Continuous and early prediction of future moderate and severe Acute Kidney Injury in critically ill patients: Development and multi-centric, multi-national external validation of a machine-learning model.
Francesca AlfieriAndrea AnconaGiovanni TripepiAndrea RubeisNiccolò ArjoldiStefano FinazziValentina CaudaRiccardo Maria FagugliPublished in: PloS one (2023)
In this study, a novel ML model for continuous and early AKI (stage 2/3 KDIGO) prediction is successfully developed, leveraging only routinely-available data. It continuously predicts AKI episodes during ICU stay, at least 14 hours in advance when the AKI episode happens after the first 24 hours of ICU admission. Its performances are validated in an extensive, multi-national and multi-centric cohort of ICU adult patients. This ML model overcomes the main limitations of currently available predictive models. The benefits of its real-world implementation enable an early proactive clinical management and the prevention of AKI episodes in ICU patients. Furthermore, the software could be directly integrated with IT system of the ICU.
Keyphrases
- intensive care unit
- acute kidney injury
- mechanical ventilation
- machine learning
- end stage renal disease
- quality improvement
- emergency department
- chronic kidney disease
- newly diagnosed
- healthcare
- primary care
- big data
- acute respiratory distress syndrome
- peritoneal dialysis
- early onset
- data analysis
- patient reported outcomes
- high intensity
- drug induced