Translating NIA-AA criteria into usual practice: Report from the ReDeMa Project.

INTRODUCTION: Biomarker-informed criteria were proposed for the diagnosis of Alzheimer's disease (AD) by the National Institute on Aging and the Alzheimer's Association (NIA-AA) in 2011; however, the adequacy of this criteria has not been sufficiently evaluated. METHODS: ReDeMa (Red de Demencias de Madrid) is a regional cohort of patients attending memory and neurology clinics. Core cerebrospinal fluid biomarkers were obtained, NIA-AA diagnostic criteria were considered, and changes in diagnosis and management were evaluated. RESULTS: A total of 233 patients were analyzed (mean age 70 years, 50% women, 73% AD). The diagnostic language was modified significantly, with a majority assumption of NIA-AA definitions (69%). Confidence in diagnosis increased from 70% to 92% (p < 0.0005) and management was changed in 71% of patient/caregivers. The influence of neurologist's age or expertise on study results was minimal. DISCUSSION: The NIA-AA criteria are adequate and utile for usual practice in memory and neurology clinics, improving diagnostic confidence and significantly modifying patient management. HIGHLIGHTS: Alzheimer's disease (AD) cerebrospinal fluid (CSF) biomarkers increase diagnostic certainty regardless of the neurologist.AD CSF biomarkers lead to changes in disease management .Biomarker-enriched, 2011 NIA-AA diagnostic criteria are adequate for usual practice.

Low anti-SARS-CoV-2 S antibody levels predict increased mortality and dissemination of viral components in the blood of critical COVID-19 patients.

BACKGROUND: Anti-SARS-CoV-2 S antibodies prevent viral replication. Critically ill COVID-19 patients show viral material in plasma, associated with a dysregulated host response. If these antibodies influence survival and viral dissemination in ICU-COVID patients is unknown. PATIENTS/METHODS: We studied the impact of anti-SARS-CoV-2 S antibodies levels on survival, viral RNA-load in plasma, and N-antigenaemia in 92 COVID-19 patients over ICU admission. RESULTS: Frequency of N-antigenaemia was >2.5-fold higher in absence of antibodies. Antibodies correlated inversely with viral RNA-load in plasma, representing a protective factor against mortality (adjusted HR [CI 95%], p): (S IgM [AUC ≥ 60]: 0.44 [0.22; 0.88], 0.020); (S IgG [AUC ≥ 237]: 0.31 [0.16; 0.61], <0.001). Viral RNA-load in plasma and N-antigenaemia predicted increased mortality: (N1-viral load [≥2.156 copies/ml]: 2.25 [1.16; 4.36], 0.016); (N-antigenaemia: 2.45 [1.27; 4.69], 0.007). CONCLUSIONS: Low anti-SARS-CoV-2 S antibody levels predict mortality in critical COVID-19. Our findings support that these antibodies contribute to prevent systemic dissemination of SARS-CoV-2.

Comparison of real-time and droplet digital PCR to detect and quantify SARS-CoV-2 RNA in plasma.

BACKGROUND: The presence of SARS-CoV-2 RNA in plasma has been linked to disease severity and mortality. We compared RT-qPCR to droplet digital PCR (ddPCR) to detect SARS-CoV-2 RNA in plasma from COVID-19 patients (mild, moderate, and critical disease). METHODS: The presence/concentration of SARS-CoV-2 RNA in plasma was compared in three groups of COVID-19 patients (30 outpatients, 30 ward patients and 30 ICU patients) using both RT-qPCR and ddPCR. Plasma was obtained in the first 24h following admission, and RNA was extracted using eMAG. ddPCR was performed using Bio-Rad SARS-CoV-2 detection kit, and RT-qPCR was performed using GeneFinder™ COVID-19 Plus RealAmp Kit. Statistical analysis was performed using Statistical Package for the Social Science. RESULTS: SARS-CoV-2 RNA was detected, using ddPCR and RT-qPCR, in 91% and 87% of ICU patients, 27% and 23% of ward patients and 3% and 3% of outpatients. The concordance of the results obtained by both methods was excellent (Cohen's kappa index = 0.953). RT-qPCR was able to detect 34/36 (94.4%) patients positive for viral RNA in plasma by ddPCR. Viral RNA load was higher in ICU patients compared with the other groups (P < .001), by both ddPCR and RT-qPCR. AUC analysis revealed Ct values (RT-qPCR) and viral RNA load values (ddPCR) can similarly differentiate between patients admitted to wards and to the ICU (AUC of 0.90 and 0.89, respectively). CONCLUSION: Both methods yielded similar prevalence of RNAemia between groups, with ICU patients showing the highest (>85%). RT-qPCR was as useful as ddPCR to detect and quantify SARS-CoV-2 RNAemia in plasma.

Viral RNA load in plasma is associated with critical illness and a dysregulated host response in COVID-19.

BACKGROUND: COVID-19 can course with respiratory and extrapulmonary disease. SARS-CoV-2 RNA is detected in respiratory samples but also in blood, stool and urine. Severe COVID-19 is characterized by a dysregulated host response to this virus. We studied whether viral RNAemia or viral RNA load in plasma is associated with severe COVID-19 and also to this dysregulated response. METHODS: A total of 250 patients with COVID-19 were recruited (50 outpatients, 100 hospitalized ward patients and 100 critically ill). Viral RNA detection and quantification in plasma was performed using droplet digital PCR, targeting the N1 and N2 regions of the SARS-CoV-2 nucleoprotein gene. The association between SARS-CoV-2 RNAemia and viral RNA load in plasma with severity was evaluated by multivariate logistic regression. Correlations between viral RNA load and biomarkers evidencing dysregulation of host response were evaluated by calculating the Spearman correlation coefficients. RESULTS: The frequency of viral RNAemia was higher in the critically ill patients (78%) compared to ward patients (27%) and outpatients (2%) (p < 0.001). Critical patients had higher viral RNA loads in plasma than non-critically ill patients, with non-survivors showing the highest values. When outpatients and ward patients were compared, viral RNAemia did not show significant associations in the multivariate analysis. In contrast, when ward patients were compared with ICU patients, both viral RNAemia and viral RNA load in plasma were associated with critical illness (OR [CI 95%], p): RNAemia (3.92 [1.183-12.968], 0.025), viral RNA load (N1) (1.962 [1.244-3.096], 0.004); viral RNA load (N2) (2.229 [1.382-3.595], 0.001). Viral RNA load in plasma correlated with higher levels of chemokines (CXCL10, CCL2), biomarkers indicative of a systemic inflammatory response (IL-6, CRP, ferritin), activation of NK cells (IL-15), endothelial dysfunction (VCAM-1, angiopoietin-2, ICAM-1), coagulation activation (D-Dimer and INR), tissue damage (LDH, GPT), neutrophil response (neutrophils counts, myeloperoxidase, GM-CSF) and immunodepression (PD-L1, IL-10, lymphopenia and monocytopenia). CONCLUSIONS: SARS-CoV-2 RNAemia and viral RNA load in plasma are associated with critical illness in COVID-19. Viral RNA load in plasma correlates with key signatures of dysregulated host responses, suggesting a major role of uncontrolled viral replication in the pathogenesis of this disease.

Clinical Characteristics and Electrophysiological Mechanisms Underlying Brugada ECG in Patients With Severe Hyperkalemia.

Background Several metabolic conditions can cause the Brugada ECG pattern, also called Brugada phenotype (BrPh). We aimed to define the clinical characteristics and outcome of BrPh patients and elucidate the mechanisms underlying BrPh attributed to hyperkalemia. Methods and Results We prospectively identified patients hospitalized with severe hyperkalemia and ECG diagnosis of BrPh and compared their clinical characteristics and outcome with patients with hyperkalemia but no BrPh ECG. Computer simulations investigated the roles of extracellular potassium increase, fibrosis at the right ventricular outflow tract, and epicardial/endocardial gradients in transient outward current. Over a 6-year period, 15 patients presented severe hyperkalemia with BrPh ECG that was transient and disappeared after normalization of their serum potassium. Most patients were admitted because of various severe medical conditions causing hyperkalemia. Six (40%) patients presented malignant arrhythmias and 6 died during admission. Multiple logistic regression analysis revealed that higher serum potassium levels (odds ratio, 15.8; 95% CI, 3.1-79; P=0.001) and male sex (odds ratio, 17; 95% CI, 1.05-286; P=0.045) were risk factors for developing BrPh ECG in patients with severe hyperkalemia. In simulations, hyperkalemia yielded BrPh by promoting delayed and heterogeneous right ventricular outflow tract activation attributed to elevation of resting potential, reduced availability of inward sodium channel conductance, and increased right ventricular outflow tract fibrosis. An elevated transient outward current gradient contributed to, but was not essential for, the BrPh phenotype. Conclusions In patients with severe hyperkalemia, a BrPh ECG is associated with malignant arrhythmias and all-cause mortality secondary to resting potential depolarization, reduced sodium current availability, and fibrosis at the right ventricular outflow tract.

Sex and age affect agreement between fasting plasma glucose and glycosylated hemoglobin for diagnosis of dysglycemia.

AIM: To assess agreement between fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) levels for diagnosis of dysglycemia (diabetes and risk of diabetes), overall and depending on clinical characteristics. METHODS: The study enrolled 1020 adult subjects without drug-treated diabetes who underwent a laboratory test at a Spanish health care center. The criteria for dysglycemia of the American Diabetes Association were used. A logistic regression analysis was used to predict de novo diagnosis of dysglycemia based on sex, age, body mass index, anemia, and iron levels. RESULTS: Overall prevalence of dysglycemia was 28.04%, and was identified by FPG only in 13.63% of subjects, by both FPG and HbA1c in 7.65%, and by HbA1c only in 6.76% (de novo diagnoses). Independent predictors of de novo diagnoses based on HbA1c were female sex (odds ratio [OR]: 2.119, 95% confidence interval [CI]: 1.133-4.020; p<0.020), age (OR for 42-56 years: 2.541, 95% CI: 0.634-17.140; OR for ≥57 years: 5.656, 95% CI: 1.516-36.980; overall p<0.007), and serum ferritin levels (borderline significance). CONCLUSIONS: In this study population, agreement between FPG and HbA1c for diagnosis of dysglycemia was poor, with FPG being the test that identified more subjects. De novo diagnoses based on HbA1c were more common in females and increased with age.