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Bloodstream infection: Derivation and validation of a reliable and multidimensional prognostic score based on a machine learning model (BLISCO) - 13/11/24

Doi : 10.1016/j.ajic.2024.07.011 
Marta Camici, PhD a, b, ⁎ , Benedetta Gottardelli, PhD c, Tommaso Novellino d, Carlotta Masciocchi, PhD e, Silvia Lamonica a, Rita Murri, MD a
a Department of Laboratory Science and Infectious Diseases, A. Gemelli University Polyclinic Foundation IRCCS, Rome, Italy 
b Clinical and Research Infectious Diseases Department, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, Rome, Italy 
c Department of Diagnostic Imaging, Oncological Radiotherapy, and Hematology, Catholic University of the Sacred Heart, Rome, Italy 
d Department of Medicine and Surgery, Catholic University of the Sacred Heart, Rome, Italy 
e Real World Data Research Core Facility, Gemelli Generator, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy 

⁎Address correspondence to Marta Camici, Clinical and Research Infectious Diseases Department, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, Via Portuense 292, 00149 Roma, Italy.Clinical and Research Infectious Diseases Department, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCSVia Portuense 292Roma00149Italy

Riassunto

Background

A bloodstream infection (BSI) prognostic score applicable at the time of blood culture collection is missing.

Methods

In total, 4,327 patients with BSIs were included, divided into a derivation (80%) and a validation dataset (20%). Forty-two variables among host-related, demographic, epidemiological, clinical, and laboratory extracted from the electronic health records were analyzed. Logistic regression was chosen for predictive scoring.

Results

The 14-day mortality model included age, body temperature, blood urea nitrogen, respiratory insufficiency, platelet count, high-sensitive C-reactive protein, and consciousness status: a score of ≥ 6 was correlated to a 14-day mortality rate of 15% with a sensitivity of 0.742, a specificity of 0.727, and an area under the curve of 0.783. The 30-day mortality model further included cardiovascular diseases: a score of ≥ 6 predicting 30-day mortality rate of 15% with a sensitivity of 0.691, a specificity of 0.699, and an area under the curve of 0.697.

Conclusions

A quick mortality score could represent a valid support for prognosis assessment and resources prioritizing for patients with BSIs not admitted in the intensive care unit.

Il testo completo di questo articolo è disponibile in PDF.

Highlights

•
A BSI prognostic score applicable at the time of blood culture collection is missing.
•
BLISCO is a multidimensional prognostic score for non-ICU patients with BSI.
•
BLISCO was derived from a machine learning model, which ensures strong performance.
•
Prognostic scores are essential instruments for optimizing resource allocation.

Il testo completo di questo articolo è disponibile in PDF.

Key Words : Non-ICU ward, Antimicrobial stewardship tool, Early mortality score, Prognostic stratification tool


Mappa


 Ethics approval: Ethics approval was given by the Ethics Commission of the Fondazione Policlinico Universitario A. Gemelli IRCCS, of Rome (ID 4801). The study was performed in accordance with the Declaration of Helsinki.
 Conflicts of interest: None of the authors have potential conflict of interests. Marta Camici received speakers’ honoraria by Gilead, Rita Murri received speakers’ honoraria by Lilly and participates on a Data Safety Monitoring Board or Advisory Board sponsored by Merck Sharp and Dome.


© 2024  Association for Professionals in Infection Control and Epidemiology, Inc.. Pubblicato da Elsevier Masson SAS. Tutti i diritti riservati.
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Vol 52 - N° 12

P. 1377-1383 - dicembre 2024 Ritorno al numero
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