# Using Novel Machine Learning Methods to Personalize Strategies for Prevention of Persistent AKI after Cardiac Surgery

> **NIH NIH K08** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2024 · $158,842

## Abstract

My long-term goal is to integrate health informatics, data mining and machine learning to improve the care for
patients with, and at risk for, acute kidney injury (AKI). I am dual trained in Nephrology and Critical Care
Medicine. I am already developing my skills in health informatics. This proposal presents a five-year career
development plan for NIH K08 award focused on training in advanced data mining, machine learning and their
applications to critical care nephrology. To that effect, I have assembled a strong mentoring team with decades
of experience in mentoring, research and leadership. The outlined career development plan in conjunction with
intensive mentoring and hands-on training will provide me the perfect platform to become a leading
independent investigator in the field.
AKI is seen in over one-third of patients undergoing cardiac surgery. Several trials investigating various
medications to prevent or treat AKI over the last two decades have proven futile. Management of AKI therefore
focuses on its prevention, measures to reduce further progression and management of its complications. The
strategy to prevent AKI and its progression relies on clinical interventions to optimize a patient’s fluid status,
blood pressure and avoiding nephrotoxins and hyperglycemia. These clinical interventions when provided to
patients requiring cardiac surgery as a care-bundle are associated with decreased incidence of AKI. This care-
bundle, however, has very low compliance with implementation and lacks the ability to personalize care for
patients. With prior work showing differential response to therapy in AKI phenotypes, there is a critical need to
determine personalized strategies to prevent the development of persistent AKI. Personalization of treatment
strategies based on dynamic clinical characteristics of patients will ensure that the right action is performed at
the right time. As transient AKI resolves spontaneously within 48 hours, focusing interventions to those at high
risk for developing persistent AKI will lead to further personalization of this approach. The overall objective of
this project is to determine a personalized strategy using machine learning to prevent the development of
persistent AKI after cardiac surgery. I will pursue following specific aims for this study: (1) Develop
reinforcement learning (RL) based strategy to prevent the development of persistent AKI after cardiac surgery.
(2) Develop digital biomarkers to predict patients at risk for persistent AKI after cardiac surgery. Completion of
these aims will provide a structured framework to provide personalized care to prevent the development of
persistent AKI after cardiac surgery. It will also provide me with preliminary data and experience necessary to
apply for R01 applications as an independent investigator leading a data science research program in critical
care nephrology.

## Key facts

- **NIH application ID:** 10894799
- **Project number:** 5K08DK131286-04
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Ankit Sakhuja
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $158,842
- **Award type:** 5
- **Project period:** 2024-01-04 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10894799

## Citation

> US National Institutes of Health, RePORTER application 10894799, Using Novel Machine Learning Methods to Personalize Strategies for Prevention of Persistent AKI after Cardiac Surgery (5K08DK131286-04). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10894799. Licensed CC0.

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