JAMA：减少儿童患者良性卵巢肿瘤不必要的卵巢切除术

2023-10-08 来源：JAMA

本文由小咖机器人翻译整理

期刊来源：JAMA

文献发表时间：2023-10-03

原文链接：https://jamanetwork.com/journals/jama/article-abstract/2810032

关键点内容如下：

问题

基于共识的术前风险分层算法能否减少儿童和青少年良性卵巢疾病患者不必要的卵巢切除术？

调查结果

在这项多机构、干预前/后研究中，纳入了519名21岁或以下的卵巢肿块患者，在实施基于共识的术前风险分层算法后，良性疾病的卵巢切除术百分比从16.1%降至8.4%。

意义

使用基于共识的术前风险分层算法管理患有卵巢肿块的儿童和青少年患者，可以防止良性疾病不必要的卵巢切除术。

摘要内容如下：

重要性

虽然大多数儿童和青少年的卵巢肿块是良性的，但许多人都接受了卵巢切除术，这可能是不必要的，而且可能对健康产生终身的负面影响。

目的

评估基于共识的术前风险分层算法区分良性和恶性卵巢病变并减少不必要的卵巢切除术的能力。

研究设计和参与者

2018年8月至2021年1月期间，在美国11家儿童医院住院的6至21岁因卵巢肿块接受手术的患者中，对危险分层算法进行了干预前/后研究，并进行了1年的随访。

干预措施

实施基于共识的术前风险分层算法，包括6个月的干预前评估、6个月的干预采用和18个月的干预。采用干预队列被排除在统计比较之外。

主要结局和措施

不必要的卵巢切除术，根据最终病理或肿块消退情况，定义为良性卵巢肿瘤的卵巢切除术。

结果

共有519名患者，中位年龄为15.1（IQR，13.0-16.8）年，分为3个阶段:96名患者处于干预前阶段（中位年龄为15.4[IQR，13.4-17.2]年；11.5%为非西班牙裔黑人；68.8%的非西班牙裔白人）；105人处于收养阶段；干预期318例，中位年龄15.0[IQR，12.9-16.6]岁；13.8%为非西班牙裔黑人；53.5%的非西班牙裔白人）。良性疾病在干预前队列中为93例（96.9%），在干预队列中为298例（93.7%）。不必要的卵巢切除术的百分比从干预前的16.1%（15/93）降至干预期间的8.4%（25/298）（绝对减少7.7%[95%CI，0.4%-15.9%]；P=.03）。在干预队列中识别良性病变的算法测试性能的敏感性为91.6%（95%CI，88.5%-94.8%），特异性为90.0%（95%CI，76.9%-100%），阳性预测值为99.3%（95%CI，98.3%-100%），阴性预测值为41.9%（95%CI，27.1%-56.6%）。在介入治疗阶段（采用保留卵巢手术治疗恶性疾病），错误分类的比例为0.7%。干预阶段的算法依从性为95.0%，保真度为81.8%。

结论和相关性

通过使用术前风险分层算法来识别适合保留卵巢手术的良性病变的高可能性病变，减少了不必要的卵巢切除术。采用这种算法可能会防止青少年时期不必要的卵巢切除术及其终身后果。需要进一步的研究来确定算法坚持的障碍。

英文原文如下：

Key Points

Question Can a consensus-based, preoperative risk stratification algorithm reduce unnecessary oophorectomies in pediatric and adolescent patients with benign ovarian disease?

Findings In this multi-institutional, pre/post interventional study that included 519 patients aged 21 years or younger with ovarian masses, the percentage of oophorectomies performed for benign disease decreased from 16.1% to 8.4% after implementation of a consensus-based preoperative risk stratification algorithm.

Meaning Managing pediatric and adolescent patients with ovarian masses using a consensus-based preoperative risk stratification algorithm can prevent unnecessary oophorectomies for benign disease.

Abstract

Importance Although most ovarian masses in children and adolescents are benign, many are managed with oophorectomy, which may be unnecessary and can have lifelong negative effects on health.

Objective To evaluate the ability of a consensus-based preoperative risk stratification algorithm to discriminate between benign and malignant ovarian pathology and decrease unnecessary oophorectomies.

Design, Setting, and Participants Pre/post interventional study of a risk stratification algorithm in patients aged 6 to 21 years undergoing surgery for an ovarian mass in an inpatient setting in 11 children’s hospitals in the United States between August 2018 and January 2021, with 1-year follow-up.

Intervention Implementation of a consensus-based, preoperative risk stratification algorithm with 6 months of preintervention assessment, 6 months of intervention adoption, and 18 months of intervention. The intervention adoption cohort was excluded from statistical comparisons.

Main Outcomes and Measures Unnecessary oophorectomies, defined as oophorectomy for a benign ovarian neoplasm based on final pathology or mass resolution.

Results A total of 519 patients with a median age of 15.1 (IQR, 13.0-16.8) years were included in 3 phases: 96 in the preintervention phase (median age, 15.4 [IQR, 13.4-17.2] years; 11.5% non-Hispanic Black; 68.8% non-Hispanic White); 105 in the adoption phase; and 318 in the intervention phase (median age, 15.0 [IQR, 12.9-16.6)] years; 13.8% non-Hispanic Black; 53.5% non-Hispanic White). Benign disease was present in 93 (96.9%) in the preintervention cohort and 298 (93.7%) in the intervention cohort. The percentage of unnecessary oophorectomies decreased from 16.1% (15/93) preintervention to 8.4% (25/298) during the intervention (absolute reduction, 7.7% [95% CI, 0.4%-15.9%]; P = .03). Algorithm test performance for identifying benign lesions in the intervention cohort resulted in a sensitivity of 91.6% (95% CI, 88.5%-94.8%), a specificity of 90.0% (95% CI, 76.9%-100%), a positive predictive value of 99.3% (95% CI, 98.3%-100%), and a negative predictive value of 41.9% (95% CI, 27.1%-56.6%). The proportion of misclassification in the intervention phase (malignant disease treated with ovary-sparing surgery) was 0.7%. Algorithm adherence during the intervention phase was 95.0%, with fidelity of 81.8%.

Conclusions and Relevance Unnecessary oophorectomies decreased with use of a preoperative risk stratification algorithm to identify lesions with a high likelihood of benign pathology that are appropriate for ovary-sparing surgery. Adoption of this algorithm might prevent unnecessary oophorectomy during adolescence and its lifelong consequences. Further studies are needed to determine barriers to algorithm adherence.

-------------------- 分割线 -------------------