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It is important to predict adverse outcomes in febrile children with hematology/oncology diseases. Procalcitonin (PCT) is a promising biomarker for the prediction of infection severity, but further studies have revealed its performance in excluding adverse outcomes of infection. IL-6 and IL-10 were reported to have a close association with those infection outcomes. The aim of the study was to investigate the performance of IL-6 and IL-10 in febrile pediatric hematology/oncology patients with normal PCT. This was a retrospective study conducted in a tertiary children's hospital in China over the past ten years. Inflammatory biomarkers, including IL-6, IL-10, PCT and C-reactive protein (CRP), were detected at the onset of infection. Separate analyses were conducted in patients with neutropenia and without neutropenia. In total, 5987 febrile cases were enrolled. For patients with neutropenia, IL-6, IL-10 and PCT were significantly increased in patients with bloodstream infection (BSI), gram-negative bacteremia (GNB) and severe sepsis (SS), but only IL-6 and IL-10 were predictive of GNB and SS. For patients without neutropenia, IL-6, IL-10 and PCT were significantly increased in patients with BSI, GNB and SS, but no biomarkers were predictive of adverse outcomes. All biomarkers failed to exclude patients with fever of unknown origin or upper respiratory infection/bronchitis in patients with neutropenia. IL-6 and IL-10 could be predictors for GNB and SS in febrile patients with neutropenia and had some association with unfavorable outcomes in febrile patients without neutropenia. All biomarkers failed to exclude patients with fever of unknown origin or upper respiratory infection/bronchitis. [ABSTRACT FROM AUTHOR]

Background Patients with advanced chronic liver disease (ACLD) are at high risk of developing hepatocellular carcinoma (HCC). Therefore, biannual surveillance is recommended. This large-scale multicenter study aimed to stratify the risk of HCC development in ACLD. Methods From 3016 patients with ACLD screened in 17 European and Chinese centers, 2340 patients with liver stiffness measurement (LSM) determined using different techniques (two-dimensional shear-wave elastography [2D-SWE], transient elastography, and point shear-wave elastography) and with different disease severities were included. Cox regression was used to explore risk factors for HCC. We used these data to create an algorithm, named PLEASE, but referred to in this manuscript as “the algorithm”; the algorithm was validated in internal and two external cohorts across elastography techniques. Results HCC developed in 127 (5.4%) patients during follow-up. LSM by 2D-SWE (hazard ratio: 2.28) was found to be associated with developing HCC, alongside age, sex, etiology, and platelet count (C-index: 0.8428). We thus established the algorithm with applicable cutoffs, assigning a maximum of six points: platelet count less than 150×109/l, LSM greater than or equal to 15 kPa, age greater than or equal to 50 years, male sex, controlled/uncontrolled viral hepatitis, or presence of steatotic liver diseases. Within 2 years, with a median follow-up of 13.7 months, patients in the high-risk group (≥4 points) had an HCC incidence of 15.6% (95% confidence interval [CI], 12.1% to 18.7%) compared with the low-risk group, at 1.7% (95% CI, 0.9% to 2.5%). Conclusions Our algorithm stratified patients into two groups: those at higher risk of developing HCC and those at lower risk. Our data provide equipoise to test the prospective utility of the algorithm with respect to clinical decisions about screening patients with ACLD for incident HCC. [ABSTRACT FROM AUTHOR]

As more advanced detection tools, including medical devices, laboratory tests, and artificial intelligence-based algorithms, become rapidly available, various statistical issues pertaining to the diagnostic characteristics of these tools need to be considered. Revisiting common terminology is also important, to distinguish and ensure different considerations for important statistical aspects in developing tools for screening versus diagnostic purposes. In this issue of NEJM Evidence, Gu et al. propose a prediction algorithm for surveillance of de novo hepatocellular carcinoma (HCC) in patients with advanced chronic liver disease. [ABSTRACT FROM AUTHOR]