Beyond Drug Efficacy: Diagnostic Informatics as a Hidden Driver of Therapeutic Value in Multidrug-Resistant Tuberculosis

Muhammad Rifqo Hafidzudin Farid; Abdul Fattah Farid

doi:10.37034/medinftech.v4i3.174

Authors

Muhammad Rifqo Hafidzudin Farid Universitas Gadjah Mada https://orcid.org/0000-0002-7953-6291
Abdul Fattah Farid Universitas Airlangga

DOI:

https://doi.org/10.37034/medinftech.v4i3.174

Keywords:

Diagnostic Informatics, Bedaquiline, Cost-Effectiveness, Health Technology Assessment, Multidrug-Resistant Tuberculosis

Abstract

This conceptual review examines whether published cost-effectiveness models of bedaquiline and delamanid account for diagnostic and pretreatment delays in multidrug-resistant/rifampicin-resistant tuberculosis (MDR/RR-TB). Evidence was retrieved through targeted searches of an AI-assisted medical evidence platform indexing Springer Nature, Wiley, NEJM Group, and JAMA Network. Searches covered five domains: diagnostic delays in Bandung’s MDR/RR-TB care cascade; clinical and epidemiological consequences; the structure of six bedaquiline/delamanid cost-effectiveness models; evidence for diagnostic and informatics interventions; and precedents for incorporating delays into economic models. Thirty-two unique studies were included. A four-year Bandung cascade analysis found that fewer than one in ten estimated MDR/RR-TB cases were diagnosed; this finding is specific to that study and is not a national estimate. Evidence from comparable high-burden settings links delays to greater disease severity, poorer treatment outcomes, and prolonged infectiousness. None of the six identified drug cost-effectiveness models formally parameterized diagnostic delay or diagnostic informatics performance. Their simulated horizons began at treatment initiation, while sensitivity analyses emphasized drug prices, outcome probabilities, and adverse-event costs. However, discrete-event simulation and related approaches have incorporated treatment waiting times into economic models in oncology and bloodstream infections. This review does not develop or validate a new economic model. It proposes a conceptual framework positioning diagnostic informatics as a determinant of therapeutic value in MDR/RR-TB. The framework aims to guide empirical research incorporating locally observed delay distributions into cost-effectiveness models for Indonesia and other high-burden settings.

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References

A. Pradipta et al., “Drug resistance of Mycobacterium tuberculosis in West Java, Indonesia,” Int. J. Med. Microbiol., vol. 320, p. 151665, 2025, doi: https://doi.org/10.1016/j.ijmm.2025.151665.

L. J. Wolfson et al., “Cost-Effectiveness of Adding Bedaquiline to Drug Regimens for the Treatment of Multidrug-Resistant Tuberculosis in the UK,” PLoS One, vol. 10, no. 3, pp. 1–20, 2015, doi: 10.1371/journal.pone.0120763.

D. Wirth, R. Dass, and R. Hettle, “Cost-effectiveness of adding novel or group 5 interventions to a background regimen for the treatment of multidrug-resistant tuberculosis in Germany.,” BMC Health Serv. Res., vol. 17, no. 1, p. 182, Mar. 2017, doi: 10.1186/s12913-017-2118-2.

G. Fekadu, J. Yao, and J. H. S. You, “Bedaquiline-based treatment for extensively drug-resistant tuberculosis in South Africa: A cost-effectiveness analysis,” PLoS One, vol. 17, no. 8, pp. 1–12, 2022, doi: 10.1371/journal.pone.0272770.

A. Y. Soeroto et al., “Evaluation of Xpert MTB-RIF guided diagnosis and treatment of rifampicin-resistant tuberculosis in Indonesia: A retrospective cohort study,” PLoS One, vol. 14, no. 2, pp. 1–16, 2019, doi: 10.1371/journal.pone.0213017.

P. Silitonga, W. Jiang, S. Wyatt, E. Burhan, E. F. M. Kes, and Q. Long, “Factors affecting time to treatment initiation after diagnosis for multidrug-resistant/rifampicin-resistant tuberculosis patients: A mixed-methods study in Jakarta, Indonesia,” Trop. Med. & Int. Heal., vol. 28, no. 1, pp. 43–52, 2023, doi: https://doi.org/10.1111/tmi.13838.

B. W. Lestari et al., “Patient pathways and delays to diagnosis and treatment of tuberculosis in an urban setting in Indonesia,” Lancet Reg. Heal. – West. Pacific, vol. 5, no. 100059, pp. 1–9, Dec. 2020, doi: 10.1016/j.lanwpc.2020.100059.

B. W. Lestari et al., “Management of drug-resistant tuberculosis in Indonesia: a four-year cascade of care analysis,” Lancet Reg. Heal. – Southeast Asia, vol. 22, pp. 1–10, Mar. 2024, doi: 10.1016/j.lansea.2023.100294.

Y. M. Htun et al., “Delay in treatment initiation and treatment outcomes among adult patients with multidrug-resistant tuberculosis at Yangon Regional Tuberculosis Centre, Myanmar: A retrospective study,” PLoS One, vol. 13, no. 12, pp. 1–20, 2019, doi: 10.1371/journal.pone.0209932.

K. A. Alene et al., “Treatment outcomes of patients with multidrug-resistant and extensively drug resistant tuberculosis in Hunan Province, China.,” BMC Infect. Dis., vol. 17, no. 1, p. 573, Aug. 2017, doi: 10.1186/s12879-017-2662-8.

G. van Cutsem, P. Isaakidis, J. Farley, E. Nardell, G. Volchenkov, and H. Cox, “Infection Control for Drug-Resistant Tuberculosis: Early Diagnosis and Treatment Is the Key.,” Clin. Infect. Dis. an Off. Publ. Infect. Dis. Soc. Am., vol. 62 Suppl 3, no. Suppl 3, pp. S238-43, May 2016, doi: 10.1093/cid/ciw012.

X. Lu, C. Smare, C. Kambili, A. C. El Khoury, and L. J. Wolfson, “Health outcomes of bedaquiline in the treatment of multidrug-resistant tuberculosis in selected high burden countries,” BMC Health Serv. Res., vol. 17, no. 1, p. 87, 2017, doi: 10.1186/s12913-016-1931-3.

A. Mpobela Agnarson, A. Williams, C. Kambili, G. Mattson, and L. Metz, “The cost-effectiveness of a bedaquiline-containing short-course regimen for the treatment of multidrug-resistant tuberculosis in South Africa.,” Expert Rev. Anti. Infect. Ther., vol. 18, no. 5, pp. 475–483, May 2020, doi: 10.1080/14787210.2020.1742109.

A. Gye, R. D. A. Lourenco, and S. Goodall, “Discrete Event Simulation to Incorporate Infusion Wait-Time When Assessing Cost-Effectiveness of a Chimeric-Antigen Receptor T Cell Therapy,” Value Heal., vol. 27, no. 4, pp. 415–424, 2024, doi: https://doi.org/10.1016/j.jval.2024.01.008.

S. van der Pol, P. Rojas Garcia, F. Antoñanzas Villar, M. J. Postma, and A. D. I. van Asselt, “Health-Economic Analyses of Diagnostics: Guidance on Design and Reporting.,” Pharmacoeconomics, vol. 39, no. 12, pp. 1355–1363, Dec. 2021, doi: 10.1007/s40273-021-01104-8.

E. E. Pliakos, N. Andreatos, F. Shehadeh, P. D. Ziakas, and E. Mylonakis, “The Cost-Effectiveness of Rapid Diagnostic Testing for the Diagnosis of Bloodstream Infections with or without Antimicrobial Stewardship.,” Clin. Microbiol. Rev., vol. 31, no. 3, Jul. 2018, doi: 10.1128/CMR.00095-17.

J. H. Lee et al., “Impact of molecular diagnostic tests on diagnostic and treatment delays in tuberculosis: a systematic review and meta-analysis,” BMC Infect. Dis., vol. 22, no. 1, p. 940, 2022, doi: 10.1186/s12879-022-07855-9.

G. M. Kassa, M. W. Merid, A. G. Muluneh, and H. F. Wolde, “Comparing the impact of genotypic based diagnostic algorithm on time to treatment initiation and treatment outcomes among drug-resistant tuberculosis patients in Amhara region, Ethiopia,” PLoS One, vol. 16, no. 2, pp. 1–12, 2021, doi: 10.1371/journal.pone.0246938.

J. Domínguez et al., “Clinical implications of molecular drug resistance testing for Mycobacterium tuberculosis: a 2023 TBnet/RESIST-TB consensus statement.,” Lancet. Infect. Dis., vol. 23, no. 4, pp. e122–e137, Apr. 2023, doi: 10.1016/S1473-3099(22)00875-1.

G. Tavaziva et al., “Chest X-ray Analysis With Deep Learning-Based Software as a Triage Test for Pulmonary Tuberculosis: An Individual Patient Data Meta-Analysis of Diagnostic Accuracy.,” Clin. Infect. Dis. an Off. Publ. Infect. Dis. Soc. Am., vol. 74, no. 8, pp. 1390–1400, Apr. 2022, doi: 10.1093/cid/ciab639.

L. Miglietta et al., “Artificial intelligence and infectious disease diagnostics: state of the art and future perspectives.,” Lancet. Infect. Dis., vol. 26, no. 3, pp. e168–e180, Mar. 2026, doi: 10.1016/S1473-3099(25)00354-8.

C. Oga-Omenka et al., “Implementation of digital tuberculosis information systems: perspectives from 10 high TB burden countries,” BMC Infect. Dis., vol. 26, no. 1, p. 1030, 2026, doi: 10.1186/s12879-026-12852-3.

S. C. van Kampen et al., “Effects of Introducing Xpert MTB/RIF on Diagnosis and Treatment of Drug-Resistant Tuberculosis Patients in Indonesia: A Pre-Post Intervention Study,” PLoS One, vol. 10, no. 6, pp. 1–11, 2015, doi: 10.1371/journal.pone.0123536.

Y. Chen, Z. Yuan, X. Shen, J. Wu, Z. Wu, and B. Xu, “Time to Multidrug-Resistant Tuberculosis Treatment Initiation in Association with Treatment Outcomes in Shanghai, China,” Antimicrob. Agents Chemother., vol. 62, no. 4, pp. 10.1128/aac.02259-17, 2018, doi: 10.1128/aac.02259-17.

T. A. Yates et al., “The transmission of Mycobacterium tuberculosis in high burden settings,” Lancet Infect. Dis., vol. 16, no. 2, pp. 227–238, 2016, doi: https://doi.org/10.1016/S1473-3099(15)00499-5.

A. Y. Soeroto, C. Pratiwi, P. Santoso, and B. W. Lestari, “Factors affecting outcome of longer regimen multidrug-resistant tuberculosis treatment in West Java Indonesia: A retrospective cohort study,” PLoS One, vol. 16, no. 2, pp. 1–13, 2021, doi: 10.1371/journal.pone.0246284.

Q. Fan, W. Ming, W. Yip, and J. H. S. You, “Cost-effectiveness of bedaquiline or delamanid plus background regimen for multidrug-resistant tuberculosis in a high-income intermediate burden city of China,” Int. J. Infect. Dis., vol. 78, pp. 44–49, 2019, doi: https://doi.org/10.1016/j.ijid.2018.10.007.

K. Naidoo et al., “The epidemiology, transmission, diagnosis, and management of drug-resistant tuberculosis—lessons from the South African experience,” Lancet Infect. Dis., vol. 24, no. 9, pp. e559–e575, 2024, doi: https://doi.org/10.1016/S1473-3099(24)00144-0.

Y. Ou et al., “Artificial Intelligence in the Diagnosis and Management of Pulmonary Tuberculosis: A Review of Current Applications and Future Perspectives,” Ther. Clin. Risk Manag., vol. 22, p. 596651, 2026, doi: 10.2147/TCRM.S596651.

D. S. Sule et al., “Diagnostic accuracy of AI-assisted chest radiographs in tuberculosis screening: A Ghanaian clinical study,” PLoS One, vol. 21, no. 3, pp. 1–11, 2026, doi: 10.1371/journal.pone.0342988.

Y. Adusi-Poku, J. S. Idan, A. Mohammed, B. W. Adu, E. K. Nakua, and E. Owusu-Dabo, “Cost-effectiveness analysis of computer aided detection for tuberculosis in Ghana: A decision-analytic modeling study.,” PLoS One, vol. 21, no. 8, p. e0356244, 2026, doi: 10.1371/journal.pone.0356244.

P. Khudyakov, L. Xu, C. Yang, D. Spiegelman, and M. Wang, “Statistical methods for cost-effectiveness analysis of left-truncated and right-censored survival data with treatment delays.,” J. R. Stat. Soc. Ser. C. Appl. Stat., vol. 74, no. 5, pp. 1476–1495, Dec. 2025, doi: 10.1093/jrsssc/qlaf035.