Survival analysis of patients with tuberculosis in Erbil, Iraqi Kurdistan region

Survival Probability in Multidrug Resistant Pulmonary Tuberculosis Patients in a South Indian Region

Background: Drug-resistant tuberculosis is a burgeoning threat to public health requiring novel strategies to combat the infection. Although national tuberculosis elimination programs focus on improving health services, challenges in eradicating tuberculosis still exist. Factors attributing to unfavorable outcomes are unknown in Warangal district of Telangana state. Methods: This study included 296 patients diagnosed with multidrug-resistant pulmonary tuberculosis. The study participants followed up for a maximum of 20 months to determine treatment outcomes. Statistical applications of Kaplan-Meier curve and log-rank test used to find the survival probabilities in subgroups. Results: The survival of multidrug-resistant pulmonary tuberculosis patients was ascertained, in male and female patients, aged between 31 and 50 years. Resistance to rifampicin was prominent. The study found a survival rate of 76.68% and a mortality rate of 23.31%. The log-rank test revealed a significant difference in survival in subcategories with and without comorbidities (P = .03), non-adherence to treatment (P = .0001), treatment duration (P = .02), regimens (P = .01), and grading of radiograph (P = .0001). Conclusion: This study identified factors that influenced the survival probability of multidrug-resistant pulmonary tuberculosis patients, including comorbidities, weight band, non-adherence to treatment, treatment duration, regimens, and grading of radiograph. These findings emphasize the need for enhanced management strategies to improve treatment outcomes.

A mixed-methods study on impact of active case finding on pulmonary tuberculosis treatment outcomes in India

BACKGROUND

Tuberculosis (TB) remains a significant public health burden in India, with elimination targets set for 2025. Active case finding (ACF) is crucial for improving TB case detection rates, although conclusive evidence of its association with treatment outcomes is lacking. Our study aims to investigate the impact of ACF on successful TB treatment outcomes among pulmonary TB patients in Gujarat, India, and explore why ACF positively impacts these outcomes.

METHODS

We conducted a retrospective cohort analysis in Gujarat, India, including 1,638 pulmonary TB cases identified through ACF and 80,957 cases through passive case finding (PCF) from January 2019 to December 2020. Generalized logistic mixed-model compared treatment outcomes between the ACF and PCF groups. Additionally, in-depth interviews were conducted with 11 TB program functionaries to explore their perceptions of ACF and its impact on TB treatment outcomes.

RESULTS

Our analysis revealed that patients diagnosed through ACF exhibited 1.4 times higher odds of successful treatment outcomes compared to those identified through PCF. Program functionaries emphasized that ACF enhances case detection rates and enables early detection and prompt treatment initiation. This early intervention facilitates faster sputum conversion and helps reduce the infectious period, thereby improving treatment outcomes. Functionaries highlighted that ACF identifies TB cases that might otherwise be missed, ensuring timely and appropriate treatment.

CONCLUSION

ACF significantly improves TB treatment outcomes in Gujarat, India. The mixed-methods analysis demonstrates a positive association between ACF and successful TB treatment, with early detection and prompt treatment initiation being key factors. Insights from TB program functionaries underscore the importance of ACF in ensuring timely diagnosis and treatment, which are critical for better treatment outcomes. Expanding ACF initiatives, especially among hard-to-reach populations, can further enhance TB control efforts. Future research should focus on optimizing ACF strategies and integrating additional interventions to sustain and improve TB treatment outcomes.

Risk factors for tuberculosis-related death among adults with drug-sensitive pulmonary tuberculosis in Selangor, Malaysia from 2013 to 2019: a retrospective cohort study using surveillance data

OBJECTIVES

Due to the paucity of literature on risk factors for tuberculosis (TB)-related death, we determine the sociodemographic and clinical risk factors associated with TB-related deaths among adult pulmonary TB (PTB) patients on treatment in Selangor, Malaysia.

DESIGN

Retrospective cohort study.

SETTING

Routinely collected primary care data from all government TB clinics in Selangor.

PARTICIPANTS

Data of 24 570 eligible adult PTB patients from 2013 to 2019 were obtained from Selangor's State Health Department surveillance records. We included PTB patients aged at least 15 years old at the time of diagnosis with complete documentation of the dates of diagnosis, treatment initiation, end of treatment/follow-up and treatment outcomes. We excluded patients whose diagnoses were changed to non-TB, post-mortem TB diagnosis and multidrug-resistant TB (MDR-TB) patients.

PRIMARY AND SECONDARY OUTCOME MEASURES

TB-related death, determined from the recorded physicians' consensus during the TB mortality meeting.

RESULTS

TB-related death was significantly associated with far (adjusted HR (aHR) 9.98, 95% CI 4.28 to 23.28) and moderately advanced (aHR 3.23, 95% CI 1.43 to 7.31) radiological findings at diagnosis; concurrent TB meningitis (aHR 7.67, 95% CI 4.53 to 12.98) and miliary TB (aHR 6.32, 95% CI 4.10 to 9.74) involvement; HIV positive at diagnosis (aHR 2.81, 95% CI 2.21 to 3.57); Hulu Selangor (aHR 1.95, 95% CI 1.29 to 2.93), Klang (aHR 1.53, 95% CI 1.18 to 1.98) and Hulu Langat (aHR 1.31, 95% CI 1.03 to 1.68) residing districts; no formal education (aHR 1.70, 95% CI 1.23 to 2.35); unemployment (aHR 1.54, 95% CI 1.29 to 1.84), positive sputum smear acid-fast bacilli (AFB) at diagnosis (aHR 1.51, 95% CI 1.22 to 1.85); rural residency (aHR 1.39, 95% CI 1.13 to 1.72) and advancing age (aHR 1.03, 95% CI 1.02 to 1.03).

CONCLUSIONS

Far and moderately advanced radiological findings, concurrent TB meningitis and miliary TB involvement, HIV positive, Hulu Selangor, Klang and Hulu Langat residing districts, no formal education, unemployment, positive sputum smear AFB, rural residency and advancing age are risk factors of TB-related death. Our findings should assist in identifying high-risk patients requiring interventions against TB-related death.

The survival analysis of rifampicin/multidrug-resistant tuberculosis patients based on the levels of inflammatory biomarkers: a retrospective cohort study

Purpose

The development of tuberculosis and inflammatory status are closely related. The aim of this study was to investigate the prognostic value of inflammatory biomarkers in patients with rifampicin/multidrug-resistant tuberculosis (RR/MDR-TB).

Patients and methods

This study recruited 504 patients with RR/MDR-TB from Wuhan Jinyintan Hospital. A total of 348 RR/MDR patients from January 2017 to December 2019 were defined as training set, the rest of patients as validation set. The patients were divided into three-risk degrees according to the levels of inflammatory biomarkers (median, 85th percentile). Kaplan-Meier curve and log-rank test were used to assess survival differences among the groups. Cox proportion risk regression was used to identify risk factors for RR/MDR-TB mortality.

Results

In training set, cox proportion risk regression analysis showed that high age (≥60 years) [OR (95%CI):1.053(1.03188-1.077)], smoking [OR (95%CI):2.206(1.191-4.085)], and bronchiectasia [OR (95%CI):2.867(1.548-5.311)] were prognostic factors for RR/MDR-TB patients. In addition, lower survival rates were observed in high CAR group [OR (95%CI):1.464(1.275-1.681)], high CPR group[OR (95%CI):1.268(1.101-1.459)], high CLR group[OR (95%CI):1.004(1.002-1.005)], high NLR group[OR (95%CI):1.103(1.069-1.139)], high PLR group[OR (95%CI):1.003(1.002-1.004)], and high MLR group[OR (95%CI):3.471(2.188-5.508)].Furthermore, AUCs of age, smoking, bronchiectasia, CAR, CPR, CLR, NLR, PLR, and MLR for predicting mortality in RR/MDR-TB patients were 0.697(95%CI:0.618-0.775), 0.603(95%CI:0.512-0.695), 0.629(95%CI:0.538-0.721), 0.748(95%CI:0.675-0.821, P<0.05), 0.754(95%CI:0.683-0.824, P<0.05), 0.759(95%CI:0.689-0.828, P<0.05), 0.789(95%CI:0.731-0.846, P<0.05), 0.740(95%CI:0.669-0.812, P<0.05), and 0.752(95%CI:0.685-0.819, P<0.05), respectively. Importantly, the AUC of predicting mortality of combination of six inflammatory biomarkers [0.823 (95%CI:0.769-0.876)] is higher than any single inflammatory biomarkers. Additionally, the similar results are also obtained in the validation set.

Conclusion

Inflammatory biomarkers could predict the survival status of RR/MDR-TB patients. Therefore, more attention should be paid to the level of inflammatory biomarkers in clinical practice.

A Nomogram Model for Mortality Risk Prediction in Pulmonary Tuberculosis Patients Subjected to Directly Observed Treatment Shortcourse (DOTS)

We analyzed the risk factors of mortality for patients with pulmonary tuberculosis under the Directly Observed Treatment Shortcourse (DOTS) and established a predictive nomogram for the risk of mortality. The retrospective cohort analysis was conducted on the treatment outcomes of 11207 tuberculosis patients in the tuberculosis management information system in Tianjin from 2014 to 2019. Based on the multivariable unconditional logistic regression, we analyzed the risk factors of mortality in patients with pulmonary TB and established the death risk prediction nomogram. We further applied cross-validation and the receiver operating characteristic (ROC) curve to explore the efficiency of the nomogram. There were 10,697 patients in the survival group and 510 in the mortality group who had successfully initiated DOTS, and the mortality rate was 4.55%. Multivariable logistic regression analysis showed that age, male, relapse cases, first sputum positivity, patient delay, and HIV-positive were independent risk factors for pulmonary TB death. The calibration curve shows that the average absolute error between the predicted mortality risk and the actual death risk is 0.003. The ROC curve shows that the area under the curve where the line-up model predicts the risk of death is 0.816 (95% CI: 0.799∼0.832). The nomogram model based on independent risk factors of mortality in TB patients shows good discrimination and accuracy, with potentially high clinical value in screening patients with a high risk of death, which could be useful for setting the interventional strategies in patients with tuberculosis who had successfully initiated DOTS.

Deep learning algorithm reveals probabilities of stage-specific time to conversion in individuals with neurodegenerative disease LATE

Introduction

Limbic-predominant age-related TAR DNA-binding protein 43 (TDP-43) encephalopathy (LATE) is a recently defined neurodegenerative disease. Currently, there is no effective way to make a prognosis of time to stage-specific future conversions at an individual level.

Methods

After using the Kaplan-Meier estimation and log-rank test to confirm the heterogeneity of LATE progression, we developed a deep learning-based approach to assess the stage-specific probabilities of time to LATE conversions for different subjects.

Results

Our approach could accurately estimate the disease incidence and transition to next stages: the concordance index was at least 82% and the integrated Brier score was less than 0.14. Moreover, we identified the top 10 important predictors for each disease conversion scenario to help explain the estimation results, which were clinicopathologically meaningful and most were also statistically significant.

Discussion

Our study has the potential to provide individualized assessment for future time courses of LATE conversions years before their actual occurrence.

Relation between Serological Findings and Expression of IL-1β and IL-6 genes in Patients Infected with Mycobacterium Tuberculosis in Iraq

Mycobacterium Tuberculosis (TB) is one of the serious bacterial infections that cause diseases and may lead to death. In this study, 178 individuals were examined for TB infection at Baghdad TB center during the period from 15^th January to 1^st October 2021. Out of 178 participants, 73 were shown to be positive for TB infection, while 105 showed negative results. According to the results, there was no significant variation between infected males and females with TB in comparison to the control group (P>0.05). The results showed that the mean age of the patients for both males and females was in the range of 2-65 years. Additionally, there were significant differences in patients with TB compared to the control group in terms of the weight loss of 8.82 ± 6.75 Kg, red blood cell (RBC) count (3.43 ± 0.56) × 10⁶/μl, white blood cell (WBC) count (3.12 ± 1.57) × 10⁶/μl, platelet count (1.03 ± 0.56) × 10⁶/μl, and hemoglobin level (6.66 ± 1.34) g/dl. A total of 30 TB patients and 50 normal individuals were genotyped to detect the IL-1β rs 114534 gene. The polymerase chain reaction (PCR) was used for exon amplification in region 5 of the ILB1 gene in the TB patients by using specific primers. The finding showed that there was an amplified product of 249bp located in chromosome 2q13-14. A total of 30 TB patients and 50 normal individuals were also genotyped to detect the IL-6 rs 1800795 gene. The PCR was used for amplification of the IL-6 gene in TB patients by using specific primers. The finding showed that there was an amplified product of 431 bp located in chromosome 7p15-p2. The expression of the ILB1 gene was investigated in TB patients and healthy controls by using qPT-PCR. Results showed that there was a high Ct value for patients and controls with a high Ct value of templates, preoperational to the total ribonucleic acid (RNA) concentration and gene expression. The expression of the IL-6 gene was investigated in TB patients and healthy controls by using qPT-PCR. Our findings revealed a high Ct value for patients and controls with a high Ct value of templates, preoperational to the total RNA concentration and gene expression.

Survival Analysis and Associated Factors for Pulmonary Tuberculosis Death: Evidence from the Information System of Tuberculosis Disease and Mortality Surveillance in China

Background

Pulmonary tuberculosis (PTB) is a severe infection, and its factors that influence survival have not been explored in some developed regions. Therefore, this study aimed to identify the factors that influence TB survival in China.

Methods

We acquired data of PTB deaths through matching two manage systems of PTB and mortality surveillance. The Kaplan–Meier method and Cox regression model were used to identify the significant factors under various survival times.

Results

A total of 283 deaths were caused by PTB, in which 53.4% occurred during the first year. In the Cox regression model, regular sputum smear test could reduce the risk of PTB death from the result of one-, three-, and five-year survival; and history of PTB treatment could decline the risk of PTB death in the first year. Additionally, age of “75–90 years” and positive result of sputum smear test significantly increased the risk of PTB death for the findings of three- and five-year survival, while age of “60–75 years” could increase the probability of PTB death for five-year survival.

Conclusion

To reduce PTB deaths, more attention should be paid to the older group especially in the first year. Regular sputum smear test could effectively reduce the mortality of PTB cases. More evidence-based interventions should be implemented to enhance the survival of patients with PTB.

The Effect of Edible Mushroom on Health and Their Biochemistry

Edible mushrooms are fungi that can be seen with the naked eye and are relatively easy to gather by hand. This review article highlights the health benefit and the biochemistry of several mushroom species. Agaricus bisporus, Pleurotus species. Lentinus edodes, and Volvariella species are the most acceptable varieties among the cultivated mushroom. Various biochemical methods such as methanol, ethanol, and water extract of different parts of the edible mushroom in the laboratory have been applied to determine and/or quantify the presence and effectiveness of their chemical compounds, food value, and medicinal properties. They contain varying amounts of carbohydrates, proteins, nucleic acids, lipids, minerals, terpenoids, phenolic compounds, steroids, and lectins and vitamins, as well as lowering cholesterol levels in the body. Due to the presence of those vital nutrients, mushrooms are the best food item with high nutritional value. These compounds have a wide range of therapeutic effects and can act as immunomodulatory, anticarcinogenic, antiviral, antioxidant, and anti-inflammatory agents. Routine consumption of edible mushrooms would give adequate protection due to the presence of all the necessary nutrients from them. Therefore, edible mushrooms are herbal antibiotics to many diseases as well as various cancers of humans.

Time-to-event prediction using survival analysis methods for Alzheimer's disease progression

INTRODUCTION

Many research studies have well investigated Alzheimer's disease (AD) detection and progression. However, the continuous-time survival prediction of AD is not yet fully explored to support medical practitioners with predictive analytics. In this study, we develop a survival analysis approach to examine interactions between patients' inherent temporal and medical patterns and predict the probability of the AD next stage progression during a time period. The likelihood of reaching the following AD stage is unique to a patient, helping the medical practitioner analyze the patient's condition and provide personalized treatment recommendations ahead of time.

METHODOLOGIES

We simulate the disease progression based on patient profiles using non-linear survival methods-non-linear Cox proportional hazard model (Cox-PH) and neural multi-task logistic regression (N-MTLR). In addition, we evaluate the concordance index (C-index) and Integrated Brier Score (IBS) to describe the evolution to the next stage of AD. For personalized forecasting of disease, we also developed deep neural network models using the dataset provided by the National Alzheimer's Coordinating Center with their multiple-visit details between 2005 and 2017.

RESULTS

The experiment results show that our N-MTLR based survival models outperform the CoxPH models, the best of which gives Concordance-Index of 0.79 and IBS of 0.09. We obtained 50 critical features out of 92 by applying recursive feature elimination and random forest techniques on the clinical data; the top ones include normal cognition and behavior, criteria for dementia, community affairs, etc. Our study demonstrates that selecting critical features can improve the effectiveness of probabilities at each time interval.

CONCLUSIONS

The proposed deep learning-based survival method and model can be used by medical practitioners to predict the patients' AD shift efficiently and recommend personalized treatment to mitigate or postpone the effects of AD. More generally, our proposed survival analysis approach for predicting disease stage shift can be used for other progressive diseases such as cancer, Huntington's disease, and scleroderma, just to mention a few, using the corresponding clinical data.

Survival analysis of patients with tuberculosis and risk factors for multidrug-resistant tuberculosis in Monrovia, Liberia

We reviewed the records of 337 confirmed cases of tuberculosis patients in Monrovia, the capital of Liberia, 2015. The risk factors affecting the survival and multidrug-resistance of tuberculosis patients were examined. Kaplan-Meier analysis and the log-rank test were used to assess the differences in survival among the patients, while Cox regression model was used for multivariate analysis. The qualitative data was tested with chi-square test in the single factor analysis of multidrug-resistant TB. Multivariate analysis was performed using binary logistic regression analysis. The significance level for all the tests were set at 0.05. The mean period of the follow-up of patients was 10 months. In the 337 patients, 33 (9.8%) died, the 21-month survival rate was 90.2%. The results of multivariate Cox regression analysis show that overcrowding (HR = 7.942, 95% CI 3.258-19.356), former smoking (HR = 3.773, 95% CI 1.601-8.889), current smoking (HR = 3.546, 95% CI 1.195-10.521), multidrug-resistance tuberculosis (HR = 4.632, 95% CI 1.913-11.217) were risk factors for death during anti-tuberculosis treatment in TB patients in Liberia. The results of binary logistic regression analysis show that extra-pulmonary (OR = 2.032, 95% CI 1.133-3.644), family history of TB (OR = 2.387, 95% CI 1.186-4.807) and current smoking (OR = 3.436, 95% CI 1.681-7.027) were risk factors for multidrug-resistant tuberculosis. These results can provide insights on local tuberculosis early intervention, increase public health awareness, and strengthen the control of factors that may affect the survival and multidrug-resistance of tuberculosis patients.

Longitudinal-Survival Models for Case-Based Tuberculosis Progression

Introduction: Tuberculosis (TB) disease continues to be responsible for a high global burden with an estimated 10 million people falling ill each year and an estimated 1.45 million deaths. Widely carried out analyses to utilize routine data coming from this disease, and well-established in literature, have paid attention to time-to-event with sputum smear results being considered only at baseline or even ignored. Also, logistic regression models have been used to demonstrate importance of sputum smear results in patient outcomes. A feature presented by this disease, however, is that each individual patient is usually followed over a period of time with sputum smear results being documented at different points of the treatment curve. This provides both repeated measures and survival times, which may require a joint modeling approach. This study aimed to investigate the association between sputum smear results and the risk of experiencing unfavorable outcome among TB patients and dynamically predict survival probabilities.

Method: A joint model for longitudinal and time-to-event data was used to analyze longitudinally measured smear test results with time to experiencing unfavorable outcome for TB patients. A generalized linear mixed-effects model was specified for the longitudinal submodel and cox proportional hazards model for the time-to-event submodel with baseline hazard approximated using penalized B-splines. The two submodels were then assumed to be related via the current value association structure. Bayesian approach was used to approximate parameter estimates using Markov Chain Monte Carlo (MCMC) algorithm. The obtained joint model was used to predict the subject's future risk of survival based on sputum smear results trajectories. Data were sourced from routinely collected TB data stored at National TB Program database.

Results: The average baseline age was 35 (SD: 15). Female TB patients constituted 36.42%. Patients with previous history of TB treatment constituted 6.38% (event: 15.25%; no event: 5.29%). TB/HIV co-infection was at 31.23% (event: 47.87%; no event: 29.20%). The association parameter 1.03 (CI[1.03,1.04]) was found to be positive and significantly different from zero, interpreted as follows: The estimate of the association parameter α = 1.033 denoted the log hazard ratio for a unit increase in the log odds of having smear positive results. HIV status (negative) 0.47 (CI [0.46,49]) and history of TB treatment (previously treated) (2.52 CI [2.41,2.63]), sex (female) (0.82 CI [0.78,0.84]), and body mass index (BMI) categories (severe malnutrition being reference) were shown to be statistically significant.

Conclusion: Sputum smear result is important in estimating the risk to unfavorable outcome among TB patients. Men, previously treated, TB/HIV co-infected and severely malnourished TB patients are at higher risk of unfavorable outcomes.

Modeling the Survival of Tuberculosis Patients in Eastern Zone of Tigray Regional State

Background

Tuberculosis (TB) is still a public health problem and amongst the top ten leading causes of death. The aim of this paper was to identify the factors that significantly affect the survival of tuberculosis patients.

Methods

A retrospective cohort study was carried out in Adigrat General and Wukro hospitals, Eastern Zone of Tigray region, Ethiopia. Data for this study were obtained from medical records of all TB cases registered from September 2016 to August 2017 in the two hospitals. Log-rank test and Kaplan–Meier plot were used to evaluate the survival pattern of TB patients. A multivariable Cox proportional regression model was employed to identify the predictors of mortality. Factors with a P-value smaller than 0.05 were taken as statistically significant facilitators of TB death.

Results

Of the 397 patients studied over the specified period, 23 (5.8%) had died. A statistically significant survival difference was observed among gender, residence, HIV status, treatment category, and age category of patients. In multivariable cox regression, lower survival rates were observed among patients aged ≥45 years (HR = 5.315, 95% CI: 1.231–22.959), relapse cases (HR = 4.069, 95% CI: 1.636–10.119), patients with extrapulmonary TB (HR = 3.054, 95% CI: 1.044–8.940), patients from rural areas (HR = 2.834, 95% CI: 1.161–6.916), patients with a bodyweight of ≤50 kg and HIV-positive patients.

Conclusion

Based on the survival experience of TB patients, advancing age, extrapulmonary TB infection, living in rural residence, lower bodyweight at beginning of treatment, HIV co-infection, and being a retreatment patient were predictors of mortality. To achieve the “End TB Strategy” goal of zero death, proper targeting of care to these vulnerable groups should be advised.