Resistance to Tuberculostatic Drugs Clinical Trial
Official title:
Artificial Neural Network as Diagnostic Tools For Rifampicin-Resistant Tuberculosis In Indonesia: A Predictive Model Study and Economic Evaluation
NCT number | NCT04208789 |
Other study ID # | 0111190912 |
Secondary ID | |
Status | Completed |
Phase | |
First received | |
Last updated | |
Start date | June 15, 2020 |
Est. completion date | October 2, 2020 |
Verified date | October 2020 |
Source | Hasanuddin University |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
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)
Status | Completed |
Enrollment | 524 |
Est. completion date | October 2, 2020 |
Est. primary completion date | September 30, 2020 |
Accepts healthy volunteers | No |
Gender | All |
Age group | N/A and older |
Eligibility | 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 |
Country | Name | City | State |
---|---|---|---|
Indonesia | Kanudjoso Djatiwibowo General Hospital | Balikpapan | East Kalimantan |
Indonesia | Balai Besar Kesehatan Paru Masyarakat | Makasar | South Sulawesi |
Indonesia | Labuang Baji General Hospital | Makasar | South Sulawesi |
Indonesia | Wahidin Sudirohusodo General Hospital | Makassar | South Sulawesi |
Indonesia | Tarakan General Hospital | Tarakan | North Kalimantan |
Lead Sponsor | Collaborator |
---|---|
Hasanuddin University | Chulalongkorn University |
Indonesia,
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. — View Citation
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. — View Citation
Dande P, Samant P. Acquaintance to Artificial Neural Networks and use of artificial intelligence as a diagnostic tool for tuberculosis: A review. Tuberculosis (Edinb). 2018 Jan;108:1-9. doi: 10.1016/j.tube.2017.09.006. Epub 2017 Sep 20. Review. — View Citation
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. — View Citation
de O Souza Filho JB, de Seixas JM, Galliez R, de Bragança Pereira B, de Q Mello FC, Dos Santos AM, Kritski AL. A screening system for smear-negative pulmonary tuberculosis using artificial neural networks. Int J Infect Dis. 2016 Aug;49:33-9. doi: 10.1016/j.ijid.2016.05.019. Epub 2016 May 24. — View Citation
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. Review. — View Citation
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. pii: S2213-2600(17)30079-6. doi: 10.1016/S2213-2600(17)30079-6. [Epub ahead of print] Review. — View Citation
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. — View Citation
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. — View Citation
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. — View Citation
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. — View Citation
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. — View Citation
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. — View Citation
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. — View Citation
Souza Filho JBOE, Sanchez M, Seixas JM, Maidantchik C, Galliez R, Moreira ADSR, da Costa PA, Oliveira MM, Harries AD, Kritski AL. Screening for active pulmonary tuberculosis: Development and applicability of artificial neural network models. Tuberculosis (Edinb). 2018 Jul;111:94-101. doi: 10.1016/j.tube.2018.05.012. Epub 2018 May 19. — View Citation
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. — View Citation
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. — View Citation
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. Review. — View Citation
* Note: There are 18 references in all — Click here to view all references
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Other | Diagnostic Ability of Artificial Intelligent Model to Drug Susceptibility Test Results | Sensitivity, Specificity, Negative Predictive Value and Positive Predictive value of Artificial Intelligent Model to Drug Susceptibility Test Results | through study completion, an average of 1 year | |
Primary | 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 | 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. | through study completion, an average of 1 year |
Status | Clinical Trial | Phase | |
---|---|---|---|
Active, not recruiting |
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