Agochukwu-Mmonu Nnenaya, Murali Adharsh, Wittmann Daniela, Denton Brian, Dunn Rodney L, Montie James, Peabody James, Miller David, Singh Karandeep
Department of Urology, New York University, New York, NY, USA.
Department of Population Health, New York University, New York, NY, USA.
Eur Urol Open Sci. 2022 Apr 18;40:1-8. doi: 10.1016/j.euros.2022.03.009. eCollection 2022 Jun.
Radical prostatectomy (RP) is the most common definitive treatment for men with intermediate-risk prostate cancer and is frequently complicated by erectile dysfunction.
To develop and validate models to predict 12- and 24-month post-RP sexual function.
Using Michigan Urological Surgery Improvement Collaborative (MUSIC) registry data from 2016 to 2021, we developed dynamic, multivariate, random-forest models to predict sexual function recovery following RP. Model factors (established a priori) included baseline patient characteristics and repeated assessments of sexual satisfaction, and Expanded Prostate Cancer Index Composite 26 (EPIC-26) overall scores and sexual domain questions.
We evaluated three outcomes related to sexual function: (1) the EPIC-26 sexual domain score (range 0-100); (2) the EPIC-26 sexual domain score dichotomized at ≥73 for "good" function; and (3) a dichotomized variable for erection quality at 12 and 24 months after RP. A gradient-boosting decision tree was used for the prediction models, which combines many decision trees into a single model. We evaluated the performance of our model using the root mean squared error (RMSE) and mean absolute error (MAE) for the EPIC-26 score as a continuous variable, and the area under the receiver operating characteristic curve (AUC) for the dichotomized EPIC-26 sexual domain score (SDS) and erection quality. All analyses were conducted using R v3.6.3.
We identified 3983 patients at 12 months and 2494 patients at 24 months who were randomized to the derivation cohort at 12 and 24 months, respectively. Using baseline information only, our model predicted the 12-month EPIC-26 SDS with RMSE of 24 and MAE of 20. The AUC for predicting EPIC-26 SDS ≥73 (a previously published threshold) was 0.82. Our model predicted 24-month EPIC-26 SDS with RMSE of 26 and MAE of 21, and AUC for SDS ≥73 of 0.81. Inclusion of post-RP data improved the AUC to 0.91 and 0.94 at 12 and 24 months, respectively. A web tool has also been developed and is available at https://ml4lhs.shinyapps.io/askmusic_prostate_pro/.
Our model provides a valid way to predict sexual function recovery at 12 and 24 months after RP. With this dynamic, multivariate (multiple outcomes) model, accurate predictions can be made for decision-making and during survivorship, which may reduce decision regret.
Our prediction model allows patients considering prostate cancer surgery to understand their probability before and after surgery of recovering their erectile function and may reduce decision regret.
根治性前列腺切除术(RP)是中度风险前列腺癌男性最常见的确定性治疗方法,且常并发勃起功能障碍。
建立并验证预测RP术后12个月和24个月性功能的模型。
设计、设置与参与者:利用2016年至2021年密歇根泌尿外科手术改进协作组(MUSIC)登记数据,我们开发了动态、多变量随机森林模型来预测RP术后性功能恢复情况。模型因素(预先设定)包括患者基线特征以及对性满意度、扩展前列腺癌指数综合评分26项(EPIC - 26)总分及性领域问题的重复评估。
我们评估了与性功能相关的三个结局:(1)EPIC - 26性领域评分(范围0 - 100);(2)EPIC - 26性领域评分,以≥73分为“良好”功能进行二分法划分;(3)RP术后12个月和24个月勃起质量的二分变量。预测模型采用梯度提升决策树,将多个决策树组合成一个单一模型。对于作为连续变量的EPIC - 26评分,我们使用均方根误差(RMSE)和平均绝对误差(MAE)评估模型性能,对于二分法划分的EPIC - 26性领域评分(SDS)和勃起质量,使用受试者操作特征曲线下面积(AUC)评估。所有分析均使用R v3.6.3进行。
我们分别确定了12个月时的3983例患者和24个月时随机分配到推导队列的2494例患者。仅使用基线信息,我们的模型预测12个月EPIC - 26 SDS的RMSE为24,MAE为20。预测EPIC - 26 SDS≥73(先前公布的阈值)的AUC为0.82。我们模型预测24个月EPIC - 26 SDS 的RMSE为26,MAE为21,SDS≥73的AUC为0.81。纳入RP术后数据分别将12个月和24个月时的AUC提高到0.91和0.94。还开发了一个网络工具,可在https://ml4lhs.shinyapps.io/askmusic_prostate_pro/获取。
我们的模型为预测RP术后12个月和24个月性功能恢复提供了一种有效的方法。通过这个动态、多变量(多个结局)模型,可以在决策制定和生存期进行准确预测,这可能会减少决策遗憾。
我们的预测模型使考虑前列腺癌手术的患者能够了解手术前后恢复勃起功能的概率,并可能减少决策遗憾。