Risk Scores in Anaesthesia: the Future is Hard to Predict
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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).
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References
1.
Protopapa K, Simpson J, Smith N, Moonesinghe S
. Development and validation of the Surgical Outcome Risk Tool (SORT). Br J Surg. 2014; 101(13):1774-83.
PMC: 4240514.
DOI: 10.1002/bjs.9638.
View
2.
Hippisley-Cox J, Coupland C, Brindle P
. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study. BMJ. 2017; 357:j2099.
PMC: 5441081.
DOI: 10.1136/bmj.j2099.
View
3.
Damen J, Hooft L, Schuit E, Debray T, Collins G, Tzoulaki I
. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ. 2016; 353:i2416.
PMC: 4868251.
DOI: 10.1136/bmj.i2416.
View
4.
Dhiman P, Ma J, Andaur Navarro C, Speich B, Bullock G, Damen J
. Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review. BMC Med Res Methodol. 2022; 22(1):101.
PMC: 8991704.
DOI: 10.1186/s12874-022-01577-x.
View
5.
Copeland G, Jones D, Walters M
. POSSUM: a scoring system for surgical audit. Br J Surg. 1991; 78(3):355-60.
DOI: 10.1002/bjs.1800780327.
View