Survival analysis of adult tuberculosis disease

Predictors of colorectal cancer survival using cox regression and random survival forests models based on gene expression data

Understanding and identifying the markers and clinical information that are associated with colorectal cancer (CRC) patient survival is needed for early detection and diagnosis. In this work, we aimed to build a simple model using Cox proportional hazards (PH) and random survival forest (RSF) and find a robust signature for predicting CRC overall survival. We used stepwise regression to develop Cox PH model to analyse 54 common differentially expressed genes from three mutations. RSF is applied using log-rank and log-rank-score based on 5000 survival trees, and therefore, variables important obtained to find the genes that are most influential for CRC survival. We compared the predictive performance of the Cox PH model and RSF for early CRC detection and diagnosis. The results indicate that SLC9A8, IER5, ARSJ, ANKRD27, and PIPOX genes were significantly associated with the CRC overall survival. In addition, age, sex, and stages are also affecting the CRC overall survival. The RSF model using log-rank is better than log-rank-score, while log-rank-score needed more trees to stabilize. Overall, the imputation of missing values enhanced the model’s predictive performance. In addition, Cox PH predictive performance was better than RSF.

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.

Does time to loss to follow-up differ among adult tuberculosis patients initiated on tuberculosis treatment and care between general hospital and health centers? A retrospective cohort study

BACKGROUND

Patients' loss to follow-up (LTFU) from tuberculosis treatment and care is a growing worry in Ethiopia. But, available information is inadequate in assessing the time to tuberculosis patient loss to follow-up difference between health centers and a general hospital in Ethiopia. We aimed to assess time to LTFU difference between health centers and a general hospital in rural Ethiopia.

METHODS

We conducted a retrospective cohort study from September 2008 to August 2015 and collected data from September 1 to October 02, 2016. A total of 1341 TB patients with known treatment outcomes were included into the study. Log rank test was used to compare the difference in time to TB patient loss to follow-up between health centers and a general hospital, whereas Cox proportional hazard model was used to assess factors associated with time to loss to follow-up in both settings.

RESULTS

We reviewed a total of 1341 patient records, and the overall follow-up time was 3074.7 and 3974 person months of observation (PMOs) for TB patients followed at health centers and a general hospital, respectively. The incidence of loss to follow-up rate was 27.3 per 1000 PMOs and 9.6 per 1000 PMOs, at health centers and a general hospital, respectively. From the overall loss to follow-ups that occurred, 55 (65.5%) and 33 (86.8%) of LTFUs occurred during the intensive phase and grew to 78 (92.9%) and 38 (100%) at health center and a general hospital, respectively, at the end of 6-month observation period. Older age (AOR = 1.7, 95%CI, 1.2-2.5, P < 0.001), being a rural resident (AHR = 2.7, 95%CI, 1.6-4.6), HIV reactive (AHR = 2.2, 95%CI, 1.5-3.2), following treatment and care in health center (AHR = 3.38, 95%CI, 2.06-5.53), and living at more than 10 km away from the health facility (AHR = 3.4, 95%CI, 2.1-5.7) were predictors for time to loss to follow-up among TB patients on treatment and care.

CONCLUSION

Time to TB patient loss to follow-up between health centers and a general hospital was significant. Loss to follow-up was high in patients with older age, rural residence, sero positive for HIV, living further from the health facilities, and following treatment and care at health centers. Strengthening the DOTs program with special emphasis on health centers is highly recommended.

The spatio-temporal analysis of the incidence of tuberculosis and the associated factors in mainland China, 2009-2015

BACKGROUND

Tuberculosis is still one of the most infectious diseases in China. This study aimed to explore the spatio-temporal distribution of TB and the associated factors in mainland China from 2009 to 2015.

METHODS

A Bayesian spatio-temporal model was utilized to analyse the correlation of socio-economic, healthcare, demographic and meteorological factors with the population level number of TB.

RESULTS

The Bayesian spatio-temporal analysis showed that for the population level number of TB, the estimated parameters of the ratio of males to females, the number of beds in medical institutions, the population density, the proportion of the population that is rural, the amount of precipitation, the largest wind speed and the sunshine duration were 0.556, 0.197, 0.199, 29.03,0.1958, 0.0854 and 0.2117, respectively, demonstrating positive associations. However, health personnel, per capita annual gross domestic product, minimum temperature and humidity indicated negative associations, and the corresponding parameters were -0.050, -0.095, -0.0022 and -0.0070, respectively.

CONCLUSIONS

Socio-economic, number of health personnel, demographic and meteorological factors could affect the case notification number of TB to different degrees and in different directions.

Diabetes mellitus increased all-cause mortality rate among newly-diagnosed tuberculosis patients in an Asian population: A nationwide population-based study

AIMS

To investigate the effect of diabetes mellitus (DM) on all-cause mortality among patients with newly-diagnosed tuberculosis (TB) in an Asian population. We also identified risk factors for mortality in these patients.

METHODS

The data were obtained from the National Health Insurance Research Database and included 9831 newly-diagnosed TB individuals and 1627 TB mortality cases in the period of 2000-2010. The mortality data were divided into a DM group and a non-DM group. We measured the incidence density of mortality and identified the risk factors of mortality.

RESULTS

The all-cause mortality of newly-diagnosed TB patients progressively increased with an average rate of 16.5% during 2000-2010. DM is an independent risk factor for all-cause mortality with HRs 1.17-1.27 by various models. TB patients with ages above 75years had the highest risk of mortality (HR=11.93) compared with those under 45 years. TB patients with heart failure, peripheral vascular disease, ischemic heart disease, cerebral vascular disease, hypertension, chronic kidney disease, pulmonary disease, liver disease, cancer, peptic ulcer disease, gout, and autoimmune disease had higher mortality compared to those without the aforementioned factors.

CONCLUSIONS

The one-year all-cause mortality after TB diagnosis was high among TB patients in Taiwan and it tended to increase in the past decade. While treating these newly-diagnosed TB patients, it is crucial to detect the factors predisposing to death, such as old age, male gender, certain kinds of aforementioned factors and diabetes.

Determinant factors for mortality during treatment among tuberculosis patients: Cox proportional hazards model

BACKGROUND

Investigating the survival of tuberculosis (TB) patients is one of the main parts of the TB control program. It can be related to many factors. This study aimed to estimate the survival experience and its associated factors among these patients.

METHODOLOGY

All TB patients detected during March 2005 to 31 September 2014 were entered into this prospective cohort. Each patient was investigated from the diagnosis date and followed until the last available information during treatment. Data analysis was performed using Kaplan Meier and multivariate Cox regression models.

RESULTS

The survival experience of 2493 TB patients was investigated 73.7% of which were pulmonary type. Mean and median survival time were 6.5 and 6.2 months respectively. The incidence rate of death among patients during the treatment courses was 0.99 (95% confidence interval: 0.84-1.1) per 100 person-months. Controlling the confounders, the incidence (95% confidence interval) of death was significantly higher among men (HR=1.8; 1.2-2.6), diabetic patients (HR=1.7; 1.2-2.6), cancerous patients (HR=4.8; 2.6-8.8) and HIV positive patients (HR=22.1; 7.3-66.4).

CONCLUSION

This study showed that male gender, TB/HIV co-infection and concurrent development of TB and cancer were determinant factors of death during the treatment period of TB.