Abstract
Background
Globally, the prevalence of drug-resistant (DR-TB) tuberculosis is rising, and Pakistan is currently one of the nations with the highest burden. It is imperative to track changes in the prevalence of DR-TB, particularly the extensively drug-resistant (XDR-TB) tuberculosis, and identify the pattern of treatment outcome.
Objective
To identify the pattern and predictors of drug resistance and treatment outcome in tuberculosis patients at the Centers for Programmatic Management of Drug-resistant Tuberculosis (PMDT) in Lahore, Pakistan.
Method
A cross-sectional analytical study was done in three PMDT centers (Programmatic Management of Drug-resistant Tuberculosis) in District Lahore. Secondary data of 387 were accessed, including files of patients of all ages and both genders enrolled during 2021. Data was collected from April to July, 2023. SPSS version 24 was used to enter the data. Information on drug resistance categories, pattern, socio-demographic and disease-related factors, adverse events, and treatment outcomes were gathered.
Results
RR resistance was found in 32.8% (127/387), MDR in 45.0% (174/387), Pre-XDR-TB in 21.2% (82/387) and XDR-TB in the remaining 1.0% (4/387). Drug-resistant TB was Pulmonary in 373/387 (96.4%) patients, while 3.6% were extra-pulmonary. 54.3% (210/387) males were enrolled with Drug-Resistant Tuberculosis with pre-dominantly RR-TB 38.1% (80/387). The mean age of 387 drug-resistant TB patients was 38.43 with SD of 17.190. Age (p-value = 0.007) and gender (p-value = 0.047) have been found significantly associated with the DRTB groups. The highest proportion (44.6%) of all types of comorbidities were observed in the MDR-TB group and being lowest (18.5%) in Pre-XDR/XDR TB group. Prior TB history was present in 65.1% (252/387) patients with majority in MDR group (46.4%). DR-TB arthralgia was the most common adverse event at 21%. The highest number of adverse events (adverse event = 208) were observed in DRTB cases who were administered with Pyrazinamide drug and the least adverse events were observed among DRTB cases taking Amikacin (adverse event = 9) and MFX (adverse event = 2). In 53.6% patients the treatment was successful, while 15.3% died.
Conclusion
Drug resistant tuberculosis patients had a high death rate and multiple adverse events associated with the treatment. Multipronged strategies need to be designed to improve treatment success, minimize adverse events and increase awareness about DR-TB.
Keywords: Drug resistance, Tuberculosis, XDR, Adverse events, Treatment outcomes
Introduction
After decades of global neglect, tuberculosis remerged on the global public health agenda, and finally in 1993 WHO declared TB a global public health emergency [1]. Subsequently, in 1994–1995, the five-element DOTS strategy was introduced [2], followed by the inclusion of TB indicators in the MDGs [3]. Stop TB Strategy 2006–2015 [4] and the resolution WHA62.15 on the prevention and control of MDR TB (multidrug-resistant tuberculosis) and XDR TB (extensively drug-resistant tuberculosis) by the World Health Assembly [5] accelerated and expanded tuberculosis treatment and control. The evolution of strategies for TB control by the World Health Organization (WHO) showed global commitment toward ending TB [6].
An estimated global total of 10.6 million people were ill with TB in 2021 equivalent to 134 cases per 10,000 population. Geographically, most TB cases in 2021 were in the WHO regions of South-East Asia (45%), Africa (23%) and the Western Pacific (18%), with smaller shares in the Eastern Mediterranean (8.1%), the Americas (2.9%) and Europe (2.2%). In 2021, eight countries accounted for more than two-thirds of global TB cases, and Pakistan (5.8%) being the 5th. Globally, there were an estimated 450,000 incident cases of MDR/RR-TB in 2021. An estimated 191,000 deaths occurred due to MDR/RR-TB in 2021. The countries contributing the largest share of MDR/RR-TB incident cases in 2021 were 26% India, 8.5% the Russian Federation, and Pakistan with 7.9% of global cases [7].
The growing global concern for multidrug-resistant TB (MDR-TB) was shifted to extensively drug-resistant TB in 2006, which was labeled as a big threat to all TB efforts so, to combat this, an XDR-TB Global Task Force was constituted in 2006. WHO updated its guidelines for the programmatic management of MDR-TB and incorporated treatment of XDR-TB and this was added to the Global Plan to Stop TB, 2006–2015 [8].
WHO’s international expert panel in 2019, reviewed the recent evidence on MDR-TB and RR-TB treatment and published new guidelines in 2020, recommending a (9 to 11 months) shorter regimen for MDR/RR-TB cases not resistant to fluoroquinolones including bedaquiline replacing injectables and all oral regimen [9]. In 2022, WHO updated consolidated guidelines to recommend a 6 month all-oral regimen including bedaquiline (B), pretomanid (Pa), linezolid (L), and moxifloxacin (M) called BPaLM, given to MDR/RR-TB including with additional fluoroquinolone resistance/pre-XDR [10].
A combination of multiple second-line drugs are used to treat MDR-TB over a long duration and these drugs are found to be associated with various adverse events resulting in severe morbidities like deafness, depression, gastrointestinal disturbances, neurotoxicity, nephrotoxicity, etc., and even death [11, 12].
There are different types of drug-resistant TB; rifampicin-resistant tuberculosis (RR-TB), multidrug-resistant tuberculosis (MDR-TB), and extensively drug-resistant tuberculosis (XDR-TB) [13]. Mycobacterium TB isolates showed variable heteroresistance (HR) in different populations [14]. Drugs used in DR-TB are known to cause side effects and adverse effects, but their rate of occurrence is found to be variable in different populations.
A study from China analyzed the trends and predictors of MDR/RR tuberculosis (TB) in culture-positive cases during 2012–2020 to scale up services and improve strategies [15]. A national-level 10-year retrospective cohort was conducted in Africa to identify predictors of treatment failure and death in MDR-TB cases concluding the need for continuous information for changing strategies in combating the TB burden [16].
The poor success and high mortality in DR-TB patients in Pakistan are drawing the attention of the program managers and clinicians [17]. In 2021, diagnosed case of drugs-resistant TB were 3,138 while 2,878 started the treatment [18]. In District Lahore, 3 PMDT centers register DR-TB patients. Improving coverage is an issue but identifying the patterns and predictors of DR in these centers can help map the situation and plan strategies to maximize enrollment for treatment initiation and adherence. Pakistan is facing a high burden of TB, poor success, and high mortality in DR-TB patients in Pakistan. Current research is planned to inform public health policies, plans, and interventions to combat drug-resistant tuberculosis in Pakistan. The objective is to identify the pattern and predictors of drug resistance and treatment outcome in tuberculosis patients at the Centers for Programmatic Management of Drug-resistant Tuberculosis (PMDT) in Lahore, enrolled during 2021.
Method
A Cross-sectional design was used, and a consecutive review of the DR-TB patients’ medical records was done. These patients enrolled for treatment in 2021 (Jan 01 to Dec 31st ) at 3 PMDT centers (Programmatic Management of Drug-resistant Tuberculosis) in District Lahore, Pakistan, namely the Jinnah Hospital, Mayo Hospital, and Gulab Devi Hospital. Patient files/records of all ages and both genders were included and files with diagnoses of HIV positive, AIDS, or any disabling lung diseases along with tuberculosis were separately categorized and not included because HIV cases are transferred to HIV and AIDS control program once diagnosed, and they get TB treatment there. All eligible patient files enrolled in that specified period at 3 centers were included in the study. IRB approval was obtained from the IRB of the parent institute of the principal investigator named Institutional Review and Research Advisory Board of Shaikh Zayed Postgraduate Medical Institute, Lahore, Pakistan. IRB waived the need for written informed consent from patients because the secondary data was used. Administrative consent was obtained from the program manager of the TB control program in Punjab, Pakistan.
Categories of drug-resistant tuberculosis
Rifampicin-resistant TB (RR-TB) is the TB strains with “resistance to rifampicin detected by genotypic or phenotypic methods, with or without resistance to other drugs”, while MDR includes patients with isolates that are resistant to at least isoniazid (H) and rifampicin (R) — the two most powerful first-line anti-TB drugs. MDR and RR-TB are often grouped together as MDR/RR-TB. There is a category of Isoniazid resistance only as HR-TB. Pre-XDR-TB strains fulfil the definition of MDR/RR-TB and are also resistant to any fluoroquinolone. XDR-TB strains that fulfil the definition of MDR/RR-TB and that are also resistant to any fluoroquinolone and at least one additional Group A drug (bedaquiline or linezolid) [19]. In this study these categories were confirmed by GeneXpert MTB/RIF assay for Rifampicin resistance and further characterization of DR, including isoniazid and other first-line drugs, was performed using the Genotype MTBDR plus (line probe assay), and for confirmation phenotypic drug-susceptibility testing (DST) was performed DST on 209/387 (54%) patients and LPA (Line Probe Assay) was done on 280/387 (50%) patients. The tests were performed at the National Reference Lab (NRL) Islamabad, under the National TB Control Program. In these centers no case of Isoniazid-resistance alone (HR-TB) was reported and documented in the data in these 3 centers of Lahore.
Social, demographic and disease factors associated with DR-TB
Gender, BMI, Age, Prior TB history, and Prior TB treatment outcome (complete, cured, not evaluated, incomplete). Prior TB disease site (Pulmonary or Extra-pulmonary site), Presence of treatment supporter in prior TB (yes, no), FLD and SLD (First line drugs and second-line drugs) used in prior TB, contact history with a DR-TB case in family or friends (Yes, No), DR-TB site (pulmonary, extra-pulmonary).
Treatment-related adverse events
These are the adverse events diagnosed by the physician on follow-up visits and recorded in files and electronic records. Gastrointestinal disturbance, Psychiatric disorder, Arthralgia, hepatitis, peripheral neuropathy, hypothyroidism, epileptic seizures, dermatological effects, ototoxicity, and nephrotoxicity.
Treatment outcome
As defined by the World Health Organization in 2013 in ‘Definitions and reporting framework for tuberculosis–2013 revision: updated December 2014 and January 2020’. Table 1.
Table 1.
Operational definitions of the outcome categories of DR-TB cases
| Cured | Treatment is completed as recommended in the national policy without any evidence of failure AND 3 or more consecutive cultures (taken at least 30 days apart) are negative after completion of the intensive phase |
| Treatment completed | Treatment is completed as recommended in the national policy without any evidence of failure BUT there is no record that three or more consecutive cultures (taken at least 30 days apart) are negative after the intensive phase |
| Treatment failed |
Treatment terminated or there was a need for permanent regimen change of at least 2 anti-TB drugs and the reason could be: − lack of conversion (by the end of the intensive phase), or − bacteriological reversion (in the continuation phase after conversion to negative), or − evidence of acquired resistance (additional) to fluoroquinolones or second-line injectable drugs, or − adverse drug reactions (ADRs). |
| Died | A patient who dies for any reason during the course of treatment |
| Lost to follow-up | A patient whose treatment was interrupted for 2 consecutive months or more. |
| Not evaluated | A patient for whom no treatment outcome is assigned. (This includes cases “transferred out” to another treatment unit and whose treatment outcome is unknown) |
| Treatment success | The sum of cured and treatment completed |
In this study, DR-TB patients were managed by using standardized PMDT regimens. Including longer all-oral regimens (LTR1-LTR4), modified longer regimens adding bedaquiline (LTR2a/2b), injectable-based regimens (STR and STR-modified) and also both standard and modified shorter treatment regimens (STR and STR-modified). These treatment regimens were selected based on drug-susceptibility testing results, national PMDT guidelines and WHO recommendations.
Data analysis
Frequencies of MDR-TB, RR-TB, Pre-XDR-TB, XDR-TB categories were identified and their associations with socio-demographic and disease-related factors, treatment outcome and adverse drug events were determined. Regression was applied to determine the predictive nature of these factors for treatment outcome. Data was entered on SPSS version 24. The current study adhered to the principles of Declaration of Helsinki and anonymity of data was ensured.
Results
Categories of drug resistance
Upon confirmation, RR resistance was found in 32.8% (127/387), MDR in 45.0% (174/387), Pre-XDR-TB in 21.2% (82/387) and XDR-TB in the remaining 1.0% (4/387). Drug-resistant TB was Pulmonary in 373/387 (96.4%) patients, while 3.6% were extra-pulmonary. Among pulmonary DR-TB, 117/373 (31.4%) were RR, 171/373 (45.8%) were MDR, 81/373 (21.7%) and 4/373 (1.1%) XDR-TB cases. In extra-pulmonary, 10/14 (71.4%) cases were RR, 3 (21.4%) MDR, 1 (7.1%) pre-XDR, and none were XDR-TB.
Out of 387, DST (Drug Susceptibility testing) was done for confirmation of resistance on 209/387 (54%) patients and LPA (Line Probe Assay) was done on 280/387 (50%) patients. Culture positives DST samples were 174/209 (83.2%) while 35/209 (16.7%) were negative and they were treated after confirmed by LPA. DST results for FLD (First Line Drugs) show Rifampicin 131/172 (76.16%), Isoniazid 147/174 (84.48%), Ethambutol 40/173 (23.12%), Pyrazanimde 64/158 (40.50%) and Streptomycin 79/168 (47%). DST results for SLD (Second Line Drugs) show 2.3% resistance against Amikacin, 39.7% resistance against Levofloxacin, 9.2% resistance against Moxifloxacin, 100% against Ethionamide, 10.3% Bedaquiline, 3.1% Delamanid, 6.1% Linazolid and 0% against Cycloserine.
Distribution of mean age among DRTB groups
The mean age of 387 drug-resistant TB patients was 38.43 with an SD of 17.190. ANOVA was applied. Multiple comparisons were done using post-hoc Tukey’s test to find the difference between groups. The mean difference of age between RR-TB vs. MDR-TB and RR-TB vs. Pre XDR-TB groups was found to be significant with p = 0.007 and p = 0.002 respectively (Table 2).
Table 2.
Distribution of mean age among DRTB groups
| DRTB Patterns | N | Mean | Std. Deviation | F | p-value |
|---|---|---|---|---|---|
| RR-TB | 127 | 42.61 | 17.778 | 4.494 | 0.004 |
| MDR-TB | 173 | 37.23 | 17.076 | ||
| Pre-XDR-TB | 82 | 34.99 | 15.554 | ||
| XDR-TB | 4 | 28.00 | 10.424 | ||
| Total | 386 | 38.43 | 17.190 |
| Multiple Comparison | Mean Difference | P-value |
|---|---|---|
| RR-TB vs. MDR-TB | 5.383 | 0.007 |
| RR-TB vs. Pre XDR-TB | 7.626 | 0.002 |
| RR-TB vs. XDR-TB | 14.614 | 0.091 |
Distribution of socio-demographic factors
There were 54.2% males and 38.2% were in 26 to 50 age group and 31% are 25 yrs or less. It was observed that only age (p-value = 0.007) and gender (p-value = 0.047) have been found significantly associated with the DRTB groups (Table 3).
Table 3.
Socio-demographic factors associated with different types of drug resistance found in tuberculosis cases enrolled at all PMDT centers of district Lahore in 2021
| Socio-demographic attributes/ Factors |
Sub-categories | Drug Resistance | Total | Chi-Square (P-Value) |
||
|---|---|---|---|---|---|---|
| RR-TB n (%) |
MDR-TB n (%) |
Pre-XDR/XDR TB n (%) |
||||
|
Age (years) |
0–25 | 32 (26.4) | 56 (46.3) | 33 (27.3) | 121 (100) |
14.193 (0.007 ) |
| 26 to 50 | 42 (28.4) | 69 (46.6) | 37 (25.0) | 148 (100) | ||
| > 50 | 53 (45.3) | 48 (41.0) | 16 (13.7) | 117 (100) | ||
| Total | 127 (32.9) | 173 (44.8) | 86 (22.3) | 386* (100) | ||
| Gender | Male | 80 (38.1) | 84 (40.0) | 46 (21.9) | 210 (100) |
6.433 (0.040) |
| Female | 47 (26.6) | 90 (50.8) | 40 (22.6) | 177 (100) | ||
| Total | 127 (32.8) | 174 (45.0) | 86 (22.2) | 387 (100) | ||
| BMI at the start of treatment | Underweight | 77 (31.6) | 112 (45.9) | 55 (21.7) | 244 (100) |
4.294 (0.637) |
| Normal | 37 (37.4) | 40 (40.4) | 22 (22.2) | 99 (100) | ||
| Overweight | 4 (23.5) | 11 (64.7) | 2 (11.8) | 17 (100) | ||
| Obese 1/Obese 2 | 9 (26.3) | 11 (36.8) | 7 (25.9) | 27 (100) | ||
| Total | 127 (32.8) | 174 (45.0) | 86 (22.2) | 387 (100) | ||
| BMI at 3rd month of treatment | Underweight | 55 (28.8) | 88 (46.1) | 48 (25.1) | 191 (100) |
0.076 (0.923) |
| Normal | 37 (35.9) | 42 (40.8) | 24 (23.3) | 103 (100) | ||
| Overweight | 6 (31.6) | 9 (47.4) | 4 (21.1) | 19 (100) | ||
| Obese 1/Obese 2 | 9 (25.0) | 14 (55.0) | 6 (20.7) | 29 (100) | ||
| Not Recorded | 20 (44.4) | 21 (46.7) | 4 (8.9) | 45 (100) |
9.566 (0.144) |
|
| Total | 127 (32.8) | 174 (45.0) | 86 (21.2) | 387 (100) | ||
| BMI at the end of treatment | Underweight | 37 (25.0) | 71 (48.0) | 40 (27.0) | 148 (100) | |
| Normal | 46 (40.4) | 45 (39.5) | 23 (28.3) | 114 (100) | ||
| Overweight | 11 (32.4) | 16 (47.1) | 7 (20.6) | 34 (100) | ||
| Obese 1/Obese 2 | 10 (18.2) | 18 (45.5) | 14 (33.3) | 42 (100) | ||
| Not Recorded | 23 (46.9) | 24 (49.0) | 2 (4.1) | 49 (100) | ||
| Total | 127 (32.8) | 174 (45.0) | 86 (21.2) | 387 (100) | ||
| Unemployed | Housewife | 28(25.7) | 56 (51.4) | 25 (22.0) | 109 (100) | |
| Others | 7 (43.8) | 6 (37.5) | 3 (12.5) | 16 (100) | ||
| Employed | Laborer | 33 (33.3) | 45 (45.5) | 21 (21.2) | 99 (100) | |
| Student | 20 (31.7) | 27 (42.9) | 16 (23.8) | 63 (100) | ||
| Worker | 38 (40.4) | 36 (38.3) | 20 (20.2) | 94 (100) | ||
| Others | 1 (50.0) | 4 (50.0) | 1 (0.0) | 6 (100) | ||
| Total | 127 (32.8) | 174 (45.0) | 86 (21.2) | 387 (100) | ||
age of one transferred out patient is not available in record
Association of pertinent co-morbidities with DRTB groups
The highest proportion 41 (44.6%) of all types of comorbidities were observed in MDR-TB group. Only hypertension (p-value = 0.037) and smoking (p-value = 0.030) were found significantly associated with the DRTB groups. (Table 4).
Table 4.
Co-morbidities association with DR-TB groups
| Co-morbidities | DRTB Groups | Total | Chi-Square (P-value) |
|||
|---|---|---|---|---|---|---|
| RR-TB n (%) |
MDR-TB n (%) |
Pre-XDR/XDR TB n (%) |
||||
| Co-morbidities | Yes | 34 (37.0) | 41 (44.6) | 17 (18.5) | 92 (100) |
1.396 (0.498) |
| No | 93 (31.5) | 133 (45.1) | 65 (22.0) | 295 (100) | ||
| Total | 127 (32.8) | 174 (45.0) | 86 (22.2) | 387 (100) | ||
| Diabetes | Yes | 25 (34.2) | 34 (46.6) | 14 (19.2) | 73 (100) |
0.483 (0.785) |
| No | 102 (32.5) | 140 (44.6) | 72 (22.9) | 314 (100) | ||
| Total | 127 (32.8) | 174 (45.0) | 82 (22.2) | 387 (100) | ||
| Hypertension | Yes | 5 (71.4) | 0 (0.0) | 2 (28.6) | 7 (100) |
6.573 (0.037) |
| No | 122 (32.1) | 174 (45.8) | 84 (22.1) | 314 (100) | ||
| Total | 127 (32.8) | 174 (45.0) | 86 (22.2) | 387 (100) | ||
| Smoking | Yes | 20 (51.3) | 14 (35.9) | 5 (12.8) | 7 (100) |
7.024 (0.030) |
| No | 107 (30.7) | 160 (46.0) | 81 (23.3) | 314 (100) | ||
| Total | 127 (32.8) | 174 (45.0) | 86 (22.2) | 387 (100) | ||
Distribution of disease-related factors
Prior TB history was present in 65.1% (252/387) patients, all were pulmonary except 1 that was extra pulmonary. Previous TB history was present in 31.3% (79/252) of current RR patients while 46.4% (117/252) in MDR, 22.2% (56/252) in Pre-XDR and XDR patients with no significant difference (P-value = 0.664). Out of 252 patients with prior TB history 96.4% (243/252) received FLD in prior TB treatment regimen while 3.6% (3/252) received SLD and 2.38% (6/252) received both FLD and SLD. Amongst 252 patients with prior TB history 116 (46%) received treatment at government hospitals, 50 (19.8%) received treatment at private hospitals while treatment centers of 86 (34.1%) patients were not reported in the patient record. Prior TB treatment outcomes mentioned in record showed that 1 (0.4%) was a treatment success, 33 (13%) were treatment failure, 186 (73.8%) were not evaluated, 9 (3.6%) were lost to follow-up and 23 (9.1%) had complete treatment but relapse occurred.
Duration of sickness prior to the diagnosis of DR-TB was self-reported by the patient and documented in the record as history of appearance of first symptoms. The duration was categorized as 0 to 6 months having 83.2% (322/387) patients, 7–18 months category with 12.1% (47/387), 19 to 24 months category with 3.1% (12/387), 25 to 36 months category with 1% (4/387) patients and more than 37 months category had only 0.5% (2/387) patients. Mean duration of sickness prior to the diagnosis of DRTB was 3.84 ± 5.755 (SD) months in RR group, 5.68 ± 10.623 months in MDR group and 6.20 ± 7.329 months in pre-XDR/XDR group.
Duration of intensive phase in DRTB groups
The mean duration of the intensive phase is highest at 7.7134 ± 4.10273 for pre-XDR/XDR TB and lowest at 6.181 ± 1.31787 for RR-TB and differs significantly (p-value < 0.001) across different types of DRTB group. The pairwise comparison shows that the mean duration of the intensive phase of RR-TB and MDR-TB differ significantly (p-value < 0.001). The mean duration of the intensive phase of MDR-TB and PRE-XDR/XDR also differ significantly (p-value < 0.001).
Duration of continuous phase in DR-TB groups
The mean duration of the continuous phase is highest at 10.710 ± 1.494 months for pre-XDR/XDR TB and lowest at 9.330 ± 3.756 months for RR-TB and differs significantly (F = 8.090, p-value < 0.001) across all DRTB. The pairwise comparison shows that the mean duration of the continuous phase of RR-TB and MDR-TB differ significantly (p-value < 0.001) and also the continuous phase of RR-TB and PRE-XDR/XDR differ significantly (p-value = 0.002).
Distribution of adverse events among DRTB groups
Overall, in all cases of DRTB arthralgia was the most common adverse event at 21%, followed by gastrointestinal disturbance at 14.5% and psychiatric disorder at 14.3%. However, presence of ototoxicity was significantly different among DRTB groups. Among all cases having ototoxicity, the highest proportion 6(75.0%) was observed among Pre-XDR-TB/XDR-TB. It was found that ototoxicity had a significant higher association with Pre-XDR-TB/XDR-TB (p = 0.005), (Table 5).
Table 5.
Major adverse drug events in different types of drug resistance found in tuberculosis cases enrolled at all PMDT centers of district Lahore in 2021
| Major Adverse drug events | Drug Resistance | Total | Chi Square (P-value) | ||
|---|---|---|---|---|---|
| RR-TB n (%) |
MDR-TB n (%) |
Pre-XDR-TB/XDR-TB n (%) |
|||
| Anemia | 6 (21.4%) | 14 (50%) | 8 (28.5%) | 28 (5.0%) | 1.911 (0.3853) |
| GI Irritation | 31 (37.8%) | 28 (34.1%) | 23 (28.0%) | 82 (14.6%) | 5.085 (0.079) |
| Psychological Issues | 21 (25.9%) | 41 (50.6%) | 19 (23.5%) | 81 (14.4%) | 2.281 (0.320) |
| Arthralgias | 40 (33.6%) | 55 (46.2%) | 24 (20.2%) | 119 (21.2%) | 0.420 (0.811) |
| Drug Induced Hepatitis | 16 (48.8%) | 10 (30.3%) | 7 (21.2%) | 33 (5.8%) | 4.439 (0.109) |
| Myelosuppression | 27 (35.0%) | 32 (41.5%) | 18 (23.4%) | 77 (13.7%) | 0.453 (0.797) |
| Nephrotoxicity | 11 (42.3%) | 7 (26.9%) | 8 (30.7%) | 26 (4.6%) | 3.698 (0.157) |
| Ototoxicity | 1 (12.5%) | 2 (12.5%) | 6 (75.0%) | 9 (1.6%) | 10.573 (0.005)* |
| Peripheral Neuropathy | 10 (22.7%) | 23 (52.2%) | 11 (25.0%) | 44 (7.8%) | 2.303 (0.316) |
| Dermatological Issues | 9 (20.4%) | 21 (47.7%) | 14 (31.8%) | 44 (7.8%) | 4.453 (0.108) |
| QTC Prolongation | 6 (35.2%) | 9 (52.9%) | 2 (11.7%) | 17 (3.0%) | 1.160 (0.560) |
| Total | 178 (31.7%) | 242 (43.2%) | 140 (25.0%) | 560 (100%) | |
*Fisher Exact test
Major adverse events with different drugs
A total of 208 adverse events were reported in all DRTB cases. The highest frequency of myelosuppression (n = 65) and PN (n = 42) was observed among cases taking Linezolid. The highest frequency of arthralgia (n = 113) was seen with Pyrazinamide. (Figures 1 and 2).
Fig. 1.
Frequency of Major Adverse Drug Events with Different Drugs administered to tuberculosis cases enrolled at all PMDT centers of District Lahore in 2021
Fig. 2.
Adverse effects of Pyrazinamide administered to tuberculosis cases enrolled at all PMDT centers of District Lahore in 2021
Treatment outcome as success (cured & completed), mortality, lost to follow up and not evaluated are mentioned in Table 6.
Table 6.
Treatment outcome in different types of drug resistance found in tuberculosis cases enrolled at all PMDT centers of district Lahore in 2021
| Treatment outcome | Drug Resistance | Total | |||
|---|---|---|---|---|---|
| RR-TB n (%) |
MDR-TB n (%) |
Pre-XDR-TB n (%) |
XDR-TB n (%) |
||
| Cured | 87 (68.5%) | 118 (68.2%) | 71 (86.5%) | 2 (66.6%) | 278 (51.5%) |
| Completed | 3 (2.3%) | 5 (2.9%) | 0 (0.0%) | 0 (0.0%) | 8 (2.1%) |
| Died | 20 (15.7%) | 32 (18.5%) | 6 (7.3%) | 1 (33.3%) | 59 (15.3%) |
| Lost to Follow-up | 12 (9.4%) | 13 (7.5%) | 3 (3.6%) | 0 (0.0%) | 28 (7.2%) |
| Not Evaluated | 5 (3.9%) | 3 (1.7%) | 0 (0.0%) | 0 (0.0%) | 8 (2.1%) |
| Total | 127 (100%) | 173 (100%) | 82 (100%) | 3 (100)) | 385 (100.0%) |
A multivariable binary logistic regression model was applied to determine the association between selected independent variables and treatment outcome (coded as 1 = successful, 0 = unsuccessful). The independent variables used in the model were gender, prior TB history, presence of comorbidities, and age group. The analysis revealed that male gender was significantly associated with higher odds of treatment success (B = 0.600, OR = 1.823, p = 0.018). This indicates that, after adjusting for other variables in the model, male patients were 1.82 times more likely to achieve a successful treatment outcome compared to female patients. Patients with a prior history of TB had 1.62 times higher odds of treatment success (B = 0.483, OR = 1.621), although this association did not reach statistical significance (p = 0.066). The presence of comorbidities was associated with a 25.5% reduction in the odds of treatment success (B = -0.294, OR = 0.745), but this relationship was not statistically significant (p = 0.323). With respect to age, patients aged < 40 years had significantly lower odds of treatment success compared to those aged ≥ 40 years (B = -0.976, OR = 0.377, p < 0.001). This suggests that, in the study population, younger patients were less likely to achieve successful treatment outcomes.
Discussion
The objective of identifying patterns and predictors of drug resistance and treatment outcome was adequately fulfilled. This facility-based cohort of 385 DR-TB patients confirmed and enrolled for the treatment in 2021 showed interesting results. Global pooled MDR prevalence estimates in 2023 were approx. 11.6% (95% CI 9.1–14.5%) but with both facility-based and population studies mixed [20]. Estimated proportion of new cases with MDR/RR TB were 3.3% approx. in 2022 while approx. 17% were previously treated cases. The burden of DR-TB was 3% increased between 2020 and 2021 [21]. In Pakistan, wide variation is reported in facility and PMDT cohorts showing a range of MDR from 15% to 40% depending upon the sample size, selection criteria and other factors [22].
The distribution of resistance in our study was RR 32.8%, MDR 45.0%, Pre-XDR 21.2%, and XDR-TB 1.0%. The proportion of RR and MDR TB is consistently higher than the reported estimates of national surveillance and WHO global [23]. Erashri et al. showed quite different results with RR-TB in 56.4% and MDR TB 13.4%. The RR resistance was more common than multidrug resistance as compared to our study [24], reflecting variation across settings, regions and testing approaches. The difference in pattern is a good predictor of how our health system should approach the single and multidrug resistance in Pakistan. In Punjab province of Pakistan, MDR-TB data of 1250 cases in nine tertiary care hospitals (5 years) showed resistance to all first-line anti-tuberculosis medications was 41% [25]. This was the data from 2010 to 14 but we can still very well appreciate approximately same multi-drug resistance pattern in Punjab (Lahore) in 2021 as well. MDR/RR-TB is present in significantly higher proportion in Pakistan. The lower resistance rate (4.5%) in Bhatti et al. may be due to the reason that they included new cases only while our cohort included previously treated cases as well [26]. Age and gender distribution in our study was quite similar to a 5-years study in Punjab [25] where the mean age was 32 (± 13.5SD) years and 53% were males. Multiple studies also showed almost similar age and gender distribution with no difference between drug resistant groups but the mean age shows more patients in productive age group (35 to 40 years) while another evidence highlighted that males made up 71.6% of the DR-TB population and 78.4% of the population was under 45 years [24, 27–29]. A different age and gender pattern was seen in another study [30]. The difference in pattern seems to be due to variable cohort characteristics and diagnostic definitions/timing etc.
In our study, FLD resistance was shown to Rifampicin at 76.16% and isoniazid at 84.48% mainly, while DST results for SLD showed 100% against Ethionamide. A meta-analysis of 38 studies (highest from Asia) on Mycobacterium TB isolates showed variable hetero-resistance (HR) with a pooled prevalence of 5% to Isoniazid, 7% to Rifampicin, 10% to fluoroquinolone and 1% to ethambutol 10% [14]. Drugs used for DR-TB are known to be associated with adverse effects, but their rate of occurrence is variable in different populations. MDR-TB isolates were resistant to at least one second-line medication in 6.3% of cases including 5.7% Pre-XDR-TB and 0.6% XDR-TB [31]. In our study, 39.7% of subjects showed resistance to Levofloxacin (SLD) mainly. These differences are probably due to different immune responses, the genetic makeup of our population, their nutritional behavior, and drug behavior. An 8-years (2014-21) national study in Gabon regions broadly supports our findings like the male predominance, high RR/MDR burden in productive/ working-age population and rising incidence of RR but the second-line resistance pattern is different. Gabon regions showed much lower pre-XDR and XDR proportions (9% and 0.3%) than ours (21.2% & 1%) probably due to national level surveillance data and the denominator being the second-line-tested isolates only [32].
In our study, pulmonary TB was in 96.4% of patients, while 3.6% were with extra-pulmonary. A similar evidence suggesting about 85.1% of the DR patients with pulmonary TB, 11.7% extrapulmonary, and 3.2% with both PTB and EPTB [33].
In our study 65.1% had a prior history of TB and showed markedly unfavorable outcomes, like extremely low treatment success (0.4%), high treatment failure (13%), loss to follow-up and relapse and unusually large group of ‘not evaluated’ category (73.8%). These findings are consistent with global evidence depicting previously treated TB as a major predicting factor of poor outcomes in DR-TB mainly due to poor adherence and accumulated resistance. Studies from Ethiopia, South Africa and Pakistan reported similar lower success and higher failure or LTFU among retreatment cases [23, 31, 34]. Treatment compliance is a big determining factor for developing resistance. We found a range of cured or treatment completed (success) rate as 36.8%, 41.4% and 63.5% in few cohorts [35–37] as compared to our 53.6% success rate. These differences are might be due to delays in diagnosis, programmatic gaps in compliance and follow-up or incomplete treatment evaluation in the case of poor outcome. Our results when compared with the study by Atif et al. showed a higher proportion of XDR patients with successful treatment outcomes (66% vs. 28.8%). Furthermore, the mortality rate of XDR patients in our study was less (8.2%) as compared to the 45.1% reported by Atif et al. This difference is perhaps due to the geographical and immune response factors in these studies [17]. Second-line TB drugs are administered for a longer duration than first-line drugs, thus the side effect profile takes more importance here with an effect on the patient’s quality of life [38]. Among the TB drugs, pyrazinamide was associated with the most side effects, which is also consistent with other studies [39]. This emphasizes the importance of early recognition of side effects especially in patients on pyrazinamide to either discontinue or take measures to ensure effective compliance [38]. A noteworthy finding from our study is arthralgia as the most common adverse event (21%), followed by gastrointestinal disturbance (14.5%) and psychiatric disorder (14.3%), aligning with previous research observations [38]. In a meta-analysis (2020), the lowest incidence of adverse effects was found with fluoroquinolones, clofazimine, and bedaquiline, whereas amino-salicylic acid, and linezolid showed the highest incidence of adverse effects [11]. In a study on 751 MDR-TB patients 90.7% experienced at least one type of adverse event; 6.8% required the permanent discontinuation of the offending medicine as a result of adverse events; and 55.2% required a change in MDR-TB treatment [40]. Another study from Bahawalpur, Pakistan showed 63.7% of MDR-TB patients with at least one adverse effect, including arthralgia (27.4%), nausea and vomiting (27.4%), and depression (33%). The presence of comorbidity and employment status were associated with adverse outcomes [41].
The World Health Organization’s recent recommendation to utilize a brief regimen showed a remarkable rate of effectiveness, especially among HIV-positive patients [42]. Patient-related demographic factors like male sex and older age were found to be associated with MDR-TB-Related mortality along with some clinical factors like undernutrition, co-morbidity, diabetes, HIV co-infection, clinical complications, history of TB, previous history of second-line anti-TB treatment, a smear-positive TB, and presence of XDR-TB [43]. Prior therapy, age 24 or less than 24, 25 to 44 years, and age of 45 to 64 years along with female gender and births other than Estonia were associated with MDR-TB in patients of this age group. Prior TB is a common risk factor for severe DR-TB and MDR-TB [44].
A bibliometric analysis of TB-related research production from 2011 to 2020 showed that Pakistan’s TB research output was low [45]. The emergence of drug-resistant tuberculosis (DR-TB) poses a challenge to the already heavily burdened and resource-poor health systems in developing countries. The rapidly evolving epidemiological pattern of drug-resistant TB further complicates this issue [46]. Moreover, the progress in DR-TB research on a global scale has been hindered by the widespread impact of the Coronavirus pandemic, further exacerbating the challenges [47]. This holds for Pakistan as well, where the fight against DR-TB has experienced a setback due to the disruptions caused by the ongoing pandemic.
This study provides valuable insights into the pattern and predictors of drug resistance tuberculosis in Pakistan. The sample included all patients under treatment at all PMDT centers in a metropolitan city/district. These findings are representative of a large population group because DR-TB treatment is only provided in government sector in only these 3 centers for this highly populated city. This study has the limitation that HIV positives with DR-TB are shifted to the HIV/AIDS program, so not included in this study. The study has policy, practice and social implications not only in Pakistani health system’s context but also for global context.
Conclusion
The current study reveals quite alarmingly poor treatment outcome among drug-resistant TB, particularly poor outcome in those with prior TB history. This study highlights the pattern and predictors of drug-resistant tuberculosis in the metropolitan city of Lahore, Pakistan, Productive age groups are mostly affected and both genders are equal. Pyrazinamide was the drug with the most adverse effects and arthralgia was the most common adverse effect of the treatment. This study highlights critical weaknesses in the care cascade for drug-resistant TB. Addressing these gaps through political will, evidence-informed policies, enhancing provider capacity, community engagement, social support and enhanced surveillance system’s capacity is essential to prevent further spread of drug resistance. This can be achieved by aligning national outcomes with global targets to ending tuberculosis.
Acknowledgements
We acknowledge the financial support by WHO for 2022 Joint EMRO/TDR Impact Grants for regional priorities focused on implementation research in infectious diseases of poverty reference # AP-22-00773.
Authors’ contributions
HA contributed to the conception of the research topic, literature review, proposal development, execution, data analysis, scientific writingAH contributed to the conception of the research topic, proposal development, execution, data analysis, scientific writingMAN contributed to the proposal development, literature review, and data collectionSWS contributed to the proposal development and analysisAK contributed to the analysis and results writeupSNM contributed to the analysis and results and discussion writeup MA contributed to the review of final draft and gave intellectual input in the discussion. NHS contributed to the review of final draft and gave intellectual input in the methods and discussion.
Funding
Financial support by WHO for 2022 Joint EMRO/TDR Impact Grants for regional priorities focused on implementation research in infectious diseases of poverty reference # AP-22-00773. This does not cover the publication cost (APC).
Data availability
Data on SPSS can be shared upon request. The request is to be sent through the email, drayeshah@gmail.com, to Dr. Ayesha Humayun, the corresponding author of this paper.
Declarations
Ethics approval and consent to participate
The study adhered to the principles of Helsinki’s declaration. Ethical approval was obtained from the IRB named Institutional Review and Research Advisory Board of Shaikh Zayed Postgraduate Medical Institute, Lahore, Pakistan. The secondary data were accessed so the need for consent was waived off by the IRB, so only permission and administrative consent were obtained from the program manager of the TB control program in Punjab, Pakistan.
Consent for publication
Not applicable.
Competing interests
Ex-manager TB control program, Punjab is one of the co-authors in this paper.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Hira Ashfaq and Ayesha Humayun contributed equally to this work and held the joint first author position.
References
- 1.Nakajima H. Tuberculosis: a global emergency. World Health. 1993;46(4):3–3. [PubMed] [Google Scholar]
- 2.Dye C, Garnett GP, Sleeman K, Williams BG. Prospects for worldwide tuberculosis control under the WHO DOTS strategy. Lancet. 1998;352(9144):1886–91. [DOI] [PubMed] [Google Scholar]
- 3.Raviglione MC. The TB epidemic from 1992 to 2002. Tuberculosis. 2003;83(1–3):4–14. [DOI] [PubMed] [Google Scholar]
- 4.Raviglione MC. The new stop TB strategy and the global plan to stop TB, 2006–2015. SciELO Public Health. 2007;85:327–327. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Organization WH. A regional response to the emerging threats of multidrug-resistant and extensively drug resistant tuberculosis. In.; 2009.
- 6.Raviglione MC, Pio A. Evolution of WHO policies for tuberculosis control, 1948–2001. Lancet. 2002;359(9308):775–80. [DOI] [PubMed] [Google Scholar]
- 7.WHO. Global Tuberculosis Report 2022. 2022:68.
- 8.Organization WH. The global MDR-TB & XDR-TB response plan 2007–2008. In.: World Health Organization; 2007. [Google Scholar]
- 9.Mirzayev F, Viney K, Linh NN, Gonzalez-Angulo L, Gegia M, Jaramillo E, Zignol M, Kasaeva T. World health organization recommendations on the treatment of drug-resistant tuberculosis, 2020 update. Eur Respir J. 2021;57(6):2003300. [DOI] [PMC free article] [PubMed]
- 10.Organization WH. WHO consolidated guidelines on tuberculosis. Module 4: treatment-drug-resistant tuberculosis treatment, 2022 update. World Health Organization; 2022. [PubMed]
- 11.Lan Z, Ahmad N, Baghaei P, Barkane L, Benedetti A, Brode SK, Brust JC, Campbell JR, Chang VWL, Falzon D. Drug-associated adverse events in the treatment of multidrug-resistant tuberculosis: an individual patient data meta-analysis. Lancet Respiratory Med. 2020;8(4):383–94. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Ahmad N, Ahuja SD, Akkerman OW, Alffenaar J-WC, Anderson LF, Baghaei P, Bang D, Barry PM, Bastos ML, Behera D. Treatment correlates of successful outcomes in pulmonary multidrug-resistant tuberculosis: an individual patient data meta-analysis. Lancet. 2018;392(10150):821–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Glasauer S, Altmann D, Hauer B, Brodhun B, Haas W, Perumal N. First-line tuberculosis drug resistance patterns and associated risk factors in Germany, 2008–2017. PLoS ONE. 2019;14(6):e0217597. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Ye M, Yuan W, Molaeipour L, Azizian K, Ahmadi A, Kouhsari E. Antibiotic heteroresistance in Mycobacterium tuberculosis isolates: a systematic review and meta-analysis. Ann Clin Microbiol Antimicrob. 2021;20(1):73. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Lecai J, Mijiti P, Chuangyue H, Mingzhen L, Qian G, Weiguo T, Jihong C. Predictors and trends of MDR/RR-TB in Shenzhen china: a retrospective 2012–2020 period analysis. Infect Drug Resist. 2021;14:4481. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Tola H, Holakouie-Naieni K, Mansournia MA, Yaseri M, Gamtesa DF, Tesfaye E, Mahamed Z, Sisay MM. National treatment outcome and predictors of death and treatment failure in multidrug-resistant tuberculosis in ethiopia: a 10-year retrospective cohort study. BMJ Open. 2021;11(8):e040862. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Atif M, Mukhtar S, Sarwar S, Naseem M, Malik I, Mushtaq A. Drug resistance patterns, treatment outcomes and factors affecting unfavourable treatment outcomes among extensively drug resistant tuberculosis patients in Pakistan; a multicentre record review. Saudi Pharm J. 2022;30(4):462–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.van den Boom M, Bennani K, Sismanidis C, Gunneberg C, Khawaja L, Safdar MA, Muhwa C, Heldal E, Cirillo DM, Khan AW, et al. : 2022 TB programme review in pakistan: strengthening governance, with better patient diagnosis and treatment. IJTLD Open. 2024;1(3):136–43. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Organization WH. Meeting report of the WHO expert consultation on the definition of extensively drug-resistant tuberculosis, 27–29 October 2020. 2021.
- 20.Salari N, Kanjoori AH, Hosseinian-Far A, Hasheminezhad R, Mansouri K, Mohammadi M. Global prevalence of drug-resistant tuberculosis: a systematic review and meta-analysis. Infect Dis Poverty. 2023;12(1):57. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Bagcchi S. WHO’s global tuberculosis report 2022. Lancet Microbe. 2023;4(1):e20. [DOI] [PubMed] [Google Scholar]
- 22.Javaid A, Khan MA, Afridi MZ, Khan AR, Ghafoor A. Prevalence and pattern of multidrug resistant tuberculosis among retreatment (Category II) patients of pulmonary tuberculosis in Khyber Pakhtunkhwa, Pakistan. Pakistan J Chest Med. 2020;26(3):121–7. [Google Scholar]
- 23.Chen Z, Wang T, Du J, Sun L, Wang G, Ni R, An Y, Fan X, Li Y, Guo R. Decoding the WHO global tuberculosis report 2024: a critical analysis of global and Chinese key data. Zoonoses. 2025;5(1):999. [Google Scholar]
- 24.Derashri G, Nayak S, Asati A, Marathe N, Jadhav T. Assessment of pattern of Drug-resistant TB and associated factors in Rewa, Madhya Pradesh, India. Epidemiol Health Syst J. 2022;9(1):34–9. [Google Scholar]
- 25.Akhtar AM, Arif MA, Kanwal S, Majeed S. Prevalence and drug resistance pattern of MDR TB in retreatment cases of Punjab, Pakistan. J Pak Med Assoc. 2016;66(8):989–983. [PubMed] [Google Scholar]
- 26.Bhatti AI, Butt NI, Zahoor MA, Ashfaq F, Aftab S, Tareen MQ. Prevalence of MDR-TB in patients of pulmonary tuberculosis at a DHQ hospital. Pakistan J Med Res. 2022;61(2):52–5. [Google Scholar]
- 27.Oladimeji O, Othman Y, Oladimeji KE, Atiba BP, Adepoju VA, Odugbemi BA. Patterns of presentation of drug-resistant tuberculosis in nigeria: A retrospective file review. Microbiol Res. 2022;13(3):609–19. [Google Scholar]
- 28.Jamil A, Aziz N, Zada I, Tarar AG, Qamber JH, Sheikh MU. Impact of treatment supporters on the treatment outcomes of drug Resistant-Tuberculosis patients. Pakistan J Chest Med. 2023;29(2):237–42. [Google Scholar]
- 29.Massud A, Khan AH, Syed Sulaiman SA, Ahmad N, Shafqat M, Ming LC. Unsuccessful treatment outcome and associated risk factors. A prospective study of DR-TB patients from a high burden country, Pakistan. PLoS ONE. 2023;18(8):e0287966. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Mughal MA, Imran A, Khan HU, Farooq M, Ikram A, Arshad F, Ashraf R, Khatoon F. Prevalence of Multidrug-Resistant tuberculosis and its association with previous treatment history in adults. Cureus. 2025;17(7):e88204. [DOI] [PMC free article] [PubMed]
- 31.Shibabaw A, Gelaw B, Gebreyes W, Robinson R, Wang S-H, Tessema B. The burden of pre-extensively and extensively drug-resistant tuberculosis among MDR-TB patients in the Amhara region, Ethiopia. PLoS ONE. 2020;15(2):e0229040. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Abdul JBPAA, Adegbite BR, Ndanga MED, Edoa JR, Mevyann RC, Mfoumbi GRAI, De Dieu TJ, Mahoumbou J, Biyogho CM, Jeyaraj S. Resistance patterns among drug-resistant tuberculosis patients and trends-over-time analysis of National surveillance data in Gabon, central Africa. Infection. 2023;51(3):697–704. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Johnson JM, Mohapatra AK, Velladath SU, Shettigar KS. Predictors of treatment outcomes in drug resistant Tuberculosis-Observational retrospective study. Int J Mycobacteriology. 2022;11(1):38–46. [DOI] [PubMed] [Google Scholar]
- 34.Javaid A, Ullah I, Masud H, Basit A, Ahmad W, Butt Z, Qasim M. Predictors of poor treatment outcomes in multidrug-resistant tuberculosis patients: a retrospective cohort study. Clin Microbiol Infect. 2018;24(6):612–7. [DOI] [PubMed] [Google Scholar]
- 35.Kibret KT, Moges Y, Memiah P, Biadgilign S. Treatment outcomes for multidrug-resistant tuberculosis under DOTS-Plus: a systematic review and meta-analysis of published studies. Infect Dis Poverty. 2017;6(1):7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Zheng X-B, Diwan VK, Zhao Q, Hu Y, Bruchfeld J, Jiang W-L, Hoffner S, Xu B. Treatment quality and outcome for multidrug-resistant tuberculosis patients in four regions of china: a cohort study. Infect Dis Poverty. 2020;9(04):75–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Indarti HT, Kristin E, Soedarsono S, Endarti D. Treatment outcomes of Multidrug-Resistant tuberculosis patients in East Java, indonesia: A retrospective cohort analysis. Int J Mycobacteriology. 2022;11(3):261–7. [DOI] [PubMed] [Google Scholar]
- 38.Yang TW, Park HO, Jang HN, Yang JH, Kim SH, Moon SH, Byun JH, Lee CE, Kim JW, Kang DH. Side effects associated with the treatment of multidrug-resistant tuberculosis at a tuberculosis referral hospital in South Korea: A retrospective study. Medicine. 2017;96(28):e7482. [DOI] [PMC free article] [PubMed]
- 39.Kwon BS, Kim Y, Lee SH, Lim SY, Lee YJ, Park JS, Cho Y-J, Yoon HI, Lee C-T, Lee JH. The high incidence of severe adverse events due to Pyrazinamide in elderly patients with tuberculosis. PLoS ONE. 2020;15(7):e0236109. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Zhang Y, Wu S, Xia Y, Wang N, Zhou L, Wang J, Fang R, Sun F, Chen M, Zhan S. Adverse events associated with treatment of Multidrug-Resistant tuberculosis in china: an ambispective cohort study. Med Sci Monit. 2017;23:2348–56. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Atif M, Ahmed W, Nouman Iqbal M, Ahmad N, Ahmad W, Malik I, Al-Worafi YM. Frequency and factors associated with adverse events among Multi-Drug resistant tuberculosis patients in pakistan: A retrospective study. Front Med. 2022;8:790718. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Trébucq A, Schwoebel V, Kashongwe Z, Bakayoko A, Kuaban C, Noeske J, Hassane S, Souleymane B, Piubello A, Ciza F, et al. Treatment outcome with a short multidrug-resistant tuberculosis regimen in nine African countries. Int J Tuberc Lung Dis. 2018;22(1):17–25. [DOI] [PubMed] [Google Scholar]
- 43.Alemu A, Bitew ZW, Worku T, Gamtesa DF, Alebel A. Predictors of mortality in patients with drug-resistant tuberculosis: A systematic review and meta-analysis. PLoS ONE. 2021;16(6):e0253848. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Kliiman K, Altraja A. Predictors of extensively drug-resistant pulmonary tuberculosis. Ann Intern Med. 2009;150(11):766–75. [DOI] [PubMed] [Google Scholar]
- 45.Abdullah M, Humayun A, Imran M, Bashir MA, Malik AA. A bibliometric analysis of global research performance on tuberculosis (2011–2020): time for a global approach to support high-burden countries. J Fam Commun Med. 2022;29(2):117. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Ou Z-J, Yu D-F, Liang Y-H, He W-Q, Li Y-Z, Meng Y-X, Xiong H-S, Zhang M-Y, He H, Gao Y-H. Trends in burden of multidrug-resistant tuberculosis in countries, regions, and worldwide from 1990 to 2017: results from the global burden of disease study. Infect Dis Poverty. 2021;10(1):1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Monedero-Recuero I, Gegia M, Wares DF, Chadha SS, Mirzayev F. Situational analysis of 10 countries with a high burden of drug-resistant tuberculosis 2 years post-UNHLM declaration: progress and setbacks in a changing landscape. Int J Infect Dis. 2021;108:557–67. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data on SPSS can be shared upon request. The request is to be sent through the email, drayeshah@gmail.com, to Dr. Ayesha Humayun, the corresponding author of this paper.