Predictive modelling for postoperative acute kidney injury: big data enhancing quality or the Emperor's new clothes?
Br J Anaesth
; 133(3): 476-478, 2024 Sep.
Article
em En
| MEDLINE
| ID: mdl-38902116
ABSTRACT
The increased availability of large clinical datasets together with increasingly sophisticated computing power has facilitated development of numerous risk prediction models for various adverse perioperative outcomes, including acute kidney injury (AKI). The rationale for developing such models is straightforward. However, despite numerous purported benefits, the uptake of preoperative prediction models into clinical practice has been limited. Barriers to implementation of predictive models, including limitations in their discrimination and accuracy, as well as their ability to meaningfully impact clinical practice and patient outcomes, are increasingly recognised. Some of the purported benefits of predictive modelling, particularly when applied to postoperative AKI, might not fare well under detailed scrutiny. Future research should address existing limitations and seek to demonstrate both benefit to patients and value to healthcare systems from implementation of these models in clinical practice.
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Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Complicações Pós-Operatórias
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Injúria Renal Aguda
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Big Data
Limite:
Humans
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article