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Systematic review and narrative synthesis of surgeons’ perception of postoperative outcomes and risk

Research output: Contribution to journalReview article

Original languageEnglish
Number of pages11
JournalBJS Open
Early online date26 Nov 2019
DateAccepted/In press - 24 Sep 2019
DateE-pub ahead of print (current) - 26 Nov 2019


Background: The accuracy with which surgeons can predict outcomes following surgery has not been explored in a systematic way. The aim of this review was to determine how accurately a surgeon’s ‘gut feeling’ or perception of risk correlates with patient outcomes and available risk scoring systems.

Methods: A systematic review was undertaken in accordance with PRISMA guidelines. A narrative syn- thesis was performed in accordance with the Guidance on the Conduct of Narrative Synthesis In System- atic Reviews. Studies comparing surgeons’ preoperative or postoperative assessment of patient outcomes were included. Studies that made comparisons with risk scoring tools were also included. Outcomes eval- uated were postoperative mortality, general and operation-specific morbidity and long-term outcomes.

Results: Twenty-seven studies comprising 20898 patients undergoing general, gastrointestinal, car- diothoracic, orthopaedic, vascular, urology, endocrine and neurosurgical operations were included. Surgeons consistently overpredicted mortality rates and were outperformed by existing risk scoring tools in six of seven studies comparing area under receiver operating characteristic (ROC) curves (AUC). Surgeons’ prediction of general morbidity was good, and was equivalent to, or better than, pre-existing risk prediction models. Long-term outcomes were poorly predicted by surgeons, with AUC values ranging from 0⋅51 to 0⋅75. Four of five studies found postoperative risk estimates to be more accurate than those made before surgery.

Conclusion: Surgeons consistently overestimate mortality risk and are outperformed by pre-existing tools; prediction of longer-term outcomes is also poor. Surgeons should consider the use of risk prediction tools when available to inform clinical decision-making.

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