Katariya, Nitin N

AbstractBiliary strictures can be a challenging clinical problem to manage and often have unclear etiologies, including benign and malignant causes. Left untreated, these problems can lead to significant morbidity and mortality linked to their underlying diagnosis. The approach to adult patients with biliary strictures requires a multidisciplinary team involving surgeons, interventional endoscopists, and interventional radiologists for diagnosis, symptom relief, palliation, as well as potential curative management. From a surgeon's perspective, there are many ways to classify and approach these strictures. It is of paramount importance to start with an excellent understanding of the patient's prior surgical history. In approaching a patient with a new diagnosis of biliary stricture, it is also critical to understand its etiology relatively quickly, as 70% are malignant in the adult population. Concurrently, one must clearly define the location and extent of the stricture: intrahepatic, hilar, or distal extrahepatic bile duct, as well as whether it is a singular lesion or multifocal phenotypes. This information provides a path forward in clinical decision-making regarding durability and efficacy of therapy, which is typically aimed at decompression and/or surgical resection to prevent cholangitis, sepsis, and progressive hepatic insufficiency.

AbstractBackgroundWhile simultaneous pancreas kidney transplant (SPKTx) is a therapeutic option for patients with type 1 diabetes (T1DM) and renal failure, few centers offer SPKTx to “select” non‐T1DM patients. To address concerns that existing insulin resistance may limit the benefits of the pancreas allograft among non‐T1DM, we compared several indices of glucose homeostasis, in “select” non‐T1DM and T1DM patients who received SPKTx.MethodsCriteria for “select” non‐T1DM included the following: positive C‐peptide, BMI <30 kg/m2, treatment with oral agents before insulin initiation, and insulin at <1 unit/kg/d. We compared several indices of glucose homeostasis within 1 yr post‐SPKTx among seven “select” patients with non‐T1DM and nine patients with T1DM with similar age, BMI, and immunosuppression. Measurements of insulin resistance included the following: homeostatic model, insulin sensitivity index, and insulin–glucose ratio; insulin secretion measures included the following: corrected insulin response.ResultsNon‐T1DM had similar pre‐transplant metabolic (fasting glucose, HbA1c, blood pressure, and lipid) parameters to the T1DM cohort. There were no significant differences in the various measures of insulin resistance and secretion between T1DM and “select” non‐T1DM patients.ConclusionOur results suggest SPKTx should be considered in the therapeutic armamentarium among carefully select non‐T1DM with features of minimal insulin resistance; however, a larger cohort with longer follow‐up is needed to confirm our results.

OBJECTIVE New-onset diabetes after kidney transplantation (NODAT) has adverse clinical and economic implications. A risk score for NODAT could help identify research subjects for intervention studies. RESEARCH DESIGN AND METHODS We conducted a single-center retrospective cohort study using pretransplant clinical and laboratory measurements to construct a risk score for NODAT. NODAT was defined by hemoglobin A1c (HbA1c) ≥6.5%, fasting serum glucose ≥126 mg/dL, or prescribed therapy for diabetes within 1 year posttransplant. Three multivariate logistic regression models were constructed: 1) standard model, with both continuous and discrete variables; 2) dichotomous model, with continuous variables dichotomized at clinically relevant cut points; and 3) summary score defined as the sum of the points accrued using the terms from the dichotomous model. RESULTS A total of 316 subjects had seven pretransplant variables with P &lt; 0.10 in univariate logistic regression analyses (age, planned corticosteroid therapy posttransplant, prescription for gout medicine, BMI, fasting glucose and triglycerides, and family history of type 2 diabetes) that were selected for multivariate models. Areas under receiver operating curves for all three models were similar (0.72, 0.71, and 0.70). A simple risk score calculated as the sum of points from the seven variables performed as well as the other two models in identifying risk of NODAT. CONCLUSIONS A risk score computed from seven simple pretransplant variables can identify risk of NODAT.