Acute kidney injury and in-hospital mortality in COVID-19: a causal analysis using directed acyclic graphs

Paola Andrea Gallego Aristizabal1,2, Tania Paola Lujan Chavarría3,4, Joaquín Rodelo-Ceballos5,4

  • 1Department of Internal Medicine, Faculty of Medicine, Universidad de Antioquia, Carrera 73 # 53-93, Medellín, Colombia. paolagallego383@gmail.com.

Journal of Nephrology
|September 8, 2025
PubMed

Insights

Acute kidney injury (AKI) significantly increases mortality risk in COVID-19 patients. This study used directed acyclic graphs to confirm AKI

Area of Science:

  • Nephrology
  • Infectious Diseases
  • Epidemiology

Background:

  • Acute kidney injury (AKI) is a frequent complication in COVID-19 patients, linked to higher mortality.
  • The independent impact of AKI on COVID-19 mortality requires further clarification.

Purpose of the Study:

  • To assess the independent association between AKI and mortality in COVID-19 patients.
  • To utilize a directed acyclic graph (DAG) approach for causal inference.

Main Methods:

  • A retrospective, multicenter cohort study included 1722 patients from June 2020 to April 2022.
  • AKI was diagnosed using KDIGO criteria.
  • A DAG was constructed to guide confounder adjustment, followed by Poisson regression to estimate mortality risk.

Main Results:

  • 30.7% of patients developed AKI, with significantly higher mortality (58.1%) compared to those without AKI (19.6%).
  • Adjusted incidence rate ratios (IRRs) for in-hospital mortality were 1.25 (Stage 1), 1.62 (Stage 2), and 1.64 (Stage 3) for AKI stages.
  • DAG analysis identified a minimal sufficient adjustment set for robust causal estimation.

Conclusions:

  • AKI is an independent predictor of increased mortality in COVID-19 patients.
  • The study demonstrates the utility of DAGs in strengthening causal inference in observational studies.
  • DAGs offer a rigorous method to identify confounders and avoid mediator bias, enhancing the validity of findings when RCTs are not feasible.
Abstract

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