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Background: Decision-making when considering major lower limb amputation is complex and requires individualized outcome estimation. It is unknown how accurate healthcare professionals or relevant outcome prediction tools are at predicting outcomes at 1-year after major lower limb amputation., Methods: An international, multicentre prospective observational study evaluating healthcare professional accuracy in predicting outcomes 1 year after major lower limb amputation and evaluation of relevant outcome prediction tools identified in a systematic search of the literature was undertaken. Observed outcomes at 1 year were compared with: healthcare professionals' preoperative predictions of death (surgeons and anaesthetists), major lower limb amputation revision (surgeons) and ambulation (surgeons, specialist physiotherapists and vascular nurse practitioners); and probabilities calculated from relevant outcome prediction tools., Results: A total of 537 patients and 2244 healthcare professional predictions of outcomes were included. Surgeons and anaesthetists had acceptable discrimination (C-statistic = 0.715), calibration and overall performance (Brier score = 0.200) when predicting 1-year death, but performed worse when predicting major lower limb amputation revision and ambulation (C-statistics = 0.627 and 0.662 respectively). Healthcare professionals overestimated the death and major lower limb amputation revision risks. Consultants outperformed trainees, especially when predicting ambulation. Allied healthcare professionals marginally outperformed surgeons in predicting ambulation. Two outcome prediction tools (C-statistics = 0.755 and 0.717, Brier scores = 0.158 and 0.178) outperformed healthcare professionals' discrimination, calibration and overall performance in predicting death. Two outcome prediction tools for ambulation (C-statistics = 0.688 and 0.667) marginally outperformed healthcare professionals., Conclusion: There is uncertainty in predicting 1-year outcomes following major lower limb amputation. Different professional groups performed comparably in this study. Two outcome prediction tools for death and two for ambulation outperformed healthcare professionals and may support shared decision-making., (© The Author(s) 2024. Published by Oxford University Press on behalf of BJS Foundation Ltd.)

Background: The accuracy with which healthcare professionals (HCPs) and risk prediction tools predict outcomes after major lower limb amputation (MLLA) is uncertain. The aim of this study was to evaluate the accuracy of predicting short-term (30 days after MLLA) mortality, morbidity, and revisional surgery., Methods: The PERCEIVE (PrEdiction of Risk and Communication of outcomE following major lower limb amputation: a collaboratIVE) study was launched on 1 October 2020. It was an international multicentre study, including adults undergoing MLLA for complications of peripheral arterial disease and/or diabetes. Preoperative predictions of 30-day mortality, morbidity, and MLLA revision by surgeons and anaesthetists were recorded. Probabilities from relevant risk prediction tools were calculated. Evaluation of accuracy included measures of discrimination, calibration, and overall performance., Results: Some 537 patients were included. HCPs had acceptable discrimination in predicting mortality (931 predictions; C-statistic 0.758) and MLLA revision (565 predictions; C-statistic 0.756), but were poor at predicting morbidity (980 predictions; C-statistic 0.616). They overpredicted the risk of all outcomes. All except three risk prediction tools had worse discrimination than HCPs for predicting mortality (C-statistics 0.789, 0.774, and 0.773); two of these significantly overestimated the risk compared with HCPs. SORT version 2 (the only tool incorporating HCP predictions) demonstrated better calibration and overall performance (Brier score 0.082) than HCPs. Tools predicting morbidity and MLLA revision had poor discrimination (C-statistics 0.520 and 0.679)., Conclusion: Clinicians predicted mortality and MLLA revision well, but predicted morbidity poorly. They overestimated the risk of mortality, morbidity, and MLLA revision. Most short-term risk prediction tools had poorer discrimination or calibration than HCPs. The best method of predicting mortality was a statistical tool that incorporated HCP estimation., (© The Author(s) 2022. Published by Oxford University Press on behalf of BJS Society Ltd. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Accurate prediction of outcomes following surgery with high morbidity and mortality rates is essential for informed shared decision-making between patients and clinicians. It is unknown how accurately healthcare professionals predict outcomes following major lower-limb amputation (MLLA). Several MLLA outcome-prediction tools have been developed. These could be valuable in clinical practice, but most require validation in independent cohorts before routine clinical use can be recommended. The primary aim of this study is to evaluate the accuracy of healthcare professionals' predictions of outcomes in adult patients undergoing MLLA for complications of chronic limb-threatening ischaemia (CLTI) or diabetes. Secondary aims include the validation of existing outcome-prediction tools., Method: This study is an international, multicentre prospective observational study including adult patients undergoing a primary MLLA for CLTI or diabetes. Healthcare professionals' accuracy in predicting outcomes at 30-days (death, morbidity and MLLA revision) and 1-year (death, MLLA revision and ambulation) will be evaluated. Sixteen existing outcome-prediction tools specific to MLLA will be examined for validity. Data collection began on 1 October 2020; the end of follow-up will be 1 May 2022. The C-statistic, Hosmer-Lemeshow test, reclassification tables and Brier score will be used to evaluate the predictive performance of healthcare professionals and prediction tools, respectively., Study Registration and Dissemination: This study will be registered locally at each centre in accordance with local policies before commencing data collection, overseen by local clinician leads. Results will be disseminated to all centres, and any subsequent presentation(s) and/or publication(s) will follow a collaborative co-authorship model., (© The Author(s) 2021. Published by Oxford University Press on behalf of BJS Society Ltd.)

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 synthesis was performed in accordance with the Guidance on the Conduct of Narrative Synthesis In Systematic 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 evaluated were postoperative mortality, general and operation-specific morbidity and long-term outcomes., Results: Twenty-seven studies comprising 20 898 patients undergoing general, gastrointestinal, cardiothoracic, 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., (© 2019 The Authors. BJS Open published by John Wiley & Sons Ltd on behalf of BJS Society Ltd.)