Identifying Subgroups with Localized Kidney Cancer Who Can Defer Surgery

描述（由申请人提供）：过去几十年来，局部肾癌的发病率一直在增加。这种增加很大程度上可能是由于在超声，CT扫描或MRI扫描后偶然发现的小肿瘤的检测，这些扫描是由于非肾癌相关的原因而进行的。这表明了两种可能性：1）许多以前在晚期发现的肿瘤只是在早期发现，2）现在发现了从未进展到症状或致命阶段的肿瘤。生物学上无关紧要的小肾脏肿块现在被检测到的可能性引发了一场关于在手术或其他介入治疗小局限性肾脏肿块之前或代替手术或其他介入治疗的积极监测的益处的辩论。许多接受手术治疗的患者会在治疗的五年内死于其他疾病。因此，对于无论治疗如何都会在五年内死亡的患者来说，肾癌治疗的生存益处是适度的。此外，有一些证据表明，某些治疗实际上可能会恶化某些局部肾癌患者的生存结局。例如，与肾部分切除术相比，根治性肾切除术与慢性肾病（CKD）的风险增加有关。鉴于围绕局部肾癌治疗的适当性的争论，特别是在老年患者和合并症患者中，需要更好的预后模型来确定谁可能从积极监测（也称为观察）中受益。 这项工作的目标是通过开发模型来改善预后建模，这些模型可以根据个体在接受或不接受治疗的情况下的潜在死亡风险对个体进行分类。我们将使用链接的SEER-Medicare数据应用模型。考虑疾病进展或死亡率异质性的重要性已经在医学决策文献中提到。然而，这些方法通常假设个体是快速或缓慢的疾病进展者。相比之下，本研究提出了在临床有效性调查中可以识别四个潜在生存率组的模型的开发：1）在治疗或不治疗的情况下具有长期存活的患者，因此可以观察到，2）在不治疗的情况下具有短期存活但在治疗的情况下具有长期存活的患者，因此应该接受立即干预，（3）不治疗生存期长，治疗生存期短者应观察;（4）治疗生存期短者或不治疗生存期短者可避免不必要手术。此外，我们假设相对较长和较短的生存风险在治疗组之间可能有所不同。我们建议使用的主要分层和鲁宾的因果模型作为概念工具，这项调查。这个项目将进一步了解局部肾癌患者生存率的异质性。 公共卫生相关性：这项工作将通过开发模型来改善局部肾癌结局的预后建模，这些模型可以根据个体在接受或不接受治疗的情况下的潜在死亡风险对其进行分类。那些预期寿命不会改变或治疗后会恶化的人可以免于手术。

DESCRIPTION (provided by applicant): The incidence of localized kidney cancer has been increasing for the past several decades. Much of this increase is likely due to the detection of small tumors found incidentally after ultrasound, CT scans, or MRI scans ordered for non-kidney cancer related reasons. This suggests two possibilities:1) many tumors that would previously have been found at a later stage are simply being found at earlier stages, and 2) tumors that would never have progressed to a symptomatic or lethal stage are now being found. The possibility that biologically inconsequential small renal masses are now being detected has opened a debate concerning the benefits of active surveillance prior to or in lieu of surgical or other interventions for the treatment of small localized renal masses. Many patients who are treated with surgery will die from other diseases within five years of treatment. The survival benefit of therapy for kidney cancer is hence modest for patients who would die within five years regardless of therapy. Further, there is some evidence that certain treatments might actually worsen survival outcomes in some patients with localized kidney cancer. Radical nephrectomy, for example, has been associated with an increased risk of chronic kidney disease (CKD) compared to partial nephrectomy. Given the debate surrounding the appropriateness of treatment for localized kidney cancer, particularly among older patients and those with comorbidities, better prognostic models are needed to identify who might benefit from active surveillance (also called observation). The goal of this work is to improve prognostic modeling by developing models that can classify individuals according to their underlying hazard of death either with or without treatment. We will apply the models using linked SEER-Medicare data. The importance of accounting for heterogeneity of progression or mortality rates has already been noted in the medical decision making literature. However, such methods often assume that individuals are either rapid or slow disease progressors. This research, in contrast, proposes the development of models that can identify four potential survival rate groups in the investigation of clinical effectiveness: 1) those that have long survival with or without treatment who can therefore be observed, 2) those that have short survival without treatment but long survival with treatment who should hence undergo immediate intervention, 3) those that have long survival without treatment but short survival with treatment who should be observed, and 4) those that have short survival with or without treatment who can avoid unnecessary surgery. Further, we assume that relatively long and short survival hazards can vary between treatment arms. We propose using principal stratification and Rubin's causal model as conceptual tools for this investigation. This project will further knowledge concerning heterogeneity in survival rates among those with localized kidney cancer. PUBLIC HEALTH RELEVANCE: This work will improve prognostic modeling of localized kidney cancer outcomes by developing models that can classify individuals according to their underlying hazard of death either with or without treatment. Those whose life expectancy would not change or would worsen with treatment could be spared surgery.