In-depth blood immune profiling of Good syndrome patients

Introduction

Good syndrome (GS) is a rare adult-onset immunodeficiency first described in 1954. It is characterized by the coexistence of a thymoma and hypogammaglobulinemia, associated with an increased susceptibility to infections and autoimmunity. The classification and management of GS has been long hampered by the lack of data about the underlying immune alterations, a controversy existing on whether it is a unique diagnostic entity vs. a subtype of Common Variable Immune Deficiency (CVID).

Methods

Here, we used high-sensitive flow cytometry to investigate the distribution of up to 70 different immune cell populations in blood of GS patients (n=9) compared to age-matched CVID patients (n=55) and healthy donors (n=61).

Results

All 9 GS patients displayed reduced B-cell counts -down to undetectable levels (<0.1 cells/μL) in 8/9 cases-, together with decreased numbers of total CD4+ T-cells, NK-cells, neutrophils, and basophils vs. age-matched healthy donors. In contrast, they showed expanded TCRγδ+ T-cells (p ≤ 0.05). Except for a deeper B-cell defect, the pattern of immune cell alteration in blood was similar in GS and (age-matched) CVID patients. In depth analysis of CD4+ T-cells revealed significantly decreased blood counts of naïve, central memory (CM) and transitional memory (TM) TCD4+ cells and their functional compartments of T follicular helper (TFH), regulatory T cells (Tregs), T helper (Th)2, Th17, Th22, Th1/Th17 and Th1/Th2 cells. In addition, GS patients also showed decreased NK-cell, neutrophil, basophil, classical monocyte and of both CD1c+ and CD141+ myeloid dendritic cell counts in blood, in parallel to an expansion of total and terminal effector TCRγδ+ T-cells. Interestingly, those GS patients who developed hypogammaglobulinemia several years after the thymoma presented with an immunological and clinical phenotype which more closely resembled a combined immune humoral and cellular defect, with poorer response to immunoglobulin replacement therapy, as compared to those in whom the thymoma and hypogammaglobulinemia were simultaneously detected.

Discussion

Our findings provide a more accurate definition of the immune cell defects of GS patients and contribute to a better discrimination among GS patients between those with a pure B-cell defect vs. those suffering from a combined immunodeficiency with important consequences on the diagnosis and management of the disease.

Analysis of the Costs Associated With the Elective Evaluation of Patients Labelled as Allergic to Beta-Lactams or Nonsteroidal Antiinflamatory Agents

Introduction: Being labelled as allergic to different drugs results in patients receiving other treatments, which are more toxic, less effective and more expensive. We aimed to analyze different studies of the costs of drug hypersensitivity assessment.

Methods: A bibliographic search on studies regarding this issue was performed, including the available scientific evidence up to June 2020. We searched three databases with terms related to costs and allergy testing in drug hypersensitivity reactions.

Results: Our search revealed 1,430 publications, of which 20 met the inclusion criteria. In the manuscript, prospective studies evaluating the costs of the evaluation of patients with suspected allergy to beta-lactams or non-steroidal anti-inflammatory drugs are analyzed. Also, comment is made on the costs associated with incorrect labeling as non-steroidal anti-inflammatory drug or penicillin hypersensitivity.

Conclusions: Taking all costs into account, the study of drug hypersensitivity is not expensive, particularly considering the economic and clinical consequences of labeling a patient with hypersensitivity to drugs.

Usefulness of an Artificial Neural Network in the Prediction of β-Lactam Allergy

BACKGROUND

An accurate diagnosis of β-lactam (BL) allergy improves the use of antibiotics, increases patients' safety, and reduces costs to health systems. Nevertheless, it requires skin and drug provocation tests, which are time-consuming and put the patient at risk. Furthermore, allergy testing is not available in circumstances such as the urgent need for antibiotic therapy.

OBJECTIVE

To evaluate the usefulness of an artificial neural network (ANN) in the prediction of hypersensitivity to BLs, and compare it with logistic regression (LR) analysis.

METHODS

In a single-center study, 656 patients evaluated for BL allergy between 1994 and 2000 were retrospectively analyzed, and the data were used to construct an ANN. The ANN predictive capabilities were compared with LR and then prospectively evaluated in 615 patients who underwent BL evaluation between 2011 and 2017.

RESULTS

A total of 1271 patients were evaluated. All patients had a definite diagnosis as allergic or nonallergic to BL. The prospective sample showed a lower percentage of patients with allergy than the retrospective sample (20.7% vs 25.8%; P = .018). In the retrospective and prospective series, the ANN reached a sensitivity of 89.5% and 81.1%, a specificity of 86.1% and 97.9%, a positive predictive value of 82.1% and 91.1%, and a negative predictive value of 92.1% and 95.2%, respectively. The ANN's performance was far superior to that of the LR, whose best performance reached a sensitivity of 31.9% and a specificity of 98.8%.

CONCLUSIONS

This ANN demonstrated a superior performance than the LR in predicting BL hypersensitivity without misdiagnosing severe allergic reactions. The ANN could be a helpful tool to classify the reaction risk, particularly in the identification of low-risk patients, in which an open challenge could be done to delabel patients.