Machine learning for fast identification of bacteraemia in SIRS patients treated on standard care wards: a cohort study.
Sci Rep
; 8(1): 12233, 2018 08 15.
Article
de En
| MEDLINE
| ID: mdl-30111827
ABSTRACT
Bacteraemia is a life-threating condition requiring immediate diagnostic and therapeutic actions. Blood culture (BC) analyses often result in a low true positive result rate, indicating its improper usage. A predictive model might assist clinicians in deciding for whom to conduct or to avoid BC analysis in patients having a relevant bacteraemia risk. Predictive models were established by using linear and non-linear machine learning methods. To obtain proper data, a unique data set was collected prior to model estimation in a prospective cohort study, screening 3,370 standard care patients with suspected bacteraemia. Data from 466 patients fulfilling two or more systemic inflammatory response syndrome criteria (bacteraemia rate 28.8%) were finally used. A 29 parameter panel of clinical data, cytokine expression levels and standard laboratory markers was used for model training. Model tuning was performed in a ten-fold cross validation and tuned models were validated in a test set (8020 random split). The random forest strategy presented the best result in the test set validation (ROC-AUC 0.729, 95%CI 0.679-0.779). However, procalcitonin (PCT), as the best individual variable, yielded a similar ROC-AUC (0.729, 95%CI 0.679-0.779). Thus, machine learning methods failed to improve the moderate diagnostic accuracy of PCT.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Bactériémie
/
Syndrome de réponse inflammatoire généralisée
Type d'étude:
Diagnostic_studies
/
Etiology_studies
/
Incidence_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limites:
Adult
/
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Langue:
En
Journal:
Sci Rep
Année:
2018
Type de document:
Article
Pays d'affiliation:
Autriche