J Biomed Inform - Towards probabilistic decision support in public health practice: predicting recent transmission of tuberculosis from patient attributes.

Tópicos

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Resumo

JECTIVE: Investigating the contacts of a newly diagnosed tuberculosis (TB) case to prevent TB transmission is a core public health activity. In the context of limited resources, it is often necessary to prioritize investigation when multiple cases are reported. Public health personnel currently prioritize contact investigation intuitively based on past experience. Decision-support software using patient attributes to predict the probability of a TB case being involved in recent transmission could aid in this prioritization, but a prediction model is needed to drive such software.METHODS: We developed a logistic regression model using the clinical and demographic information of TB cases reported to Montreal Public Health between 1997 and 2007. The reference standard for transmission was DNA fingerprint analysis. We measured the predictive performance, in terms of sensitivity, specificity, negative predictive value, positive predictive value, the Receiver Operating Characteristic (ROC) curve and the Area Under the ROC (AUC).RESULTS: Among 1552 TB cases enrolled in the study, 314 (20.2%) were involved in recent transmission. The AUC of the model was 0.65 (95% confidence interval: 0.61-0.68), which is significantly better than random prediction. The maximized values of sensitivity and specificity on the ROC were 0.53 and 0.67, respectively.CONCLUSIONS: The characteristics of a TB patient reported to public health can be used to predict whether the newly diagnosed case is associated with recent transmission as opposed to reactivation of latent infection.

Resumo Limpo

jectiv investig contact newli diagnos tuberculosi tb case prevent tb transmiss core public health activ context limit resourc often necessari priorit investig multipl case report public health personnel current priorit contact investig intuit base past experi decisionsupport softwar use patient attribut predict probabl tb case involv recent transmiss aid priorit predict model need drive softwaremethod develop logist regress model use clinic demograph inform tb case report montreal public health refer standard transmiss dna fingerprint analysi measur predict perform term sensit specif negat predict valu posit predict valu receiv oper characterist roc curv area roc aucresult among tb case enrol studi involv recent transmiss auc model confid interv signific better random predict maxim valu sensit specif roc respectivelyconclus characterist tb patient report public health can use predict whether newli diagnos case associ recent transmiss oppos reactiv latent infect

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