GeneXpert-Detected Mycobacterium tuberculosis in Sudan: Insights into Rifampicin Resistance

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Methods: Data were extracted from the medical records of presumptive tuberculosis patients who attended the hospital from February to October 2025. Sputum samples from presumptive Tuberculosis patients were analyzed using the GeneXpert MTB/RIF assay to detect Mycobacterium tuberculosis and Rifampicin resistance. Results : A total of 153 sputum samples were analyzed. There were male (71.9%) and female (28.1%) participants. The majority of participants (41.8%) were aged 40-64 years, followed by those aged 25-39 years (33.3%). Younger participants aged 15-24years represented (12.4%). At the same time, only a small fraction were children aged 1-14 years (2%). Mycobacterium tuberculosis was detected in 29 cases (19.0%), and 81% were negative. Among the positive cases, moderate bacillary levels were most common (48.3%). Rifampicin resistance was detected in only one case (0.7%), with one additional inconclusive result; the remaining positive cases were sensitive (18.3%). A small number of participants reported previous TB Infection (4.6%) or HIV Infection (1.3%). Chi-square analysis revealed no statistically significant relationship between age (p=0.454) and gender (p=0.228). In contrast, previous TB Infection(p0.05) Conclusions: The findings reveal a high prevalence of tuberculosis and significant recurrence among presumptive cases, suggesting gaps in the treatment cascade. The low rate of rifampicin resistance is a cautiously optimistic, yet preliminary, finding, highlighting the need for strengthened surveillance and continuity of care in Sudan's conflict-affected setting. Mycobacterium Tuberculosis Rifampicin resistance GeneXpert MTB/RIF assay Khartoum Sudan Introduction Tuberculosis (TB) remains a leading cause of morbidity and mortality worldwide, with a disproportionate impact on developing countries ( 15 ). It is an airborne infectious disease caused by Mycobacterium tuberculosis. A major challenge in global TB control is the rise of drug-resistant strains. Multi-drug-resistant tuberculosis (MDR-TB), defined as resistance to both isoniazid and rifampicin, poses a significant Public Health threat ( 12 ). With an estimated 10.8 million people globally falling ill with TB and 1.25 million succumbing to the disease in 2023, the urgency of the situation cannot be overstated ( 15 ). Sudan, a high-burden country in the Eastern Mediterranean Region, reported over 21,000 Cases with a mortality rate of 25 per 100,000 Population in the same year ( 15 ). The emergence of rifampicin-resistant TB (RR-TB), a Key proxy for MDR-TB, is particularly alarming, with approximately 410,000 New cases globally and a treatment success rate of only 68% ( 14 ). The diagnosis of TB and drug resistance in low-resource settings like Sudan remains challenging. While traditional smear microscopy is widely used, it has limited sensitivity, especially for paucibacillary and extrapulmonary TB ( 9 ). Although culture is the gold standard, it is time-consuming, with results taking up to 8 weeks ( 8 ). Molecular tools such as the GeneXpert Mycobacterium tuberculosis/rifampicin (MTB/RIF) assay have revolutionized TB Diagnosis by simultaneously detecting M. tuberculosis and rifampicin resistance within 2 hours, with high sensitivity and specificity ( 13 ). Despite the development of GeneXpert technology in Sudan, significant barriers persist, including limited accessibility, insufficient laboratory capacity, and a lack of comprehensive nationwide surveillance. These barriers cannot be overstated, as they are crucial for effective TB control. These challenges are compounded by general issues in resource-limited settings, such as suboptimal treatment management and HIV co-infection. The situation in Sudan is challenging due to underdeveloped diagnostic systems, treatment discontinuation, and delayed therapy initiation ( 12 ). Even before the current conflict, the country faced a troubling MDR-TB burden, with prevalence estimates of 22.3% among new cases and 30% among previously treated cases ( 1 ). The ongoing war has critically damaged Sudan’s public health infrastructure, disrupting laboratory services, destroying facilities, and displacing Healthcare Workers. This collapsed and undoubtedly exacerbates TB transmission and severely limits access to diagnosis and treatment, underlining the gravity of the situation. While several diagnostic centers in Sudan are equipped with GeneXpert technology, no nationwide study has comprehensively evaluated the prevalence of rifampicin resistance. The convergence of high pre-war resistance rates and the current healthcare system collapse create a public health emergency. This study, therefore, aims to determine the prevalence of pulmonary tuberculosis and rifampicin-resistant tuberculosis among patients tested with GeneXpert in Sudan, and to identify associated sociodemographic and clinical risk factors. Materials and Methods 2.1. Study Design, Setting, and Period A retrospective, Cross-sectional study was conducted using the records of presumptive pulmonary tuberculosis (TB) patients at Omdurman Military Hospital in Khartoum, Sudan. Data were extracted from the medical records of patients who were tested between February and October 2025. Omdurman Military is a tertiary-care facility with a capacity of over 500 beds. It serves as one of the state’s key centers, equipped with the GeneXpert MTB/RIF assay. The hospital’s microbiology department receives and processes an average of 19 sputum samples for TB testing per month. 2.2. Study population and Sampling The study population comprised all presumptive pulmonary TB patients with complete laboratory data entries in the GeneXpert TN registration logbook during the study period. Patients with missing or incomplete data for key variables under investigation (e.g., test results, demographic information) were excluded from the analysis. A formal sample size calculation was performed using Cochran’s formula for finite populations, assuming a projected TB positivity proportion of 33.76% ( 2 ), a 5% margin of error, and a 95% confidence level. This calculation yielded a minimum required sample size of 344 participants. However, due to this study’s retrospective design and fixed study period, a convenience sample of all eligible records from the specified timeframe was used. 2.3. Laboratory Methods Sputum samples collected from presumptive TB patients were processed and analyzed using the GeneXpert MTB/RIF assay (Cepheid, Sunnyvale, CA, USA) according to the manufacturer’s instructions. The test simultaneously detects complex (MTB) and rifampicin resistance (RIF) through real-time PCR amplification of the rpoB gene. 2.4. Data Collection A structured data extraction sheet was developed to collect information from the GeneXpert TB Laboratory Registration Logbook. The extracted variable included: Sociodemographic data: Age, gender, and residence. Clinical data: HIV status and previous history of TB treatment. Laboratory results: GeneXpert MTB/RIF result (categorized as MTB-detected /RIF-resistant MTB- detected /RIF-susceptible, or MTB-not-detected. 2.5. Ethical Consideration Ethical approval for this study was obtained from the Institutional Review Board (or Ethical Committee) of Al-Fajr College for Science and Technology. The requirement for informed consent was waived due to the study's retrospective nature and the analysis of anonymized pre-existing data. All patient identifiers were removed to ensure confidentiality. 2.6. Data analysis Involved cleaning and coding the collected data in Microsoft Excel before importing it into the Statistical Package for the Social Sciences (SPSS), version 26, for further analysis. Descriptive statistics summarized participants' characteristics by presenting frequencies and percentages for categorical variables. The prevalence of pulmonary TB and rifampicin-resistant TB was calculated by determining the proportion of positive results among the total samples tested. Associations between independent variables (such as age, gender, HIV status, and TB history) and outcomes (PTB and rifampicin resistance) were assessed using the Chi-square test or Fisher’s exact test when cell counts were less than 5. A p-value of less than 0.05 was deemed statistically significant. Results Sociodemographic and clinical characteristics of the study participants. A total of 153 presumptive pulmonary TB patients were included in this cross-sectional study. The demographic characteristics are summarized as follows: the majority of participants were males (71.9%, n = 110) and were aged 40–64 (41.8%, n = 64). The clinical characteristics of the participants are shown in Table 1 . All samples tested were sputum (100%, n = 153). Mycobacterium tuberculosis (MTB) was found in 19.0% (n = 29) of the samples. Most positive cases had a medium bacillary load (9.2%, n = 14), followed by low (5.9%, n = 9) and high loads (3.9%, n = 6). Rifampicin resistance was detected in one case (0.7% of total samples; 3.4% of MTB-positive cases). One positive case (0.7%) had an indeterminate rifampicin result. A small percentage of participants had a previous TB history (4.6%, n = 7) or were HIV-positive (1.3%, n = 2). Table 1 details the clinical characteristics of the 153 participants. The relationship between participant characteristics and MTB detection is shown in Table 2 . A previous TB infection was significantly linked to a positive MTB result (p < 0.001). HIV status also showed a significant association (p = 0.018), but all MTB-positive cases were HIV-negative. There was no significant link between MTB detection and age (p = 0.454) or gender (p = 0.228), though more males were positive (n = 23) compared to females (n = 6). Table 2 illustrates the associations between participant traits and MTB detection. Factors related to Rifampicin-resistant TB (RR-TB) are in Table 3 . No significant links were found between RR-TB and age (p = 0.306), gender (p = 0.124), or previous TB history (p = 0.929). The potential association with HIV status couldn’t be assessed, as the only resistant case and both HIV-positive cases were in different groups. Table 3 presents the relationship between participant characteristics and rifampicin resistance among MTB-positive cases (n = 29). Table 1 Clinical characteristics of the study participants (N = 153) Frequency n (%) Category Variable 153 (100%) Sputum Specimen Type 29 (19.0%) Detected MTB Detection 124 (81.0%) Not Detected 9 (31.0%) Low Bacillary Load (n = 29) 14 (48.3%) Medium 6 (20.7%) High 1 (3.4%) Resistant Rifampicin Resistance (n = 29) 27 (93.1%) Sensitive 1 (3.4%) Indeterminate 7 (4.6%) Yes Previous TB History 146 (95.4%) No 2 (1.3%) Positive HIV Status 151 (98.7%) Negative Table 2 Association between participant characteristics and MTB detection Variable Category MTB Detected (n = 29) MTB Not Detected (n = 124) p-value Age Group (years) 1–14 0 (0.0%) 3 (100.0%) 0.454 15–24 6 (31.6%) 13 (68.4%) 25–39 7 (13.7%) 44 (86.3%) 40–64 13 (20.3%) 51 (79.7%) ≥ 65 3 (18.8%) 13 (81.3%) Gender Male 23 (20.9%) 87 (79.1%) 0.228 Female 6 (14.0%) 37 (86.0%) Previous TB Yes 2 (28.6%) 5 (71.4%) < 0.001 No 27 (18.5%) 119 (81.5%) HIV Status Positive 0 (0.0%) 2 (100.0%) 0.018 Negative 29 (19.2%) 122 (80.8%) Table 3 Association between participant characteristics and rifampicin resistance (among MTB-positive cases, n = 29) p-value Rifampicin Sensitive/Indet. (n = 28) Rifampicin Resistant (n = 1) Category Variable 0.306 6 (85.7%) 1 (14.3%) 25–39 Age Group (years) 22 (100.0%) 0 (0.0%) All Other Age Groups 0.124 5 (83.3%) 1 (16.7%) Gender Gender 23 (100.0%) 0 (0.0%) Male 0.929 26 (96.3%) 1 (3.7%) No Previous TB 2 (100.0%) 0 (0.0%) Yes N/E 28 (96.6%) 1 (3.4%) Negative HIV Status 2 (100.0%) 0 (0.0%) Positive N/E, Not Evaluable due to low cell counts. Discussion This hospital-based study provides a critical, time-stamped assessment of the tuberculosis landscape in Sudan, a nation grappling with a pre-existing High disease burden and a currently fractured health care system. Our principal findings indicate a high prevalence of Pulmonary TB (19.0%) among presumptive cases at the tertiary facility, a robust Association between current TB infection and a history of previous disease, and a surprisingly Low rate of rifampicin-resistant TB (RR-TB) at 3.4% of confirmed cases. These results, set against the backdrop of conflict and systemic collapse, offer nuanced insights that challenge some national estimates and highlight urgent public health priorities. The 19.0% yield of GeneXpert-confirmed TB in our cohort is substantially higher than the 5–10% typically observed in routine national surveillance of presumptive cases in stable settings ( 15 ). This disparity is not unexpected but is highly significant. Tertiary care hospitals like Omdurman Military Hospital function as clinical sentinels; their elevated case rates are a classic indicator of advanced disease presentation and the breakdown of Primary Health Care screening ( 3 ). In Sudan, this likely reflects the compounding effects of the Ongoing conflict, which has disrupted primary care services, delayed health-seeking behavior, This has forced the concentration of severe cases into the few remaining functional referral centers. Our findings align with the funnel effect documented in conflict-affected regions like Yemen and Syria, where the collapse of peripheral health infrastructure leads to a disproportionate burden of advanced disease at Central hospitals ( 4 ). Therefore, this 19% prevalence should be interpreted not as a general population rate but as a marker of a healthcare system under severe duress, where diagnoses are occurring late in the disease pathway. The most striking and clinically alarming finding of our study is the influential association between a history of previous TB and current active disease (p < 0.001). This points to a high rate of recurrent TB control program performance. Recurrence can stem from two primary mechanisms: relapse of the original infection due to inadequate treatment, or reinfection with a new strain in a high-transmission environment ( 7 , 11 ) The distinction between relapse and reinfection has important implications for TB control programs, as relapse often indicates treatment failure while reinfection suggests ongoing community transmission ( 11 ). The chaotic conditions in Sudan, characterized by treatment interruption, loss to follow-up, and overcrowded living conditions, create a perfect storm for both mechanisms. However, the high recurrence rate strongly suggests fundamental weaknesses in the treatment cascade, potentially including non-adherence due to drug stockouts, the use of non-WHO-recommended regimens, or a complete absence of a patient support system ( 5 ) . This finding moves beyond a simple statistical association; it is a direct indicator of a failing TB control program and a significant risk factor for the breeding of drug-resistant strains, even if not yet fully manifested in our cohort. Our identification of a single RR-TB case (3.4% of positives) stands in stark contrast to the substantial pre-war drug-resistant TB burden in Sudan, where a national meta-analysis estimated pooled MDR-TB prevalence at 22.3% among new cases and 46.4% among previously treated cases ( 10 ). This rate also differs from recent findings in neighboring Ethiopia, where GeneXpert-based surveillance revealed a 2.9% RR-TB prevalence among presumptive cases ( 6 ). This discrepancy must be interpreted with caution. While it is a positive finding for this specific patient cohort, several confounding factors are at play. First, our sample size of MTB-positive cases (n = 29) provides low precision for estimating resistance prevalence; the 95% confidence interval around 3.4% is wide. A key methodological limitation is that this study was underpowered; the small number of MTB-positive cases (n = 29) provides insufficient precision to estimate the true prevalence of RR-TB reliably or to analyze its associated risk factors. This directly explains the wide confidence interval around our resistance rate and its apparent discrepancy with higher pre-war national estimates. Second, our study was conducted in a military hospital, which may serve a population with better initial access to care and a lower baseline risk for drug resistance compared to the general public or internally displaced population. Most critically, the collapse of surveillance and diagnostic systems during the war means the current national prevalence of RR-TB is unknown. Our data may reflect a temporary snapshot before the conflict-driven interruptions in treatment lead to a surge in drug resistance, a well-documented phenomenon in humanitarian crises. This paradox underscores that the absence of evidence is not evidence of absence. It highlights the catastrophic gap in national surveillance and the urgent need to re-establish diagnostic networks to monitor this critical threat. The significant association between HIV status and TB detection, where all cases were HIV-negative, is a statistical artifact of a rare exposure (HIV prevalence of 1.3% in our sample). This is a classic example of a Type I statistical error in an underpowered analysis and should not be interpreted biologically. The low HIV prevalence is consistent with national seroprevalence data (< 0.1%), confirming that HIV is not a primary driver of the TB epidemic in Sudan, unlike in Southern and Eastern African regions. Limitations and Methodological Rigor The limitations of this study are inherent to its context. The retrospective, single-center design limits generalizability and introduces potential selection bias. The sample size, particularly for the analysis of RR-TB risk factors, was underpowered, a direct consequence of conducting research in a resource-constrained and conflict-affected setting. However, these limitations do not invalidate the finding; instead, they define the scope within which the results should be applied. The initial data inconsistencies we identified and rectified underscore the challenges of relying on routine health information systems during a period of collapse and reinforce the value of rigorous data cleaning and validation. Conclusion and Imperative Recommendation To address these challenges, we propose the following targeted recommendation; 1- Implement Sentinel Surveillance: Immediately established a sentinel surveillance system for TB drug resistance within functional tertiary hospitals to track trends and provide early warning of rising resistance. 2-Decentralized and Fortify Diagnosis: Expand access to molecular diagnosis, such as GeneXpert, at stable, accessible peripheral sites to reduce diagnostic delays and the burden on central hospitals. 3- Launch a Zero Recurrence Initiative Prioritize strengthening the treatment cascade by scaling up community-based directly observed therapy (DOT), patient support mechanisms, and guaranteed drug supplies to break the cycle of recurrence and prevent DR-TB emergence. 4-Integrate TB Care into Humanitarian Response: Advanced for the seamless integration of TB diagnosis and treatment into the broader humanitarian health response, ensuring continuity of care for displaced and vulnerable populations. Ultimately, the battle against TB in Sudan cannot be won without stability and a functional health system. However, until that is achieved, targeted, data-driven interventions focused on preventing treatment failure and monitoring resistance are our most critical tools for mitigating a worsening catastrophe. In conclusion , this study illuminates two sides of the TB crisis in Sudan: a health system overwhelmed with active cases and a treatment cascade that is failing to prevent recurrence. The low RR-TB rate is a cautiously optimistic finding, but it poses a latent threat given the systemic breakdown. Abbreviations DOT Directly Observed Therapy DR-TB Drug-resistant Tuberculosis HIV Human Immunodeficiency Virus MDR-TB Multidrug-resistant Tuberculosis MTB Mycobacterium Tuberculosis MTB/RIF Mycobacterium Tuberculosis / Rifampicin PCR Polymerase Chain Reaction PTB Pulmonary Tuberculosis RIF Rifampicin RR-TB Rifampicin-resistant Tuberculosis SPSS Statistical Package for the Social Sciences TB Tuberculosis WHO World Health Organization Declarations Ethics approval and consent to participate: Ethical approval for this study was obtained from the Institutional Review Board of Al-Fajr College for Science and Technology. Informed consent was waived due to the retrospective nature of the study and the use of anonymized data. Consent for publication: Not applicable Availability of data and materials: The datasets used and analyzed during this study are available from the corresponding author upon reasonable request. Competing interests: The authors declare that they have no competing interests. Funding: No funding was received for this study. Authors' contributions: Saif Eldowla Ayoub: Conceptualization, Data curation, Formal analysis, Writing original draft. Alaa Osman: Methodology, Validation, Writing review & editing. Nada Mohamed: Investigation, Resources, Data curation . Hewida Ahmed: Writing review & editing, Visualization. Rayan Elamin: Supervision, Project administration, Writing review & editing. All authors read and approved the final manuscript. Acknowledgements: We are grateful to the staff of the Microbiology Department at the Central Laboratory for their help in accessing the laboratory register data, and to Mr. Mujtaba Ahmed Alnoor for his valuable support during data collection. References Al Zamel AM, Saeed AA, Elmubarak M, Alsarraj MA (2025) Sudan's tuberculosis response needs global support amid conflict. 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Cureus 16:e71402. https://doi.org/10.7759/cureus.71402 Hajissa K, Zakaria R, Suppian R, Mohamed Z (2021) Prevalence of drug-resistant tuberculosis in Sudan: A systematic review and meta-analysis. Antibiot (Basel) 10:932. https://doi.org/10.3390/antibiotics10080932 Marx FM, Dunbar R, Enarson DA, Williams BG, Warren RM, van der Spuy GD et al (2014) The temporal dynamics of relapse and reinfection with tuberculosis after successful treatment: a retrospective cohort study. Clin Infect Dis 58:1676–1683. https://doi.org/10.1093/cid/ciu186 Noor SKM, Bushara MOE, Taha ZI, Salah M, Anwar T, Osman AA et al (2021) Prevalence of multidrug-resistant Mycobacterium tuberculosis (MDR-TB) using GeneXpert; how serious is the situation? Int J Pharm Res Allied Sci 10:116–121. https://doi.org/10.51847/v0mtrrkFKF Omar A, Abo Elfadl AE, Ahmed Y, Hosny M (2019) Valuing the use of the GeneXpert test as an unconventional approach to diagnose pulmonary tuberculosis. Egypt J Bronchol 13:403–407. https://doi.org/10.4103/ejb.ejb_88_18 Shi Z, Peng J, Li X, Fu X, Zou L, Chen Q et al (2025) Mycobacterium tuberculosis infection status and associated factors among household close contacts of rifampicin-resistant pulmonary tuberculosis patients: a single-center cross-sectional study. J Clin Tuber Other Mycobact Dis 41:100561. https://doi.org/10.1016/j.jctube.2025.100561 World Health Organization (2024) Global tuberculosis report 2024. World Health Organization, Geneva. https://www.who.int/teams/global-programme-on-tuberculosis-and-lung-health/tb-reports/global-tuberculosis-report-2024 . Accessed 14 Oct 2025 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Mar, 2026 Read the published version in Bulletin of the National Research Centre → Version 1 posted Editorial decision: Revision requested 07 Feb, 2026 Reviews received at journal 03 Feb, 2026 Reviews received at journal 31 Jan, 2026 Reviews received at journal 30 Jan, 2026 Reviewers agreed at journal 26 Jan, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviewers agreed at journal 20 Jan, 2026 Reviewers invited by journal 20 Jan, 2026 Editor assigned by journal 19 Jan, 2026 Submission checks completed at journal 15 Jan, 2026 First submitted to journal 15 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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10:03:14","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":77067,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8495858/v1/4ef896f7063f670436822bdf.html"},{"id":105755193,"identity":"9a9e9dfb-404e-486a-bbf1-2bf6c5e38ba7","added_by":"auto","created_at":"2026-03-30 16:26:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1178388,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8495858/v1/ace3352f-fe91-4ff9-a2c3-c3190ba80736.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"GeneXpert-Detected Mycobacterium tuberculosis in Sudan: Insights into Rifampicin Resistance","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTuberculosis (TB) remains a leading cause of morbidity and mortality worldwide, with a disproportionate impact on developing countries (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). It is an airborne infectious disease caused by Mycobacterium tuberculosis. A major challenge in global TB control is the rise of drug-resistant strains. Multi-drug-resistant tuberculosis (MDR-TB), defined as resistance to both isoniazid and rifampicin, poses a significant Public Health threat (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWith an estimated 10.8\u0026nbsp;million people globally falling ill with TB and 1.25\u0026nbsp;million succumbing to the disease in 2023, the urgency of the situation cannot be overstated (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Sudan, a high-burden country in the Eastern Mediterranean Region, reported over 21,000 Cases with a mortality rate of 25 per 100,000 Population in the same year (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The emergence of rifampicin-resistant TB (RR-TB), a Key proxy for MDR-TB, is particularly alarming, with approximately 410,000 New cases globally and a treatment success rate of only 68% (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe diagnosis of TB and drug resistance in low-resource settings like Sudan remains challenging. While traditional smear microscopy is widely used, it has limited sensitivity, especially for paucibacillary and extrapulmonary TB (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough culture is the gold standard, it is time-consuming, with results taking up to 8 weeks (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Molecular tools such as the GeneXpert Mycobacterium tuberculosis/rifampicin (MTB/RIF) assay have revolutionized TB Diagnosis by simultaneously detecting M. tuberculosis and rifampicin resistance within 2 hours, with high sensitivity and specificity (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the development of GeneXpert technology in Sudan, significant barriers persist, including limited accessibility, insufficient laboratory capacity, and a lack of comprehensive nationwide surveillance. These barriers cannot be overstated, as they are crucial for effective TB control. These challenges are compounded by general issues in resource-limited settings, such as suboptimal treatment management and HIV co-infection.\u003c/p\u003e \u003cp\u003eThe situation in Sudan is challenging due to underdeveloped diagnostic systems, treatment discontinuation, and delayed therapy initiation (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Even before the current conflict, the country faced a troubling MDR-TB burden, with prevalence estimates of 22.3% among new cases and 30% among previously treated cases (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe ongoing war has critically damaged Sudan\u0026rsquo;s public health infrastructure, disrupting laboratory services, destroying facilities, and displacing Healthcare Workers. This collapsed and undoubtedly exacerbates TB transmission and severely limits access to diagnosis and treatment, underlining the gravity of the situation.\u003c/p\u003e \u003cp\u003eWhile several diagnostic centers in Sudan are equipped with GeneXpert technology, no nationwide study has comprehensively evaluated the prevalence of rifampicin resistance. The convergence of high pre-war resistance rates and the current healthcare system collapse create a public health emergency. This study, therefore, aims to determine the prevalence of pulmonary tuberculosis and rifampicin-resistant tuberculosis among patients tested with\u003c/p\u003e \u003cp\u003eGeneXpert in Sudan, and to identify associated sociodemographic and clinical risk factors.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. \u003cb\u003eStudy Design, Setting, and Period\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eA retrospective, Cross-sectional study was conducted using the records of presumptive pulmonary tuberculosis (TB) patients at Omdurman Military Hospital in Khartoum, Sudan. Data were extracted from the medical records of patients who were tested between February and October 2025. Omdurman Military is a tertiary-care facility with a capacity of over 500 beds. It serves as one of the state\u0026rsquo;s key centers, equipped with the GeneXpert MTB/RIF assay. The hospital\u0026rsquo;s microbiology department receives and processes an average of 19 sputum samples for TB testing per month.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. \u003cb\u003eStudy population and Sampling\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe study population comprised all presumptive pulmonary TB patients with complete laboratory data entries in the GeneXpert TN registration logbook during the study period. Patients with missing or incomplete data for key variables under investigation (e.g., test results, demographic information) were excluded from the analysis.\u003c/p\u003e \u003cp\u003eA formal sample size calculation was performed using Cochran\u0026rsquo;s formula for finite populations, assuming a projected TB positivity proportion of 33.76% (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), a 5% margin of error, and a 95% confidence level. This calculation yielded a minimum required sample size of 344 participants. However, due to this study\u0026rsquo;s retrospective design and fixed study period, a convenience sample of all eligible records from the specified timeframe was used.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. \u003cb\u003eLaboratory Methods\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eSputum samples collected from presumptive TB patients were processed and analyzed using the GeneXpert MTB/RIF assay (Cepheid, Sunnyvale, CA, USA) according to the manufacturer\u0026rsquo;s instructions. The test simultaneously detects complex (MTB) and rifampicin resistance (RIF) through real-time PCR amplification of the rpoB gene.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. \u003cb\u003eData Collection\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eA structured data extraction sheet was developed to collect information from the GeneXpert TB Laboratory Registration Logbook. The extracted variable included:\u003c/p\u003e \u003cp\u003eSociodemographic data: Age, gender, and residence.\u003c/p\u003e \u003cp\u003eClinical data: HIV status and previous history of TB treatment.\u003c/p\u003e \u003cp\u003eLaboratory results: GeneXpert MTB/RIF result (categorized as MTB-detected /RIF-resistant\u003c/p\u003e \u003cp\u003eMTB- detected /RIF-susceptible, or MTB-not-detected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. \u003cb\u003eEthical Consideration\u003c/b\u003e\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003e for this study was obtained from the Institutional Review Board (or Ethical Committee) of Al-Fajr College for Science and Technology. The requirement for informed consent was waived due to the study's retrospective nature and the analysis of anonymized pre-existing data. All patient identifiers were removed to ensure confidentiality.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. \u003cb\u003eData analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eInvolved cleaning and coding the collected data in Microsoft Excel before importing it into the Statistical Package for the Social Sciences (SPSS), version 26, for further analysis. Descriptive statistics summarized participants' characteristics by presenting frequencies and percentages for categorical variables. The prevalence of pulmonary TB and rifampicin-resistant TB was calculated by determining the proportion of positive results among the total samples tested. Associations between independent variables (such as age, gender, HIV status, and TB history) and outcomes (PTB and rifampicin resistance) were assessed using the Chi-square test or Fisher\u0026rsquo;s exact test when cell counts were less than 5. A p-value of less than 0.05 was deemed statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eSociodemographic and clinical characteristics of the study participants. A total of 153 presumptive pulmonary TB patients were included in this cross-sectional study. The demographic characteristics are summarized as follows: the majority of participants were males (71.9%, n\u0026thinsp;=\u0026thinsp;110) and were aged 40\u0026ndash;64 (41.8%, n\u0026thinsp;=\u0026thinsp;64).\u003c/p\u003e \u003cp\u003eThe clinical characteristics of the participants are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. All samples tested were sputum (100%, n\u0026thinsp;=\u0026thinsp;153). Mycobacterium tuberculosis (MTB) was found in 19.0% (n\u0026thinsp;=\u0026thinsp;29) of the samples. Most positive cases had a medium bacillary load (9.2%, n\u0026thinsp;=\u0026thinsp;14), followed by low (5.9%, n\u0026thinsp;=\u0026thinsp;9) and high loads (3.9%, n\u0026thinsp;=\u0026thinsp;6). Rifampicin resistance was detected in one case (0.7% of total samples; 3.4% of MTB-positive cases). One positive case (0.7%) had an indeterminate rifampicin result. A small percentage of participants had a previous TB history (4.6%, n\u0026thinsp;=\u0026thinsp;7) or were HIV-positive (1.3%, n\u0026thinsp;=\u0026thinsp;2). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e details the clinical characteristics of the 153 participants. The relationship between participant characteristics and MTB detection is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. A previous TB infection was significantly linked to a positive MTB result (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). HIV status also showed a significant association (p\u0026thinsp;=\u0026thinsp;0.018), but all MTB-positive cases were HIV-negative. There was no significant link between MTB detection and age (p\u0026thinsp;=\u0026thinsp;0.454) or gender (p\u0026thinsp;=\u0026thinsp;0.228), though more males were positive (n\u0026thinsp;=\u0026thinsp;23) compared to females (n\u0026thinsp;=\u0026thinsp;6). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the associations between participant traits and MTB detection. Factors related to Rifampicin-resistant TB (RR-TB) are in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. No significant links were found between RR-TB and age (p\u0026thinsp;=\u0026thinsp;0.306), gender (p\u0026thinsp;=\u0026thinsp;0.124), or previous TB history (p\u0026thinsp;=\u0026thinsp;0.929). The potential association with HIV status couldn\u0026rsquo;t be assessed, as the only resistant case and both HIV-positive cases were in different groups. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the relationship between participant characteristics and rifampicin resistance among MTB-positive cases (n\u0026thinsp;=\u0026thinsp;29).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of the study participants (N\u0026thinsp;=\u0026thinsp;153)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e153 (100%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSputum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecimen Type\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29 (19.0%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMTB Detection\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e124 (81.0%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNot Detected\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9 (31.0%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLow\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBacillary Load (n\u0026thinsp;=\u0026thinsp;29)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e14 (48.3%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMedium\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6 (20.7%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHigh\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1 (3.4%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eResistant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eRifampicin Resistance (n\u0026thinsp;=\u0026thinsp;29)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e27 (93.1%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSensitive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1 (3.4%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIndeterminate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7 (4.6%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePrevious TB History\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e146 (95.4%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2 (1.3%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePositive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eHIV Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e151 (98.7%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between participant characteristics and MTB detection\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMTB Detected (n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMTB Not Detected (n\u0026thinsp;=\u0026thinsp;124)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge Group (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;14\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (100.0%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (31.6%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (68.4%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e25\u0026ndash;39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e7 (13.7%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e44 (86.3%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e40\u0026ndash;64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e13 (20.3%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e51 (79.7%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3 (18.8%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e13 (81.3%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e23 (20.9%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e87 (79.1%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.228\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e6 (14.0%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e37 (86.0%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrevious TB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2 (28.6%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5 (71.4%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e27 (18.5%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e119 (81.5%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHIV Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePositive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0 (0.0%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2 (100.0%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e29 (19.2%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e122 (80.8%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between participant characteristics and rifampicin resistance (among MTB-positive cases, n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRifampicin Sensitive/Indet. (n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRifampicin Resistant (n\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (85.7%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (14.3%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u0026ndash;39\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAge Group (years)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (100.0%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAll Other Age Groups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0.124\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5 (83.3%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1 (16.7%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e23 (100.0%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0 (0.0%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0.929\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e26 (96.3%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1 (3.7%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ePrevious TB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2 (100.0%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0 (0.0%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN/E\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e28 (96.6%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1 (3.4%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eHIV Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2 (100.0%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0 (0.0%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ePositive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eN/E, Not Evaluable due to low cell counts.\u003c/b\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e This hospital-based study provides a critical, time-stamped assessment of the tuberculosis landscape in Sudan, a nation grappling with a pre-existing High disease burden and a currently fractured health care system. Our principal findings indicate a high prevalence of Pulmonary TB (19.0%) among presumptive cases at the tertiary facility, a robust Association between current TB infection and a history of previous disease, and a surprisingly Low rate of rifampicin-resistant TB (RR-TB) at 3.4% of confirmed cases. These results, set against the backdrop of conflict and systemic collapse, offer nuanced insights that challenge some national estimates and highlight urgent public health priorities.\u003c/p\u003e \u003cp\u003eThe 19.0% yield of GeneXpert-confirmed TB in our cohort is substantially higher than the 5\u0026ndash;10% typically observed in routine national surveillance of presumptive cases in stable settings (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). This disparity is not unexpected but is highly significant.\u003c/p\u003e \u003cp\u003eTertiary care hospitals like Omdurman Military Hospital function as clinical sentinels; their elevated case rates are a classic indicator of advanced disease presentation and the breakdown of Primary Health Care screening (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In Sudan, this likely reflects the compounding effects of the\u003c/p\u003e \u003cp\u003eOngoing conflict, which has disrupted primary care services, delayed health-seeking behavior,\u003c/p\u003e \u003cp\u003eThis has forced the concentration of severe cases into the few remaining functional referral centers. Our findings align with the funnel effect documented in conflict-affected regions like Yemen and Syria, where the collapse of peripheral health infrastructure leads to a disproportionate burden of advanced disease at Central hospitals (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, this 19% prevalence should be interpreted not as a general population rate but as a marker of a healthcare system under severe duress, where diagnoses are occurring late in the disease pathway.\u003c/p\u003e \u003cp\u003eThe most striking and clinically alarming finding of our study is the influential association between a history of previous TB and current active disease (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This points to a high rate of recurrent TB control program performance. Recurrence can stem from two primary mechanisms: relapse of the original infection due to inadequate treatment, or reinfection with a new strain in a high-transmission environment (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) The distinction between relapse and reinfection has important implications for TB control programs, as relapse often indicates treatment failure while reinfection suggests ongoing community transmission (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The chaotic conditions in Sudan, characterized by treatment interruption, loss to follow-up, and overcrowded living conditions, create a perfect storm for both mechanisms.\u003c/p\u003e \u003cp\u003eHowever, the high recurrence rate strongly suggests fundamental weaknesses in the treatment cascade, potentially including non-adherence due to drug stockouts, the use of non-WHO-recommended regimens, or a complete absence of a patient support system (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e. This finding moves beyond a simple statistical association; it is a direct indicator of a failing TB control program and a significant risk factor for the breeding of drug-resistant strains, even if not yet fully manifested in our cohort.\u003c/p\u003e \u003cp\u003eOur identification of a single RR-TB case (3.4% of positives) stands in stark contrast to the substantial pre-war drug-resistant TB burden in Sudan, where a national meta-analysis estimated pooled MDR-TB prevalence at 22.3% among new cases and 46.4% among previously treated cases (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). This rate also differs from recent findings in neighboring Ethiopia, where GeneXpert-based surveillance revealed a 2.9% RR-TB prevalence among presumptive cases (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). This discrepancy must be interpreted with caution. While it is a positive finding for this specific patient cohort, several confounding factors are at play. First, our sample size of MTB-positive cases (n\u0026thinsp;=\u0026thinsp;29) provides low precision for estimating resistance prevalence; the 95% confidence interval around 3.4% is wide. A key methodological limitation is that this study was underpowered; the small number of MTB-positive cases (n\u0026thinsp;=\u0026thinsp;29) provides insufficient precision to estimate the true prevalence of RR-TB reliably or to analyze its associated risk factors. This directly explains the wide confidence interval around our resistance rate and its apparent discrepancy with higher pre-war national estimates. Second, our study was conducted in a military hospital, which may serve a population with better initial access to care and a lower baseline risk for drug resistance compared to the general public or internally displaced population. Most critically, the collapse of surveillance and diagnostic systems during the war means the current national prevalence of RR-TB is unknown.\u003c/p\u003e \u003cp\u003eOur data may reflect a temporary snapshot before the conflict-driven interruptions in treatment lead to a surge in drug resistance, a well-documented phenomenon in humanitarian crises. This paradox underscores that the absence of evidence is not evidence of absence. It highlights the catastrophic gap in national surveillance and the urgent need to re-establish diagnostic networks to monitor this critical threat.\u003c/p\u003e \u003cp\u003eThe significant association between HIV status and TB detection, where all cases were HIV-negative, is a statistical artifact of a rare exposure (HIV prevalence of 1.3% in our sample). This is a classic example of a Type I statistical error in an underpowered analysis and should not be interpreted biologically. The low HIV prevalence is consistent with national seroprevalence data (\u0026lt;\u0026thinsp;0.1%), confirming that HIV is not a primary driver of the TB epidemic in Sudan, unlike in Southern and Eastern African regions.\u003c/p\u003e"},{"header":"Limitations and Methodological Rigor","content":"\u003cp\u003eThe limitations of this study are inherent to its context. The retrospective, single-center design limits generalizability and introduces potential selection bias. The sample size, particularly for the analysis of RR-TB risk factors, was underpowered, a direct consequence of conducting research in a resource-constrained and conflict-affected setting. However, these limitations do not invalidate the finding; instead, they define the scope within which the results should be applied. The initial data inconsistencies we identified and rectified underscore the challenges of relying on routine health information systems during a period of collapse and reinforce the value of rigorous data cleaning and validation.\u003c/p\u003e"},{"header":"Conclusion and Imperative","content":"\u003cp\u003e \u003cb\u003eRecommendation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo address these challenges, we propose the following targeted recommendation;\u003c/p\u003e\n\u003ch3\u003e1- Implement Sentinel Surveillance:\u003c/h3\u003e\n\u003cp\u003eImmediately established a sentinel surveillance system for TB drug resistance within functional tertiary hospitals to track trends and provide early warning of rising resistance.\u003c/p\u003e\n\u003ch3\u003e2-Decentralized and Fortify Diagnosis:\u003c/h3\u003e\n\u003cp\u003eExpand access to molecular diagnosis, such as GeneXpert, at stable, accessible peripheral sites to reduce diagnostic delays and the burden on central hospitals.\u003c/p\u003e\n\u003ch3\u003e3- Launch a Zero Recurrence Initiative\u003c/h3\u003e\n\u003cp\u003ePrioritize strengthening the treatment cascade by scaling up community-based directly observed therapy (DOT), patient support mechanisms, and guaranteed drug supplies to break the cycle of recurrence and prevent DR-TB emergence.\u003c/p\u003e \u003cp\u003e 4-Integrate TB Care into Humanitarian Response: Advanced for the seamless integration of TB diagnosis and treatment into the broader humanitarian health response, ensuring continuity of care for displaced and vulnerable populations.\u003c/p\u003e \u003cp\u003eUltimately, the battle against TB in Sudan cannot be won without stability and a functional health system. However, until that is achieved, targeted, data-driven interventions focused on preventing treatment failure and monitoring resistance are our most critical tools for mitigating a worsening catastrophe.\u003c/p\u003e \u003cp\u003eIn \u003cb\u003econclusion\u003c/b\u003e, this study illuminates two sides of the TB crisis in Sudan: a health system overwhelmed with active cases and a treatment cascade that is failing to prevent recurrence. The low RR-TB rate is a cautiously optimistic finding, but it poses a latent threat given the systemic breakdown.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDOT Directly Observed Therapy\u003c/p\u003e\n\u003cp\u003eDR-TB Drug-resistant Tuberculosis\u003c/p\u003e\n\u003cp\u003eHIV Human Immunodeficiency Virus\u003c/p\u003e\n\u003cp\u003eMDR-TB Multidrug-resistant Tuberculosis\u003c/p\u003e\n\u003cp\u003eMTB Mycobacterium Tuberculosis\u003c/p\u003e\n\u003cp\u003eMTB/RIF Mycobacterium Tuberculosis / Rifampicin\u003c/p\u003e\n\u003cp\u003ePCR Polymerase Chain Reaction \u003c/p\u003e\n\u003cp\u003ePTB Pulmonary Tuberculosis\u003c/p\u003e\n\u003cp\u003eRIF Rifampicin \u003c/p\u003e\n\u003cp\u003eRR-TB Rifampicin-resistant Tuberculosis\u003c/p\u003e\n\u003cp\u003eSPSS Statistical Package for the Social Sciences\u003c/p\u003e\n\u003cp\u003eTB Tuberculosis\u003c/p\u003e\n\u003cp\u003eWHO World Health Organization\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was obtained from the Institutional Review Board of Al-Fajr College for Science and Technology. Informed consent was waived due to the retrospective nature of the study and the use of anonymized data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSaif Eldowla Ayoub:\u003c/strong\u003e Conceptualization, Data curation, Formal analysis, Writing original draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlaa Osman:\u003c/strong\u003e Methodology, Validation, Writing review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNada Mohamed:\u003c/strong\u003e \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eInvestigation, Resources, Data curation\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHewida Ahmed:\u0026nbsp;\u003c/strong\u003eWriting review \u0026amp; editing, Visualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRayan Elamin:\u003c/strong\u003e Supervision, Project administration, Writing review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the staff of the Microbiology Department at the Central Laboratory for their help in accessing the laboratory register data, and to Mr. Mujtaba Ahmed Alnoor for his valuable support during data collection.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAl Zamel AM, Saeed AA, Elmubarak M, Alsarraj MA (2025) Sudan's tuberculosis response needs global support amid conflict. 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Accessed 14 Oct 2025\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bulletin-of-the-national-research-centre","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnrc","sideBox":"Learn more about [Bulletin of the National Research Centre](https://BNRC.springeropen.com)","snPcode":"42269","submissionUrl":"https://submission.springernature.com/new-submission/42269/3","title":"Bulletin of the National Research Centre","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mycobacterium Tuberculosis, Rifampicin resistance, GeneXpert MTB/RIF assay, Khartoum, Sudan","lastPublishedDoi":"10.21203/rs.3.rs-8495858/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8495858/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: This study aimed to determine the prevalence of Tuberculosis (TB) and Rifampicin-resistant Tuberculosis (RR-TB) among Sudanese patients tested using the GeneXpert method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eData were extracted from the medical records of presumptive tuberculosis patients who attended the hospital from February to October 2025. Sputum samples from presumptive Tuberculosis patients were analyzed using the GeneXpert MTB/RIF assay to detect Mycobacterium tuberculosis and Rifampicin resistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A total of 153 sputum samples were analyzed. There were male (71.9%) and female (28.1%) participants. The majority of participants (41.8%) were aged 40-64 years, followed by those aged 25-39 years (33.3%). Younger participants aged 15-24years represented (12.4%). At the same time, only a small fraction were children aged 1-14 years (2%). Mycobacterium tuberculosis was detected in 29 cases (19.0%), and 81% were negative. Among the positive cases, moderate bacillary levels were most common (48.3%). Rifampicin resistance was detected in only one case (0.7%), with one additional inconclusive result; the remaining positive cases were sensitive (18.3%). A small number of participants reported previous TB Infection (4.6%) or HIV Infection (1.3%).\u003c/p\u003e\n\u003cp\u003eChi-square analysis revealed no statistically significant relationship between age (p=0.454) and gender (p=0.228). In contrast, previous TB Infection(p\u0026lt;0.001), and HIV Status (p=0.018) showed a strong association with TB detection. No significant association between age, gender, previous TB infection, and rifampicin resistance (p\u0026gt;0.05)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings reveal a high prevalence of tuberculosis and significant recurrence among presumptive cases, suggesting gaps in the treatment cascade. The low rate of rifampicin resistance is a cautiously optimistic, yet preliminary, finding, highlighting the need for strengthened surveillance and continuity of care in Sudan's conflict-affected setting.\u003c/p\u003e","manuscriptTitle":"GeneXpert-Detected Mycobacterium tuberculosis in Sudan: Insights into Rifampicin Resistance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-22 10:03:09","doi":"10.21203/rs.3.rs-8495858/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-07T08:35:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-03T08:45:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-31T19:32:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-30T20:08:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202448876788641161810641747107013315651","date":"2026-01-26T05:20:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"289190602661874159703438251281183692391","date":"2026-01-21T20:48:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"118241279795322320043067646308156118558","date":"2026-01-21T11:39:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"113782483941979765845563588543862718169","date":"2026-01-21T03:03:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-20T20:59:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-19T08:56:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-15T23:38:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Bulletin of the National Research Centre","date":"2026-01-15T20:33:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bulletin-of-the-national-research-centre","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnrc","sideBox":"Learn more about [Bulletin of the National Research Centre](https://BNRC.springeropen.com)","snPcode":"42269","submissionUrl":"https://submission.springernature.com/new-submission/42269/3","title":"Bulletin of the National Research Centre","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d54b6ddf-3388-4dee-b4d3-ad7882ad1c0e","owner":[],"postedDate":"January 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:21:38+00:00","versionOfRecord":{"articleIdentity":"rs-8495858","link":"https://doi.org/10.1186/s42269-026-01428-5","journal":{"identity":"bulletin-of-the-national-research-centre","isVorOnly":false,"title":"Bulletin of the National Research Centre"},"publishedOn":"2026-03-27 16:10:50","publishedOnDateReadable":"March 27th, 2026"},"versionCreatedAt":"2026-01-22 10:03:09","video":"","vorDoi":"10.1186/s42269-026-01428-5","vorDoiUrl":"https://doi.org/10.1186/s42269-026-01428-5","workflowStages":[]},"version":"v1","identity":"rs-8495858","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8495858","identity":"rs-8495858","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}