Clinical trial NCT04208789: Artificial Inteligent for Diagnosing Drug-Resistant Tuberculosis
Official title: Artificial Neural Network as Diagnostic Tools For Rifampicin-Resistant Tuberculosis In Indonesia: A Predictive Model Study and Economic Evaluation
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: MDR Tuberculosis
Resistance to Tuberculostatic Drugs
Keywords: Artificial Neural Network
GeneXpert MTB/RIF
Drug-Resistant Tb
Diagnosis
Lead sponsor: Hasanuddin University
Sponsor class: OTHER
Collaborators: name: Chulalongkorn University
First posted: 2019-12-23
Enrollment: 524
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_CONTROL
Time perspective: RETROSPECTIVE
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

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)

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

Eligibility criteria: 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

Primary outcomes:
measure: Accuracy of Artificial Intelligent Model to Drug Susceptibility Test Results
timeFrame: through study completion, an average of 1 year
Secondary outcomes:
measure: Accuracy of Rapid Molecular Drug Resistant Tuberculosis test to Drug Susceptibility Test Results
timeFrame: through study completion, an average of 1 year
Other outcomes:
measure: Diagnostic Ability of Artificial Intelligent Model to Drug Susceptibility Test Results
timeFrame: through study completion, an average of 1 year
Locations:
city: Balikpapan
country: Indonesia
facility: Kanudjoso Djatiwibowo General Hospital
state: East Kalimantan
city: Tarakan
country: Indonesia
facility: Tarakan General Hospital
state: North Kalimantan
city: Makasar
country: Indonesia
facility: Labuang Baji General Hospital
state: South Sulawesi
city: Makasar
country: Indonesia
facility: Balai Besar Kesehatan Paru Masyarakat
state: South Sulawesi
city: Makassar
country: Indonesia
facility: Wahidin Sudirohusodo General Hospital
state: South Sulawesi
Study officials:
affiliation: Chulalongkorn University
name: Sathirakorn Pongpanich, Prof
role: STUDY_DIRECTOR
affiliation: Chulalongkorn University
name: Wandee Sirichokchatchawan, Ph.D
role: PRINCIPAL_INVESTIGATOR
affiliation: Hasanuddin University
name: Bumi Herman, MD
role: PRINCIPAL_INVESTIGATOR
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