BMC infectious diseases · 2025
Hospital-wide, dynamic, individualized prediction of central line-associated bloodstream infections-development and temporal evaluation of six prediction models.
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Albu Elena, Gao Shan, Stijnen Pieter, Rademakers Frank E, Janssens Christel, Cossey Veerle, Debaveye Yves, Wynants Laure, Van Calster Ben
Аннотация
BACKGROUND: Central line-associated bloodstream infections (CLABSI) are preventable hospital-acquired infections. Predicting CLABSI helps improve early intervention strategies and enhance patient safety.
AIM: To develop and temporally evaluate dynamic prediction models for continuous CLABSI risk monitoring.
METHODS: Data from hospitalized patients with central catheter(s) admitted to University Hospitals Leuven between 2014 and 2017 were used to develop five dynamic models (a landmark cause-specific model, two random forest models, and two XGBoost models) to predict 7-day CLABSI risk, accounting for competing events (death, discharge, and catheter removal). The models' predictions were then combined using a superlearner model. All models were temporally evaluated on data from the same hospital from 2018 to 2020 using performance metrics for discrimination, calibration, and clinical utility.
FINDINGS: Among 61629 catheter episodes in the training set, 1930 (3.1%) resulted in CLABSI, while in the test set of 44544 catheter episodes, 1059 (2.4%) experienced CLABSI. Among individual models, one XGBoost model achieved the highest AUROC of 0.748. Calibration was good for predicted risks up to 5%, while the cause-specific and XGBoost models overestimated higher predicted risks. The superlearner displayed a modest improvement in discrimination (AUROC up to 0.751) and better calibration than the cause-specific and XGBoost models, but worse than the random forest models. The models showed clinical utility to support standard care interventions (at risk thresholds between 0.5-4%), but not to support advanced interventions (at thresholds 15-25%).
CONCLUSION: Hospital-wide CLABSI prediction models offer clinical utility based on medium-risk thresholds. Clinical utility at present may be limited as the model performance deteriorated over time.
AIM: To develop and temporally evaluate dynamic prediction models for continuous CLABSI risk monitoring.
METHODS: Data from hospitalized patients with central catheter(s) admitted to University Hospitals Leuven between 2014 and 2017 were used to develop five dynamic models (a landmark cause-specific model, two random forest models, and two XGBoost models) to predict 7-day CLABSI risk, accounting for competing events (death, discharge, and catheter removal). The models' predictions were then combined using a superlearner model. All models were temporally evaluated on data from the same hospital from 2018 to 2020 using performance metrics for discrimination, calibration, and clinical utility.
FINDINGS: Among 61629 catheter episodes in the training set, 1930 (3.1%) resulted in CLABSI, while in the test set of 44544 catheter episodes, 1059 (2.4%) experienced CLABSI. Among individual models, one XGBoost model achieved the highest AUROC of 0.748. Calibration was good for predicted risks up to 5%, while the cause-specific and XGBoost models overestimated higher predicted risks. The superlearner displayed a modest improvement in discrimination (AUROC up to 0.751) and better calibration than the cause-specific and XGBoost models, but worse than the random forest models. The models showed clinical utility to support standard care interventions (at risk thresholds between 0.5-4%), but not to support advanced interventions (at thresholds 15-25%).
CONCLUSION: Hospital-wide CLABSI prediction models offer clinical utility based on medium-risk thresholds. Clinical utility at present may be limited as the model performance deteriorated over time.