Predicting mortality among critically ill patients with acute kidney injury treated with renal replacement therapy: Development and validation of new prediction models

Daniel H Li; Ron Wald; Daniel Blum; Eric McArthur; Matthew T James; Karen E A Burns; Jan O Friedrich; Neill K J Adhikari; Danielle M Nash; Gerald Lebovic; Andrea K Harvey; Stephanie N Dixon; Samuel A Silver; Sean M Bagshaw; William Beaubien-Souligny

doi:10.1016/j.jcrc.2019.12.015

Predicting mortality among critically ill patients with acute kidney injury treated with renal replacement therapy: Development and validation of new prediction models

J Crit Care. 2020 Apr:56:113-119. doi: 10.1016/j.jcrc.2019.12.015. Epub 2019 Dec 18.

Authors

Affiliations

¹ Division of Nephrology, St. Michael's Hospital and University of Toronto, Toronto, Canada.
² Division of Nephrology, St. Michael's Hospital and University of Toronto, Toronto, Canada; ICES, Ontario, Canada.
³ ICES, Ontario, Canada.
⁴ Division of Nephrology, Foothills Medical Center, Calgary, Canada.
⁵ Critical Care and Medicine Departments, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada.
⁶ Department of Critical Care Medicine, Sunnybrook Health Sciences Centre; Interdepartmental Division of Critical Care, University of Toronto, Toronto, Canada.
⁷ Applied Health Research Centre, University of Toronto, Toronto, Canada.
⁸ ICES, Ontario, Canada; Department of Epidemiology and Biostatistics, Western University, London, Canada; Department of Mathematics and Statistics, University of Guelph, Guelph, Canada.
⁹ ICES, Ontario, Canada; Division of Nephrology, Kingston Health Sciences Center, Queen's University, Kingston, Canada.
¹⁰ Department of Critical Care Medicine, Faculty of Medicine and Dentistry, School of Public Health, University of Alberta, Edmonton, Canada.
¹¹ Division of Nephrology, St. Michael's Hospital and University of Toronto, Toronto, Canada; Division of Nephrology, Centre Hospitalier de l'Université de Montréal, Montréal, Canada. Electronic address: [email protected].

PMID: 31896444
DOI: 10.1016/j.jcrc.2019.12.015

Abstract

Purpose: Severe acute kidney injury (AKI) is associated with a significant risk of mortality and persistent renal replacement therapy (RRT) dependence. The objective of this study was to develop prediction models for mortality at 90-day and 1-year following RRT initiation in critically ill patients with AKI.

Methods: All patients who commenced RRT in the intensive care unit for AKI at a tertiary care hospital between 2007 and 2014 constituted the development cohort. We evaluated the external validity of our mortality models using data from the multicentre OPTIMAL-AKI study.

Results: The development cohort consisted of 594 patients, of whom 320(54%) died and 40 (15% of surviving patients) remained RRT-dependent at 90-day Eleven variables were included in the model to predict 90-day mortality (AUC:0.79, 95%CI:0.76-0.82). The performance of the 90-day mortality model declined upon validation in the OPTIMAL-AKI cohort (AUC:0.61, 95%CI:0.54-0.69) and showed modest calibration. Similar results were obtained for mortality model at 1-year.

Conclusions: Routinely collected variables at the time of RRT initiation have limited ability to predict mortality in critically ill patients with AKI who commence RRT.

Keywords: Acute kidney injury; Dialysis; Prediction model; Renal recovery; Renal replacement therapy.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Acute Kidney Injury / mortality*
Acute Kidney Injury / therapy*
Aged
Area Under Curve
Critical Illness
Decision Making, Shared
Female
Humans
Intensive Care Units
Male
Middle Aged
Models, Theoretical
Multicenter Studies as Topic
Predictive Value of Tests
Prognosis
Renal Replacement Therapy*
Retrospective Studies
Risk