TY  - JOUR
AU  - Persson, Inger
AU  - Östling, Andreas
AU  - Arlbrandt, Martin
AU  - Söderberg, Joakim
AU  - Becedas, David
PY  - 2021
DA  - 2021/9/30
TI  - A Machine Learning Sepsis Prediction Algorithm for Intended Intensive Care Unit Use (NAVOY Sepsis): Proof-of-Concept Study
JO  - JMIR Form Res
SP  - e28000
VL  - 5
IS  - 9
KW  - sepsis
KW  - prediction
KW  - early detection
KW  - machine learning
KW  - electronic health record
KW  - EHR
KW  - software as a medical device
KW  - algorithm
KW  - detection
KW  - intensive care unit
KW  - ICU
KW  - proof of concept
AB  - Background: Despite decades of research, sepsis remains a leading cause of mortality and morbidity in intensive care units worldwide. The key to effective management and patient outcome is early detection, for which no prospectively validated machine learning prediction algorithm is currently available for clinical use in Europe. Objective: We aimed to develop a high-performance machine learning sepsis prediction algorithm based on routinely collected intensive care unit data, designed to be implemented in European intensive care units. Methods: The machine learning algorithm was developed using convolutional neural networks, based on Massachusetts Institute of Technology Lab for Computational Physiology MIMIC-III clinical data from intensive care unit patients aged 18 years or older. The model uses 20 variables to produce hourly predictions of onset of sepsis, defined by international Sepsis-3 criteria. Predictive performance was externally validated using hold-out test data. Results: The algorithm—NAVOY Sepsis—uses 4 hours of input and can identify patients with high risk of developing sepsis, with high performance (area under the receiver operating characteristics curve 0.90; area under the precision-recall curve 0.62) for predictions up to 3 hours before sepsis onset. Conclusions: The prediction performance of NAVOY Sepsis was superior to that of existing sepsis early warning scoring systems and comparable with those of other prediction algorithms designed to predict sepsis onset. The algorithm has excellent predictive properties and uses variables that are routinely collected in intensive care units. 
SN  - 2561-326X
UR  - https://formative.jmir.org/2021/9/e28000
UR  - https://doi.org/10.2196/28000
UR  - http://www.ncbi.nlm.nih.gov/pubmed/34591016
DO  - 10.2196/28000
ID  - info:doi/10.2196/28000
ER  -