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

Background: A 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. Methods: We 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. Results: The 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%). Conclusion: Here 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

An acoustically-driven biochip - Impact of flow on the cell-association of targeted drug carriers

The interaction of targeted drug carriers with epithelial and endothelial barriers in vivo is largely determined by the dynamics of the body fluids. To simulate these conditions in binding assays, a fully biocompatible in vitro model was developed which can accurately mimic a wide range of physiological flow conditions on a thumbnail-format cell-chip. This acoustically-driven microfluidic system was used to study the interaction characteristics of protein-coated particles with cells. Poly(D,L-lactide-co-glycolide) (PLGA) microparticles (2.86 {\pm} 0.95 {\mu}m) were conjugated with wheat germ agglutinin (WGA-MP, cytoadhesive protein) or bovine serum albumin (BSA-MP, nonspecific protein) and their binding to epithelial cell monolayers was investigated under stationary and flow conditions. While mean numbers of 1500 {\pm} 307 mm-2 WGA-MP and 94 {\pm} 64 mm-2 BSA-MP respectively were detected to be cell-bound in the stationary setup, incubation at increasing flow velocities increasingly antagonized the attachment of both types of surface-modified particles. However, while binding of BSA-MP was totally inhibited by flow, grafting with WGA resulted in a pronounced anchoring effect. This was indicated by a mean number of 747 {\pm} 241 mm-2 and 104 {\pm} 44 mm-2 attached particles at shear rates of 0.2 s-1 and 1 s-1 respectively. Due to the compactness of the fluidic chip which favours parallelization, this setup represents a highly promising approach towards a screening platform for the performance of drug delivery vehicles under physiological flow conditions. In this regard, the flow-chip is expected to provide substantial information for the successful design and development of targeted micro- and nanoparticulate drug carrier systems.Comment: 19 page

Testing lupus anticoagulants in a real-life scenario - a retrospective cohort study

Introduction: Lupus anticoagulant (LAC) testing is challenging. Most data are derived from a well-controlled study environment with potential alterations to daily routines. The aim of this retrospective cohort study was to assess the capacity of various LAC screening tests and derived mixing tests to predict a positive result in subsequent confirmation tests in a large cohort of patients. Materials and methods: In 5832 individuals, we retrospectively evaluated the accuracy of the aPTT-A, aPTT-LAscreen, aPTT-FS and dRVVTscreen and of their derived mixing tests in detecting a positive confirmation test result within the same blood specimen. The group differences, degree of correlation and the predictive accuracy of LAC coagulation tests were analysed using the Mann-Whitney U test, the Spearman-rank-correlation and by area under the receiver operating characteristic curve (ROC-AUC) analysis. ROC-AUCs were compared with the Venkatraman´s permutation test. Results: The pre-test probability of patients with clinically suspected LAC was 36% in patients without factor deficiency or anticoagulation therapy. The aPTT-LAscreen showed the best diagnostic accuracy with a ROC-AUC of 0.84 (95% CI: 0.82 – 0.86). No clear advantage of the dRVVT-derived mixing test was detectable when compared to the dRVVTscreen (P = 0.829). Usage of the index of circulating anticoagulant (ICA) did not improve the diagnostic power of respective mixing tests. Conclusions: Among the parameters evaluated, aPTT-LAscreen and derived mixing test parameters were the most accurate tests. In our study cohort, neither other mixing test nor the ICA presented any further advantage in LAC diagnostics

Molecular quantification of tissue disease burden is a new biomarker and independent predictor of survival in mastocytosis

A high allele burden of the KIT D816V mutation in peripheral blood or bone marrow aspirates indicates multi-lineage hematopoietic involvement and has been associated with an aggressive clinical course of systemic mastocytosis. Since mast cells are substantially underrepresented in these liquid specimens, their mutation burden likely underestimates the tumor burden of the disease. We used a novel previously validated digital polymerase chain reaction (PCR) method for KIT D816V analysis to systematically analyze the mutation burden in formalin-fixed, paraffin-embedded bone marrow tissue sections of 116 mastocytosis patients (91 with indolent and 25 with advanced systemic mastocytosis), and to evaluate for the first time the clinical value of the tissue mutation burden as a novel biomarker. The KIT D816V mutation burden in the tissue was significantly higher and correlated better with bone marrow mast cell infiltration (r=0.68 vs. 0.48) and serum tryptase levels (r=0.68 vs. 0.58) compared to that in liquid specimens. Furthermore, the KIT D816V tissue mutation burden was: (i) significantly higher in advanced than in indolent systemic mastocytosis (P=0.001); (ii) predicted survival of patients in multivariate analyses independently; and (iii) was significantly reduced after response to cytoreductive therapy. Finally, digital PCR was more sensitive in detecting KIT D816V in bone marrow sections of indolent systemic mastocytosis patients than melting curve analysis after peptide nucleic acid-mediated PCR clamping (97% vs. 89%;

Machine Learning Strategien zur Identifizierung von Bakteriämie

Schwere Infektionen sind lebensbedrohliche Zustände, die schnelles diagnostisches und therapeutisches Handeln erfordern. Entscheidungsunterstützungssysteme für eine schnellere Erkennung von Patienten mit schweren Infektionen könnten das Überleben der Patienten verbessern oder Patienten vor nicht-notwendigen Medikationen bewahren. Im Rahmen dieser Doktorarbeit wurden verschiedene Ziele von Entscheidungsunterstützungssystemen mit Patienten zweier unterschiedlicher Kohorten evaluiert. Eine retrospektive Kohorte inkludierte Patienten mit klinischem Bakteriämie-Verdacht, die zwischen 2006 und 2010 am Allgemeinen Krankenhaus Wien behandelt wurden. Prospektiv wurden Patienten mit einem Systemischen Inflammatorischen Reaktionssyndrom (SIRS), welche am Allgemeinen Krankenhaus Wien an der Normalstation zwischen Juli 2011 und September 2012 behandelt wurden, rekrutiert. Die Vorhersagekraft von Routineparametern sowie von experimentellen Biomarkern für die Erkennung von Bakteriämie sowie des Bakterientyps wurde erhoben. Machine-Learning-Modelle (ML-Modelle) wurden erstellt, um die Vorhersagekraft der Einzelparameter zu erhöhen. Bei 298 SIRS-Patienten auf der Normalstation zeigte Prokalzitonin (PCT) 0,78 ROC-AUC (95%CI: 0,72–0,83) für die Vorhersage von Bakteriämie. Bei 466 SIRS-Patienten konnte jedoch diese Vorhersagekraft durch lineare oder nicht-lineare ML Strategien nicht verbessert werden. Eine Random-Forest-Strategie zeigte die beste Vorhersagekraft, welche die diagnostische Vorhersagekraft von PCT nicht verbesserte. In der retrospektiven krankenhausweiten Kohorte mit 15.985 Patienten mit Bakteriämieverdacht wurden Routineparameter für die Bakteriämievorhersage evaluiert. Das beste Modell zeigte 0,80 ROC-AUC (95%CI: 0,76–0,84), welches in 98,8% negativen Vorhersagewert resultierte. Daten von 1.180 bakteriämischen Patienten wurden verwendet, um Modelle zu erstellen, die zwischen Gram-positiver und Gram-negativer Bakteriämie unterscheiden können. Jedoch schlug dieser Ansatz fehl. Zusammenfassend gesagt würden ML-Strategien sich eignen, um in unselektierten Kohorten hoch- oder niedrig-Risikopatienten für Bakteriämie zu erkennen. Bei selektierten Patienten erfordert es weitere Studien, welche evaluieren, ob zelluläre oder genetische Marker die Vorhersagekraft von PCT erhöhen.Severe infections are life-threating conditions, requiring fast diagnostic and therapeutic actions. Decision support systems for faster detection of patients with severe infections might improve patients’ survival rate or prevent patients from receiving unnecessary therapies. Within this thesis, several goals for decision support systems were evaluated using data of two different patient cohorts. A retrospective cohort included patients with clinically suspected bacteraemia treated at Vienna General Hospital between 2006 and 2010. Further, a prospective cohort of patients with systemic inflammatory response syndrome (SIRS) treated at the Vienna General Hospital standard care wards between July 2011 and September 2012 was recruited. The capacity of standard parameters and experimental biomarkers used to identify bacteraemia or the type of bacteria (Gram staining) was evaluated. Machine learning (ML) models were trained to enhance the predictive performance of individual parameters. Among 298 evaluated standard care patients with SIRS, procalcitonin (PCT) presented 0.78 ROC-AUC (95%CI: 0.72–0.83) for identifying bacteraemia. However, this discriminatory potency could not be improved by applying linear or non-linear ML strategies, in 466 SIRS patients treated on standard care wards. A random forest strategy showed the best results but failed to improve the diagnostic accuracy of PCT. In a retrospective hospital-wide cohort study including 15,985 patients with suspected bacteraemia, routinely available laboratory parameters were evaluated regarding their predictive capacity to identify bacteraemia. The best model resulted in 0.80 ROC-AUC (95%CI: 0.76–0.84), which yielded a 98.8% negative predictive value for bacteraemia. Data from 1,180 bactaeremic patients were utilized to establish models for predicting the presence of Gram-positive or Gram-negative bacteraemia, which failed. In conclusion, ML strategies might be applied in unselected patients to identify high-risk or low-risk patients. For application in selected patients, further studies are required to evaluate the potency of cellular or genetic markers to enhance the capacity of PCT.submitted by Franz RatzingerAbweichender Titel laut Übersetzung der Verfasserin/des VerfassersMedizinische Universität Wien, Diss., 201

Machine learning strategies for identification of bacteraemia

Schwere Infektionen sind lebensbedrohliche Zustände, die schnelles diagnostisches und therapeutisches Handeln erfordern. Entscheidungsunterstützungssysteme für eine schnellere Erkennung von Patienten mit schweren Infektionen könnten das Überleben der Patienten verbessern oder Patienten vor nicht-notwendigen Medikationen bewahren. Im Rahmen dieser Doktorarbeit wurden verschiedene Ziele von Entscheidungsunterstützungssystemen mit Patienten zweier unterschiedlicher Kohorten evaluiert. Eine retrospektive Kohorte inkludierte Patienten mit klinischem Bakteriämie-Verdacht, die zwischen 2006 und 2010 am Allgemeinen Krankenhaus Wien behandelt wurden. Prospektiv wurden Patienten mit einem Systemischen Inflammatorischen Reaktionssyndrom (SIRS), welche am Allgemeinen Krankenhaus Wien an der Normalstation zwischen Juli 2011 und September 2012 behandelt wurden, rekrutiert. Die Vorhersagekraft von Routineparametern sowie von experimentellen Biomarkern für die Erkennung von Bakteriämie sowie des Bakterientyps wurde erhoben. Machine-Learning-Modelle (ML-Modelle) wurden erstellt, um die Vorhersagekraft der Einzelparameter zu erhöhen. Bei 298 SIRS-Patienten auf der Normalstation zeigte Prokalzitonin (PCT) 0,78 ROC-AUC (95%CI: 0,720,83) für die Vorhersage von Bakteriämie. Bei 466 SIRS-Patienten konnte jedoch diese Vorhersagekraft durch lineare oder nicht-lineare ML Strategien nicht verbessert werden. Eine Random-Forest-Strategie zeigte die beste Vorhersagekraft, welche die diagnostische Vorhersagekraft von PCT nicht verbesserte. In der retrospektiven krankenhausweiten Kohorte mit 15.985 Patienten mit Bakteriämieverdacht wurden Routineparameter für die Bakteriämievorhersage evaluiert. Das beste Modell zeigte 0,80 ROC-AUC (95%CI: 0,760,84), welches in 98,8% negativen Vorhersagewert resultierte. Daten von 1.180 bakteriämischen Patienten wurden verwendet, um Modelle zu erstellen, die zwischen Gram-positiver und Gram-negativer Bakteriämie unterscheiden können. Jedoch schlug dieser Ansatz fehl. Zusammenfassend gesagt würden ML-Strategien sich eignen, um in unselektierten Kohorten hoch- oder niedrig-Risikopatienten für Bakteriämie zu erkennen. Bei selektierten Patienten erfordert es weitere Studien, welche evaluieren, ob zelluläre oder genetische Marker die Vorhersagekraft von PCT erhöhen.Severe infections are life-threating conditions, requiring fast diagnostic and therapeutic actions. Decision support systems for faster detection of patients with severe infections might improve patients survival rate or prevent patients from receiving unnecessary therapies. Within this thesis, several goals for decision support systems were evaluated using data of two different patient cohorts. A retrospective cohort included patients with clinically suspected bacteraemia treated at Vienna General Hospital between 2006 and 2010. Further, a prospective cohort of patients with systemic inflammatory response syndrome (SIRS) treated at the Vienna General Hospital standard care wards between July 2011 and September 2012 was recruited. The capacity of standard parameters and experimental biomarkers used to identify bacteraemia or the type of bacteria (Gram staining) was evaluated. Machine learning (ML) models were trained to enhance the predictive performance of individual parameters. Among 298 evaluated standard care patients with SIRS, procalcitonin (PCT) presented 0.78 ROC-AUC (95%CI: 0.720.83) for identifying bacteraemia. However, this discriminatory potency could not be improved by applying linear or non-linear ML strategies, in 466 SIRS patients treated on standard care wards. A random forest strategy showed the best results but failed to improve the diagnostic accuracy of PCT. In a retrospective hospital-wide cohort study including 15,985 patients with suspected bacteraemia, routinely available laboratory parameters were evaluated regarding their predictive capacity to identify bacteraemia. The best model resulted in 0.80 ROC-AUC (95%CI: 0.760.84), which yielded a 98.8% negative predictive value for bacteraemia. Data from 1,180 bactaeremic patients were utilized to establish models for predicting the presence of Gram-positive or Gram-negative bacteraemia, which failed. In conclusion, ML strategies might be applied in unselected patients to identify high-risk or low-risk patients. For application in selected patients, further studies are required to evaluate the potency of cellular or genetic markers to enhance the capacity of PCT.submitted by Franz RatzingerAbweichender Titel laut Übersetzung der Verfasserin/des VerfassersMedizinische Universität Wien, Diss., 2018(VLID)287817