Assessment of a novel BLOOMY score for predicting mortality in hospitalised adults with bloodstream infection
Johanna Tietäväinen, Tapio Seiskari, Janne Aittoniemi, Heini Huhtala, Jukka Mustonen, Reetta Huttunen, Jaana Syrjänen, Juha Rannikko

TL;DR
A new machine learning-based BLOOMY score accurately predicts mortality in bloodstream infection patients, outperforming existing methods.
Contribution
The BLOOMY score was validated in a new cohort and shown to outperform established mortality prediction tools for bloodstream infections.
Findings
The BLOOMY 14-day score had an AUC of 0.87 for predicting 14-day mortality.
The BLOOMY 6-month score had an AUC of 0.79 for predicting 6-month mortality.
BLOOMY scores outperformed quick SOFA and Pitt Bacteraemia Score in 14-day mortality prediction.
Abstract
A German multicentre study BLOOMY was the first to use machine learning approach to develop mortality prediction scores for bloodstream infection (BSI) patients, but the scores have not been assessed in other cohorts. Our aim was to assess how the BLOOMY 14-day and 6-month scores estimate mortality in our cohort of 497 cases with BSI. Clinical data, laboratory data, and patient outcome were gathered retrospectively from patient records. The scores were calculated as presented in the BLOOMY study with the exception in the day of the evaluation. In our cohort, BLOOMY 14-day score estimated death by day 14 with an area under curve (AUC) of 0.87 (95% Confidence Interval 0.80–0.94). Using ≥ 6 points as a cutoff, sensitivity was 68.8%, specificity 88.1%, positive predictive value (PPV) 39.3%, and negative predictive value (NPV) 96.2%. These results were similar in the original BLOOMY cohort…
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Taxonomy
TopicsSepsis Diagnosis and Treatment · Streptococcal Infections and Treatments · Bacterial Identification and Susceptibility Testing
