Ahn, Joseph C

Introduction Despite extensive studies on the relationship between diabetes mellitus (DM) and depression, the associations of depressive symptom severity with prevalence, awareness, treatment, and control of diabetes remain unclear. We aimed to investigate changes in these outcomes of diabetes as depressive symptoms aggravate. Research design and methods We conducted a cross-sectional analysis of 14 328 participants in the 2011–2016 National Health and Nutrition Examination Survey. Participants were classified into depressive symptom groups of none, mild, moderate, moderately severe, and severe depending on their Patient Health Questionnaire-9 scores. Multivariate logistic regression analyses were conducted in three models adjusted for expanding confounders to evaluate the associations between severity of depressive symptoms and prevalence, awareness, treatment, and control of DM. Results As depressive symptom severity worsened, both prevalence and awareness of DM increased regardless of models (p value for trend <0.01 in all models for prevalence and awareness; adjusted OR (aOR) 2.14, 95% CI 1.29 to 3.56 for prevalence in the severe group, model 1; aOR 2.43, 95% CI 1.27 to 4.64 for awareness in the moderately severe group, model 1). Notwithstanding higher awareness of diabetes in the moderately severe and severe groups (84.5% and 86.2%, respectively, vs 71.3% in the none group), these groups were treated less (aOR 0.25, 95% CI 0.11 to 0.55 in the severe group, model 3) or inadequately controlled (aOR 0.51, 95% CI 0.27 to 0.98 in the moderately severe group, model 3). Conclusions The gap between patients’ higher awareness and lower treatment rate or control of diabetes among individuals with severe depressive symptoms highlights the unmet needs for postdiagnostic multidisciplinary care for patients with comorbid depression and DM.

Background and Aims Patients with cirrhosis on the liver transplant (LT) waiting list may die or be removed because of complications of portal hypertension (PH) or infections. von Willebrand factor antigen (vWF‐Ag) and C‐reactive protein (CRP) are simple, broadly available markers of these processes. Approach and Results We determined whether addition of vWF‐Ag and CRP to the Model for End‐Stage Liver Disease‐Sodium (MELD‐Na) score improves risk stratification of patients awaiting LT. CRP and vWF‐Ag at LT listing were assessed in two independent cohorts (Medical University of Vienna [exploration cohort] and Mayo Clinic Rochester [validation cohort]). Clinical characteristics, MELD‐Na, and mortality on the waiting list were recorded. Prediction of 3‐month waiting list mortality was assessed by receiver operating characteristics curve (ROC‐AUC). In order to explore potential mechanisms underlying the prognostic utility of vWF‐Ag and CRP in this setting, we evaluated their association with PH, bacterial translocation, systemic inflammation, and circulatory dysfunction. In the exploration cohort (n = 269) vWF‐Ag and CRP both improved the predictive value of MELD‐Na for 3‐month waitlist mortality and showed the highest predictive value when combined (AUC: MELD‐Na, 0.764; MELD‐Na + CRP, 0.790; MELD‐Na + vWF, 0.803; MELD‐Na + CRP + vWF‐Ag, 0.824). Results were confirmed in an independent validation cohort (n = 129; AUC: MELD‐Na, 0.677; MELD‐Na + CRP + vWF‐Ag, 0.882). vWF‐Ag was independently associated with PH and inflammatory biomarkers, whereas CRP closely, and MELD independently, correlated with biomarkers of bacterial translocation/inflammation. Conclusions The addition of vWF‐Ag and CRP—reflecting central pathophysiological mechanisms of PH, bacterial translocation, and inflammation, that are all drivers of mortality on the waiting list for LT—to the MELD‐Na score improves prediction of waitlist mortality. Using the vWFAg‐CRP‐MELD‐Na model for prioritizing organ allocation may improve prediction of waitlist mortality and decrease waitlist mortality.

Modern medical care produces large volumes of multimodal patient data, which many clinicians struggle to process and synthesize into actionable knowledge. In recent years, artificial intelligence (AI) has emerged as an effective tool in this regard. The field of hepatology is no exception, with a growing number of studies published that apply AI techniques to the diagnosis and treatment of liver diseases. These have included machine‐learning algorithms (such as regression models, Bayesian networks, and support vector machines) to predict disease progression, the presence of complications, and mortality; deep‐learning algorithms to enable rapid, automated interpretation of radiologic and pathologic images; and natural‐language processing to extract clinically meaningful concepts from vast quantities of unstructured data in electronic health records. This review article will provide a comprehensive overview of hepatology‐focused AI research, discuss some of the barriers to clinical implementation and adoption, and suggest future directions for the field.

Hepatocellular carcinoma (HCC) is among the leading causes of worldwide cancer‐related morbidity and mortality. Poor prognosis of HCC is attributed primarily to tumor presentation at an advanced stage when there is no effective treatment to achieve the long term survival of patients. Currently available tests such as alpha‐fetoprotein have limited accuracy as a diagnostic or prognostic biomarker for HCC. Liver biopsy provides tissue that can reveal tumor biology but it is not used routinely due to its invasiveness and risk of tumor seeding, especially in early‐stage patients. Liver biopsy is also limited in revealing comprehensive tumor biology due to intratumoral heterogeneity. There is a clear need for new biomarkers to improve HCC detection, prognostication, prediction of treatment response, and disease monitoring with treatment. Liquid biopsy could be an effective method of early detection and management of HCC. Circulating tumor cells (CTCs) are cancer cells in circulation derived from the original tumor or metastatic foci, and their measurement by liquid biopsy represents a great potential in facilitating the implementation of precision medicine in patients with HCC. CTCs can be detected by a simple peripheral blood draw and potentially show global features of tumor characteristics. Various CTC detection platforms using immunoaffinity and biophysical properties have been developed to identify and capture CTCs with high efficiency. Quantitative abundance of CTCs, as well as biological characteristics and genomic heterogeneity among the CTCs, can predict disease prognosis and response to therapy in patients with HCC. This review article will discuss the currently available technologies for CTC detection and isolation, their utility in the clinical management of HCC patients, their limitations, and future directions of research.