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妇科肿瘤大手术围手术期风险预测:英国临床实践的全国性诊断调查

Perioperative Risk Prediction in Major Gynaecological Oncology Surgery: A National Diagnostic Survey of UK Clinical Practice.

作者信息

Sevinyan Lusine, Tailor Anil, Prabhu Pradeep, Williams Peter, Flint Melanie, Madhuri Thumuluru Kavitha

机构信息

School of Applied Sciences, University of Brighton, Brighton BN2 4GJ, UK.

Department of Gynaecological Oncology, Royal Surrey NHS Foundation Trust, Guildford GU2 7XX, UK.

出版信息

Diagnostics (Basel). 2025 Jul 6;15(13):1723. doi: 10.3390/diagnostics15131723.

Abstract

: Gynaecological oncology (GO) surgery involves a wide range of procedures, from minor diagnostic interventions to highly complex cytoreductive operations. Accurate perioperative diagnostics-particularly in major surgery-are critical to optimise patient care, predict morbidity, and facilitate shared decision-making. This study aimed to evaluate current practices in perioperative risk assessment amongst UK GO specialists, focusing on the use, perception, and applicability of diagnostic risk prediction tools. : A national multicentre survey was distributed via the British Gynaecological Cancer Society (BGCS) to consultants, trainees, and nurse specialists. The questionnaire examined clinician familiarity with and use of existing tools such as POSSUM, P-POSSUM, and ACS NSQIP, as well as perceived reliability and areas for improvement. : Fifty-four clinicians responded, two-thirds of whom were consultant gynaecological oncologists. While 51.9% used morbidity prediction tools selectively, only 7.4% used them routinely for all major surgeries. The most common models were P-POSSUM (39.6%) and ACS NSQIP (25%), though over 20% did not use any formal tool. Despite this, 80% of respondents expressed a desire for more accurate, GO-specific models. : This study reveals a gap between available perioperative diagnostics and real-world clinical use in GO surgical planning. There is an urgent need for validated, user-friendly, and GO-specific risk prediction tools-particularly for high-risk, complex surgical cases. Further research should focus on prospective validation of tools such as ACS NSQIP and their integration into routine practice to improve outcomes in gynaecological oncology.

摘要

妇科肿瘤手术涵盖了广泛的程序,从微小的诊断性干预到高度复杂的肿瘤细胞减灭术。准确的围手术期诊断——尤其是在大型手术中——对于优化患者护理、预测发病率以及促进共同决策至关重要。本研究旨在评估英国妇科肿瘤专家围手术期风险评估的当前实践,重点关注诊断风险预测工具的使用、认知和适用性。

一项全国多中心调查通过英国妇科癌症协会(BGCS)分发给顾问、实习生和护士专家。问卷考察了临床医生对现有工具(如POSSUM、P-POSSUM和美国外科医师协会国家外科质量改进计划(ACS NSQIP))的熟悉程度和使用情况,以及感知到的可靠性和改进领域。

54名临床医生做出了回应,其中三分之二是妇科肿瘤顾问医生。虽然51.9%的人选择性地使用发病率预测工具,但只有7.4%的人在所有大型手术中都常规使用这些工具。最常用的模型是P-POSSUM(39.6%)和ACS NSQIP(25%),不过超过20%的人没有使用任何正式工具。尽管如此,80%的受访者表示希望有更准确的、针对妇科肿瘤的模型。

这项研究揭示了在妇科肿瘤手术规划中,可用的围手术期诊断与实际临床应用之间存在差距。迫切需要经过验证的、用户友好的且针对妇科肿瘤的风险预测工具——特别是针对高风险、复杂手术病例。进一步的研究应侧重于对ACS NSQIP等工具进行前瞻性验证,并将其整合到常规实践中,以改善妇科肿瘤的治疗效果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/762d/12248713/56051198865a/diagnostics-15-01723-g001.jpg

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