Personalized clinical management of oncology patients with acute kidney injury associated to immune inhibitors.

Purpose and goal:

Immune checkpoint inhibitors (ICI) have increased survival rates of cancer patients. Up to 29% of the ICIs treated cancer patients develop acute kidney injury (AKI) with acute tubulointerstitial nephritis (ATIN), which may stunt patients´ therapeutic options. The diagnosis of ICI-AKI etiology is performed by kidney biopsy, which is risky and cannot identify ICIs-ATIN etiology.

We propose to develop a cost-effective artificial intelligence (AI)-based risk stratification tool to early diagnose ICIs-AKI and allow for personalized clinical interventions for these patients. Expected results and effects:

It is expected that the development of a cost-effective artificial intelligence (AI)-based risk stratification tool to early diagnose ICIs-AKI will allow personalizing clinical management of these patients, avoid unnecessary invasive risky procedures such as kidney biopsies and consequently contribute to improved patient quality of life.

Approach and implementation:

To achieve this goal, we will collect retrospective and prospective demographic and clinical data of ICI-AKI patients as well as serial urine, blood and kidney tissue samples of ICI-treated cancer patients during two years to study novel biomarkers related to loss of tolerance of T-cells to self-antigens. These data will be used to develop an AI-based risk stratification method to early diagnose ICIs-AKI.

Further, to ensure viability of the tool implementation, we will conduct a cost-effectiveness analysis.