Zhuang Liu

BACKGROUND: Early detection of the Acute Respiratory Distress Syndrome (ARDS) has the potential to improve the prognosis of critically ill patients admitted to the intensive care unit (ICU). However, no reliable biomarkers are currently available for accurate early detection of ARDS in patients with predisposing conditions. OBJECTIVES: This study examined risk factors and biomarkers for ARDS development and mortality in two prospective cohort studies. METHODS: We examined clinical risk factors for ARDS in a cohort of 178 patients in Beijing, China who were admitted to the ICU and were at high risk for ARDS. Identified biomarkers were then replicated in a second cohort of1,878 patients in Boston, USA. RESULTS: Of 178 patients recruited from participating hospitals in Beijing, 75 developed ARDS. After multivariate adjustment, sepsis (odds ratio [OR]:5.58, 95% CI: 1.70-18.3), pulmonary injury (OR: 3.22; 95% CI: 1.60-6.47), and thrombocytopenia, defined as platelet count <80×10(3)/µL, (OR: 2.67; 95% CI: 1.27-5.62)were significantly associated with increased risk of developing ARDS. Thrombocytopenia was also associated with increased mortality in patients who developed ARDS (adjusted hazard ratio [AHR]: 1.38, 95% CI: 1.07-1.57) but not in those who did not develop ARDS(AHR: 1.25, 95% CI: 0.96-1.62). The presence of both thrombocytopenia and ARDS substantially increased 60-day mortality. Sensitivity analyses showed that a platelet count of <100×10(3)/µL in combination with ARDS provide the highest prognostic value for mortality. These associations were replicated in the cohort of US patients. CONCLUSIONS: This study of ICU patients in both China and US showed that thrombocytopenia is associated with an increased risk of ARDS and platelet count in combination with ARDS had a high predictive value for patient mortality.

BACKGROUND: To develop a machine learning model for predicting acute respiratory distress syndrome (ARDS) events through commonly available parameters, including baseline characteristics and clinical and laboratory parameters. METHODS: A secondary analysis of a multi-centre prospective observational cohort study from five hospitals in Beijing, China, was conducted from January 1, 2011, to August 31, 2014. A total of 296 patients at risk for developing ARDS admitted to medical intensive care units (ICUs) were included. We applied a random forest approach to identify the best set of predictors out of 42 variables measured on day 1 of admission. RESULTS: All patients were randomly divided into training (80%) and testing (20%) sets. Additionally, these patients were followed daily and assessed according to the Berlin definition. The model obtained an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.82 and yielded a predictive accuracy of 83%. For the first time, four new biomarkers were included in the model: decreased minimum haematocrit, glucose, and sodium and increased minimum white blood cell (WBC) count. CONCLUSIONS: This newly established machine learning-based model shows good predictive ability in Chinese patients with ARDS. External validation studies are necessary to confirm the generalisability of our approach across populations and treatment practices.

Inspired by the lipoprotein molecular structure, dual-functional phospholipid-poly(acrylic acid) brushes grafted porous poly(vinyl alcohol) beads were developed for selective removal of low-density lipoprotein from hyperlipidemia blood.

Background: Sepsis is the most common contributing factor towards development of acute kidney injury (AKI), which is strongly associated to poor prognostic outcomes. There are numerous epidemiological studies about sepsis-associated acute kidney injury (S-AKI), however current literature is limited with the majority of studies being conducted only in the intensive care unit (ICU) setting. The aim of this study was to assess the epidemiology of S-AKI in all hospitalized in-patients. Methods: This was a retrospective population-based study using a large regional population database in Beijing city from January, 2005 to December, 2017. It included patients with S-AKI. Patients with pre-existing end-stage kidney disease (ESKD), previous history of kidney transplantation, or being pregnant were excluded. Patients’ demographic characteristics, incidence, risk factors and outcomes of S-AKI were analyzed. The contrast between different time periods, different levels of hospitals, and types of the hospitals (traditional Chinese medicine hospitals (TCMHs) and western medicine hospitals (WMHs)) was also compared using Mann–Whitney U -test. Results: A total of 19,579 patients were included. The overall incidence of S-AKI in all in-patients was 48.1%. The significant risk factors by multivariate analysis for AKI included: age, male, being treated in a level-II hospital, pre-existing hypertension, chronic kidney disease (CKD), cirrhosis, atrial fibrillation (AF), ischemic heart disease (IHD), being admitted from emergency room, ICU admission, shock, pneumonia, intra-abdominal infection, bloodstream infection, respiratory insufficiency, acute liver injury, disseminated intravascular coagulation (DIC) and metabolic encephalopathy. The overall mortality rate in this cohort was 55%. The multivariate analysis showed that the significant risk factors for mortality included: age, being treated in a level-II hospital and TCMHs, being admitted from emergency room, pre-existing comorbidities (CKD, malignancy, cirrhosis and AF), shock, pneumonia, intra-abdominal infection, bloodstream infection, central nervous system (CNS) infection and respiratory insufficiency. Conclusion: AKI is a common complication in patients with sepsis, and its incidence increases over time, especially when ICU admission is required. Exploring interventional strategies to address modifiable risk factors will be important to reduce incidence and mortality of S-AKI. Keywords: epidemiology, sepsis-associated acute kidney injury, traditional Chinese medicine hospitals, western medicine hospitals

BACKGROUND: Septic acute kidney injury (AKI), identified when sepsis and AKI present concurrently, is a syndrome of acute function impairment and organ damage, which accounts for ~50% AKI in the intensive care unit (ICU). METHODS: This study retrospectively reviewed 591 patients who were diagnosed with sepsis and admitted to the ICU of Beijing Friendship Hospital from January 2009 to December 2014. According to the concentration of serum sodium, the 591 patients were further divided into 3 groups: normal group, hyponatremia group, and hypernatremia group. RESULTS: The arterial partial pressure of carbon dioxide (PaCO2, P=0.014), concentration of sodium (Na+, P<0.001), and chloride ion (Cl-, P<0.001), blood urea nitrogen (BUN, P<0.001), acute physiology and chronic health evaluation (APACHE) score (P<0.001), sequential organ failure assessment (SOFA) score (P<0.001), and Glasgow score (P<0.001) showed significant differences. The SOFA score [P=0.040; odds ratio (OR) =1.261], body mass index (BMI, P=0.041; OR =1.229), P content (P=0.032; OR =7.180) and creatine kinase myocardial band (CK-MB, P=0.006; OR =1.168) may be risk factors for occurrence of AKI in patients with hypernatremia. The AKI (P<0.001; OR =6.850) and P content (P=0.027; OR =3.676) may be risk factors for death in patients with hypernatremia. The Na+ suggested a predictive ability for AKI (P<0.001; area under the curve (AUC): 0.586) but not for death (P=0.104). CONCLUSIONS: Hypernatremia is independently associated with an increased risk and has a predictive ability of AKI in patients with sepsis.