METHODS: This is a prospective observational study on patients with SIRS. Plasma creatinine (pCr) and NGAL were measured on ICU admission. Patients were classified according to the occurrence of AKI and sepsis.
RESULTS: Of 225 patients recruited, 129 (57%) had sepsis of whom 67 (52%) also had AKI. 96 patients (43%) had non-infectious SIRS, of whom 20 (21%) also had AKI. NGAL concentrations were higher in AKI patients within both the sepsis and non-infectious SIRS cohorts (both P
MATERIALS: We recruited consecutively adult patients with SIRS admitted to an intensive care unit. They were divided into sepsis and noninfectious SIRS based on clinical assessment with or without positive cultures. Concentrations of PCT and IL-6 were measured daily over the first 3 days.
RESULTS: A total of 239 patients were recruited, 164 (68.6%) had sepsis, and 68 (28.5%) died in hospital. The PCT levels were higher in sepsis compared with noninfectious SIRS throughout the 3-day period (P < .0001). On admission, PCT concentration was diagnostic of sepsis (area under the curve of 0.63 [0.55-0.71]), and IL-6 was predictive of mortality, (area under the curve of 0.70 [0.62-0.78]). Peak IL-6 concentration improved the risk assessment of Sequential Organ Failure Assessment (SOFA) score for prediction of mortality among those who went on to die by an average of 5% and who did not die by 2%
CONCLUSIONS: Procalcitonin measured on intensive care unit admission was diagnostic of sepsis, and IL-6 was predictive of mortality. Addition of IL-6 concentration to SOFA score improved risk assessment for prediction of mortality. Future studies should include clinical indices, for example, SOFA score, for prognostic evaluation of biomarkers.
METHODS: We used aptamer-based affinity-capture plasma proteomics to measure 1305 plasma proteins at 1 month post-MI in a New Zealand cohort (CDCS [Coronary Disease Cohort Study]) including 181 patients post-MI who were subsequently hospitalized for HF in comparison with 250 patients post-MI who remained event free over a median follow-up of 4.9 years. We then correlated plasma proteins with left ventricular ejection fraction measured at 4 months post-MI and identified proteins potentially coregulated in post-MI HF using weighted gene co-expression network analysis. A Singapore cohort (IMMACULATE [Improving Outcomes in Myocardial Infarction through Reversal of Cardiac Remodelling]) of 223 patients post-MI, of which 33 patients were hospitalized for HF (median follow-up, 2.0 years), was used for further candidate enrichment of plasma proteins by using Fisher meta-analysis, resampling-based statistical testing, and machine learning. We then cross-referenced differentially expressed proteins with their differentially expressed genes from single-cell transcriptomes of nonmyocyte cardiac cells isolated from a murine MI model, and single-cell and single-nucleus transcriptomes of cardiac myocytes from murine HF models and human patients with HF.
RESULTS: In the CDCS cohort, 212 differentially expressed plasma proteins were significantly associated with subsequent HF events. Of these, 96 correlated with left ventricular ejection fraction measured at 4 months post-MI. Weighted gene co-expression network analysis prioritized 63 of the 212 proteins that demonstrated significantly higher correlations among patients who developed post-MI HF in comparison with event-free controls (data set 1). Cross-cohort meta-analysis of the IMMACULATE cohort identified 36 plasma proteins associated with post-MI HF (data set 2), whereas single-cell transcriptomes identified 15 gene-protein candidates (data set 3). The majority of prioritized proteins were of matricellular origin. The 6 most highly enriched proteins that were common to all 3 data sets included well-established biomarkers of post-MI HF: N-terminal B-type natriuretic peptide and troponin T, and newly emergent biomarkers, angiopoietin-2, thrombospondin-2, latent transforming growth factor-β binding protein-4, and follistatin-related protein-3, as well.
CONCLUSIONS: Large-scale human plasma proteomics, cross-referenced to unbiased cardiac transcriptomics at single-cell resolution, prioritized protein candidates associated with post-MI HF for further mechanistic and clinical validation.
STUDY DESIGN: Literature-based meta-analysis and individual-study-data meta-analysis of diagnostic studies following PRISMA-IPD guidelines.
SETTING & STUDY POPULATIONS: Studies of adults investigating AKI, severe AKI, and AKI-D in the setting of cardiac surgery, intensive care, or emergency department care using either urinary or plasma NGAL measured on clinical laboratory platforms.
SELECTION CRITERIA FOR STUDIES: PubMed, Web of Science, Cochrane Library, Scopus, and congress abstracts ever published through February 2020 reporting diagnostic test studies of NGAL measured on clinical laboratory platforms to predict AKI.
DATA EXTRACTION: Individual-study-data meta-analysis was accomplished by giving authors data specifications tailored to their studies and requesting standardized patient-level data analysis.
ANALYTICAL APPROACH: Individual-study-data meta-analysis used a bivariate time-to-event model for interval-censored data from which discriminative ability (AUC) was characterized. NGAL cutoff concentrations at 95% sensitivity, 95% specificity, and optimal sensitivity and specificity were also estimated. Models incorporated as confounders the clinical setting and use versus nonuse of urine output as a criterion for AKI. A literature-based meta-analysis was also performed for all published studies including those for which the authors were unable to provide individual-study data analyses.
RESULTS: We included 52 observational studies involving 13,040 patients. We analyzed 30 data sets for the individual-study-data meta-analysis. For AKI, severe AKI, and AKI-D, numbers of events were 837, 304, and 103 for analyses of urinary NGAL, respectively; these values were 705, 271, and 178 for analyses of plasma NGAL. Discriminative performance was similar in both meta-analyses. Individual-study-data meta-analysis AUCs for urinary NGAL were 0.75 (95% CI, 0.73-0.76) and 0.80 (95% CI, 0.79-0.81) for severe AKI and AKI-D, respectively; for plasma NGAL, the corresponding AUCs were 0.80 (95% CI, 0.79-0.81) and 0.86 (95% CI, 0.84-0.86). Cutoff concentrations at 95% specificity for urinary NGAL were>580ng/mL with 27% sensitivity for severe AKI and>589ng/mL with 24% sensitivity for AKI-D. Corresponding cutoffs for plasma NGAL were>364ng/mL with 44% sensitivity and>546ng/mL with 26% sensitivity, respectively.
LIMITATIONS: Practice variability in initiation of dialysis. Imperfect harmonization of data across studies.
CONCLUSIONS: Urinary and plasma NGAL concentrations may identify patients at high risk for AKI in clinical research and practice. The cutoff concentrations reported in this study require prospective evaluation.