T-cell Subset Profile in Kidney Recipients of Extended or Standard Donors

IntroductionThe usage of extended-criteria donors (ECD) became a routinely accepted manner in the last decade. ECD is a potential risk factor for antibody-mediated rejection. Analysis of lymphocyte subsets might be a complementary diagnostic toolkit because there is limited knowledge about this term.MethodBetween May 12, 2016, and September 4, 2019, a total of 130 patients who had undergone kidney transplant were investigated. Patients were divided in ECD and standard criteria donor (SCD) groups. Blood samples were collected before the operation, then in the first week and after 30, 60, 180, and 365 days. Besides routine laboratory tests, multicolor flow cytometry was performed for lymphocyte subsets.ResultsECD grafts were transplanted to older recipients. The number of CD4+ cells increased in the SCDs from the first week to until the end of first month, and then decreased. The number of CD4+ cells decreased from the beginning of the study until the end of first year to 66% of its original value in ECDs. At the first month, the number of CD19+ cells was higher in SCD compared with ECD cases; the number then decreased in both groups. T-regulatory cells had a drop at the first week that lasted until the first month. A bigger increase in SCD and a moderate increase in ECD group were then observed. The kinetics of CD19+ and CD19+ naive cells are similar in the ECD and SCD groups. In the SCD group, cell count decreased in both CD19+ (13%) and CD19+ naive (12%) between third and sixth month. The count of CD19+ cells decreased by 9%, but the count of CD19+ naive cells increased by 11% between the sixth month and first year.DiscussionThe prolonged postoperative uremic state caused by the poorer initial function, together with an aging immune system, explains the weaker immune response in ECD patients, which may be the cause of the decreased number of memory and regulatory T cells. Older patients with an ECD graft need a tailored, personalized, and less aggressive immunosuppressive treatment.     

SALAD‐BAAR: A numerical risk score for hospital admission or emergency department presentation in ambulatory patients with cardiovascular disease

BackgroundWhile many interventions to reduce hospital admissions and emergency department (ED) visits for patients with cardiovascular disease have been developed, identifying ambulatory cardiac patients at high risk for admission can be challenging.HypothesisA computational model based on readily accessible clinical data can identify patients at risk for admission.MethodsElectronic health record (EHR) data from a tertiary referral center were used to generate decision tree and logistic regression models. International Classification of Disease (ICD) codes, labs, admissions, medications, vital signs, and socioenvironmental variables were used to model risk for ED presentation or hospital admission within 90 days following a cardiology clinic visit. Model training and testing were performed with a 70:30 data split. The final model was then prospectively validated.ResultsA total of 9326 patients and 46 465 clinic visits were analyzed. A decision tree model using 75 patient characteristics achieved an area under the curve (AUC) of 0.75 and a logistic regression model achieved an AUC of 0.73. A simplified 9‐feature model based on logistic regression odds ratios achieved an AUC of 0.72. A further simplified numerical score assigning 1 or 2 points to each variable achieved an AUC of 0.66, specificity of 0.75, and sensitivity of 0.58. Prospectively, this final model maintained its predictive performance (AUC 0.63–0.60).ConclusionNine patient characteristics from routine EHR data can be used to inform a highly specific model for hospital admission or ED presentation in cardiac patients. This model can be simplified to a risk score that is easily calculated and retains predictive performance.