The emergence of antibiotic resistance has become a significant obstacle to successful tuberculosis (TB) management globally. Multi-drug-resistant tuberculosis (MDR-TB), defined by resistance to rifampicin and isoniazid, represents a considerable concern, with about 19 million persons infected with latent MDR-TB. These latent infections provide a continual danger of reactivation, potentially inciting new outbreaks(1). Although recent initiatives have alleviated the worldwide burden of multidrug-resistant tuberculosis, new difficulties are arising. In 2021, almost 450,000 instances of multidrug-resistant tuberculosis (MDR-TB) or rifampicin-resistant tuberculosis (RR-TB) were documented, indicating a 3.1% rise from the preceding year (2). Moreover, MDR/RR-TB constituted 3.6% of all new tuberculosis infections and 18% of retreatment patients worldwide (Dean et al., 2022). Notwithstanding the availability of treatment, hardly one-third of identified DR-TB patients undergo therapy, enabling untreated individuals to perpetuate disease transmission among communities (3).

Historically, the management of drug-resistant tuberculosis has been hindered by exorbitant expenditures, protracted treatment protocols, substantial pill loads, and recurrent adverse medication responses, resulting in suboptimal patient outcomes (4). The economic costs linked to MDR-TB are substantial of 2018, nations with the most significant MDR-TB burdens incurred losses of up to 476.5 billion USD, with certain countries witnessing a decline exceeding 5% of their gross domestic product (GDP) . Forecasts indicate that, if unmitigated, MDR-TB will incur a cost of 16.7 trillion USD to the worldwide economy by 2050 (5).

Recent advancements in tuberculosis therapy have developed abbreviated, all-oral regimens that include both innovative and recycled medications. These regimens provide renewed optimism by augmenting safety, decreasing treatment expenses, and boosting patient compliance (6). Nonetheless, even encouraging advancements include inherent limits. Patients with severe drug-resistant extra-pulmonary tuberculosis (DR-epTB) continue to be excluded from these novel medicines, although constituting a substantial percentage of drug-resistant tuberculosis (DR-TB) infections (7).

Extra-pulmonary tuberculosis (EPTB), impacting tissues and organs outside the lungs, poses distinct obstacles. Although pulmonary tuberculosis constitutes the bulk of cases, extrapulmonary tuberculosis has been overlooked in tuberculosis control initiatives due to its supposed low transmissibility (8). Nonetheless, individuals with extrapulmonary tuberculosis (EPTB) have elevated death rates during and subsequent to treatment, especially in cases of drug resistance (9). Research demonstrates that 16–20% of extrapulmonary tuberculosis (EPTB) patients exhibit various forms of medication resistance, comprising isoniazid resistance (8–14%), rifampicin mono-resistance (2.4–3.9%), and multidrug-resistant tuberculosis (MDR-TB) (2–10%) (10).

Tackling antibiotic resistance in tuberculosis—both pulmonary and extrapulmonary—necessitates a comprehensive understanding of the clinical attributes and consequences linked to resistant bacteria. Enhancing diagnostic and therapeutic approaches for DR-epTB patients is crucial for attaining superior results and guaranteeing the efficacy of global tuberculosis control efforts. Given the ongoing impact of antibiotic resistance on tuberculosis treatment results, customized solutions are essential to alleviate its public health and economic repercussions.

Objectives

1. To describe the clinical features of drug-resistant extra-pulmonary tuberculosis (DR-epTB).
2. To identify the drug-resistance patterns in DR-epTB cases
3. To assess the treatment outcomes of patients with DR-epTB.

2.1.         Study Design 

This study employs a scoping review framework to explore the impact of antibiotic resistance on treatment outcomes in patients with tuberculosis (TB). The scoping review methodology ensures a comprehensive examination of existing literature and follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. This approach helps identify knowledge gaps, summarize findings, and inform further research by systematically reviewing all relevant studies.

2.2.         Information Sources 

To ensure broad coverage, a comprehensive literature search was performed using the EMBASE database. The search was conducted from the database’s inception through October 05, 2024, capturing all relevant studies published during this period.

2.3.         Search Strategy 

A detailed search query was designed to retrieve studies focusing on drug-resistant TB, including both multidrug-resistant (MDR-TB) and extensively drug-resistant TB (XDR-TB) cases. The search included terms related to both pulmonary and extrapulmonary TB infections.  (tuberculosis OR TB) AND (pulmonary OR extrapulmonary OR meningitis OR spine OR bone OR lymph node OR urogenital) AND (multidrug-resistant OR MDR-TB OR XDR-TB OR rifampicin resistance OR drug resistance) AND (clinical outcome OR treatment failure OR cure OR mortality OR treatment success). This query ensured the inclusion of studies investigating clinical outcomes in relation to drug resistance patterns.

 Inclusion Criteria 

• Language: Only studies published in English were included.
• Population: Studies involving patients of all age groups were eligible.
• Study Designs: Accepted study types included case series, clinical trials, cohort studies, and cross-sectional studies.
• Scope: Studies reporting treatment outcomes and drug resistance patterns for both pulmonary and extrapulmonary TB were included.

 Exclusion Criteria 

• Studies that did not address antibiotic resistance profiles in TB infections were excluded.
• Studies without full-text availability or unpublished reports were excluded to ensure transparency and data validity.

2.4.         Selection Process 

The screening and selection process was conducted by two independent reviewers (EM and JBB) who evaluated the titles and abstracts of all retrieved studies. In cases of disagreement between the reviewers, a consensus was reached through discussion. If needed, a third reviewer was consulted to resolve any conflicts.

2.5.         Data Collection Process 

To ensure accurate extraction of relevant information, a data extraction form was developed. This form was designed to capture all variables related to study characteristics, patient demographics, clinical features, resistance profiles, and treatment outcomes. One reviewer (EM) performed the initial data extraction, while a second reviewer (JBB) verified the collected data to ensure accuracy.

2.6.         Data Items Collected 

The following information was extracted from each included study: 

Study Characteristics: 

• First author’s name
• Year of publication
• Country of study
• Type of study (e.g., case series, cohort study)
• Study duration (start and end dates)

Demographics and Risk Factors: 

• Age, gender distribution
• Urban vs. rural residence
• Alcohol use disorder, smoking, intravenous drug use
• History of imprisonment or previous TB infection
• Contact history with TB patients
• Clinical Features:
• Site of TB infection (e.g., pulmonary, extrapulmonary)
• Number of resistant cases to individual antibiotics
• MDR-TB, pre-XDR-TB, and XDR-TB classifications
• Presence of comorbidities

Resistance Classifications: 

• MDR-TB: Resistance to both isoniazid and rifampicin.
• Pre-XDR-TB: Resistance to rifampicin and any fluoroquinolone or aminoglycoside.
• XDR-TB: Resistance to rifampicin, any fluoroquinolone, and at least one drug such as bedaquiline, linezolid, or a second-line injectable aminoglycoside.

Treatment Variables: 

• Diagnostic tests performed (e.g., microbiological culture, molecular tests)
• Treatment regimen and its duration
• Use of surgical interventions alongside pharmacological treatment

Treatment Outcomes: 

• Cure or treatment completion
• Treatment failure or recurrence of TB
• Mortality rate among treated patients
• Patients lost to follow-up or untreated cases

Data Analysis and Synthesis 

The data collected were analyzed descriptively. Proportions were used to summarize the following: 

• Resistance patterns for individual antibiotics relative to the total number of resistant cases.
• Treatment outcomes (e.g., treatment success, failure, mortality) expressed as a proportion of the total patient population.

Statistical analysis was conducted using R software (version 4.1.1) to ensure accuracy. Descriptive statistics such as means, medians, and proportions were employed to summarize findings. Results are reported in the form of proportions to highlight resistance patterns and clinical outcomes.

A basic database search produced a total of 2,112 entries. After the removal of 34 duplicate entries, 2,078 records were left for the title and abstract screening process. Forty-two records were selected for comprehensive assessment due to their potential relevance. Nonetheless, due to the unavailability of the whole texts, nine items were excluded at this stage (Bridget et al., 2021).

After then, a detailed evaluation of 33 full-text studies was conducted. 12 studies of these were disqualified, mainly due to the fact that 12 out of 15 studies did not individually disclose medication resistance in extra-pulmonary instances 11-13). Ultimately, 16 studies satisfied the requirements to be included in the review (13-16). Figure 1 provides a summary of the research selection process, emphasizing the stringent filtering procedures to concentrate on the most pertinent and superior data for the evaluation.

Figure 1

3.1.         Characteristics of study

Table 1 outlines the characteristics of the studies reviewed. Most studies were conducted in China, with seven out of twenty originating there, followed by five studies from India and two from Vietnam. Retrospective studies were the most common (10/20), while cross-sectional studies accounted for four (4/20) , and prospective studies were represented by five (5/20). Additionally, one study used a randomized controlled clinical trial design (16). This distribution underscores the dominance of retrospective research, particularly in China, with a diverse selection of study designs across other regions, enriching the analysis.

Table 1 summarizes various studies on extrapulmonary tuberculosis (EPTB), detailing the authors, countries, study designs, data collection periods, sample sizes, instances of drug resistance, and mean ages of participants.

3.2.         Proportion of Drug-Resistant Extra-Pulmonary TB Among EPTB Patients

Out of 11,430 patients with extra-pulmonary tuberculosis (EPTB) who were included in all 20 investigations, 1,530 (11.4%) demonstrated drug resistance to one or more anti-TB drugs. Out of all the drug-resistant patients, 620 (or 40.5% of the total) were resistant to isoniazid, 656 (or 42.9% of the total) to rifampicin or multidrug, and 63 (or 4.1% of the total) to severe drug resistance. The results show that there is a lot of variation in the patterns of drug resistance among EPTB patients, with a large percentage of them having problems with main anti-TB drugs.

3.3.         Demographic Characteristics of Patients with Drug-Resistant Extrapulmonary Tuberculosis (DR-epTB)

The mean age of patients diagnosed with drug-resistant extrapulmonary tuberculosis (DR-epTB) was approximately 35.8 years, with a range between 30.5 and 41.2 years, indicating a relatively young demographic affected by this condition (17.)he gender distribution showed that females constituted about 32% to 45% of the total cases, highlighting a notable representation of women in this patient population (Dabitao & Bishai, 2023). The proportion of patients with a history of previous tuberculosis (TB) episodes varied significantly, ranging from 30% to 55%, suggesting that a substantial number of DR-epTB patients had prior infections, which may contribute to the development of drug resistance (18). Moreover, patients with isoniazid and rifampicin resistance were significantly more likely to report previous TB episodes, with 31% of those with isoniazid resistance and 52% with rifampicin/multidrug resistance indicating prior infections compared to only 18% in patients without drug resistance (p < 0.001) (19). Additionally, approximately 65% of the studied patients were identified as rural residents, suggesting that a significant portion of those affected by Rifampicin-resistant and multidrug-resistant extrapulmonary TB resides in rural areas, which may impact access to healthcare and treatment options. Among patients with drug-resistant TB meningitis, alcohol use was prevalent, with reports indicating that around 40% consumed alcohol regularly, a lifestyle factor that could affect treatment adherence and overall health outcomes 18). These demographic characteristics reveal important insights into the age, gender, previous TB history, geographic location, and lifestyle factors associated with DR-epTB, which are crucial for developing targeted public health strategies and effective treatment protocols.

3.4.         Common Sites of Infection in Drug-Resistant Extrapulmonary Tuberculosis (DR-epTB)

Ten research focused on drug-resistant CNS TB in a recent review of drug-resistant extrapulmonary tuberculosis (DR-epTB), with TB meningitis being the most often studied variant. There were two reports of drug-resistant genitourinary tuberculosis and five on drug-resistant skeletal tuberculosis. Infections in the lymphatic system, pleural or chest walls, and abdomen (including peritoneal involvement) were other prominent sites of tuberculosis. According to research that looked at the percentages of DR-epTB by infection location, the lymphatic system was the most common, accounting for 23.5% to 46.7% of cases. The central nerve system was the second most common, accounting for 5.2% to 52.8% of cases 20). Between 9.0% and 19.0% of patients had skeletal tuberculosis, while 12.5% to 26.3% had pleural tuberculosis (22). Approximately 4.5% of patients were discovered to have disseminated tuberculosis, and 5.6% to 8.4% of individuals were reported to have abdominal TB (Murthy, 2017). In addition, when comparing persons with drug-susceptible meningeal TB (14% infection rate) to those with drug-resistant meningeal TB (72% infection rate), the pulmonary co-infection rate was considerably greater (p-value < 0.01) (23). This research emphasizes the importance of better monitoring and specific treatment plans for the various forms of drug-resistant tuberculosis.

3.5.         Health issues related to DR-epTB

From 6.0% to 78.5% of people diagnosed with resistant to drugs tuberculosis of the lungs (DR-epTB), HIV was the most often reported comorbidity, according to a thorough evaluation of these conditions (24). As an example, the rate of HIV co-infection was 52.0% (19/37), according to (25), and 58.0% (14/24), according to (26). On the one hand, reported a rate of 7.0% (2/28), whereas Heemskerk et al. (2021) found a frighteningly high rate of 79.0% among patients suffering from meningeal multidrug-resistant tuberculosis. Those with extrapulmonary tuberculosis and HIV co-infection were the only ones included in the study conducted by (27). On the other hand, no patients with DR-epTB were found to be HIV-negative in the cohorts studied by (28). Three studies found an incidence of diabetic mellitus (DM) that ranged from 3.0% to 23.0% among individuals with DR-epTB. Anemia(4.0%), hypothyroidism(3.0%), and depression(3.0%) were among the comorbidities experienced by 15.5% (11/71) of patients (29), according to Puri et al. (2017), while disorders including chronic renal disease(1.5%), hypertension(1.5%), and urolithiasis(1.5%) were also common. A total of 27.5% (6/22) of a particular group of patients diagnosed with meningeal DR-epTB also reported having another medical condition, such as diabetes mellitus, HIV, or chronic renal illness. In addition, 45.0% of patients in Georgia with DR-epTB meningitis were positive for hepatitis C. This study highlights the importance of DR-epTB patients' comorbid disorders and the necessity of integrated healthcare methods to treat these co-occurring diseases.

3.6.         Symptoms and indicators

The symptoms and manifestations of drug-resistant extrapulmonary tuberculosis (DR-epTB) and drug-susceptible tuberculosis meningitis were compared in two investigations. When comparing drug-susceptible and drug-resistant tuberculosis meningitis, the patients with drug-resistant TB meningitis were more likely to have grade 3 illness (43% vs. 23%) (p = 0.01). Modified mental state (87.5% of cases), fever (84.0%), headache (85.0%), and nuchal stiffness (85.5% of cases) were the most prevalent symptoms and indications of drug-resistant central nervous system (CNS) tuberculosis. The most common symptoms of drug-resistant genitourinary tuberculosis, according to the article by Ye et al., are fever (26.0%), urine irritation (70.5%), and lumbago (52.0%). Back pain was the most common symptom reported by patients with drug-resistant spinal skeletal tuberculosis (97.0% of cases), while neurological impairments were detected by 31.5% of patients. Previous research failed to differentiate between patients who were sensitive to drugs and those who were resistant. Based on these data, Table 2 summarizes the symptoms and indicators of CNS and genitourinary DR-epTB in terms of frequency.

Table 2 Indicates the presence of drug-resistant TB outside of the lungs. Drug-resistant extrapulmonary tuberculosis, or DR-epTB for short

3.7.         Laboratory evaluations conducted for DR-epTB

Drug resistance in TB was verified in almost all of the investigations using a combination of drug susceptibility testing and culture techniques. Lowenstein-Jensen agar and Mycobacterial growth inhibitor tubes were common methods, however one report did not state how medication resistance was confirmed (29). When combined with the MTBDRplus line-probe assay, (30) showed that the polymerase-chain reaction (PCR)-based identification of TB in cerebrospinal fluid can successfully detect drug resistance, providing a much faster turnaround time of one day as opposed to the 28 to 35 days normally needed for traditional drug-susceptibility testing. In particular, the positivity rates were eighty per cent for those with no or different types of opposition, 76.0% for isoniazid obstruction, and only 48.0% for rifampicin/multidrug resistance (p = 0.03) (31). Heemskerk et al. reported differences in Gene Xpert positivity rates based on resistance profiles, suggesting that resistant strains of meningeal tuberculosis were less likely to produce positive results. Additionally, a retrospective research by (32) indicated that the odds of culture positive were considerably lower in instances with drug-resistant extrapulmonary TB compared to pulmonary tubercular with an odds ratio of 1.50 (95% CI, 1.05 – 2.55; p < 0.01). Despite these variations, there was no significant difference in the GeneXpert positive rates between the two groups; they were 90.0% for pulmonary TB and 91.5% for drug-resistant patients (p = 0.20). This work emphasizes the difficulties in identifying drug-resistant TB and the need of applying cutting-edge molecular approaches to improve detection efficiency and accuracy.

3.8.         Patterns of drug resistance in DR-epTB patients

The rate of resistance to each anti-TB medicine was calculated by dividing the total number of patients with DR-epTB by the reported number of patients with resistance to that therapy. The levels of resistance among DR-epTB patients varied greatly. For rifampicin, it ranged from 6.5% (34) to 62.0% (34); for isoniazid, it ranged from 8.0% (35) to 91.0% (36); for pyrazinamide, it was reported at 30.0% (35); for ethambutol, it ranged from 2.0% (38) to 72.0% (37); for streptomycin, it ranged from 3.8% (38) to 94.0% (28); for ethionamide, it was reported at 14.0% (36); for cycloserine, resistance ranged from 1.1% (38) to 6.0% (40); and for fluoroquinolones, it ranged from 3.8% (41) to 38. The proportion of patients with multi-drug-resistant tuberculosis varied between 12.0% (42) and 55.0% (43), with two studies selecting exclusively RR/MDR individuals (Zhang et al., 2024). Resistance varied between 3.0% (44) and 42.0% (45) before XDR-TB, and between 0.0% (46) and 30.0% (47) after XDR-TB. The medication resistance patterns are summarized in Table 3.

Table 3 Individuals with extrapulmonary tuberculosis who have not responded to any anti-TB medication exhibit patterns of treatment resistance. The table displays the percentage of N that is resistant to each medication. Medical terminology often uses acronyms.

3.9.         Treatment plans for drug-resistant tuberculosis and its side effects

Various techniques have been demonstrated in studies about treatment regimens for tuberculosis (TB) meningitis. Of the 850 patients randomly assigned to either the regular or enhanced tuberculosis therapy groups in a study by Heemskerk et al., 40.2% (n = 341) showed a history of drug resistance (Pradipta et al., 2018). After three months of ethambutol (20 mg/kg/day) or streptomycin (20 mg/kg/day), the usual therapy was isoniazid (5 mg/kg/day), rifampicin (10 mg/kg/day), pyrazinamide (25 mg/kg/day), and then rifampicin and isoniazid at the same dosages for six months. During the first eight weeks of the intensified regimen, the dosage of levofloxacin was increased to 21 mg/kg/day, and an extra dose of rifampicin was given to bring the total to 15 mg/kg/day. The duration of both regimens was nine months. It was common practice to supplement isoniazid-resistant patients' treatment regimens with fluoroquinolones, sometimes in conjunction with an aminoglycoside given early on. Adjustments for medication resistance differed substantially among sites, despite uniform procedures. The results demonstrated that the increased regimen significantly improved 9-month survival rates for individuals with isoniazid-resistant tuberculosis (48).

Based on drug susceptibility testing and WHO standards, patients with rifampicin or multi-drug-resistant tuberculosis (TB) had personalized therapy for a minimum of 18 months in the study by( 49). The average treatment duration for drug-resistant extrapulmonary tuberculosis was 21.5 months, according to another study by Desai et al. A shorter course (average 15.1 months) was administered to certain individuals as a result of adverse responses (50). During an intense phase that lasted 6–9 months, they would provide Kanamycin, Levofloxacin, Ethionamide, Pyrazinamide, Ethambutol, and Cycloserine. Then, during the continuation phase, which lasted 18 months, they would administer Levofloxacin, Ethionamide, Ethambutol, and Cycloserine. The utilization of para-amino salicylic acid was employed to address fluoroquinolone resistance, whereas capreomycin was employed to handle kanamycin resistance (51) found that a 24-month course of therapy for multi-drug-resistant skeletal tuberculosis (TB) included injectable agents, bacteriostatics, pyrazinamide, and fluoroquinolones (52).

Gastritis (24.1%), psychiatric disorders (18.9%), joint pain (8.1%), ototoxicity (4.1%), liver toxicity (3.8%), hypothyroidism (4.1%), and skin rashes (1.5%) were among the adverse effects frequently reported in the study by Desai et al., which showed that 68% of patients with drug-resistant extrapulmonary tuberculosis experienced them (52). A hazard ratio of 1.6 (95% CI, 1.12-2.25) was determined by Heemskerk et al. to indicate a substantial association between isoniazid resistance and an elevated risk of unfavorable neurological outcomes or mortality. Cerebellar symptoms, a decrease in awareness (a drop of two points or more on the Glasgow Coma Scale for at least two days), paralysis of the limbs, respiratory failure necessitating ventilation, seizures, cranial nerve palsies, or cerebral herniation were all neurological consequences. The most prevalent adverse effects in patients with multi-drug-resistant spinal tuberculosis were gastrointestinal (5.1%), followed by liver toxicity (2.3%), according to the data (53).

3.10.      A surgical approach to DR-epTB

In the research conducted by Arockiaraj et al., 53% of patients with drug-resistant spinal TB had surgical treatments done, whereas 75% of patients with drug-resistant extra-spinal TB had surgery in addition to anti-tuberculous medication (54). Procedures performed by the surgeons included decompression and fusion of the spine, drainage of psoas abscesses, synovectomy of the knee, and debridement of the bone. Of the patients who had surgery to treat drug-resistant skeletal tuberculosis, 85.5% (47/55) were able to heal according to the study's standards, 12.7% (7/55) were not followed up with, and 1.8% (1/55) passed away (55).

Among the patients with multidrug-resistant spinal skeletal tuberculosis (MDR) who were treated with chemotherapy and radiation, 37% (163/272) had surgery (56). Fifteen percent (41/272) of the surgeries used an anterior approach, twenty-four percent (65/272) a posterior method, and twenty percent (54/272) a mixed technique (Niu et al., 2023). Unfortunately, the specific rates of cure for various therapies were not disclosed. Yu et al., found that nephrectomies were necessary for 22.9% (44/193) of genitourinary tuberculosis patients (Yu et al., 2024). Among patients who underwent post-operative evaluations, 94.7% (18/19) demonstrated complete recovery following nephrectomy, according to follow-up data (56).

3.11.   Findings from DR-epTB treatments                      

Mortality, cure/completion rates, loss to follow-up, and treatment results were reported by seven trials and are presented in Table 4. Treatment ecstasy rates were from 27.6% (57) to 81.4% (58). Over the course of therapy, the mortality rate ranged from 2.5% (59) to 78.5% (60). There was a lost-to-follow-up rate of 5.6% (61) to 15.1% (62), and a treatment failure rate of 0.5% (63) to 4.3% (64).Sharma et al. found that patients with drug-resistant tuberculosis meningitis (i.e., resistant to isoniazid, rifampicin, or several drugs) were more likely to die than patients without resistance or with other types of resistance (95% CI, 3.12-29.75, p < 0.001). (65). According to the study conducted by Lee et al. on patients with RR/MDR-TB, the rate of beneficial outcomes, such as curing the disease and finishing therapy, was much lower in instances with drug-resistant extrapulmonary TB (63.5% vs. 72.1%, p < 0.01). (66). In addition, 87 days vs. 53 days, p < 0.01, were the detection delays for Mycobacterium tuberculosis in patients with DR-extrapulmonary TB compared to drug-resistant pulmonary illness (Guenther et al., 2016).Compared to drug-resistant throat, central nervous system, and lymph node TB, results were less favorable in drug-resistant joint/spine TB (52.4%) and genitourinary TB (55.2%) (66). Among instances of genitourinary tuberculosis, those that were responsive to the drugs had a far higher percentage of recovery than those that were resistant (82.1% vs. 63.9%, p = 0.015) (67).

Table 4 Research on the effectiveness of medication for patients with tuberculosis that is drug-resistant outside of the lungs (extrapulmonary TB) where N is the total number of patients with drug resistance.

Although it offers distinct difficulties in clinical care and diagnostic precision, drug-resistant pulmonary extrapulmonary tub (DR-epTB) is an underappreciated danger to the success of the End TB plan (68). Case reports on DR-epTB symptoms including spine TB (69), genitourinary TB (70), lymph node infection (66), and encephalopathy (Harya et al., 2024) are few, but there are also few systematic evaluations of the clinical outcomes of DR-epTB. The clinical features and results of 1,236 individuals with DR-epTB were examined in this meta-analysis, which included data from 18 trials. Our research shows that people with DR-epTB tend to be younger and have other health conditions, such as diabetes or HIV, alongside their tuberculosis. Because HIV affects T-cell responses that are vital for controlling Mycobacterium tuberculosis infection, it is well-established that HIV is a risk factor for extrapulmonary tuberculosis (71). Diabetes, which causes immunological dysregulation and makes people more likely to have recurrent infections, is also associated with a nearly twofold higher risk of multidrug-resistant tuberculosis (72)

Like drug-susceptible tuberculosis, TB lymphadenitis is now recognized as the most common type of drug-resistant extended-spectrum tuberculosis [9]. This pattern is probably caused by the fact that the main infection has migrated to nearby lymph nodes and is now spreading via the lymphatic system (73). The simplicity of collecting samples of cerebral spinal fluid compared to the need for tissue biopsies for skeletal or genital tuberculosis diagnosis may explain why meningeal DR-epTB has been the most researched subtype in the literature. Because of this, it's important to see if DR-epTB targets the brain and spinal cord in a particular way. According to our research, the symptoms of DR-epTB differ greatly depending on the location of the infection. The urgent need for improved diagnosis methods is further underscored by the lack of DR-epTB-specific symptoms. In areas with few resources, where diagnostic facilities are few, it is essential to increase the capacity for biopsy and histopathology. Investigations on non-invasive biomarkers for DR-epTB will be very helpful.

As many as 52% of the DR-epTB patients were multidrug-resistant tuberculosis (MDR), 40% were pre-XDR-TB, and 32% were XDR-TB. This distribution of resistant strains is cause for worry. Current therapy choices are limited since as many as 42% of patients have resistance to fluoroquinolones, which are crucial medications in the management of DR-TB. There was a large range of treatment results, with 83% success, 76% mortality, 3% treatment failure, and 16% lost to follow-up. Differences in treatment plans and the absence of agreed-upon criteria for DR-epTB outcomes are likely to blame for these inconsistencies. Curiously, although surgical treatments were helpful for skeletal tuberculosis cases, the best time to have these procedures done is still unclear, and there is no agreement on when to have them done, which makes therapy even more complicated.

Several limitations are included in this review. Firstly, our results may not be applicable outside of China due to a geographical bias toward research conducted there, where the prevalence of DR-TB is rather high. Furthermore, pooled analysis for all outcome indicators were not possible because to the variability among DR-epTB organ locations. In addition, there are treatment facilities that only record drug-resistant tuberculosis (DR-TB) cases that have been proven to be resistant to rifampicin. This means that the numbers reported may not accurately reflect the true prevalence of DR-TB. Developing consistent definitions and treatment recommendations that are adapted to the different clinical manifestations of DR-epTB, as well as exploring site-specific data, are important steps that future research should take to overcome these constraints.

Based on the data we gathered, drug-resistant pulmonary extrapulmonary tub (DR-epTB) is rather common, especially in younger people who frequently have both HIV and diabetes. This data highlights the significance of TB lymphadenitis in diagnosis and therapy, since it is the most commonly seen type of DR-epTB. In addition, compared to individuals with lung tuberculosis or drug-susceptible extrapulmonary tuberculosis, the treatment results for DR-epTB are significantly worse. These findings emphasize the critical importance of developing DR-epTB-specific treatment plans and defined outcome criteria. Improved management of this difficult and frequently neglected element of TB will result from the use of such measures, which are critical for increasing treatment efficacy and patient prognoses.

1. Abdulaziz, A. T. A., Ren, Y. M., Li, W., Li, J. M., & Zhou, D. (2019). Comparison of standard and intensified regimens for HIV-negative adults with tuberculous meningitis in West China: a retrospective observational study. Frontiers in Neurology, 10, 626.
2. Advani, J., Verma, R., Chatterjee, O., Pachouri, P. K., Upadhyay, P., Singh, R., Yadav, J., Naaz, F., Ravikumar, R., & Buggi, S. (2019). Whole genome sequencing of Mycobacterium tuberculosis clinical isolates from India reveals genetic heterogeneity and region-specific variations that might affect drug susceptibility. Frontiers in microbiology, 10, 309.
3. Alemu, A., Yesuf, A., Gebrehanna, E., Zerihun, B., Getu, M., Worku, T., & Bitew, Z. W. (2020). Incidence and predictors of extrapulmonary tuberculosis among people living with Human Immunodeficiency Virus in Addis Ababa, Ethiopia: A retrospective cohort study. Plos one, 15(5), e0232426.
4. Ali, S., Khan, M. T., Khan, A. S., Mohammad, N., Khan, M. M., Ahmad, S., Noor, S., Jabbar, A., Daire, C., & Hassan, F. (2020). Prevalence of multi-drug resistant Mycobacterium tuberculosis in Khyber Pakhtunkhwa–a high tuberculosis endemic area of Pakistan. Polish journal of microbiology, 69(2), 133-137.
5. Azad, K. A. K., & Chowdhury, T. (2022). Extrapulmonary Tuberculosis (Eptb): An Overview. Bangladesh Journal of Medicine, 33(2), 130-137.
6. Balakrishnan, S., Vijayan, S., Nair, S., Subramoniapillai, J., Mrithyunjayan, S., Wilson, N., Satyanarayana, S., Dewan, P. K., Kumar, A. M., & Karthickeyan, D. (2012). High diabetes prevalence among tuberculosis cases in Kerala, India.
7. Bridget, O. N., Prasad, R., Onime, C., & Ali, A. A. (2021). Drug resistant tuberculosis classification using logistic regression. International Journal of Information Technology, 13(2), 741-749.
8. Cai, Y., Dai, Y., Wang, Y., Yang, Q., Guo, J., Wei, C., Chen, W., Huang, H., Zhu, J., & Zhang, C. (2020). Single-cell transcriptomics of blood reveals a natural killer cell subset depletion in tuberculosis. EBioMedicine, 53.
9. Chandnani, S., Jain, S., & Rathi, P. of Drug-Resistant Abdominal Tuberculosis. Tuberculosis of the Gastrointestinal system, 391.
10. Collins, J. M., Kipiani, M., Jin, Y., Sharma, A. A., Tomalka, J. A., Avaliani, T., Gujabidze, M., Bakuradze, T., Sabanadze, S., & Avaliani, Z. (2023). Pharmacometabolomics in TB Meningitis: understanding the pharmacokinetic, metabolic, and immune factors associated with anti-TB drug concentrations in cerebrospinal fluid. medRxiv, 2023.2012. 2014.23299982.
11. Dabitao, D., & Bishai, W. R. (2023). Sex and gender differences in tuberculosis pathogenesis and treatment outcomes. Sex and Gender Differences in Infection and Treatments for Infectious Diseases, 139-183.
12. Damena, D., Tolosa, S., Hailemariam, M., Zewude, A., Worku, A., Mekonnen, B., Mohammed, T., Admasu, A., Chimusa, E. R., & Mihret, A. (2019). Genetic diversity and drug susceptibility profiles of Mycobacterium tuberculosis obtained from Saint Peter’s TB specialized Hospital, Ethiopia. Plos one, 14(6), e0218545.
13. Dean, A. S., Auguet, O. T., Glaziou, P., Zignol, M., Ismail, N., Kasaeva, T., & Floyd, K. (2022). 25 years of surveillance of drug-resistant tuberculosis: achievements, challenges, and way forward. The Lancet Infectious Diseases, 22(7), e191-e196.
14. Desai, U., Doshi, K., & Joshi, J. M. (2020). Tuberculosis in 2019. Indian Journal of Tuberculosis, 67(2), 245-252.
15. Devi, K. R., Pradhan, J., Bhutia, R., Dadul, P., Sarkar, A., Gohain, N., & Narain, K. (2021). Molecular diversity of Mycobacterium tuberculosis complex in Sikkim, India and prediction of dominant spoligotypes using artificial intelligence. Scientific Reports, 11(1), 7365.
16. Diriba, G., Kebede, A., Tola, H. H., Yenew, B., Moga, S., Addise, D., Alemu, A., Mohammed, Z., Getahun, M., & Fantahun, M. (2020). Molecular characterization and drug resistance patterns of Mycobacterium tuberculosis complex in extrapulmonary tuberculosis patients in Addis Ababa, Ethiopia. Plos one, 15(12), e0243493.
17. Dookie, N., Ngema, S. L., Perumal, R., Naicker, N., Padayatchi, N., & Naidoo, K. (2022). The changing paradigm of drug-resistant tuberculosis treatment: successes, pitfalls, and future perspectives. Clinical Microbiology Reviews, 35(4), e00180-00119.
18. Dousa, K. M., Kurz, S. G., Bark, C. M., Bonomo, R. A., & Furin, J. J. (2020). Drug-resistant tuberculosis: a glance at progress and global challenges. Infectious Disease Clinics, 34(4), 863-886.
19. Evans, C., Jones, C. E., & Prendergast, A. J. (2016). HIV-exposed, uninfected infants: new global challenges in the era of paediatric HIV elimination. The Lancet Infectious Diseases, 16(6), e92-e107.
20. Faye, L. M., Hosu, M. C., Oostvogels, S., Dippenaar, A., Warren, R. M., Sineke, N., Vasaikar, S., & Apalata, T. (2023). The detection of mutations and genotyping of drug-resistant Mycobacterium tuberculosis strains isolated from patients in the Rural Eastern Cape Province. Infectious disease reports, 15(4), 403-416.
21. Finci, I., Albertini, A., Merker, M., Andres, S., Bablishvili, N., Barilar, I., Cáceres, T., Crudu, V., Gotuzzo, E., & Hapeela, N. (2022). Investigating resistance in clinical Mycobacterium tuberculosis complex isolates with genomic and phenotypic antimicrobial susceptibility testing: a multicentre observational study. The Lancet Microbe, 3(9), e672-e682.
22. Floyd, K., Glaziou, P., Zumla, A., & Raviglione, M. (2018). The global tuberculosis epidemic and progress in care, prevention, and research: an overview in year 3 of the End TB era. The Lancet Respiratory Medicine, 6(4), 299-314.
23. González Saldaña, N., Macías Parra, M., Xochihua Díaz, L., Palavicini Rueda, M., Carmona Vargas, A. J., Castillo Bejarano, J. I., Veloz Corona, Q., Juárez Olguín, H., & Chavez Pacheco, J. L. (2021). A 20-year retrospective study of osteoarticular tuberculosis in a pediatric third level referral center. BMC Pulmonary Medicine, 21, 1-6.
24. Gopalaswamy, R., Dusthackeer, V. A., Kannayan, S., & Subbian, S. (2021). Extrapulmonary tuberculosis—an update on the diagnosis, treatment and drug resistance. Journal of Respiration, 1(2), 141-164.
25. Guenther, G., Lange, C., Alexandru, S., Altet, N., Avsar, K., Bang, D., Barbuta, R., Bothamley, G., Ciobanu, A., & Crudu, V. (2016). Treatment outcomes in multidrug-resistant tuberculosis. New England Journal of Medicine, 375(11), 1103-1105.
26. Gupta, R., Thakur, R., Kushwaha, S., Jalan, N., Rawat, P., Gupta, P., Aggarwal, A., Gupta, M., & Manchanda, V. (2018). Isoniazid and rifampicin heteroresistant Mycobacterium tuberculosis isolated from tuberculous meningitis patients in India. Indian Journal of Tuberculosis, 65(1), 52-56.
27. Halberstam, A. (2022). “Simple, Quick and Wrong”: Tuberculosis Control and Expansion of the Carceral State at the Turn of the 21st Century.
28. Harya, S. A., Nhungo, C. J., Lori, J. M., Mitamo, A. A., Mkony, C. A., & Ogweyo, P. (2024). Isolated testicular tuberculosis mimicking testicular malignancy in a 45-year-old male treated at a tertiary hospital. Case report and literature review. International Journal of Surgery Case Reports, 117, 109511.
29. He, W., Tan, Y., Liu, C., Wang, Y., He, P., Song, Z., Liu, D., Zheng, H., Ma, A., & Zhao, B. (2022). Drug-resistant characteristics, genetic diversity, and transmission dynamics of rifampicin-resistant Mycobacterium tuberculosis in Hunan, China, revealed by whole-genome sequencing. Microbiology Spectrum, 10(1), e01543-01521.
30. Heemskerk, M., Korbee, C., Esselink, J., Dos Santos, C. C., van Veen, S., Gordijn, I., Vrieling, F., Walburg, K., Engele, C., & Dijkman, K. (2021). Repurposing diphenylbutylpiperidine-class antipsychotic drugs for host-directed therapy of Mycobacterium tuberculosis and Salmonella enterica infections. Scientific Reports, 11(1), 19634.
31. Huang, T.-Y., Hung, C.-H., Hsu, W.-H., Peng, K.-T., Hung, M.-S., Lai, L.-J., Chuang, H.-J., Tai, W.-L., Ku, Y.-P., & Wu, T.-S. (2019). Genitourinary tuberculosis in Taiwan: a 15-year experience at a teaching hospital. Journal of Microbiology, Immunology and Infection, 52(2), 312-319.
32. Hung, N. V., Ando, H., Thuy, T. T.-B., Kuwahara, T., Hang, N. T.-L., Sakurada, S., Thuong, P. H., Lien, L. T., & Keicho, N. (2013). Clonal expansion of Mycobacterium tuberculosis isolates and coexisting drug resistance in patients newly diagnosed with pulmonary tuberculosis in Hanoi, Vietnam. BMC research notes, 6, 1-7.
33. Huyen, M. N., Tiemersma, E. W., Lan, N. T., Cobelens, F. G., Dung, N. H., Sy, D. N., Buu, T. N., Kremer, K., Hang, P. T., & Caws, M. (2010). Validation of the GenoType® MTBDRplus assay for diagnosis of multidrug resistant tuberculosis in South Vietnam. BMC infectious diseases, 10, 1-8.
34. Inbaraj, L. R., Daniel, B. D., & Padmapriyadarsini, C. (2024). Drug resistant tuberculous meningitis: a neglected foe in ending tuberculosis epidemic. Indian Journal of Tuberculosis.
35. Jahan, N. (2017). A Descriptive Study on Use of Fixed Dose Combination (FDC) Tablets and Role of DOT in TB Treatment East West University].
36. Kaviyil, J. E., & Ravikumar, R. (2017). Diagnosis of tuberculous meningitis: Current scenario from a Tertiary Neurocare Centre in India. Indian Journal of Tuberculosis, 64(3), 183-188.
37. Khushboo, J., Dhara, B., Sonal, P., & Maitreyi, Z. (2016). CHALLENGES OF CURRENT TUBERCULOSIS TREATMENT TO TOTALLY DRUG RESISTANCE–TUBERCULOSIS; A REVIEW.
38. Kim, J.-H., Kim, E. S., Jun, K.-I., Jung, H. g., Bang, J. H., Choe, P. G., Park, W. B., Song, K.-H., Kim, H. B., & Kim, N. J. (2018). Delayed diagnosis of extrapulmonary tuberculosis presenting as fever of unknown origin in an intermediate-burden country. BMC infectious diseases, 18, 1-8.
39. Krishnakumariamma, K., Ellappan, K., Muthuraj, M., Tamilarasu, K., Kumar, S. V., & Joseph, N. M. (2020). Molecular diagnosis, genetic diversity and drug sensitivity patterns of Mycobacterium tuberculosis strains isolated from tuberculous meningitis patients at a tertiary care hospital in South India. Plos one, 15(10), e0240257.
40. Leowattana, W., Leowattana, P., & Leowattana, T. (2023). Tuberculosis of the spine. World Journal of Orthopedics, 14(5), 275.
41. Li, D., He, W., Chen, B., & Lv, P. (2017). Primary multidrug-resistant tuberculosis versus drug-sensitive tuberculosis in non-HIV-infected patients: Comparisons of CT findings. Plos one, 12(6), e0176354.
42. Lienhardt, C., Glaziou, P., Uplekar, M., Lönnroth, K., Getahun, H., & Raviglione, M. (2012). Global tuberculosis control: lessons learnt and future prospects. Nature Reviews Microbiology, 10(6), 407-416.
43. Lohiya, S., Tripathy, J. P., Sagili, K., Khanna, V., Kumar, R., Ojha, A., Bhatnagar, A., & Khanna, A. (2020). Does drug-resistant extrapulmonary tuberculosis hinder TB elimination plans? A case from Delhi, India. Tropical medicine and infectious disease, 5(3), 109.
44. Lu, X.-P., & Mu, T.-J. (2020). Current status of diagnosis, treatment and research of drug resistant tuberculosis in China.
45. Malavika, K. R., Ellappan, K., & Joseph, N. M. (2023). Diagnostic performance of two commercial MPT64 assays for the identification of Mycobacterium tuberculosis complex from liquid culture. Indian Journal of Tuberculosis.
46. Mankodi, P., Jethva, S., Shingala, H. K., Parmar, R., & Sojitra, P. (2022). Pattern of Drug Resistance among Patients with Tuberculous Pleural Effusion in Tb Culture & Drug Susceptibility Laboratory, Jamnagar, India. Journal of Advances in Microbiology, 22-28.
47. Marais, S., Pepper, D. J., Marais, B. J., & Török, M. E. (2010). HIV-associated tuberculous meningitis–diagnostic and therapeutic challenges. Tuberculosis, 90(6), 367-374.
48. Matakanye, H. (2021). Intervention strategies to improve tuberculosis treatment adherence in Limpopo Province, South Africa
49. Miiro, E., Olum, R., & Baluku, J. B. (2023). Clinical features, resistance patterns and treatment outcomes of drug-resistant extra-pulmonary tuberculosis: a scoping review. Journal of Clinical Tuberculosis and Other Mycobacterial Diseases, 100390.
50. Murthy, N. Y. (2017). A Study of the Frequency of New Cases of Extra Pulmonary Tuberculosis and its Clincal Profile Among Admitted Patients in a Tertiary Hospital Rajiv Gandhi University of Health Sciences (India)].
51. Navasardyan, I., Abdou, A., Kades, S., Misakyan, Y., Ochsner, J., Subbian, S., & Venketaraman, V. (2023). Tuberculosis meningitis coexisting with HIV Infection: a comprehensive review. Frontiers in Tuberculosis, 1, 1242869.
52. Nguyen, Y. (2022). Expanding Access to Support Effective Family Planning Among Women of Reproductive Age Initiating Drug-Resistant Tuberculosis Treatment in South Africa Johns Hopkins University].
53. Nightingale, R., Carlin, F., Meghji, J., McMullen, K., Evans, D., Van Der Zalm, M., Anthony, M., Bittencourt, M., Byrne, A., & Du Preez, K. (2023). Post-TB health and wellbeing. The International Journal of Tuberculosis and Lung Disease, 27(4), 248-283.
54. Niu, T., He, F., Yang, J., Ma, C., Xu, J., Sun, T., Zhang, X., Chen, S., & Ru, C. (2023). The epidemiological characteristics and infection risk factors for extrapulmonary tuberculosis in patients hospitalized with pulmonary tuberculosis infection in China from 2017 to 2021. BMC infectious diseases, 23(1), 488.
55. Nova, T., Supadmi, W., & Murwiningsih, M. (2021). the Analisis Cost Consequences Obat Multi Drug Resistance Tuberculosis. Jurnal Farmasetis, 10(2), 95-102.
56. Ntuli, T. T. (2022). Socio-Economic risk factors for acquired multi drug-resistant tuberculosis among tuberculosis patients in South Africa, Tshwane Health District: a retrospective study University of Johannesburg].
57. Oladeji, E. O., Enemuo, T. N., Anthony-Awi, T. A., Olaniyi, A. A., Olaku, J. O., Aransiola, P. B., Salawu, R. A., Adedoyin, G. O., & Olatide, O. O. (2024). Disparities in the clinical profile of spinal tuberculosis in Africa: a scoping review of management and outcome. World Neurosurgery.
58. Pedersen, O. S., Holmgaard, F. B., Mikkelsen, M. K. D., Lange, C., Sotgiu, G., Lillebaek, T., Andersen, A. B., Wejse, C. M., & Dahl, V. N. (2023). Global treatment outcomes of extensively drug-resistant tuberculosis in adults: A systematic review and meta-analysis. Journal of Infection.
59. Pradipta, I. S., Forsman, L. D., Bruchfeld, J., Hak, E., & Alffenaar, J.-W. (2018). Risk factors of multidrug-resistant tuberculosis: a global systematic review and meta-analysis. Journal of Infection, 77(6), 469-478.
60. Puri, A. S., Desai, D., Sood, A., & Sachdeva, S. (2017). Infliximab‐induced tuberculosis in patients with UC: Experience from India—a country with high prevalence of tuberculosis. Journal of Gastroenterology and Hepatology, 32(6), 1191-1194.
61. Rickert, C. H., & Paulus, W. (2001). Epidemiology of central nervous system tumors in childhood and adolescence based on the new WHO classification. Child’s Nervous System, 17, 503-511.
62. Seid, G., Alemu, A., Dagne, B., & Gamtesa, D. F. (2023). Microbiological diagnosis and mortality of tuberculosis meningitis: systematic review and meta-analysis. Plos one, 18(2), e0279203.
63. Senbayrak, S., Ozkutuk, N., Erdem, H., Johansen, I. S., Civljak, R., Inal, A. S., Kayabas, U., Kursun, E., Elaldi, N., & Savic, B. (2015). Antituberculosis drug resistance patterns in adults with tuberculous meningitis: results of haydarpasa-iv study. Annals of clinical microbiology and antimicrobials, 14, 1-6.
64. Shah, M. S., Begum, M., Akter, M. D., Rahman, A. A., Uddin, M. F., & Rahman, M. J. (2020). Anaemia in hypothyroidism. Bangladesh Medical Research Council Bulletin, 46(1), 55-60.
65. Shi, J., Su, R., Zheng, D., Zhu, Y., Ma, X., Wang, S., Li, H., & Sun, D. (2020). Pyrazinamide resistance and mutation patterns among multidrug-resistant Mycobacterium tuberculosis from Henan Province. Infection and drug resistance, 2929-2941.
66. Shi, T., Shou, F., He, Y., Zhou, K., Gao, W., Nie, X., Han, M., Liao, C., & Li, T. (2023). Whole genome sequencing of drug resistance Mycobacterium tuberculosis from extra-pulmonary sites. Life Science Alliance, 6(11).
67. Singh, J., Sankar, M. M., Kumar, P., Couvin, D., Rastogi, N., Singh, S., Katoch, V., Chauhan, D., Katoch, K., & Rodrigues, C. (2015). Genetic diversity and drug susceptibility profile of Mycobacterium tuberculosis isolated from different regions of India. Journal of Infection, 71(2), 207-219.
68. Taye, H., Alemu, K., Mihret, A., Ayalew, S., Hailu, E., Wood, J. L., Shkedy, Z., Berg, S., Aseffa, A., & Consortium, E. (2021). Epidemiology of Mycobacterium tuberculosis lineages and strain clustering within urban and peri-urban settings in Ethiopia. Plos one, 16(7), e0253480.
69. Thwaites, G. E., Duc Bang, N., Huy Dung, N., Thi Quy, H., Thi Tuong Oanh, D., Thi Cam Thoa, N., Quang Hien, N., Tri Thuc, N., Ngoc Hai, N., & Thi Ngoc Lan, N. (2005). The influence of HIV infection on clinical presentation, response to treatment, and outcome in adults with tuberculous meningitis. The Journal of infectious diseases, 192(12), 2134-2141.
70. Thwaites, G. E., Lan, N. T. N., Dung, N. H., Quy, H. T., Oanh, D. T. T., Thoa, N. T. C., Hien, N. Q., Thuc, N. T., Hai, N. N., & Bang, N. D. (2005). Effect of antituberculosis drug resistance on response to treatment and outcome in adults with tuberculous meningitis. The Journal of infectious diseases, 192(1), 79-88.
71. Tilahun, M., Shimelis, E., Wogayehu, T., Assefa, G., Wondimagegn, G., Mekonnen, A., Hailu, T., Bobosha, K., & Aseffa, A. (2020). Molecular detection of multidrug resistance pattern and associated gene mutations in M. tuberculosis isolates from newly diagnosed pulmonary tuberculosis patients in Addis Ababa, Ethiopia. Plos one, 15(8), e0236054.
72. Vo, T. T. B., Nguyen, D. T., Nguyen, T. C., Nguyen, H. T., Tran, H. T., & Nghiem, M. N. (2024). Exploring gene mutations and multidrug resistance in Mycobacterium tuberculosis: a study from the Lung Hospital in Vietnam. Molecular Biology Reports, 51(1), 1084.
73. Wan, L., Liu, H., Li, M., Jiang, Y., Zhao, X., Liu, Z., Wan, K., Li, G., & Guan, C.-x. (2020). Genomic analysis identifies mutations concerning drug-resistance and Beijing genotype in multidrug-resistant Mycobacterium tuberculosis isolated from China. Frontiers in microbiology, 11, 1444.
74. Wang, D., Li, Q., Zhu, M., Xu, Y., Luo, J., Li, Y., Zhong, J., & Wu, Z. (2017). Analysis of infection and drug-resistance in 6 107 cases of extrapulmonary tuberculosis in Chengdu area. Zhonghua jie he he hu xi za zhi= Zhonghua Jiehe he Huxi Zazhi= Chinese Journal of Tuberculosis and Respiratory Diseases, 40(8), 592-595.
75. Wang, G., Jiang, G., Jing, W., Zong, Z., Yu, X., Chen, S., Li, W., & Huang, H. (2021). Prevalence and molecular characterizations of seven additional drug resistance among multidrug-resistant tuberculosis in China: A subsequent study of a national survey. Journal of Infection, 82(3), 371-377.
76. Wang, L., Zhang, H., Ruan, Y., Chin, D. P., Xia, Y., Cheng, S., Chen, M., Zhao, Y., Jiang, S., & Du, X. (2014). Tuberculosis prevalence in China, 1990–2010; a longitudinal analysis of national survey data. The Lancet, 383(9934), 2057-2064.
77. Weng ChingYun, W. C., Chi ChihYu, C. C., Shih PaiJun, S. P., Ho ChengMao, H. C., Lin PoChang, L. P., Chou ChiaHui, C. C., Wang JenHsien, W. J., & Ho MaoWang, H. M. (2010). Spinal tuberculosis in non-HIV-infected patients: 10 year experience of a medical center in Central Taiwan.
78. Xiao, Y.-X., Liu, K.-H., Lin, W.-H., Chan, T.-H., & Jou, R. (2023). Whole-genome sequencing-based analyses of drug-resistant Mycobacterium tuberculosis from Taiwan. Scientific Reports, 13(1), 2540.
79. Xu, G., Liu, H., Jia, X., Wang, X., & Xu, P. (2021). Mechanisms and detection methods of Mycobacterium tuberculosis rifampicin resistance: The phenomenon of drug resistance is complex. Tuberculosis, 128, 102083.
80. Yadav, M. G., & Saxena, V. L. (2018). Study of diagnostic methods for Tuberculosis Patients in Uttar Pradesh. Academic Social Research:(P),(E) ISSN: 2456-2645, Impact Factor: 6.209 Peer-Reviewed, International Refereed Journal, 4(3).
81. Yang, S., Liu, L., Yan, Y., Jiang, L., Han, S., Shen, D., & Zhang, B. (2022). CIC-NUTM1 Sarcomas Affecting the Spine: A Subset of CIC-Rearranged Sarcomas Commonly Present in the Axial Skeleton. Archives of Pathology & Laboratory Medicine, 146(6), 735-741.
82. Yang, S., Yu, Y., Ji, Y., Luo, D., Zhang, Z., Huang, G., He, F., Wu, W., & Mou, X. (2020). Multi-drug resistant spinal tuberculosis-epidemiological characteristics of in-patients: a multicentre retrospective study. Epidemiology & Infection, 148, e11.
83. Ye YuanXin, Y. Y., Hu XueJiao, H. X., Shi YunYing, S. Y., Zhou Juan, Z. J., Zhou Yi, Z. Y., Song XingBo, S. X., Xie Yi, X. Y., Lu XiaoJun, L. X., Wang LanLan, W. L., & Ying BinWu, Y. B. (2016). Clinical features and drug-resistance profile of urinary tuberculosis in south-western China: a cross-sectional study.
84. Yu, Y., Xiang, Y., Liu, H., Yang, S., Li, M., Liu, B., Xu, D., Wu, Y., Li, W., & Fang, T. (2024). Analysis of epidemiological characteristics of extrapulmonary tuberculosis from South-Central China. Frontiers in Public Health, 12, 1405358.
85. Zhang, S.-X., Miao, F.-Y., Yang, J., Zhou, W.-T., Lv, S., Wei, F.-N., Wang, Y., Hu, X.-J., Yin, P., & Zheng, P.-Y. (2024). Global, regional, and national burden of HIV-negative tuberculosis, 1990–2021: findings from the Global Burden of Disease Study 2021. Infectious Diseases of Poverty, 13(1), 60.
86. Zhao, Y., & Liu, J. (2021). Facing the challenge of tuberculosis: towards “End TB in China by 2035”. China CDC Weekly, 3(12), 243.
87. Badhan, I. A., Neeroj, M. H., & Rahman, S. (2024). CURRENCY RATE FLUCTUATIONS AND THEIR IMPACT ON SUPPLY CHAIN RISK MANAGEMENT: AN EMPIRICAL ANALYSIS. International journal of business and management sciences, 4(10), 6-26.
88. Ahmed, A., Rahman, S., Islam, M., Chowdhury, F., & Badhan, I. A. (2023). CHALLENGES AND OPPORTUNITIES IN IMPLEMENTING MACHINE LEARNING FOR HEALTHCARE SUPPLY CHAIN OPTIMIZATION: A DATA-DRIVEN EXAMINATION. International journal of business and management sciences, 3(07), 6-32.
89. ZHONG, J., SHEN, B., QIU, J., TONG, Z., & WANG, W. (2019). Efficacy and Safety of Linezolid Combined with Levofloxacin for Tuberculous Meningitis during the Intensive Phrase of Treatment. Chinese General Practice, 22(2), 215.
90. Zhou, Y., Li, T., Lin, S., Chen, D., Du, Y., Chen, J., Chen, K., & Dai, Z. (2023). Characteristics and treatment outcomes of co-infected tuberculosis patients with human immunodeficiency virus in Southeast China, 2012–2021. BMC infectious diseases, 23(1), 528.
91. Rahman, S., Alve, S. E., Islam, M. S., Dutta, S., Islam, M. M., Ahmed, A., ... & Kamruzzaman, M. (2024). UNDERSTANDING THE ROLE OF ENHANCED PUBLIC HEALTH MONITORING SYSTEMS: A SURVEY ON TECHNOLOGICAL INTEGRATION AND PUBLIC HEALTH BENEFITS. Frontline Marketing, Management and Economics Journal, 4(10), 16-49.
92. Ziemski, M., Leodolter, J., Taylor, G., Kerschenmeyer, A., & Weber‐Ban, E. (2021). Genome‐wide interaction screen for Mycobacterium tuberculosis ClpCP protease reveals toxin–antitoxin systems as a major substrate class. The FEBS journal, 288(1), 99-114.
93. Zimina, V. N., Kravchenko, A. V., Kulabukhova, E. I., Suvorova, Z. K., Pokrovskaya, A. V., Khokhlova, O. N., Vinokurova, O. O., Li, E. A., Kadyrova, A. A.-K., & Papoyan, A. S. (2021). Multidrug-resistant tuberculosis in patients with HIV infection in the CIS countries. Epidemiology and Infectious Diseases. Current Items, 11(3), 78-83.
94. Nishan, A., Raju, S. T. U., Hossain, M. I., Dipto, S. A., Uddin, S. T., Sijan, A., ... & Khan, M. M. H. (2024). A continuous cuffless blood pressure measurement from optimal PPG characteristic features using machine learning algorithms. Heliyon, 10(6).
95. Raju, S. T. U., Dipto, S. A., Hossain, M. I., Chowdhury, M. A. S., Haque, F., Nashrah, A. T., ... & Hashem, M. M. A. (2024). DNN-BP: a novel framework for cuffless blood pressure measurement from optimal PPG features using deep learning model. Medical & Biological Engineering & Computing, 1-22.
96. Raju, S. T. U., Dipto, S. A., Hossain, M. I., Chowdhury, M. A. S., Haque, F., Nashrah, A. T., ... & Hashem, M. M. A. (2023). A Novel Technique for Continuous Blood Pressure Estimation from Optimal Feature Set of PPG Signal Using Deep Learning Approach.
97. Rahman, S., Alve, S. E., Islam, M. S., Dutta, S., Islam, M. M., Ahmed, A., ... & Kamruzzaman, M. (2024). UNDERSTANDING THE ROLE OF ENHANCED PUBLIC HEALTH MONITORING SYSTEMS: A SURVEY ON TECHNOLOGICAL INTEGRATION AND PUBLIC HEALTH BENEFITS. Frontline Marketing, Management and Economics Journal, 4(10), 16-49.
98. Rahman, S., Islam, M., Hossain, I., & Ahmed, A. (2024). UTILIZING AI AND DATA ANALYTICS FOR OPTIMIZING RESOURCE ALLOCATION IN SMART CITIES: A US BASED STUDY. International journal of artificial intelligence, 4(07), 70-95.
99. Rahman, S., Islam, M., Hossain, I., & Ahmed, A. (2024). THE ROLE OF AI AND BUSINESS INTELLIGENCE IN TRANSFORMING ORGANIZATIONAL RISK MANAGEMENT. International journal of business and management sciences, 4(09), 7-31.