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MDR TuberculosisNCT04208789

Artificial Inteligent for Diagnosing Drug-Resistant Tuberculosis

An observational study of MDR Tuberculosis and Resistance to Tuberculostatic Drugs, run by Hasanuddin University at 5 sites in Indonesia.

Completed

Finished. The study has ended and is not enrolling. Results may be posted on the official record.

Official record
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Trial 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. Read more about this study

Who is it for?
Any age
What is being tested?
Rapid Molecular Drug-Resistant Tuberculosis Test, Artificial Intelligent Model and Drug Susceptibility Test
How many people
524 participants
Where
5 sitesIndonesia
Study dates
15 Jun 2020to 2 Oct 2020
Phase
Observational
Are there results?
None posted in the registry

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

  • Ages Any age
  • All sexes
  • No healthy volunteers
  • Child, Adult, Older adult

Study population: All suspected/presumptive Drug-Resistant Tuberculosis cases that were sent to the appointed Study Center within the last 3 years

Who may be able to join

  • Default cases under WHO criteria
  • Failure cases under WHO criteria
  • 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)

Who cannot join

  • Incomplete Information on Rapid Molecular Test Results, and Culture Results
  • Participants or family are unable/unwilling to provide additional information obtained through questionnaire

Trial locations and contacts

5 sites in 1 country.

5 sites in 1 country

Indonesia5 sites

  • Kanudjoso Djatiwibowo General Hospital

    Balikpapan, East Kalimantan, 76115

  • Wahidin Sudirohusodo General Hospital

    Makassar, South Sulawesi, 76124

  • Labuang Baji General Hospital

    Makassar, South Sulawesi, 90132

  • Balai Besar Kesehatan Paru Masyarakat

    Makassar, South Sulawesi

  • Tarakan General Hospital

    Tarakan, North Kalimantan, 77113

Other trials in Indonesia

About this study

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

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.

Read more

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)

Read the 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 :
  3. Presence of Diabetes Mellitus (Including years of being diagnosed, HbA1c Before DST examination and treatment, medication either insulin or oral anti-diabetic)
  4. Presence of HIV ((Including years of being diagnosed, CD4 level Before DST examination and treatment, and anti-retroviral medication)
  5. Tobacco cessation (Brinkman Index)
  6. Alcohol consumption
  7. History of Immunosuppressant use (steroid)
  8. Presence of other diseases (cancer, stroke, cardiovascular disease)
  9. History of drug abuse
  10. History of adverse drug reaction during tuberculosis treatment
  11. Adherence of previous tuberculosis therapy
  12. Presence of COPD
  13. Body Mass Index Environment
  14. History of Contact with Tuberculosis Patients
  15. Healthy Index of Living Environment (Household crowds) Agent
  16. Level of Bacterial Smear Before DST
  17. Extension of Lesion in Chest X-Ray
  18. Presence of Cavitation Sociodemographic Factors
  19. Age
  20. Gender
  21. Education
  22. Income Level
  23. Health Insurance
  24. Marital Status
  25. Employment Status
  26. For incomplete information, a confirmation to the health center that was referring the cases will be done using the Tuberculosis Registration or questionnaire.
  27. 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 :
  28. Determine Significant Parameter
  29. Dealing with Insufficient and Imbalanced data class (over or under-sampling)
  30. Normalization (Batch, Min-Max)
  31. Layer and design
  32. Training and test distribution (70:30)
  33. Model Selection
  34. External Validation will be done to the appointed study center. Precision: (true positive + True Negative)/All cases
  35. 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
Being tested
Rapid Molecular Drug-Resistant Tuberculosis Test, Artificial Intelligent Model, Drug Susceptibility Test
Keywords
Artificial Neural Network, GeneXpert MTB/RIF, Drug-Resistant Tb, Diagnosis
Medical subject headings
Tuberculosis, Multidrug-Resistant, Disease

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

  • Rapid Molecular Drug-Resistant Tuberculosis TestDiagnostic test

    Also called GeneXpert MTB/RIF

    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.

  • Artificial Intelligent ModelOther

    Also called Artificial Neural Network

    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.

  • Drug Susceptibility TestDiagnostic test

    Also called Lowenstein-Jensen Medium 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.

Study groups in NCT04208789
Study groupRoleWhat they receive
Positive Rifampicin-Resistant TuberculosisAll suspected cases that yielded Positive Rifampicin-Resistant Tuberculosis under the Gold-Standard Test (Culture on Lowenstein-Jensen Medium)
Receives: Diagnostic Test: Rapid Molecular Drug-Resistant Tuberculosis Test; Other: Artificial Intelligent Model; Diagnostic Test: Drug Susceptibility Test
Negative Rifampicin-Resistant TuberculosisAll suspected cases that yielded Negative Rifampicin-Resistant Tuberculosis under the Gold-Standard Test (Culture on Lowenstein-Jensen Medium)
Receives: Diagnostic Test: Rapid Molecular Drug-Resistant Tuberculosis Test; Other: Artificial Intelligent Model; Diagnostic Test: Drug Susceptibility Test

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. Accuracy of Artificial Intelligent Model to Drug Susceptibility Test Results

    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.

    Measured: through study completion, an average of 1 year

Secondary outcomes (1)

  1. Accuracy of Rapid Molecular Drug Resistant Tuberculosis test to Drug Susceptibility Test Results

    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.

    Measured: through study completion, an average of 1 year

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted23 Dec 2019
  2. Study start15 Jun 2020
  3. Primary completion30 Sep 2020
  4. Study completion2 Oct 2020

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Observational
Observational model
Case-control
Time perspective
Retrospective
Enrolment
524 participants (actual)
IDs, dates and publications
ClinicalTrials.gov ID
NCT04208789
Sponsor's protocol ID
0111190912
Status last verified
Oct 2020
First submitted
6 Dec 2019
Last updated
27 Oct 2020
Results posted
No
Data monitoring committee
Yes
FDA-regulated drug
No
FDA-regulated device
No
Individual data sharing
Undecided

Publications

  • GBD Tuberculosis Collaborators. The global burden of tuberculosis: results from the Global Burden of Disease Study 2015. Lancet Infect Dis. 2018 Mar;18(3):261-284. doi: 10.1016/S1473-3099(17)30703-X. Epub 2017 Dec 7.

    PubMed 29223583

  • Dean AS, Cox H, Zignol M. Epidemiology of Drug-Resistant Tuberculosis. Adv Exp Med Biol. 2017;1019:209-220. doi: 10.1007/978-3-319-64371-7_11.

    PubMed 29116637

  • Kendall EA, Azman AS, Cobelens FG, Dowdy DW. MDR-TB treatment as prevention: The projected population-level impact of expanded treatment for multidrug-resistant tuberculosis. PLoS One. 2017 Mar 8;12(3):e0172748. doi: 10.1371/journal.pone.0172748. eCollection 2017.

    PubMed 28273116

  • Dheda K, Gumbo T, Maartens G, Dooley KE, McNerney R, Murray M, Furin J, Nardell EA, London L, Lessem E, Theron G, van Helden P, Niemann S, Merker M, Dowdy D, Van Rie A, Siu GK, Pasipanodya JG, Rodrigues C, Clark TG, Sirgel FA, Esmail A, Lin HH, Atre SR, Schaaf HS, Chang KC, Lange C, Nahid P, Udwadia ZF, Horsburgh CR Jr, Churchyard GJ, Menzies D, Hesseling AC, Nuermberger E, McIlleron H, Fennelly KP, Goemaere E, Jaramillo E, Low M, Jara CM, Padayatchi N, Warren RM. The epidemiology, pathogenesis, transmission, diagnosis, and management of multidrug-resistant, extensively drug-resistant, and incurable tuberculosis. Lancet Respir Med. 2017 Mar 15:S2213-2600(17)30079-6. doi: 10.1016/S2213-2600(17)30079-6. Online ahead of print.

    PubMed 28344011

  • Collins D, Hafidz F, Mustikawati D. The economic burden of tuberculosis in Indonesia. Int J Tuberc Lung Dis. 2017 Sep 1;21(9):1041-1048. doi: 10.5588/ijtld.16.0898.

    PubMed 28826455

  • Falzon D, Mirzayev F, Wares F, Baena IG, Zignol M, Linh N, Weyer K, Jaramillo E, Floyd K, Raviglione M. Multidrug-resistant tuberculosis around the world: what progress has been made? Eur Respir J. 2015 Jan;45(1):150-60. doi: 10.1183/09031936.00101814. Epub 2014 Sep 26.

    PubMed 25261327

  • Falzon D, Jaramillo E, Wares F, Zignol M, Floyd K, Raviglione MC. Universal access to care for multidrug-resistant tuberculosis: an analysis of surveillance data. Lancet Infect Dis. 2013 Aug;13(8):690-7. doi: 10.1016/S1473-3099(13)70130-0. Epub 2013 Jun 4.

    PubMed 23743044

  • van Kampen SC, Susanto NH, Simon S, Astiti SD, Chandra R, Burhan E, Farid MN, Chittenden K, Mustikawati DE, Alisjahbana B. Effects of Introducing Xpert MTB/RIF on Diagnosis and Treatment of Drug-Resistant Tuberculosis Patients in Indonesia: A Pre-Post Intervention Study. PLoS One. 2015 Jun 15;10(6):e0123536. doi: 10.1371/journal.pone.0123536. eCollection 2015.

    PubMed 26075722

  • Soeroto AY, Lestari BW, Santoso P, Chaidir L, Andriyoko B, Alisjahbana B, van Crevel R, Hill PC. Evaluation of Xpert MTB-RIF guided diagnosis and treatment of rifampicin-resistant tuberculosis in Indonesia: A retrospective cohort study. PLoS One. 2019 Feb 28;14(2):e0213017. doi: 10.1371/journal.pone.0213017. eCollection 2019.

    PubMed 30818352

  • Prada-Medina CA, Fukutani KF, Pavan Kumar N, Gil-Santana L, Babu S, Lichtenstein F, West K, Sivakumar S, Menon PA, Viswanathan V, Andrade BB, Nakaya HI, Kornfeld H. Systems Immunology of Diabetes-Tuberculosis Comorbidity Reveals Signatures of Disease Complications. Sci Rep. 2017 May 17;7(1):1999. doi: 10.1038/s41598-017-01767-4.

    PubMed 28515464

  • Pradipta IS, Forsman LD, Bruchfeld J, Hak E, Alffenaar JW. Risk factors of multidrug-resistant tuberculosis: A global systematic review and meta-analysis. J Infect. 2018 Dec;77(6):469-478. doi: 10.1016/j.jinf.2018.10.004. Epub 2018 Oct 16.

    PubMed 30339803

  • Wang MG, Huang WW, Wang Y, Zhang YX, Zhang MM, Wu SQ, Sandford AJ, He JQ. Association between tobacco smoking and drug-resistant tuberculosis. Infect Drug Resist. 2018 Jun 12;11:873-887. doi: 10.2147/IDR.S164596. eCollection 2018.

    PubMed 29928135

  • Alipanah N, Jarlsberg L, Miller C, Linh NN, Falzon D, Jaramillo E, Nahid P. Adherence interventions and outcomes of tuberculosis treatment: A systematic review and meta-analysis of trials and observational studies. PLoS Med. 2018 Jul 3;15(7):e1002595. doi: 10.1371/journal.pmed.1002595. eCollection 2018 Jul.

    PubMed 29969463

  • Tegegne BS, Mengesha MM, Teferra AA, Awoke MA, Habtewold TD. Association between diabetes mellitus and multi-drug-resistant tuberculosis: evidence from a systematic review and meta-analysis. Syst Rev. 2018 Oct 15;7(1):161. doi: 10.1186/s13643-018-0828-0.

    PubMed 30322409

  • Darsey JA, Griffin WO, Joginipelli S, Melapu VK. Architecture and biological applications of artificial neural networks: a tuberculosis perspective. Methods Mol Biol. 2015;1260:269-83. doi: 10.1007/978-1-4939-2239-0_17.

    PubMed 25502388

Questions about this trial

What is this clinical trial studying?

An observational study of MDR Tuberculosis and Resistance to Tuberculostatic Drugs, run by Hasanuddin University at 5 sites in Indonesia.

The full registered description is in the About this study section and the official record.

Who may be eligible to take part?

The sponsor lists 4 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.

Is this clinical trial currently recruiting?

The record says: finished, as of the sponsor's last update on 27 Oct 2020. Status can change between updates, so confirm with the study contact before making plans.

Where is the study taking place?

The record lists 5 sites in Indonesia. Whether a site is taking part right now should be confirmed with that site. See every site.

What are the possible benefits and risks?

They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.

Are results available for this study?

The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.

How can a doctor refer a patient or contact the research team?

The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

Accuracy of Artificial Intelligent Model to Drug Susceptibility Test Results (time frame: through study completion, an average of 1 year). It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT04208789, which also lists 19 linked publications.

Does a listing mean the treatment is approved or proven to work?

No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.

How often is this information updated?

AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 27 Oct 2020.

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