Identifying Subgroups with Localized Kidney Cancer Who Can Defer Surgery

描述(申请人提供)：在过去的几十年里，局部肾癌的发病率一直在增加。这一增加很可能是由于非肾癌相关原因订购的超声波、CT扫描或MRI扫描后偶然发现的小肿瘤。这暗示了两种可能性：1)许多以前可能在较晚阶段发现的肿瘤只是在较早阶段被发现，以及2)永远不会发展到有症状或致命阶段的肿瘤现在正在被发现。现在发现了生物学上无关紧要的小肾肿块的可能性，这引发了一场关于在治疗小的局限性肾肿块之前或代替外科手术或其他干预措施的积极监测的好处的辩论。许多接受手术治疗的患者会在治疗后五年内死于其他疾病。因此，对于那些在五年内死亡的患者来说，肾癌治疗的生存益处是有限的，无论采用何种治疗。此外，有一些证据表明，某些治疗实际上可能会恶化一些局限性肾癌患者的生存结果。例如，与部分肾切除术相比，根治性肾切除术与慢性肾脏疾病(CKD)的风险增加有关。鉴于围绕局部肾癌治疗是否合适的争论，尤其是在老年患者和合并肾癌的患者中，需要更好的预后模型来确定谁可能从积极监测中受益(也称为观察)。这项工作的目标是通过开发模型来改进预后建模，该模型可以根据患者接受治疗或不接受治疗的潜在死亡风险对个人进行分类。我们将使用链接的SEER-Medicare数据应用模型。在医学决策文献中，已经注意到对进展或死亡率的异质性进行解释的重要性。然而，这种方法通常假设个人要么是疾病进展的快速者，要么是进展缓慢的人。相反，这项研究提出了在临床疗效调查中可以确定四个潜在存活率组的模型的发展：1)可以观察到的有无治疗的长期存活率组，2)没有治疗但有治疗的存活期短的应该立即接受干预的人，3)应该观察的无治疗但有治疗的存活期长的人，以及4)有或没有治疗但可以避免不必要的手术的存活率较短的人。此外，我们假设不同的治疗方案，相对较长和较短的生存风险可能有所不同。我们建议使用主体分层和鲁宾的因果模型作为本研究的概念性工具。该项目将进一步了解局限性肾癌患者存活率的异质性。 公共卫生相关性：这项工作将通过开发模型来改进局部肾癌预后的建模，该模型可以根据个体接受治疗或不接受治疗的潜在死亡风险对个体进行分类。那些预期寿命不会改变或随着治疗而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.