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Open Access 01-12-2024 | Acute Kidney Injury | Research

Machine learning derived serum creatinine trajectories in acute kidney injury in critically ill patients with sepsis

Authors: Kullaya Takkavatakarn, Wonsuk Oh, Lili Chan, Ira Hofer, Khaled Shawwa, Monica Kraft, Neomi Shah, Roopa Kohli-Seth, Girish N. Nadkarni, Ankit Sakhuja

Published in: Critical Care | Issue 1/2024

Abstract

Background

Current classification for acute kidney injury (AKI) in critically ill patients with sepsis relies only on its severity-measured by maximum creatinine which overlooks inherent complexities and longitudinal evaluation of this heterogenous syndrome. The role of classification of AKI based on early creatinine trajectories is unclear.

Methods

This retrospective study identified patients with Sepsis-3 who developed AKI within 48-h of intensive care unit admission using Medical Information Mart for Intensive Care-IV database. We used latent class mixed modelling to identify early creatinine trajectory-based classes of AKI in critically ill patients with sepsis. Our primary outcome was development of acute kidney disease (AKD). Secondary outcomes were composite of AKD or all-cause in-hospital mortality by day 7, and AKD or all-cause in-hospital mortality by hospital discharge. We used multivariable regression to assess impact of creatinine trajectory-based classification on outcomes, and eICU database for external validation.

Results

Among 4197 patients with AKI in critically ill patients with sepsis, we identified eight creatinine trajectory-based classes with distinct characteristics. Compared to the class with transient AKI, the class that showed severe AKI with mild improvement but persistence had highest adjusted risks for developing AKD (OR 5.16; 95% CI 2.87–9.24) and composite 7-day outcome (HR 4.51; 95% CI 2.69–7.56). The class that demonstrated late mild AKI with persistence and worsening had highest risks for developing composite hospital discharge outcome (HR 2.04; 95% CI 1.41–2.94). These associations were similar on external validation.

Conclusions

These 8 classes of AKI in critically ill patients with sepsis, stratified by early creatinine trajectories, were good predictors for key outcomes in patients with AKI in critically ill patients with sepsis independent of their AKI staging.

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Title: Machine learning derived serum creatinine trajectories in acute kidney injury in critically ill patients with sepsis
Authors: Kullaya Takkavatakarn
Wonsuk Oh
Lili Chan
Ira Hofer
Khaled Shawwa
Monica Kraft
Neomi Shah
Roopa Kohli-Seth
Girish N. Nadkarni
Ankit Sakhuja
Publication date: 01-12-2024
Publisher: BioMed Central
Keywords: Acute Kidney Injury
Acute Kidney Injury
Septicemia
Septicemia
Published in: Critical Care / Issue 1/2024
Electronic ISSN: 1364-8535
DOI: https://doi.org/10.1186/s13054-024-04935-x

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Machine learning derived serum creatinine trajectories in acute kidney injury in critically ill patients with sepsis

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

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