Citations

ABSTRACT Background and Aims It remains unclear whether different factors influence 30‐ and 90‐day readmission rates. We identified predictors of 90‐day mortality in a global cohort of hospitalised patients with cirrhosis. Methods Variables associated with 30‐ and 90‐day post‐discharge readmission, liver transplantation (LT), and mortality were compared in prospectively enrolled, non‐electively hospitalised adult patients with cirrhosis. Results We enrolled 4208 patients from 125 centres across 37 countries. Alcohol‐related liver disease was the most common aetiology of cirrhosis (40%), followed by hepatitis B virus infection (21%); 47% were readmitted, 8% received liver transplantation (LT), and 23% died within 90 days post‐discharge. In multivariable analysis, country income was significantly associated with both 30‐ and 90‐day readmission rates, the receipt of LT, and mortality within 30 and 90 days post‐discharge (all p ‐values < 0.001). Independent predictors of higher rates of 30‐ and 90‐day readmissions, respectively, were disease severity ( p < 0.001, p = 0.003), prior ascites ( p = 0.025, p = 0.016), prior hospitalisation ( p < 0.001), hyponatremia ( p < 0.001, p = 0.008), and in‐hospital vasopressor use ( p = 0.002, p = 0.011). Prior variceal bleeding independently predicted higher 90‐day readmission rates, whereas age, disease severity, mechanical ventilation, and in‐hospital vasopressor use (all, p < 0.001) increased odds of 30‐ and 90‐day post‐discharge death. Additionally, admission ( p = 0.011) and nosocomial ( p = 0.022) infections increased the odds of 90‐day post‐discharge death. Conclusion In a global cohort of hospitalised patients with cirrhosis, patients in high‐income countries exhibited the highest rates of 30‐ and 90‐day readmission, alongside lower mortality rates and a higher incidence of LT. Variables that predict 30‐day readmission also predicted 90‐day readmission. Understanding these disparities is essential for achieving health equity.

INTRODUCTION: Nosocomial infections (NIs) in cirrhosis are associated with high mortality but could be preventable. Logistic regression (LR) models have failed to identify high-risk patients. We aimed to develop machine learning (ML) models to predict NI. METHODS: The CLEARED consortium consists of prospectively enrolled cirrhosis inpatients from >120 centers. Using day-of-admission clinical data, 3 ML approaches (random forest [RF], extreme gradient boosting, and neural networks [NNs]) were used to predict NI. Data were split 80:20 for training and testing stratified by the outcome. Models were compared using area under the receiver operating characteristic curve (AUC). RESULTS: In total, 8,263 patients (55.90 ± 13.34 years; 64.1% men) from 127 centers in 37 countries were included. NI developed in 869 (10.5%), a median of 6 (4–11) days of postadmission. Major NIs were respiratory (29.6%) and urinary tract infection (15.7%), and spontaneous bacterial peritonitis (13.5%). NIs occurred more frequently in patients from low/low-middle income countries and those with severe liver disease, alcohol etiology, and admission infections. NIs were associated with inpatient mortality (31.9% vs 8.1%, P < 0.001) and liver transplantation (4.8% vs 1.9%, P < 0.001). Although the RF model (AUC 0.69) showed good calibration (Brier score 0.09), outperforming extreme gradient boosting, neural network, and LR models (AUC 0.66 for all; LR comparison P = 0.043), no model achieved AUC ≥0.80 for clinical utility. At 10% predicted probability threshold, the RF model demonstrated only 75.4% sensitivity, 52.9% specificity, and 15.9% positive predictive value (PPV). DISCUSSION: NIs cannot be accurately predicted from day-of-admission data using ML models, even in a large, prospective, global cirrhosis cohort. Every hospitalized patient with cirrhosis should receive protocolized infection control measures.

Background/Aims: Infections with drug-resistant organisms (DROs) are associated with poor outcomes in cirrhosis. Rifaximin, widely used for hepatic encephalopathy (HE), could promote cross-resistance, but data regarding clinical impact are conflicting. Aim: Determine predictors of DROs in a global cirrhosis inpatient cohort focusing on preadmission rifaximin use.Methods: From the global CLEARED consortium, we focused on cirrhosis inpatients with infections on/during admission. Clinical/demographic/medication, especially rifaximin details were recorded. The primary outcome was DRO development. Multivariable regression for DRO including clinical, medications, and country income was performed.Results: 2,949 infected inpatients (55.3 years, 62.9% male) were included. 12.2% of all and 24.4% of culture-positive infections developed DROs; these patients had higher HE (39 vs. 31%, <i>P</i>=0.003), hepatorenal syndrome (25 vs. 19%, <i>P</i>=0.006), lactulose (55 vs. 47%, <i>P</i>=0.008) and rifaximin use (34 vs. 27%, <i>P</i>=0.006) on crude comparisons but country-income distributions were similar. 29.7% were on pre-admission rifaximin, mostly HE-related; they had more advanced cirrhosis and from low/low-middle-income countries. Daptomycin was used in 1.5%, linked with DROs (5.0 vs. 1.0%, <i>P</i><0.0001) without a difference in rifaximin use (1.4 vs.1.7%, <i>P</i>=0.61). On adjusted analysis, MELD-Na (1.03, 95% CI 1.02–1.04, <i>P</i><0.001) increased, whereas male sex (0.73, 95% CI 0.58–0.92, <i>P</i>=0.008) and hepatitis-B (0.65, 95% CI 0.45–0.92, <i>P</i>=0.020) decreased DRO. Rifaximin was not associated with DROs overall (OR 1.07, 95% CI 0.80–1.43, <i>P</i>=0.65) or within income strata (high: 1.07, 95% CI 0.59–1.92, <i>P</i>=0.82, upper-middle: 1.18, 95% CI 0.74–1.85, <i>P</i>=0.49, low/low-middle: 1.04, 95% CI 0.60–1.81, <i>P</i>=0.90) despite sensitivity analyses.Conclusions: In this large global cohort of hospitalized patients with cirrhosis and infections, 12% developed infections involving DROs. 30% had pre-admission rifaximin use which was not linked with daptomycin use or with DRO development on adjusted analysis overall or across country income groups.

Infectious diseases contribute significantly to the global disease burden. Sensitive and accurate screening methods are some of the most effective means of identifying sources of infection and controlling infectivity. Conventional detecting strategies such as quantitative polymerase chain reaction (qPCR), DNA sequencing, and mass spectrometry typically require bulky equipment and well-trained personnel. Therefore, mass screening of a large population using conventional strategies during pandemic periods often requires additional manpower, resources, and time, which cannot be guaranteed in resource-limited settings. Recently, emerging microfluidic technologies have shown the potential to replace conventional methods in performing point-of-care detection because they are automated, miniaturized, and integrated. By exploiting the spatial separation of detection sites, microfluidic platforms can enable the multiplex detection of infectious diseases to reduce the possibility of misdiagnosis and incomplete diagnosis of infectious diseases with similar symptoms. This review presents the recent advances in microfluidic platforms used for multiplex detection of infectious diseases, including microfluidic immunosensors and microfluidic nucleic acid sensors. As representative microfluidic platforms, lateral flow immunoassay (LFIA) platforms, polymer-based chips, paper-based devices, and droplet-based devices will be discussed in detail. In addition, the current challenges, commercialization, and prospects are proposed to promote the application of microfluidic platforms in infectious disease detection.

Abstract Background: We aimed to analyse clinical characteristics and find potential factors predicting poor prognosis in patients with coronavirus disease 2019 (COVID-19). Methods: We analyzed the demographic and clinical data of COVID-19 patients and detected SARS-CoV-2 RNA in urine sediments collected from 53 COVID-19 patients enrolled in Renmin Hospital of Wuhan University from January 31, 2020 to February 18, 2020 with qRT-PCR analysis, and then classified those patients based on clinical conditions (severe or non-severe syndrome) and urinary SARS-CoV-2 RNA (URNA- or URNA+). Results: We found that COVID-19 patients with severe syndrome (severe patients) showed significantly higher positive rate (11 of 23, 47.8%) of urinary SARS-CoV-2 RNA than non-severe patients (4 of 30, 13.3%, p = 0.006). URNA+ patients or severe URNA+ subgroup exhibited higher prevalence of inflammation and immune discord, cardiovascular diseases, liver damage and renal disfunction, and higher risk of death than URNA- patients. To understand the potential mechanisms underlying the viral urine shedding, we performed renal histopathological analysis on postmortems of patients with COVID-19 and found that severe renal vascular endothelium lesion characterized by increase of the expression of thrombomodulin and von Willebrand factor, markers to assess the endothelium dysfunction. We proposed a theoretical and mathematic model to depict the potential factors determining the urine shedding of SARS-CoV-2. Conclusions: This study indicated that urinary SARS-CoV-2 RNA detected in urine specimens can be used to predict the progression and prognosis of COVID-19 severity.

AbstractBackgroundProfiling of mitochondrial DNA is surely to provide valuable investigative clues for forensic cases involving highly degraded specimens or complex maternal lineage kinship determination. But traditionally used hypervariable region sequencing of mitochondrial DNA is less frequently suggested by the forensic community for insufficient informativeness. Genome‐wide sequencing of mitochondrial DNA can provide considerable amount of variant information but can be high cost at the same time.MethodsEfficiency of the 60 mitochondrial DNA polymorphic sites dispersing across the control region and coding region of mitochondrial DNA genome was evaluated with 106 Mongolians recruited from the Xinjiang Uyghur Autonomous Region, China, and allele‐specific PCR technique was employed for mitochondrial DNA typing.ResultsAltogether 58 haplotypes were observed and the haplotypic diversity, discrimination power and random match probability were calculated to be 0.981, 0.972, and 0.028, respectively. Mitochondrial DNA haplogroup affiliation exhibited an exceeding percentage (12.26%) of west Eurasian lineage (H haplogroup) in the studied Mongolian group, which needed to be further verified with more samples. Furthermore, the genetic relationships between the Xinjiang Mongolian group and the comparison populations were also investigated and the genetic affinity was discovered between the Xinjiang Mongolian group and the Xinjiang Kazak group in this study.ConclusionIt was indicated that the panel was potentially enough to be used as a supplementary tool for forensic applications. And the matrilineal genetic structure analyses based on mitochondrial DNA variants in the Xinjiang Mongolian group could be helpful for subsequent anthropological studies.

AbstractIn the forensic field, ancestry‐informative markers (AIMs) showing remarkable allele frequency discrepancies can be useful in deducing the likely ancestral origin of a person or estimating the ancestry component proportions of an admixed population or individual. Diallelic single nucleotide polymorphisms are genetic markers commonly used for ancestry inference, but the genotyping methods of single nucleotide polymorphisms fail to fulfil the demands of cost‐effectiveness and simplicity of experimental manipulation. To overcome the limitations, a 39 ancestry‐informative insertion/deletion polymorphism multiplex panel was developed in the present study to perform ancestry assignment of individuals from three distinct biogeographic regions (Africa, Europe, East Asia). And in the panel design, we also attempted to incorporate AIM‐insertion/deletion polymorphisms exhibiting allelic frequency differences in Han, Uyghur, and Tibetan populations into the multiplex assay, further expecting to provide valuable information for refining ancestry inference within Chinese populations. Statistical analyses were performed to estimate efficiency of this panel in clustering individuals from three continents mentioned above into their corresponding populations, which indicated the potential of the panel in ancestry inference. Besides, we also estimated the ancestral component proportions of Uyghur group and STRUCTURE analysis revealed that Uyghurs from Urumchi city of northern Xinjiang exhibited a distinctly admixed pattern of East Asian and European ancestry components with a ratio of 49:44, reflecting the relatively higher East Asian ancestry component contribution in the gene pool of the Uyghur group.