University
    • Principal Investigator: Bumi Herman, MD, Hasanuddin University

    Study Documents (Full-Text)

    None provided.

    More Information

    Publications

    Responsible Party:
    Bumi Herman, Researcher, Hasanuddin University
    ClinicalTrials.gov Identifier:
    NCT04208789
    Other Study ID Numbers:
    • 0111190912
    First Posted:
    Dec 23, 2019
    Last Update Posted:
    Oct 27, 2020
    Last Verified:
    Oct 1, 2020
    Individual Participant Data (IPD) Sharing Statement:
    Undecided
    Plan to Share IPD:
    Undecided
    Studies a U.S. FDA-regulated Drug Product:
    No
    Studies a U.S. FDA-regulated Device Product:
    No
    Keywords provided by Bumi Herman, Researcher, Hasanuddin University
    Artificial Neural Network
    GeneXpert MTB/RIF
    Drug-Resistant Tb
    Diagnosis
    Additional relevant MeSH terms:
    Tuberculosis
    Tuberculosis, Multidrug-Resistant

    Study Results

    No Results Posted as of Oct 27, 2020