A novel urinary biomarker predicts 1-year mortality after discharge from intensive care

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.

Primary care nurses’ preparedness for COVID-19 in the Western Cape province, South Africa

“… 1 To ensure nurses’ protection during the COVID-19 pandemic, the following is advised: intense education and training; reasonable shift schedules; making full use of existing infection, prevention and control (IPC) systems; providing psychological counselling; and avoiding unnecessary contact. 7 …”

A Review of Clinical Practice Guidelines and Treatment Recommendations for Cancer Care in the COVID-19 Pandemic

“…Among the areas of China hardest hit by COVID-19 was Heilongjiang province. A series of protocols were established when the first confirmed case emerged, and authors summarized their experience in medical management strategies including protection of medical staff, reallocation of medical resources, plans for hierarchical treatment, and utilization of a network platform [ 19 ].…”

“…We conducted a literature search using PubMed to identify articles published in English language that reported on cancer patient care recommendations during the COVID-19 pandemic from inception up to 1st June 2020, using the terms “(cancer or tumor) AND (COVID-19)” ( Table 1 ) [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , ...…”

Urinary Peptides Significantly Associate with COVID‐19 Severity: Pilot Proof‐of‐Principle Data and Design of a Multicentric Diagnostic Study

“…The capillary electrophoresis -mass sepectometry (CE-MS) technology was employed for the comparable analysis of currently >70 000 samples [11] and has previously been qualified for prognosis of progression and outcome in large-scale prospective and longitudinal clinical studies, for example, in the setting of diabetic nephropathy, [9] in the context of graft versushost disease, [12] or in the context of predicting death after ICU stay. [10] Samples from patients and matching controls were analyzed using CE-MS, and lists of peptides were generated, as described. [11] The proteomics data were deposited in PRoteomics IDEntifications database (PRIDE) under the dataset identifier PXD020444.…”

“…In addition, the kidney was reported as an organ substantially affected especially in severe cases of COVID-19, [6,7] as indicated by early albuminuria excretion. [8] Based on these observations, the hypothesis was generated that COVID-19 results in a significant change of not only albumin, but a disease-specific cluster of urinary peptides, especially collagen derived, [9,10] and that these changes are associated with disease severity. To test this hypothesis, we initiated a pilot study with the aim to identify peptides that undergo >50% change in disease.…”