Development and external validation of a clinical prediction model for predicting quality of recovery up to 1 week after surgery

Stefan van Beek*, Daan Nieboer, Markus Klimek, Robert Jan Stolker, Hendrik Jan Mijderwijk

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Downloads (Pure)

Abstract

The Quality of Recovery Score-40 (QoR-40) has been increasingly used for assessing recovery after patients undergoing surgery. However, a prediction model estimating quality of recovery is lacking. The aim of the present study was to develop and externally validate a clinical prediction model that predicts quality of recovery up to one week after surgery. The modelling procedure consisted of two models of increasing complexity (basic and full model). To assess the internal validity of the developed model, bootstrapping (1000 times) was applied. At external validation, the model performance was evaluated according to measures for overall model performance (explained variance (R 2)) and calibration (calibration plot and slope). The full model consisted of age, sex, previous surgery, BMI, ASA classification, duration of surgery, HADS and preoperative QoR-40 score. At model development, the R 2 of the full model was 0.24. At external validation the R 2 dropped as expected. The calibration analysis showed that the QoR-40 predictions provided by the developed prediction models are reliable. The presented models can be used as a starting point for future updating in prediction studies. When the predictive performance is improved it could be implemented clinically in the future.

Original languageEnglish
Article number387
JournalScientific Reports
Volume14
Issue number1
DOIs
Publication statusPublished - 3 Jan 2024

Bibliographical note

Publisher Copyright:
© 2024, The Author(s).

Fingerprint

Dive into the research topics of 'Development and external validation of a clinical prediction model for predicting quality of recovery up to 1 week after surgery'. Together they form a unique fingerprint.

Cite this