Cardiothoracic & vascular anesthesia
Predicting Postoperative Cognitive Dysfunction in Older Cardiac Surgery Patients: An Integrated Machine Learning Approach With a Visual Nomogram
Authors' conclusion
A precise and effective nomogram was developed using surgical approach, underlying comorbidities, surgery duration, blood loss, ICU sleep quality score, APACHE II score, and self-care ability as predictors of POCD in older cardiac surgery patients.
Study context
- Population
- Consecutive older patients (aged ≥ 60 years) undergoing cardiac surgery, surgical intensive care unit (ICU) of a tertiary academic medical centerconsecutive
- Participants
- 353 patients
- Outcome
- POCD incidence and nomogram performance
Methodological detailsprediction model development, nomogram, LASSO regression with multivariate logistic regression, single-centre
- Prediction model development
- Nomogram
- LASSO regression with multivariate logistic regression
- Single-centre
Main finding
As reported in the abstract
Overall incidence of postoperative cognitive dysfunction (POCD)
49.86%
176 of 353
Seven independent predictors: surgical approach, pre-existing comorbidities, surgery duration, intraoperative blood loss, ICU sleep quality score, APACHE II score, preoperative self-care ability
7
Nomogram discrimination, AUC
0.786
95% CI 0.738–0.834
Observational data: associations, not effects.
Parts of this Brief are withheld: the automatic checks could not confirm them against the abstract.
Original publication
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