A Novel Urinary Biomarker Predicts 1-Year Mortality After Discharge From Intensive Care

Esther Nkuipou-Kenfack; Agnieszka Latosinska; Wen-Yi Yang; Marie-Céline Fournier; Alice Blet; Blerim Mujaj; Lutgarde Thijs; Elodie Feliot; Etienne Gayat; Harald Mischak; Jan A. Staessen; Alexandre Mebazaa; Zhen-Yu Zhang

Disclosures

Crit Care. 2020;24(10) 

In This Article

Abstract and Introduction

Abstract

Rationale: The urinary proteome reflects molecular drivers of disease.

Objectives: To construct a urinary proteomic biomarker predicting 1-year post-ICU mortality.

Methods: In 1243 patients, the urinary proteome was measured on ICU admission, using capillary electrophoresis coupled with mass spectrometry along with clinical variables, circulating biomarkers (BNP, hsTnT, active ADM, and NGAL), and urinary albumin. Methods included support vector modeling to construct the classifier, Cox regression, the integrated discrimination (IDI), and net reclassification (NRI) improvement, and area under the curve (AUC) to assess predictive accuracy, and Proteasix and protein-proteome interactome analyses.

Measurements and main results: In the discovery (deaths/survivors, 70/299) and test (175/699) datasets, the new classifier ACM128, mainly consisting of collagen fragments, yielding AUCs of 0.755 (95% CI, 0.708–0.798) and 0.688 (0.656–0.719), respectively. While accounting for study site and clinical risk factors, hazard ratios in 1243 patients were 2.41 (2.00–2.91) for ACM128 (+ 1 SD), 1.24 (1.16–1.32) for the Charlson Comorbidity Index (+ 1 point), and ≥ 1.19 (P ≤ 0.022) for other biomarkers (+ 1 SD). ACM128 improved (P ≤ 0.0001) IDI (≥ + 0.50), NRI (≥ + 53.7), and AUC (≥ + 0.037) over and beyond clinical risk indicators and other biomarkers. Interactome mapping, using parental proteins derived from sequenced peptides included in ACM128 and in silico predicted proteases, including/excluding urinary collagen fragments (63/35 peptides), revealed as top molecular pathways protein digestion and absorption, lysosomal activity, and apoptosis.

Conclusions: The urinary proteomic classifier ACM128 predicts the 1-year post-ICU mortality over and beyond clinical risk factors and other biomarkers and revealed molecular pathways potentially contributing to a fatal outcome.

Introduction

In high- and middle-income countries, millions of patients survive critical illness thanks to the highly specialized life-sustaining management in intensive care units (ICU). However, cumulative mortality over the first year after ICU discharge ranges from 26 to 63%.[1] Large cohort studies conducted in Canada,[2] Australia,[3] and the USA[1] demonstrated that ICU survivors followed up from 3[1,2] up to 15[3] years experienced mortality rates 2 to 5 times higher than sex- and age-matched population controls. The number of patients who survive intensive care is growing fast, because of the demographic transition in aging populations[4] and the ongoing sophistication of critical care resulting in a lower in-ICU fatality rate.[5–7] Several risk factors determine the 1-year risk of death after ICU discharge. Clinical risk indicators include older age, the indication for critical care, comorbidities, the number of failing organs, the length of ICU care, and newly diagnosed malignancies.[3] The risk of death is also associated with circulating and urinary biomarkers indicative of myocardial, vascular, or renal distress.[8] Stakeholder conferences advised prioritizing research on reliable predictors of post-ICU impairments and death to identify patients in need of further diagnostic work-up and targeted treatment.[5,7] Urinary proteomic profiling developed over the past 15 years into a state-of-the-art technology, which enables discovery of disease-specific multidimensional biomarkers indicative of molecular pathogenic processes.[9,10] Along these lines, the current study aimed to develop a urinary proteomic classifier predictive of the 1-year mortality in ICU survivors. The French and European Outcome Registry in Intensive Care Unit Investigators (FROG-ICU; (NCT01367093) compiled the analyzed database.[8,11]

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