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The purposes of this study were to construct a comprehensive nomogram for providing a simple, precise and personalized prediction of incident multidrug-resistant tuberculosis (MDR-TB) after completing pulmonary tuberculosis treatment (CPTBT). A matched case–control study (1:2 ratios) was performed between 2005 and 2018. A multivariable Cox regressi...
Contexts in source publication
Context 1
... This study workflow was summarized in Fig. 1. Two separate datasets were used to develop and validate a risk-prediction tool based on predictors of incident MDR-TB in individuals with CPTBT. Data of a matched case-control study (1:2 ratios) from January 1, 2005 to December 31, 2018 (n = 1719) were used to derive the risk of MDR-TB among individuals with CPTBT (i.e., a training ...
Context 2
... they had a history of MDR-TB infection before the present study; (b) no DST results were reported; (c) TB patients were being treated (i.e., patients with an anti-TB drug therapy during the course of study); (d) no treatment outcome could be obtained; (e) subjects who lost or died during the follow-up visit; and (f) the missing data was severe (Fig. ...
Context 3
... of the subjects. A flow diagram summarizing the identified eligible subjects and the study participants was shown in Fig. 1. Baseline characteristics of the study population were listed in Table ...
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Introduction:
We analyzed the trends in primary multidrug-resistant tuberculosis (MDR-TB).
Methods:
We performed a time series analysis of primary MDR-TB cases reported in the State of Rio de Janeiro (RJ) during 2000-2019. The annual percent change and the average annual percentage change (AAPC) were computed using joinpoint regression analysis....
Citations
... The prediction accuracy of the risk score was better than that in a study conducted on the prediction of lost to follow-up (C-index = 0.65) and death (C-index = 0.70) among MDR-TB patients and the prediction model of poor treatment outcomes among MDR-TB patients (C-statistics = 0.69) (28, 34,35). However, this model had a lower prediction accuracy compared with that in a study conducted in China on individualized predictions of incident MDR-TB after the completion of pulmonary TB treatment (C-index = 0.83) (36). This could be due to a difference in terms of the quality of data recording and handling. ...
... The prediction accuracy of the risk score was better than that in a study conducted on the prediction of lost to follow-up (C-index = 0.65) and death (C-index = 0.70) among MDR-TB patients and the prediction model of poor treatment outcomes among MDR-TB patients (C-statistics = 0.69) (28, 34,35). However, this model had a lower prediction accuracy compared with that in a study conducted in China on individualized predictions of incident MDR-TB after the completion of pulmonary TB treatment (C-index = 0.83) (36). This could be due to a difference in terms of the quality of data recording and handling. ...
... The prediction accuracy of the risk score was better than that in a study conducted on the prediction of lost to follow-up (C-index = 0.65) and death (C-index = 0.70) among MDR-TB patients and the prediction model of poor treatment outcomes among MDR-TB patients (C-statistics = 0.69) (28, 34,35). However, this model had a lower prediction accuracy compared with that in a study conducted in China on individualized predictions of incident MDR-TB after the completion of pulmonary TB treatment (C-index = 0.83) (36). This could be due to a difference in terms of the quality of data recording and handling. ...
Introduction
Drug compliance is the act of taking medication on schedule or taking medication as prescribed and obeying other medical instructions. It is the most crucial aspect in the treatment of chronic diseases particularly for patients with multidrug-resistant tuberculosis (MDR-TB). Drug non-compliance is the main reason for causing drug resistance and poor treatment outcomes. Hence, developing a risk prediction model by using early obtainable prognostic determinants of non-compliance is vital in averting the existing, unacceptably high level of poor treatment outcomes and reducing drug resistance among MDR-TB patients.
Materials and methods
A retrospective follow-up study was conducted on a total of 517 MDR-TB patients in Northwest Ethiopia. A logistic regression–based machine learning algorithm was used to develop a risk score for the prediction of treatment non-compliance among MDR-TB patients in selected referral hospitals of Northwest Ethiopia. The data were incorporated in EpiData version 3.1 and exported to STATA version 16 and R version 4.0.5 software for analysis. A simplified risk prediction model was developed, and its performance was reported. It was also internally validated by using a bootstrapping method.
Results
Educational status, registration group (previously treated/new), treatment support, model of care, and khat use were significant prognostic features of treatment non-compliance. The model has a discriminatory power of area under curve (AUC) = 0.79 with a 95% CI of 0.74–0.85 and a calibration test of p -value = 0.5. It was internally validated by using a bootstrapping method, and it has a relatively corrected discriminatory performance of AUC = 0.78 with a 95% CI of 0.73–0.86 and an optimism coefficient of 0.013.
Conclusion
Educational status, registration group, treatment supporter, model of care, and khat use are important features that can predict treatment non-compliance of MDR-TB patients. The risk score developed has a satisfactory level of accuracy and good calibration. In addition, it is clinically interpretable and easy to use in clinical practice, because its features are easily ascertainable even at the initial stage of patient enrolment. Hence, it becomes important to reduce poor treatment outcomes and drug resistance.
... The prediction accuracy of the risk score was better than that in a study conducted on the prediction of lost to follow-up (C-index = 0.65) and death (C-index = 0.70) among MDR-TB patients and the prediction model of poor treatment outcomes among MDR-TB patients (C-statistics = 0.69) (28, 34,35). However, this model had a lower prediction accuracy compared with that in a study conducted in China on individualized predictions of incident MDR-TB after the completion of pulmonary TB treatment (C-index = 0.83) (36). This could be due to a difference in terms of the quality of data recording and handling. ...
... The prediction accuracy of the risk score was better than that in a study conducted on the prediction of lost to follow-up (C-index = 0.65) and death (C-index = 0.70) among MDR-TB patients and the prediction model of poor treatment outcomes among MDR-TB patients (C-statistics = 0.69) (28, 34,35). However, this model had a lower prediction accuracy compared with that in a study conducted in China on individualized predictions of incident MDR-TB after the completion of pulmonary TB treatment (C-index = 0.83) (36). This could be due to a difference in terms of the quality of data recording and handling. ...
Introduction: Drug compliance is the act of taking medication on schedule or taking
medication as prescribed and obeying other medical instructions. It is the most crucial
aspect in the treatment of chronic diseases particularly for patients with multidrug-resistant
tuberculosis (MDR-TB). Drug non-compliance is the main reason for
causing drug resistance and poor treatment outcomes. Hence, developing a risk
prediction model by using early obtainable prognostic determinants of non-compliance is vital in averting the existing, unacceptably high level of poor treatment
outcomes and reducing drug resistance among MDR-TB patients.
Materials and methods: A retrospective follow-up study was conducted on a total of 517
MDR-TB patients in Northwest Ethiopia. A logistic regression–based machine learning
algorithm was used to develop a risk score for the prediction of treatment noncompliance
among MDR-TB patients in selected referral hospitals of Northwest Ethiopia. The
data were incorporated in EpiData version 3.1 and exported to STATA version 16 and R version
4.0.5 software for analysis. A simplified risk prediction model was developed, and its
performance was reported. It was also internally validated by using a bootstrapping method.
Results: Educational status, registration group (previously treated/new), treatment
support, model of care, and khat use were significant prognostic features of treatment
non-compliance. The model has a discriminatory power of area under the curve (AUC) = 0.79
with a 95% CI of 0.74–0.85 and a calibration test of p-value = 0.5. It was internally
validated by using a bootstrapping method, and it has a relatively corrected discriminatory
performance of AUC = 0.78 with a 95% CI of 0.73–0.86 and an optimism coefficient of 0.013.
Conclusion: Educational status, registration group, treatment supporter, model of care, and khat use are important features that can predict treatment non-compliance of MDR-TB patients. The risk score developed has a satisfactory level of accuracy and good calibration. In addition, it is clinically interpretable and easy to use in clinical practice, because its features are easily ascertainable even at the initial stage of patient enrolment. Hence, it becomes important to reduce poor treatment outcomes and drug resistance.
... When compared with BACTEC MGIT 960 system, LPAs such as The Genotype ® MTBDRplus (version 2) have been found to be efficient and reliable rapid molecular DST assay for rapid susceptibility screening of MDR and even XDR-TB and are particularly useful in high MDR/XDR burden countries (Maningi et al. 2017). Likewise a study conducted in Ethiopia by Yigzaw et al. 2021, comparing (Cheng et al. 2020). Such approaches provide avenue for monitoring, estimating and intervening the risk of incident MDR-TB. ...
Background
Previous studies have shown significant differences and lack clarity on whether resistance to either isoniazid or rifampicin can predict multidrug resistance tuberculosis (MDR-TB). Some consider rifampicin resistance to be a surrogate for MDR-TB. We, therefore, conducted this study to determine resistance to either isoniazid or rifampicin can predict MDR-TB.
Results
A total of 315 Mycobacteria tuberculosis isolates were tested for resistance against isoniazid, rifampicin, ethambutol and streptomycin using the phenotypic proportion method on Lowenstein–Jensen media. Results showed most isolates (88.9%, 280/315) were not resistant to any anti-TB tested, 5.7% (18/315) were resistant to both isoniazid and rifampicin, 2.5% (8/315) were resistant to rifampicin only and 1.3% (4/315) were resistant to all four first-line anti-tuberculosis. Prediction of MDR TB basing on rifampicin results showed sensitivity of 100.0%, specificity of 96.3%, diagnostic accuracy of 96.5%, and positive and negative predictive values of 62.1% and 100.0%, respectively. Isoniazid had sensitivity of 100.0%, specificity of 98.3%, diagnostic accuracy of 98.4%, and positive and negative predictive values of 78.3% and 100.0%, respectively. Prediction of rifampicin resistance based on isoniazid results had sensitivity of 62.1%, specificity of 98.3%, diagnostic accuracy of 94.9%, a positive predictive value of 78.3% and a negative predictive value of 96.2%.
Conclusions
Resistance to either rifampicin or isoniazid sub-optimally predicts MDR-TB. Despite having high sensitivity and specificity, the positive predictive value of rifampicin was only 62.1% and for isoniazid was 78.3%, suggesting that if either is tested in isolation both could result in false positives MDR-TB cases, resulting into patients being unnecessarily subjected to the more toxic and expensive second-line anti-TB drugs, which are less effective compared to first-line anti-TB drugs.
... However, in reviewing the literature, we found that there were two distinct standpoints for non-standardized TRs. On the one hand, from a programmatic perspective, one would like to see standardized treatment regimens (32). On the other hand, from a personalized medicine perspective, if proper DST or sequencing is available, personalized regimens might be indicated for specific patients (33). ...
... There were many differences in predictors for incident MDR-TB between the sexes. These findings might be attributed to certain gender disparities with sociodemographic, therapeutic and managed factors implicated in the development of MDR-TB (32,36). In fact, the 21-30 year age group, LFI, and PMTCF in males with PTBH are associated with incident MDR-TB. ...
Background: To date, too little attention has been paid to monitoring and estimating the risk of incident multidrug-resistant tuberculosis (MDR-TB) among individuals with a previous tuberculosis history (PTBH). The purpose of this study was to assess the incidence of and risk factors for MDR-TB in those individuals.
Methods: Between 2005 and 2020, a large, retrospective, population-based cohort study was performed in Hangzhou, China. A multivariable Cox regression model was used to evaluate independent predictors of incident MDR-TB among individuals with PTBH.
Results: The incidence density of MDR-TB was 22.6 per 1,000 person-years (95% confidence level and an interval of 20.9–24.3) for individuals with PTBH. The incidence of MDR-TB increased significantly in individuals who
• were under 60 years old.
• were male.
• had a history of direct contact.
• came from low-income families.
• worked in high-risk occupations.
• lived in rural areas.
• had a retreatment TB history.
• had an unfavorable outcome in their previous treatment (P < 0.05).
In addition, we found that the following factors were significantly linked to the MDR-TB risk among individuals with PTBH (P < 0.05):
• sociodemographic factors such as the 21–30 and 31–40 year age groups, or a history of direct contact.
• clinical factors like passive modes of TB case finding (PMTCF), human immunodeficiency virus infection, unfavorable treatment outcomes, retreated TB history, non-standardized treatment regimens of retreatment TB patients, and duration of pulmonary cavities (DPC).
• microbiological factors, such as duration of positive sputum culture.
We also found that the 21–30 year age group, low family income, and PMTCF were significantly linked to incident MDR-TB only in males with PTBH, whilst the 41–50 year age group, extended treatment course, and DPC were significantly associated with female MDR-TB only.
Conclusion: The incidence of MDR-TB was high, with a higher rate among subjects with a history of direct contact and unfavorable treatment outcomes. There was a gender difference in the incidence density and risk factors of MDR-TB among individuals with PTBH. Long-term monitoring and gender-specific risk-factor modifications should be given to individuals with PTBH.
... We also validated our finding by two randomized controlled trials 26,27 dataand revealed the recurrence pattern of PCNSL with the information of patients in our hospital. Finally, we developed a practical clinical tool for individualized risk prediction 28,29 based on the population-based data for PCNSL patients. ...
Background
Before the introduction of the chemotherapeutic agent methotrexate, radiotherapy (RT) and steroids have been the sole, first-line treatment of primary central nervous system lymphoma (PCNSL). With the application of methotrexate, the role of RT in the treatment of PCNSL has been challenged. Methods
We performed observation analysis on 2,486 PCNSL patients between 1988 and 2016 from the Surveillance, Epidemiology and End Results (SEER) database. Propensity score matching (PSM) was employed to ensure well-balanced characteristics of two groups of patients who received RT and those who did not receive it. Two randomized controlled trials (RCTs) were pooled to further evaluate the role of consolidation whole-brain radiotherapy (WBRT) in PCNSL. To clarify whether WBRT is necessary for PCNSL, 27 relapsed patients who attained complete response (CR), partial response (PR) or stable disease (SD) during or after first-line treatment without WBRT for newly diagnosed PCNSL in our institution was retrospectively analyzed; the pattern and location of relapse was identified. ResultsAfter matching, there was no statistical difference on survival between the two groups. In patients did not received chemotherapy, RT significantly improved the survival of patients who undergone biopsy (All P < .0001) or subtotal resection (All P < .0001). In particular, RT helped improve survival for patients with other infectious and parasitic diseases including HIV (OIPDH). Pool-analysis shown the better progression free survival (PFS) of patients with WBRT arm compared with no WBRT arm in per-protocol (PP) population (HR 0.71, 95% CI 0.52 to 0.98). In the 27 relapse patients, 17 (63%) had new measurable enhancing lesions at relapse at a spatially distinct site, the remote recurrence after CR was 9/11 (82%), and after PR was 8/15 (53%). Single lesion occurred remote recurrence was 11/13 (85%), while multiple lesions were 7/14 (50%). We also established a novel prediction model with excellent performance to estimate the potential benefit from RT with respect to the end point of overall survival. ConclusionsRT is still an important method in the treatment of PCNSL, which cannot be removed. More precise studies should be carried out to perfect the treatment strategies of the disease.
... However, there is a striking scarcity of studies analyzing new techniques to improve early detection and diagnosis of TB in high incidence countries. In our previous study, we found that a nomogram is a useful tool for the individualized prediction of MDR-TB among individuals with PTBH [42]. Although this tool cannot be used in the diagnosis of MDR-TB, this approach is suitable for early screening of MDR-TB. ...
Objectives
The impact of tuberculosis history on the risk of multidrug-resistant tuberculosis (MDR-TB) is not yet fully understood. We aimed to identify the impact of different TB history at the onset of future MDR-TB.
Methods
A large, retrospective, and population-based cohort study was performed between 2005 and 2019. The multivariable Cox model was used to evaluate independent risk factors of MDR-TB from individuals with differently previous TB history (PTBH) [such as newly diagnosed TB history (NDTH) and re-treated TB history (RTH)].
Results
Overall, 12, 172 individuals with PTBH were included in this study. The main impacts of different PTBH at the onset of future MDR-TB are as follows: (a) family income of low level, high-risk occupation, TB patients with severe infection, extended or shortened treatment course, 2H3R3Z3E3/4H3R3, and frequencies of sputum culture were significantly linked to incident MDR-TB only in individuals with NDTH (P＜ 0.05); (b) passive mode of TB case finding, individualized treatment regimens, 3HRZES/6HRE, duration of pulmonary cavities, excellent frequencies of chest X-ray examination, and duration of negative sputum smear were significantly associated with incident MDR-TB only in individuals with RTH (P＜ 0.05); (c) age < 60 years, a history of direct contact, human immunodeficiency virus infection, unsuccessful treatment, and duration of positive sputum culture were related to incident MDR-TB in both PTBH individuals (P＜ 0.05).
Conclusions
Early and differential surveillances, assessments and interventions for reducing the risk of MDR-TB among individuals with different PTBH play a key role.
Background
To verify the differential expression of miR‐30c and miR‐142‐3p between tuberculosis patients and healthy controls and to investigate the performance of microRNA (miRNA) and subsequently models for the diagnosis of tuberculosis (TB).
Methods
We followed up 460 subjects suspected of TB, and finally enrolled 132 patients, including 60 TB patients, 24 non‐TB disease controls (TB‐DCs), and 48 healthy controls (HCs). The differential expression of miR‐30c and miR‐142‐3p in serum samples of the TB patients, TB‐DCs, and HCs were identified by reverse transcription–quantitative real‐time PCR. Diagnostic models were developed by analyzing the characteristics of miRNA and electronic health records (EHRs). These models evaluated by the area under the curves (AUC) and calibration curves were presented as nomograms.
Results
There were differential expression of miR‐30c and miR‐142‐3p between TB patients and HCs (p < 0.05). Individual miRNA has a limited diagnostic value for TB. However, diagnostic performance has been both significantly improved when we integrated miR‐142‐3p and ordinary EHRs to develop two models for the diagnosis of tuberculosis. The AUC of the model for distinguishing tuberculosis patients from healthy controls has increased from 0.75 (95% CI: 0.66–0.84) to 0.96 (95% CI: 0.92–0.99) and the model for distinguishing tuberculosis patients from non‐TB disease controls has increased from 0.67 (95% CI: 0.55–0.79) to 0.94 (95% CI: 0.89–0.99).
Conclusions
Integrating serum miR‐142‐3p and EHRs is a good strategy for improving TB diagnosis.
