Joanne Palmer

Objectives: To evaluate the acceptability and feasibility of intraoperative cell salvage (ICS) in women with ovarian cancer. A prospective multicenter randomized controlled feasibility trial. Setting: Four U.K. cancer centers. Women FIGO stage III/IV ovarian cancer supported by CT scan evidence. We randomized women to receive ICS or donor blood (as required) during surgery for ovarian cancer. The acceptability and feasibility of ICS in women with ovarian cancer having cytoreductive surgery; rates of recruitment for a larger trial and the likely completeness of resource use and outcome data; and blinding of allocation for participants and outcome assessors. A total of 57 women were included; the rate of recruitment was 1.4 cases per month, which closely aligns with the target, and 66% of the eligible patients were recruited. Overall, 91% of women completed the 30-day follow-up, and 75% completed the six-week follow-up. Mean (SD) blood loss in the ICS group was 1022 mL (SD 929 mL) and 924 mL (SD 646 mL) in the control group. A total of 16 (62%) of the participants undergoing surgery in the ICS arm received ICS reinfusion. Of the ten participants in the intervention group who did not receive ICS, six participants lost a significant volume of blood requiring transfusion. In the donor blood group, 14 of the 29 participants received donor blood. In the ICS group, 20/24 (83%) of participants, and 23/24 (96%) of research nurses did not know their group allocation. In the control group, 24/28 (86%) and 25/29 (86%) of participants and research nurses did not know the group allocation. Our study provides clinical evidence of the feasibility and acceptability of using ICS in ovarian cancer surgery and provides useful insights into the use of ICS within clinical trials. Women are open to having ICS as an alternative to blood transfusion. An appropriately powered randomized controlled trial is now required.

Artificial intelligence as a medical device is increasingly being applied to healthcare for diagnosis, risk stratification and resource allocation. However, a growing body of evidence has highlighted the risk of algorithmic bias, which may perpetuate existing health inequity. This problem arises in part because of systemic inequalities in dataset curation, unequal opportunity to participate in research and inequalities of access. This study aims to explore existing standards, frameworks and best practices for ensuring adequate data diversity in health datasets. Exploring the body of existing literature and expert views is an important step towards the development of consensus-based guidelines. The study comprises two parts: a systematic review of existing standards, frameworks and best practices for healthcare datasets; and a survey and thematic analysis of stakeholder views of bias, health equity and best practices for artificial intelligence as a medical device. We found that the need for dataset diversity was well described in literature, and experts generally favored the development of a robust set of guidelines, but there were mixed views about how these could be implemented practically. The outputs of this study will be used to inform the development of standards for transparency of data diversity in health datasets (the STANDING Together initiative).