Predictive model of unfavorable outcomes for multidrug-resistant tuberculosis

Abstract

OBJECTIVE: to analyze the temporal trend, identify the factors related and elaborate a predictive model for unfavorable treatment outcomes for multidrug-resistant tuberculosis (MDR-TB). METHODS: Retrospective cohort study with all cases diagnosed with MDR-TB between the years 2006 and 2015 in the state of São Paulo. The data were collected from the state system of TB cases notifications (TB-WEB). The temporal trend analyzes of treatment outcomes was performed through the Prais-Winsten analysis. In order to verify the factors related to the unfavorable outcomes, abandonment, death with basic cause TB and treatment failure, the binary logistic regression was used. Pictorial representations of the factors related to treatment outcome and their prognostic capacity through the nomogram were elaborated. RESULTS: Both abandonment and death have a constant temporal tendency, whereas the failure showed it as decreasing. Regarding the risk factors for such outcomes, using illicit drugs doubled the odds for abandonment and death. Besides that, being diagnosed in emergency units or during hospitalizations was a risk factor for death. On the contrary, having previous multidrug-resistant treatments reduced the odds for the analyzed outcomes by 33%. The nomogram presented a predictive model with 65% accuracy for dropouts, 70% for deaths and 80% for failure. CONCLUSIONS: The modification of the current model of care is an essential factor for the prevention of unfavorable outcomes. Through predictive models, as presented in this study, it is possible to develop patient-centered actions, considering their risk factors and increasing the chances for cure.

Prevalence and risk factors associated with latent tuberculosis infection among household contacts of smear positive pulmonary tuberculosis patients in South India

“…Generating further evidence regarding the burden of LTBI and its possible risk factors among HHC would aid policymakers to target TB preventive therapy. Previous studies have explored the use of prediction modelling to identify the risk factors for disease transmission among HHCs and treatment failure in varied study settings [12,13]. Nevertheless, research work exploring the risk factors for LTBI, especially among the HHCs of TB patients in India, is limited.…”

A Score to Predict the Risk of Major Adverse Drug Reactions Among Multi-Drug Resistant Tuberculosis Patients in Southern Ethiopia, 2014–2019

“…However, the prediction model developed in this study was lower than the risk scores developed to predict MDR-TB treatment failure (c-index = 0.8), 25 risk of atrial fibrillation (AUC = 0.78), 28 pediatrics mortality (c-index = 0.88), 29 and adverse outcomes in patients with pulmonary embolism (AUC = 0.85). 30 These prediction models were done using a prospective study design where there is a possibility of identifying all key prognostic indicators, a possible reason for the higher discriminatory performance of the aforementioned scores than the current prediction score.…”

“…The risk prediction score developed for major adverse events has a satisfactory level of accuracy with AUC of 0.77. The discriminatory performance is higher than a prediction score developed in Mexico (c-statistics = 0.68) for prediction of TB treatment failure, death, and drug resistance, 23 for poor MDR-TB treatment outcome in China (AUROC = 0.69), 24 the risk score developed to predict dropouts in MDR-TB patients in Brazil (c-statistics = 0.65), 25 prediction of hospital readmission in general medical patients (AUC = 0.61), 26 and a risk score to predict the risk of advanced nasopharyngeal carcinoma (c-index = 0.748). 27 …”

Translating drug resistant tuberculosis treatment guidelines to reality in war-torn Kandahar, Afghanistan: A retrospective cohort study

“…Our cohort displayed a five-fold higher proportion of children, which may be explained by systematic household contact tracing [ 32 ]. In other studies, between 0.2% -11.2% of children were diagnosed with RR-TB disease after RR-TB household contact screening [ 33 – 39 ].…”