Machine learning-derived blood culture classification with both predictive and prognostic values in the intensive care unit: A retrospective cohort study

Objectives: Diagnosis and management of intensive care unit (ICU)-acquired bloodstream infections are often based on positive blood culture results. This retrospective cohort study aimed to develop a classification model using data-driven characterisation to optimise the management of intensive care patients with blood cultures.Setting, methodology/design: An unsupervised clustering model was developed based on the clinical characteristics of patients with blood cultures in the Medical Information Mart for Intensive Care (MIMIC)-IV database (n = 2451). It was tested using the data from the MIMIC-III database (n = 2047).Main outcome measures: The prognosis, blood culture outcomes, antimicrobial interventions, and trajectories of infection indicators were compared between clusters.Results: Four clusters were identified using machine learning-based k-means clustering based on data obtained 48 h before the first blood culture sampling. Cluster gamma was associated with the highest 28-day mortality rate, followed by clusters alpha, delta, and beta. Cluster gamma had a higher fungal isolation rate than cluster beta (P < 0.05). Cluster delta was associated with a higher isolation rate of Gram-negative organisms and fungi (P < 0.05). Patients in clusters gamma and delta underwent more femoral site vein catheter placements than those in cluster beta (P < 0.001, all). Patients with a duration of antibiotics treatment of 4, 6, and 7 days in clusters alpha, delta, and gamma, respectively, had the lowest 28-day mortality rate.Conclusions: Machine learning identified four clusters of intensive care patients with blood cultures, which yielded different prognoses, blood culture outcomes, and optimal duration of antibiotic treatment. Such data-driven blood culture classifications suggest further investigation should be undertaken to optimise treatment and improve care.Implications for clinical practice: Intensive care unit-acquired bloodstream infections are heterogeneous. Meaningful classifications associated with outcomes should be described. Using machine learning and cluster analysis could help in understanding heterogeneity. Data-driven blood culture classification could identify distinct physiological states and prognoses before deciding on blood culture sampling, optimise treatment, and improve care.