How surgeons use risk calculators and non-clinical factors for informed consent and shared decision making: A qualitative study

Panton, Jasmine; Beaulieu‐Jones, Brendin R.; Marwaha, Jayson S.; Woods, Amanda; Nakikj, Drashko; Gehlenborg, Nils; Brat, Gabriel

doi:10.1016/j.amjsurg.2023.07.017

Cited by 1 publication

(2 citation statements)

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“…To engage in a meaningful process of informed consent and mitigate anticipated surgical risks, patient, disease, and surgical factors must be considered in a robust risk assessment. Application of this information using surgical risk calculators can clarify the risk-to-benefit profile of surgery, particularly within the context of major cytoreduction surgery, which can often involve multiple visceral organ resections impacting quality of life [ 12 , 13 , 14 ]. Risk calculators are important instruments for shared decision-making between patients and doctors [ 13 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

“…Application of this information using surgical risk calculators can clarify the risk-to-benefit profile of surgery, particularly within the context of major cytoreduction surgery, which can often involve multiple visceral organ resections impacting quality of life [ 12 , 13 , 14 ]. Risk calculators are important instruments for shared decision-making between patients and doctors [ 13 , 15 , 16 ]. However, pre-existing surgical risk calculators are limited in their ability to accurately predict risk for major gynecological oncology surgeries and are not validated for use in such a population.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Risk of Post-Operative Morbidity and Mortality following Gynaecological Oncology Surgery (PROMEGO): A Global Gynaecological Oncology Surgical Outcomes Collaborative Led Study

Gaba,

Mohammadi,

Krivonosov

et al. 2024

Cancers

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The medical complexity of surgical patients is increasing, and surgical risk calculators are crucial in providing high-value, patient-centered surgical care. However, pre-existing models are not validated to accurately predict risk for major gynecological oncology surgeries, and many are not generalizable to low- and middle-income country settings (LMICs). The international GO SOAR database dataset was used to develop a novel predictive surgical risk calculator for post-operative morbidity and mortality following gynecological surgery. Fifteen candidate features readily available pre-operatively across both high-income countries (HICs) and LMICs were selected. Predictive modeling analyses using machine learning methods and linear regression were performed. The area-under-the-receiver-operating characteristic curve (AUROC) was calculated to assess overall discriminatory performance. Neural networks (AUROC 0.94) significantly outperformed other models (p < 0.001) for evaluating the accuracy of prediction across three groups, i.e., minor morbidity (Clavien–Dindo I-II), major morbidity (Clavien–Dindo III-V), and no morbidity. Logistic-regression modeling outperformed the clinically established SORT model in predicting mortality (AUROC 0.66 versus 0.61, p < 0.001). The GO SOAR surgical risk prediction model is the first that is validated for use in patients undergoing gynecological surgery. Accurate surgical risk predictions are vital within the context of major cytoreduction surgery, where surgery and its associated complications can diminish quality-of-life and affect long-term cancer survival. A model that requires readily available pre-operative data, irrespective of resource setting, is crucial to reducing global surgical disparities.

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