Likelihood of blood culture positivity using SeptiCyte RAPID
Navalkar, K.; Wheelock, A.; Gregory, M.; Clark, D. V.; Kibuuka, H.; Okello, S.; Atukunda, S.; Wailagala, A.; Waitt, P.; Kakooza, F.; Oduro, G.; Adams, N.; Dietrich, M.; von der Forst, M.; Schultz, M. J.; Greenberg, J. A.; Aggarwal, N. R.; Yager, T. D.; Brandon, R. B.
Show abstract
Early diagnosis and identification of causative pathogens using blood culture in patients suspected of Blood Stream Infection (BSI) and sepsis are critical for improving patient outcomes through early and more targeted treatment. There is a need for tools that can guide the use of microbiologic diagnostics, especially where resources are limited, such as in lower and middle income countries (LMICs), pandemic and mass-casualty scenarios, and prolonged field care settings during military operations. MethodsPost-hoc retrospective analysis of individual patient data from three prospective clinical studies, conducted in North America, Europe and Africa, to investigate the association between SeptiCyte RAPID test results (SeptiScores) and blood culture (BC) results. Hypothesisthat a significant correlation exists between elevated SeptiScores and positive blood culture results, and between low SeptiScores and negative blood culture results. ResultsThe area under the receiver operating characteristic curve (ROC AUC) was 0.91 for 85 BC(+) versus 257 SIRS, and was 0.80 for 164 BC(-) versus 257 SIRS. As the SeptiScore increases, the relative probability of a septic patient being BC(+) as opposed to BC(-) also increases. A non-linear positive correlation is observed. Below a crossover point at SeptiScore 10, the ratio of probabilities of BC(+) sepsis / BC(-) sepsis is <1 while above the crossover point this ratio is >1. Thus, septic patients with SeptiScores >10 have a higher probability of being BC(+) compared to BC(-). ConclusionsElevated SeptiScores, obtained before blood culture results, are indicative of increased blood culture positivity. This may have clinical utility, particularly in resource limited settings, as an aid for improving the efficiency of blood culture practice, for instance by informing patient selection and interpretation of blood culture results.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Clustering ICU patients with sepsis based on the patterns of their circulating biomarkers: a secondary analysis of the CAPTAIN prospective multicenter cohort study 96%
- Application of the Sepsis-3 criteria to describe sepsis epidemiology in the AmsterdamUMCdb intensive care dataset 96%
- Development and validation of a cellular host response test as an early diagnostic for sepsis 96%
Similar papers in this journal
- A Transcriptomic Severity Metric that Predicts Clinical Outcomes in Critically Ill Surgical Sepsis Patients 96%
- Early prediction of impending septic shock in children using age-adjusted Sepsis-3 criteria 92%
- A Multicenter Evaluation of Blood Purification with Seraph 100 Microbind Affinity Blood Filter for the Treatment of Severe COVID-19: A Preliminary Report 92%
Similar papers in this journal
- Androgen receptor pathway activity assay for sepsis diagnosis and prediction of favorable prognosis 95%
- Three Distinct Trajectories of Red Blood Cell Distribution Width and Their Significant Association with Mortality in Sepsis Patients: A Group-Based Trajectory Modeling Study with Validation 94%
- Unsupervised clustering reveals phenotypes of AKI in ICU Covid19 patients 92%
Similar papers in this journal
- COVID-19 as cause of viral sepsis: A Systematic Review and Meta-Analysis 95%
- Immune profiling demonstrates a common immune signature of delayed acquired immunodeficiency in patients with various etiologies of severe injury 93%
- Prognostic and predictive biomarkers in patients with COVID-19 treated with tocilizumab in a randomised controlled trial 90%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.