Artificial Neural Network as Diagnostic Tools For Rifampicin-Resistant Tuberculosis In Indonesia: A Predictive Model Study and Economic Evaluation
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 524
- 试验地点
- 5
- 主要终点
- Accuracy of Artificial Intelligent Model to Drug Susceptibility Test Results
研究概览
简要总结
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 :
- To develop a model using an artificial intelligence approach that is able to classify the possibility of rifampicin-resistant tuberculosis.
- To assess the diagnostic ability and the accuracy of the model in comparison to existing rapid test and the gold standard
- To evaluate the cost-effectiveness evaluation of Artificial Neural Network model in Web-Based Application in comparison with the standard diagnostic tools
Methodology
- 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.
- A comprehensive, retrospective medical records assessment and tuberculosis individual report will be performed to obtain a variable of interest.
- Questionnaire assessment for confirmation of insufficient information.
- Model Building through machine learning and deep learning procedure
- 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)
详细描述
PROCEDURE
- Under the permission granted by the study centers, the team will obtain the medical records of all eligible cases within the past 5 years
- 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 :
- Presence of Diabetes Mellitus (Including years of being diagnosed, HbA1c Before DST examination and treatment, medication either insulin or oral anti-diabetic)
- Presence of HIV ((Including years of being diagnosed, CD4 level Before DST examination and treatment, and anti-retroviral medication)
- Tobacco cessation (Brinkman Index)
- Alcohol consumption
- History of Immunosuppressant use (steroid)
- Presence of other diseases (cancer, stroke, cardiovascular disease)
- History of drug abuse
- History of adverse drug reaction during tuberculosis treatment
- Adherence of previous tuberculosis therapy
- Presence of COPD
- Body Mass Index
Environment
- History of Contact with Tuberculosis Patients
- Healthy Index of Living Environment (Household crowds)
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Retrospective
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- 未提供
排除标准
- •Incomplete Information on Rapid Molecular Test Results, and Culture Results
- •Participants or family are unable/unwilling to provide additional information obtained through questionnaire
结局指标
主要结局
Accuracy of Artificial Intelligent Model to Drug Susceptibility Test Results
时间窗: 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.
次要结局
- Accuracy of Rapid Molecular Drug Resistant Tuberculosis test to Drug Susceptibility Test Results(through study completion, an average of 1 year)
研究者
Bumi Herman
Researcher
Hasanuddin University