1. Machine learning for fast identification of bacteraemia in SIRS patients treated on standard care wards: a cohort study.
- Author
-
Ratzinger F, Haslacher H, Perkmann T, Pinzan M, Anner P, Makristathis A, Burgmann H, Heinze G, and Dorffner G
- Subjects
- Adult, Aged, Area Under Curve, Bacteremia blood, Bacteremia classification, Biomarkers blood, Calcitonin blood, Cohort Studies, Female, Forecasting, Humans, Machine Learning, Male, Middle Aged, Models, Theoretical, Prospective Studies, Protein Precursors blood, ROC Curve, Systemic Inflammatory Response Syndrome blood, Systemic Inflammatory Response Syndrome microbiology, Bacteremia diagnosis, Systemic Inflammatory Response Syndrome complications
- 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 (80:20 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.
- Published
- 2018
- Full Text
- View/download PDF