A Clinical Decision Aid to Support Personalized Treatment Selection for Patients with Clinical T1 Renal Masses: Results from a Multi-institutional Competing-risks Analysis.
A Clinical Decision Aid to Support Personalized Treatment Selection for Patients with Clinical T1 Renal Masses: Results from a Multi-institutional Competing-risks Analysis.
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DOI：
10.1016/j.eururo.2021.11.002
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发表时间：
2022-06
期刊：
影响因子：
23.4
通讯作者：
Leibovich, Bradley C.
中科院分区：
文献类型：
--
作者：
Psutka, Sarah P.;Gulati, Roman;Jewett, Michael A. S.;Fadaak, Kamel;Finelli, Antonio;Legere, Laura;Morgan, Todd M.;Pierorazio, Phillip M.;Allaf, Mohamad E.;Herrin, Jeph;Lohse, Christine M.;Thompson, R. Houston;Boorjian, Stephen A.;Atwell, Thomas D.;Schmit, Grant D.;Costello, Brian A.;Shah, Nilay D.;Leibovich, Bradley C.
关键词：
临床T1肾皮质肿块（RCMs）的个性化治疗应考虑与肿瘤和患者特征相关的竞争风险。建立癌症特异性死亡率（CSM）、其他原因死亡率（OCM）和90天Clavien≥3并发症的治疗特异性预测模型，包括根治性肾切除术（RN）、部分肾切除术（PN）、热消融（TA）和主动监测（AS）。收集了4个大容量转诊中心（2000-2019）连续接受初始RN、PN、TA或AS治疗的成年RCM患者的预处理临床和影像学特征。预测模型对CSM和OCM采用竞争风险回归，对90天Clavien级≥3级并发症采用logistic回归。使用自举验证评估性能。该队列包括5300名接受RN（1277）、PN（2967）、TA（476）或AS（580）治疗的患者。中位随访5.2年（IQR 2.5-8.7）， CSM 117例，OCM 607例，并发症198例。CSM、OCM和并发症预测模型的c指数分别为0.80、0.77和0.64。在线计算器（https://small-renal-mass-risk-calculator.fredhutch.org）提供了拟合模型的预测结果。举例来说，假设一名74岁男性，RCM为4.5cm， BMI为32 kg/m2， eGFR为50 mL/min， ECOG PS为3，CCI为3，预测5年CSM为2.9-5.6%，但5年OCM为29%，90天Clavien 3 - 5并发症风险为1.9%，RN， PN和TA分别为5.8%和3.6%。局限性包括选择偏倚、不同治疗地点和研究时间段的实践异质性，以及缺乏对外科医生/医院数量的控制。我们提出了一个包含预处理特征的风险计算器，以估计在共享决策和个性化治疗选择期间使用的治疗特异性死亡率和并发症的竞争风险。我们提出了一个风险计算器，可以对1期肾肿瘤患者因癌症或其他原因死亡的风险以及手术、消融和监测治疗选择的并发症进行个性化估计。
Personalized treatment for clinical T1 renal cortical masses (RCMs) should account for competing risks related to tumor and patient characteristics. To develop treatment-specific prediction models for cancer-specific mortality (CSM), other-cause mortality (OCM), and 90-day Clavien ≥3 complications across radical nephrectomy (RN), partial nephrectomy (PN), thermal ablation (TA), and active surveillance (AS). Pretreatment clinical and radiological features were collected for consecutive adult RCM patients treated with initial RN, PN, TA, or AS at four high-volume referral centers (2000–2019). Prediction models used competing risks regression for CSM and OCM and logistic regression for 90-day Clavien grade ≥3 complications. Performance was assessed using bootstrap validation. The cohort comprised 5300 patients treated with RN (1277), PN (2967), TA (476), or AS (580). With median follow-up of 5.2 years (IQR 2.5–8.7), there were 117 CSM, 607 OCM, and 198 complication events. C-indices for the predictive models were 0.80, 0.77, and 0.64 for CSM, OCM, and complications, respectively. Predictions from the fitted models are provided in an online calculator (https://small-renal-mass-risk-calculator.fredhutch.org). To illustrate, a hypothetical 74-year-old male with a 4.5cm RCM, BMI of 32 kg/m2, eGFR of 50 mL/min, ECOG PS of 3, and CCI of 3, has a predicted 5-year CSM of 2.9–5.6% across treatments, but a 5-year OCM of 29%, and 90-day risk of Clavien 3–5 complications of 1.9%, 5.8%, and 3.6% for RN, PN, and TA, respectively. Limitations include selection bias, heterogeneity in practice across treatment sites and the study time period, and lack of control for surgeon/hospital volume. We present a risk calculator incorporating pretreatment features to estimate treatment-specific competing risks of mortality and complications for use during shared decision-making and personalized treatment election We present a risk calculator that generates personalized estimates of the risks of death from cancer or other causes and complications for surgical, ablation, and surveillance treatment options for patients with stage 1 kidney tumors.
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