BMF CP132: Mapping High-Risk Predictors of Cavitary Pulmonary Tuberculosis

Ni Putu Wulan Purnama Sari

Faculty of Nursing, Widya Mandala Surabaya Catholic University, East Java, Indonesia

June 6, 2026


“The course covers style, techniques, and performance.

The techniques seem the heaviest, for it includes flying, bolting, darting, lifting, sneaking, and even holding one’s breath…”

In “No-Fish Dietary”; Wild Wise Weird (2024)


1. Project description

This study is a systems-oriented, theory-driven exploration of cavitary pulmonary Tuberculosis (CPTB). Employing the Granular Interaction Thinking Theory (GITT) [1,2], this study reframes CPTB from a clinical feature into a primary endpoint influenced by biological/clinical granules (Diabetes Mellitus or DM, nutritional status, and TB history), behavioral granules (smoking), and socio-demographic granules (sex). This study approaches TB epidemiology differently, providing evidence to understand predictors of severe disease progression (CPTB), which is academically distinctive. This methodological innovation positions this work at the intersection of clinical epidemiology and systems thinking.

1.1. Main objectives

The current study is conducted to examine the following research questions:

• How are the high-risk predictors, i.e., sex, smoking, DM, nutritional status, and TB history, associated with CPTB?
• How does nutritional status moderate the relationship between DM and CPTB?

1.2. Materials

The GITT will be employed for the conceptual development of this study [1,2], while the Bayesian Mindsponge Framework (BMF) analytics will be utilized for statistical analysis [3]. The dataset comprises responses from 73 multidrug-resistant TB (MDR-TB) patients and 219 TB patients in West Sumatra, Indonesia [4]. Statistical analyses will be conducted using the bayesvl R package, which utilizes the Markov chain Monte Carlo (MCMC) algorithm for estimation [5].

1.3. Main findings

Results of preliminary analysis showed that only sex, smoking, and TB history are positively associated with CPTB, while DM and nutritional status exhibit negative associations.

The main finding of this study is that nutritional status positively moderates the relationship between DM and CPTB (see Figure 1). This finding suggests a synergistic effect between nutritional status and DM in the development of CPTB as a severe disease progression.



Figure 1: The estimated posterior distributions with HPDI at 95%

2. Collaboration procedure

Portal users should follow these steps to register to participate in this research project:

1. Create an account on the website (preferably using an institution email).
2. Comment your name, affiliation, and your desired role in the project below this post.
3. Patiently wait for the formal agreement on the project from the AISDL mentor.

If you have further inquiries, please contact us at aisdl_team@mindsponge.info

If you have been invited to join the project by an AISDL member, you are still encouraged to follow the above formal steps.

All the resources for conducting and writing the research manuscript will be distributed upon project participation.

Mentor for this project: Ni Putu Wulan Purnama Sari.

Other members who have joined this project: Minh-Hoang Nguyen and Quan-Hoang Vuong.

The research project strictly adheres to scientific integrity standards, including authorship rights and obligations, without incurring an economic burden at participants’ expenses.

References

[1] Vuong QH, Nguyen MH (2025). Developing Bayesian probabilistic reasoning capacity in HSS disciplines: Qualitative evaluation on bayesvl and BMF analytics for ECRs. https://dipot.ulb.ac.be/dspace/bitstream/2013/397326/3/wp25008.pdf

[2] Vuong QH, La VP, Nguyen MH. (2025). Informational entropy-based value formation: A new paradigm for a deeper understanding of value. Evaluation Review, 50(3), 516-540.

[3] Vuong QH, Nguyen MH, La VP. (2022). The mindsponge and BMF analytics for innovative thinking in social sciences and humanities. Walter de Gruyter GmbH. https://books.google.com/books?id=EGeEEAAAQBAJ

[4] Nindrea RD, Sari NP, Harahap WA, Haryono SJ, Kusnanto H, Dwiprahasto I, Lazuardi L, Aryandono T. (2020). Survey data of multidrug-resistant tuberculosis, Tuberculosis patients characteristics and stress resilience during COVID-19 pandemic in West Sumatera Province, Indonesia. Data in Brief, 32, 106293.

[5] Vuong QH, La VP. (2025). Package ‘bayesvl’ version 1.0.0. https://books.google.com/books/about?id=znleEQAAQBAJ