Risk scores in anaesthesia: the future is hard to predict

Daniel James Drayton; Michael Ayres; Samuel D Relton; Matthew Sperrin; Marlous Hall

doi:10.1016/j.bjao.2022.100027

Risk scores in anaesthesia: the future is hard to predict

Daniel James Drayton, Michael Ayres, Samuel D Relton, Matthew Sperrin, Marlous Hall

Division of Informatics, Imaging & Data Sciences (L5)

University of Leeds

Research output: Contribution to journal › Article › peer-review

Abstract

External validation helps to assess whether a given risk prediction model will perform well in a target population. Validation is an important step in maintaining the utility of risk prediction models, as their ability to provide reliable risk estimates will deteriorate over time (calibration drift).

Original language	English
Pages (from-to)	100027
Journal	BJA open
Volume	3
DOIs	https://doi.org/10.1016/j.bjao.2022.100027
Publication status	Published - Sept 2022

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10.1016/j.bjao.2022.100027

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title = "Risk scores in anaesthesia: the future is hard to predict",

abstract = "External validation helps to assess whether a given risk prediction model will perform well in a target population. Validation is an important step in maintaining the utility of risk prediction models, as their ability to provide reliable risk estimates will deteriorate over time (calibration drift).",

author = "Drayton, \{Daniel James\} and Michael Ayres and Relton, \{Samuel D\} and Matthew Sperrin and Marlous Hall",

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year = "2022",

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doi = "10.1016/j.bjao.2022.100027",

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T2 - the future is hard to predict

AU - Drayton, Daniel James

AU - Ayres, Michael

AU - Relton, Samuel D

AU - Sperrin, Matthew

AU - Hall, Marlous

PY - 2022/9

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N2 - External validation helps to assess whether a given risk prediction model will perform well in a target population. Validation is an important step in maintaining the utility of risk prediction models, as their ability to provide reliable risk estimates will deteriorate over time (calibration drift).

AB - External validation helps to assess whether a given risk prediction model will perform well in a target population. Validation is an important step in maintaining the utility of risk prediction models, as their ability to provide reliable risk estimates will deteriorate over time (calibration drift).

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U2 - 10.1016/j.bjao.2022.100027

DO - 10.1016/j.bjao.2022.100027

M3 - Article

C2 - 37588581

SN - 2772-6096

VL - 3

SP - 100027

JO - BJA open

JF - BJA open

ER -

Risk scores in anaesthesia: the future is hard to predict

Abstract

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