Characteristics and outcomes of patients admitted to adult intensive care units in Hong Kong: a population retrospective cohort study from 2008 to 2018.

BACKGROUND: Globally, mortality rates of patients admitted to the intensive care unit (ICU) have decreased over the last two decades. However, evaluations of the temporal trends in the characteristics and outcomes of ICU patients in Asia are limited. The objective of this study was to describe the characteristics and risk adjusted outcomes of all patients admitted to publicly funded ICUs in Hong Kong over a 11-year period. The secondary objective was to validate the predictive performance of Acute Physiology And Chronic Health Evaluation (APACHE) IV for ICU patients in Hong Kong. METHODS: This was an 11-year population-based retrospective study of all patients admitted to adult general (mixed medical-surgical) intensive care units in Hong Kong public hospitals. ICU patients were identified from a population electronic health record database. Prospectively collected APACHE IV data and clinical outcomes were analysed. RESULTS: From 1 April 2008 to 31 March 2019, there were a total of 133,858 adult ICU admissions in Hong Kong public hospitals. During this time, annual ICU admissions increased from 11,267 to 14,068, whilst hospital mortality decreased from 19.7 to 14.3%. The APACHE IV standard mortality ratio (SMR) decreased from 0.81 to 0.65 during the same period. Linear regression demonstrated that APACHE IV SMR changed by - 0.15 (95% CI - 0.18 to - 0.11) per year (Pearson's R = - 0.951, p < 0.001). Observed median ICU length of stay was shorter than that predicted by APACHE IV (1.98 vs. 4.77, p < 0.001). C-statistic for APACHE IV to predict hospital mortality was 0.889 (95% CI 0.887 to 0.891) whilst calibration was limited (Hosmer-Lemeshow test p < 0.001). CONCLUSIONS: Despite relatively modest per capita health expenditure, and a small number of ICU beds per population, Hong Kong consistently provides a high-quality and efficient ICU service. Number of adult ICU admissions has increased, whilst adjusted mortality has decreased over the last decade. Although APACHE IV had good discrimination for hospital mortality, it overestimated hospital mortality of critically ill patients in Hong Kong.

Predictive value of plasma neutrophil gelatinase-associated lipocalin for acute kidney injury in intensive care unit patients after major non-cardiac surgery.

AIM: The performance of plasma neutrophil gelatinase-associated lipocalin (pNGAL) for prediction of acute kidney injury (AKI) in non-cardiac surgical patients has not been well described. This study investigates the use of pNGAL for early detection of AKI in patients admitted to an intensive care unit (ICU) after major or ultra-major non-cardiac surgery. METHODS: A total of 151 patients were recruited. Blood samples at 0 h and 6 h post-ICU admission were collected. Primary outcome was occurrence of AKI within 48 h of ICU admission defined using Acute Kidney Injury Network (AKIN) classification. RESULTS: Forty-five (29.8%) patients developed AKI within 48 h of ICU admission. Among them, 22, 14, and nine were classified as AKIN Stage 1, 2, and 3 respectively. pNGAL levels at 0 h and 6 h were significantly related to AKI severity. The AUROC for pNGAL at 0 h and 6 h increased with AKI severity (AKIN stage ≥1 0.671 ± 0.048 and 0.691 ± 0.047; stage ≥2 0.737 ± 0.055 and 0.796 ± 0.048; stage 3 0.829 ± 0.072 and 0.860 ± 0.065, respectively) and requirement of renal replacement therapy (0.880 ± 0.059 & 0.837 ± 0.088). Change of pNGAL from 0 h to 6 h showed no advantage in predictive power compared with pNGAL level at 0 h or 6 h alone. The addition of pNGAL into clinical AKI prediction model could only provide marginal benefit. CONCLUSION: pNGAL correlated with severity of AKI and requirement of renal replacement therapy in ICU patients who received major or ultra-major non-cardiac surgery. However, the benefit of adding pNGAL into clinical AKI prediction model is marginal.

A distinct influenza infection signature in the blood transcriptome of patients with severe community-acquired pneumonia.

INTRODUCTION: Diagnosis of severe influenza pneumonia remains challenging because of a lack of correlation between the presence of influenza virus and clinical status. We conducted gene-expression profiling in the whole blood of critically ill patients to identify a gene signature that would allow clinicians to distinguish influenza infection from other causes of severe respiratory failure, such as bacterial pneumonia, and noninfective systemic inflammatory response syndrome. METHODS: Whole-blood samples were collected from critically ill individuals and assayed on Illumina HT-12 gene-expression beadarrays. Differentially expressed genes were determined by linear mixed-model analysis and overrepresented biological pathways determined by using GeneGo MetaCore. RESULTS: The gene-expression profile of H1N1 influenza A pneumonia was distinctly different from those of bacterial pneumonia and systemic inflammatory response syndrome. The influenza gene-expression profile is characterized by upregulation of genes from cell-cycle regulation, apoptosis, and DNA-damage-response pathways. In contrast, no distinctive gene-expression signature was found in patients with bacterial pneumonia or systemic inflammatory response syndrome. The gene-expression profile of influenza infection persisted through 5 days of follow-up. Furthermore, in patients with primary H1N1 influenza A infection in whom bacterial co-infection subsequently developed, the influenza gene-expression signature remained unaltered, despite the presence of a superimposed bacterial infection. CONCLUSIONS: The whole-blood expression-profiling data indicate that the host response to influenza pneumonia is distinctly different from that caused by bacterial pathogens. This information may speed the identification of the cause of infection in patients presenting with severe respiratory failure, allowing appropriate patient care to be undertaken more rapidly.