Perioperative medicine & outcomes
Predicting postoperative infections: prediction models and their evaluation: A systematic review
Authors' conclusion
Out of 267 prediction models for postoperative infections identified, ACS SRC and SURPAS were most frequently validated.
Study context
- Population
- Studies of multivariable models using pre-operative predictors to estimate risk of postoperative infections within 30 days of elective, major noncardiac, non-intracranial surgery
- Participants
- 151 included studies
- Comparison
- ACS SRC vs SURPAS
- Outcome
- Prediction models for postoperative infection: development, validation, implementation
Pooled result
As reported in the abstract
- Studies reporting model development (267 distinct models)
- 88
- Studies assessing model validity (314 validation analyses)
- 88
- Studies describing implementation
- 0
- Models predicting surgical site infections (SSI)
- 88
- Models predicting pneumonia
- 45
- Models predicting general (unspecified) infections
- 57
- Most common predictors: age
- 66%
- Most common predictors: sex
- 53%
- Most common predictors: ASA score
- 48%
Further pooled results
| Outcome | ACS SRC | SURPAS |
|---|---|---|
| External validations and c-statistics of most frequently validated modelsACS SRC value: c-statistic for SSI · SURPAS value: c-statistic for UTI · median | 0.61 (range 0.43–0.85) | 0.62 (range 0.52–0.78) |
| External validations | 225 | 35 |
| C-statistic, pneumonia (median [range]) | 0.66 [0.44 to 0.95] | — not stated in the abstract |
| C-statistic, UTI (median [range]) | 0.64 [0.31 to 0.97] | — not stated in the abstract |
| C-statistic, general infection (median [range]) | — not stated in the abstract | 0.59 [0.52 to 0.82] |
— not stated in the abstract
Original publication
Predicting postoperative infections: prediction models and their evaluation: A systematic review. Eur J Anaesthesiol. Published 28 July 2026. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →