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 effects and result

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.

Planned 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.