Prediction of Mortality and Major Adverse Kidney Events in Critically Ill Patients With Acute Kidney Injury

Javier A. Neyra, Victor Ortiz-Soriano, Lucas J. Liu, Taylor D. Smith, Xilong Li, Donglu Xie, Beverley Adams-Huet, Orson W. Moe, Robert D. Toto, Jin Chen

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Rationale & Objective: Risk prediction tools for assisting acute kidney injury (AKI) management have focused on AKI onset but have infrequently addressed kidney recovery. We developed clinical models for risk stratification of mortality and major adverse kidney events (MAKE) in critically ill patients with incident AKI. Study Design: Multicenter cohort study. Setting & Participants: 9,587 adult patients admitted to heterogeneous intensive care units (ICUs; March 2009 to February 2017) who experienced AKI within the first 3 days of their ICU stays. Predictors: Multimodal clinical data consisting of 71 features collected in the first 3 days of ICU stay. Outcomes: (1) Hospital mortality and (2) MAKE, defined as the composite of death during hospitalization or within 120 days of discharge, receipt of kidney replacement therapy in the last 48 hours of hospital stay, initiation of maintenance kidney replacement therapy within 120 days, or a ≥50% decrease in estimated glomerular filtration rate from baseline to 120 days from hospital discharge. Analytical Approach: Four machine-learning algorithms (logistic regression, random forest, support vector machine, and extreme gradient boosting) and the SHAP (Shapley Additive Explanations) framework were used for feature selection and interpretation. Model performance was evaluated by 10-fold cross-validation and external validation. Results: One developed model including 15 features outperformed the SOFA (Sequential Organ Failure Assessment) score for the prediction of hospital mortality, with areas under the curve of 0.79 (95% CI, 0.79-0.80) and 0.71 (95% CI, 0.71-0.71) in the development cohort and 0.74 (95% CI, 0.73-0.74) and 0.71 (95% CI, 0.71-0.71) in the validation cohort (P < 0.001 for both). A second developed model including 14 features outperformed KDIGO (Kidney Disease: Improving Global Outcomes) AKI severity staging for the prediction of MAKE: 0.78 (95% CI, 0.78-0.78) versus 0.66 (95% CI, 0.66-0.66) in the development cohort and 0.73 (95% CI, 0.72-0.74) versus 0.67 (95% CI, 0.67-0.67) in the validation cohort (P < 0.001 for both). Limitations: The models are applicable only to critically ill adult patients with incident AKI within the first 3 days of an ICU stay. Conclusions: The reported clinical models exhibited better performance for mortality and kidney recovery prediction than standard scoring tools commonly used in critically ill patients with AKI in the ICU. Additional validation is needed to support the utility and implementation of these models. Plain-Language Summary: Acute kidney injury (AKI) occurs commonly in critically ill patients admitted to the intensive care unit (ICU) and is associated with high morbidity and mortality rates. Prediction of mortality and recovery after an episode of AKI may assist bedside decision making. In this report, we describe the development and validation of a clinical model using data from the first 3 days of an ICU stay to predict hospital mortality and major adverse kidney events occurring as long as 120 days after hospital discharge among critically ill adult patients who experienced AKI within the first 3 days of an ICU stay. The proposed clinical models exhibited good performance for outcome prediction and, if further validated, could enable risk stratification for timely interventions that promote kidney recovery.

Original languageEnglish
Pages (from-to)36-47
Number of pages12
JournalAmerican Journal of Kidney Diseases
Volume81
Issue number1
DOIs
StatePublished - Jan 2023

Bibliographical note

Funding Information:
Javier A. Neyra, MD, MSCS, Victor Ortiz-Soriano, MD, Lucas J. Liu, MS, Taylor D. Smith, MS, Xilong Li, PhD, Donglu Xie, MS, Beverley Adams-Huet, MS, Orson W. Moe, MD, Robert D. Toto, MD, and Jin Chen, PhD. Contributed to study concept and design: JAN, VO-S, LJL, TDS, JC; helped with data extraction, management, harmonization, and validation: VO-S, LJL, TDS, XL, DX, BA-H; performed data analysis: LJL, TDS, XL, JC; performed model evaluation and interpretation: JAN, VO-S, LJL, XL, JC; supervised study procedures at UTSW: OWM, RDT; supervised study procedures at UKY: JC; supervised the overall study procedures and execution: JAN. Each author contributed important intellectual content during manuscript drafting or revision and agrees to be personally accountable for the individual's own contributions and to ensure that questions pertaining to the accuracy or integrity of any portion of the work, even one in which the author was not directly involved, are appropriately investigated and resolved, including with documentation in the literature if appropriate. This study was supported in part by the University of Texas Southwestern Medical Center O'Brien Kidney Research Core Center (NIH, P30 DK079328-06) and the University of Kentucky Center for Clinical and Translational Science (NIH/NCATS, UL1TR001998). Dr Neyra is supported by grants from NIDDK (R01DK128208, R56 DK126930 and P30 DK079337) and NHLBI (R01 HL148448-01). Funders did not have a role in study design, data collection, analysis, reporting, or the decision to submit this publication. The authors declare that they have no relevant financial interests. Received November 1, 2021. Evaluated by 2 external peer reviewers, with direct editorial input from a Statistics/Methods Editor, an Associate Editor, and the Editor-in-Chief. Accepted in revised form June 6, 2022.

Funding Information:
This study was supported in part by the University of Texas Southwestern Medical Center O’Brien Kidney Research Core Center (NIH, P30 DK079328-06) and the University of Kentucky Center for Clinical and Translational Science (NIH/NCATS, UL1TR001998). Dr Neyra is supported by grants from NIDDK (R01DK128208, R56 DK126930 and P30 DK079337) and NHLBI (R01 HL148448-01). Funders did not have a role in study design, data collection, analysis, reporting, or the decision to submit this publication.

Publisher Copyright:
© 2022 National Kidney Foundation, Inc.

Keywords

  • AKI staging
  • Mortality
  • acute kidney injury (AKI)
  • clinical decision making
  • critically ill patients
  • intensive care unit (ICU)
  • kidney recovery
  • machine learning
  • major adverse kidney events (MAKE)
  • renal prognosis
  • risk prediction tool
  • risk stratification

ASJC Scopus subject areas

  • Nephrology

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