Serial measurement of plasma neutrophil gelatinase associated lipocalin in mortality prediction in critically ill patients with systemic inflammatory disease and sepsis - Md Ralib, Azrina; Mat Nor, Mohd Basri (2016)
Introduction: Plasma Neutrophil Gelatinase Associated Lipocalin (NGAL) is the most promising acute kidney injury biomarker to date. NGAL expression is also increased by inflammation and infection. NGAL’s ability to predict hard outcome such as mortality in this group of patients is of interest. Sepsis is the leading cause of ICU admission in Malaysia, and contributes to a high mortality rate. Previous studies showed conflicting results on the NGAL’s ability to predict of mortality. We hypothesised that serial measurement of biomarker may offer extra advantage compared to a single measurement. Objective: We aim to assess the ability of serial measurement of NGAL in prediction of mortality in critically ill patients with systemic inflammatory diseases. Methods: This is a secondary analysis of a prospective observational study of Hospital Tengku Ampuan Afzan, Kuantan. The study was registered under the National Medical Research Register (NMR-11-1102-9248) and has received ethical approval. Patients whom fulfill the Systemic Inflammatory Response Syndrome (SIRS) criteria were recruited in the study. Delta NGAL at 24 and 48 hours (NGAL-24 and NGAL-48) were defined as 24 and 48 h NGAL minus Day-1 NGAL. Results: A total of 151 patients with three days of NGAL measurement were analyzed. Of this, 53 (35.1%) died. Non-survivors were older (51 vs 45, p=0.03) and had higher SOFA (9±7 vs 7±4, p=0.02) and SAPSII (47±15 vs 40±15, p=0.01) scores compared to survivors. NGAL concentrations over three days were higher in non-survivors compared to survivors (Repeated Measures ANOVA, p=0.02). Day 1-NGAL and NGAL-24 were not independently predictive of mortality. However, NGAL-48 was predictive after adjusted for age and severity of illness (OR 9.1 (1.97 to 41.7, p=0.005). Conclusions: NGAL dynamics over 48 hours independently predicted mortality in critically ill patients with SIRS. This could assist clinicians in risk stratification of this group of high-risk patients.