Rapid Diagnostic Algorithms as a Screening Tool for Tuberculosis: An Assessor Blinded Cross-Sectional Study

Abstract

BackgroundA major obstacle to effectively treat and control tuberculosis is the absence of an accurate, rapid, and low-cost diagnostic tool. A new approach for the screening of patients for tuberculosis is the use of rapid diagnostic classification algorithms.MethodsWe tested a previously published diagnostic algorithm based on four biomarkers as a screening tool for tuberculosis in a Central European patient population using an assessor-blinded cross-sectional study design. In addition, we developed an improved diagnostic classification algorithm based on a study population at a tertiary hospital in Vienna, Austria, by supervised computational statistics.ResultsThe diagnostic accuracy of the previously published diagnostic algorithm for our patient population consisting of 206 patients was 54% (CI: 47%–61%). An improved model was constructed using inflammation parameters and clinical information. A diagnostic accuracy of 86% (CI: 80%–90%) was demonstrated by 10-fold cross validation. An alternative model relying solely on clinical parameters exhibited a diagnostic accuracy of 85% (CI: 79%–89%).ConclusionHere we show that a rapid diagnostic algorithm based on clinical parameters is only slightly improved by inclusion of inflammation markers in our cohort. Our results also emphasize the need for validation of new diagnostic algorithms in different settings and patient populations.

Sepsis biomarkers in neutropaenic systemic inflammatory response syndrome patients on standard care wards

“…Therefore, we restricted our statistical analyses mostly to single variable methods. Predictive models analysis might increase the discriminatory power of biomarkers [37][38][39]. In a logistic regression model, including LBP, PCT and body temperature, we found 0Á899 ROC-AUC (CI: 0Á813-0Á985) for identifying bacteraemic SIRS patients with neutropaenia.…”

“…In contrast, in a prospective cohort study including 47 children, no significant difference was found in LBP levels when it came to predicting clinical sepsis or bacteraemia [31]. Predictive models analysis might increase the discriminatory power of biomarkers [37][38][39]. The function of LBP is the binding of bacterial surface patterns including lipopolysaccharides from Gram-negative bacteria and lipoteichoic acid from Gram-positive bacteria [35,36].…”

Utility of Sepsis Biomarkers and the Infection Probability Score to Discriminate Sepsis and Systemic Inflammatory Response Syndrome in Standard Care Patients

“…In IPS studies, no pre-selection of cases was done, leading to a lower pre-test probability and subsequently to a higher NPV. Therefore, our findings emphasize the need for careful validation in different patient populations [ 51 ]. In this survey a cohort study design was chosen, including only patients fulfilling two or more SIRS criteria.…”

Diagnostic ‘omics’ for active tuberculosis

“…Indeed, the need to validate diagnostic models in independently recruited patient populations and to define the target group in which the diagnostic test is likely to be successful (e.g. ethnic background, HIV status) has been convincingly illustrated by Ratzinger et al [ 78 ], who applied the diagnostic algorithm previously devised by Agranoff et al [ 68 ] to a new Central European patient cohort of 36 active TB cases and 170 patients with other diseases. The originally published diagnostic algorithm predicted disease status in the new cohort with a poor accuracy of only 54 % (19 % sensitivity and 62 % specificity).…”

“…The originally published diagnostic algorithm predicted disease status in the new cohort with a poor accuracy of only 54 % (19 % sensitivity and 62 % specificity). Ratzinger et al [ 78 ] argued that the performance difference to the initial study may be attributable to differences in the composition of the comparison groups and the pre-test probability due to study design (approximately 1:1 distribution of TB cases and controls in the original case-control study [ 68 ] versus 1:4 distribution in the following cross-sectional study [ 78 ]).…”