ArXiv Preprint
Despite decades of clinical research, sepsis remains a global public health
crisis with high mortality, and morbidity. Currently, when sepsis is detected
and the underlying pathogen is identified, organ damage may have already
progressed to irreversible stages. Effective sepsis management is therefore
highly time-sensitive. By systematically analysing trends in the plethora of
clinical data available in the intensive care unit (ICU), an early prediction
of sepsis could lead to earlier pathogen identification, resistance testing,
and effective antibiotic and supportive treatment, and thereby become a
life-saving measure. Here, we developed and validated a machine learning (ML)
system for the prediction of sepsis in the ICU. Our analysis represents the
largest multi-national, multi-centre in-ICU study for sepsis prediction using
ML to date. Our dataset contains $156,309$ unique ICU admissions, which
represent a refined and harmonised subset of five large ICU databases
originating from three countries. Using the international consensus definition
Sepsis-3, we derived hourly-resolved sepsis label annotations, amounting to
$26,734$ ($17.1\%$) septic stays. We compared our approach, a deep
self-attention model, to several clinical baselines as well as ML baselines and
performed an extensive internal and external validation within and across
databases. On average, our model was able to predict sepsis with an AUROC of
$0.847 \pm 0.050$ (internal out-of sample validation) and $0.761 \pm 0.052$
(external validation). For a harmonised prevalence of $17\%$, at $80\%$ recall
our model detects septic patients with $39\%$ precision 3.7 hours in advance.
Michael Moor, Nicolas Bennet, Drago Plecko, Max Horn, Bastian Rieck, Nicolai Meinshausen, Peter Bühlmann, Karsten Borgwardt
2021-07-12
