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Surgical Risk Preoperative Assessment System (SURPAS): III. Accurate Preoperative Prediction of 8 Adverse Outcomes Using 8 Predictor Variables.
- Source :
-
Annals of surgery [Ann Surg] 2016 Jul; Vol. 264 (1), pp. 23-31. - Publication Year :
- 2016
-
Abstract
- Objective: To develop accurate preoperative risk prediction models for multiple adverse postoperative outcomes applicable to a broad surgical population using a parsimonious common set of risk variables and outcomes.<br />Summary Background Data: Currently, preoperative assessment of surgical risk is largely based on subjective clinician experience. We propose a paradigm shift from the current postoperative risk adjustment for cross-hospital comparison to patient-centered quantitative risk assessment during the preoperative evaluation.<br />Methods: We identify the most common and important predictor variables of postoperative mortality, overall morbidity, and 6 complication clusters from previously published prediction analyses that used forward selection stepwise logistic regression. We then refit the prediction models using only the 8 most common and important predictor variables, and compare the discrimination and calibration of these models to the original full-variable models using the c-index, Hosmer-Lemeshow analysis, and Brier scores.<br />Results: Accurate risk models for 30-day outcomes of mortality, overall morbidity, and 6 clusters of complications were developed using a set of 8 preoperative risk variables. C-indexes of the 8 variable models are between 97.9% and 99.2% of those of the full models containing up to 28 variables, indicating excellent discrimination using fewer predictor variables. Hosmer-Lemeshow analyses showed observed to expected event rates to be nearly identical between parsimonious models and full models, both showing good calibration.<br />Conclusions: Accurate preoperative risk assessment of postoperative mortality, overall morbidity, and 6 complication clusters in a broad surgical population can be achieved with as few as 8 preoperative predictor variables, improving feasibility of routine preoperative risk assessment for surgical patients.
Details
- Language :
- English
- ISSN :
- 1528-1140
- Volume :
- 264
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Annals of surgery
- Publication Type :
- Academic Journal
- Accession number :
- 26928465
- Full Text :
- https://doi.org/10.1097/SLA.0000000000001678