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Published on: August 1, 2019
An Online Preoperative Screening Tool to Optimize Care for Patients Undergoing Cancer Surgery: A Mixed-Method Study
Alexandria Paige Petridis1,2, Cherry Koh1,2,3, Michael Solomon1,2,3
1Surgical Outcomes Research Centre (SOuRCe), Royal Prince Alfred Hospital, Sydney 2050, Australia.
Background/Objective:
Despite surgery being the primary curative treatment for cancer, patients with compromised preoperative physical, nutritional, and psychological status are often at a higher risk for complications. While various screening tools exist to assess physical, nutritional, and psychological status, there is currently no standardised self-reporting tool, or established cut-off points for comprehensive risk assessment. This study aims to develop, validate, and implement an online self-reporting preoperative screening tool that identifies modifiable risk factors in cancer surgery patients.
Methods:
This mixed-methods study consists of three distinct stages: (1) Development-(i) a scoping review to identify available physical, nutritional, and psychological screening tools; (ii) a Delphi study to gain consensus on the use of available screening tools; and (iii) a development of the online screening tool to determine patients at high risk of postoperative complications. (2) Testing-a prospective cohort study determining the correlation between at-risk patients and postoperative complications. (3) Implementation-the formulation of an implementation policy document considering feasibility.
Conclusions:
The timely identification of high-risk patients, based on their preoperative physical, nutritional, and psychological statuses, would enable referral to targeted interventions. The implementation of a preoperative online screening tool would streamline this identification process while minimising unwarranted variation in preoperative treatment optimisation. This systematic approach would not only support high-risk patients but also allow for more efficient provision of surgery to low-risk patients through effective risk stratification.

