Artificial Inteligent for Diagnosing Drug-Resistant Tuberculosis

NCT04208789 | Completed DMC

• 1 other identifier: 0111190912
• Study Type: observational
• Target: 524
• Locations: 1 country
• Sites: 5

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. 1.To develop a model using an artificial intelligence approach that is able to classify the possibility of rifampicin-resistant tuberculosis.
2. 2.To assess the diagnostic ability and the accuracy of the model in comparison to existing rapid test and the gold standard
3. 3.To evaluate the cost-effectiveness evaluation of Artificial Neural Network model in Web-Based Application in comparison with the standard diagnostic tools
4. 4.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.
5. 5.A comprehensive, retrospective medical records assessment and tuberculosis individual report will be performed to obtain a variable of interest.
6. 6.Questionnaire assessment for confirmation of insufficient information.
7. 7.Model Building through machine learning and deep learning procedure
8. 8.Model Validation and testing using training data set and data from the different study center

Conditions

MDR Tuberculosis; Resistance to Tuberculostatic Drugs

Keywords

Artificial Neural Network; GeneXpert MTB/RIF; Drug-Resistant Tb; Diagnosis

Outcome Measures

Primary Outcomes (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.

  through study completion, an average of 1 year

Secondary Outcomes (1)

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

  through study completion, an average of 1 year

Other Outcomes (1)

• Diagnostic Ability of Artificial Intelligent Model to Drug Susceptibility Test Results

  through study completion, an average of 1 year

Study Arms (2)

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; Other: Artificial Intelligent Model; Diagnostic Test: 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; Other: Artificial Intelligent Model; Diagnostic Test: Drug Susceptibility Test

Interventions

Rapid Molecular Drug-Resistant Tuberculosis Test DIAGNOSTIC_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.

Also known as: GeneXpert MTB/RIF

Negative Rifampicin-Resistant Tuberculosis; Positive Rifampicin-Resistant Tuberculosis

Artificial Intelligent Model OTHER

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.

Also known as: Artificial Neural Network

Negative Rifampicin-Resistant Tuberculosis; Positive Rifampicin-Resistant Tuberculosis

Drug Susceptibility Test DIAGNOSTIC_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.

Also known as: Lowenstein-Jensen Medium Drug Susceptibility Test

Negative Rifampicin-Resistant Tuberculosis; Positive Rifampicin-Resistant Tuberculosis

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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

You may qualify if:

• 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)

You may not qualify if:

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

Sponsors & Collaborators

• Hasanuddin University: lead
• Chulalongkorn University: collaborator

Study Sites (5)

Kanudjoso Djatiwibowo General Hospital

Balikpapan, East Kalimantan, 76115, Indonesia

Location

Tarakan General Hospital

Tarakan, North Kalimantan, 77113, Indonesia

Location

Wahidin Sudirohusodo General Hospital

Makassar, South Sulawesi, 76124, Indonesia

Location

Labuang Baji General Hospital

Makassar, South Sulawesi, 90132, Indonesia

Location

Balai Besar Kesehatan Paru Masyarakat

Makassar, South Sulawesi, Indonesia

Location

Related Publications (19)

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• 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.

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• 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.

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• Herman B, Sirichokchatchawan W, Pongpanich S, Nantasenamat C. Development and performance of CUHAS-ROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia. PLoS One. 2021 Mar 25;16(3):e0249243. doi: 10.1371/journal.pone.0249243. eCollection 2021.

  PMID: 33765092 DERIVED

MeSH Terms

Conditions

Tuberculosis, Multidrug-Resistant; Disease

Condition Hierarchy (Ancestors)

Tuberculosis; Mycobacterium Infections; Actinomycetales Infections; Gram-Positive Bacterial Infections; Bacterial Infections; Bacterial Infections and Mycoses; Infections; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Sathirakorn Pongpanich, Prof

  Chulalongkorn University

  STUDY DIRECTOR

• Wandee Sirichokchatchawan, Ph.D

  Chulalongkorn University

  PRINCIPAL INVESTIGATOR

• Bumi Herman, MD

  Hasanuddin University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Researcher

Study Record Dates

• First Submitted: December 6, 2019
• First Posted: December 23, 2019
• Study Start: June 15, 2020
• Primary Completion: September 30, 2020
• Study Completion: October 2, 2020
• Last Updated: October 27, 2020

Record last verified: 2020-10

Locations

ID (5)