High Anti-CoV2S Antibody Levels at Hospitalization Are Associated with Improved Survival in Patients with COVID-19 Vaccine Breakthrough Infection.

BACKGROUND: Although vaccination against COVID-19 is highly effective, breakthrough infections occur, often leading to severe courses and death. The extent of protection provided by individual antibody levels in breakthrough infections is still unknown and cut-off levels have yet to be determined. METHODS: In 80 consecutive fully vaccinated patients hospitalized between August and December 2021 with COVID-19 breakthrough infection (Delta variant), anti-CoV2S antibody levels were analyzed for the endpoint of death. RESULTS: Ten out of the 12 patients who died (83.3%) had antibody levels < 600 U/mL; 5 (41.7%) of these had antibody levels < 200 U/mL. Only 2 patients with a level of >600 U/mL died from vaccine breakthrough infection. Correction for the number of comorbidities and age revealed that anti-CoV2S antibody levels at the time of hospitalization were a significant predictor for reduced risk of death (OR = 0.402 for every 1000 U/mL, p = 0.018). CONCLUSIONS: In this retrospective data analysis, we show that almost all patients who died from COVID-19 vaccine breakthrough infection had antibody levels < 600 U/mL, most of them below 200 U/mL. In logistic regression corrected for the number of comorbidities and age, anti-CoV2S antibody levels at the time of hospitalization proved to be a significantly protective predictor against death.

Biomarkers Predictive for In-Hospital Mortality in Patients with Diabetes Mellitus and Prediabetes Hospitalized for COVID-19 in Austria: An Analysis of COVID-19 in Diabetes Registry.

BACKGROUND: This study assessed the predictive performance of inflammatory, hepatic, coagulation, and cardiac biomarkers in patients with prediabetes and diabetes mellitus hospitalized for COVID-19 in Austria. METHODS: This was an analysis of a multicenter cohort study of 747 patients with diabetes mellitus or prediabetes hospitalized for COVID-19 in 11 hospitals in Austria. The primary outcome of this study was in-hospital mortality. The predictor variables included demographic characteristics, clinical parameters, comorbidities, use of medication, disease severity, and laboratory measurements of biomarkers. The association between biomarkers and in-hospital mortality was assessed using simple and multiple logistic regression analyses. The predictive performance of biomarkers was assessed using discrimination and calibration. RESULTS: In our analysis, 70.8% had type 2 diabetes mellitus, 5.8% had type 1 diabetes mellitus, 14.9% had prediabetes, and 8.6% had other types of diabetes mellitus. The mean age was 70.3 ± 13.3 years, and 69.3% of patients were men. A total of 19.0% of patients died in the hospital. In multiple logistic regression analysis, LDH, CRP, IL-6, PCT, AST-ALT ratio, NT-proBNP, and Troponin T were significantly associated with in-hospital mortality. The discrimination of NT-proBNP was 74%, and that of Troponin T was 81%. The calibration of NT-proBNP was adequate (p = 0.302), while it was inadequate for Troponin T (p = 0.010). CONCLUSION: Troponin T showed excellent predictive performance, while NT-proBNP showed good predictive performance for assessing in-hospital mortality in patients with diabetes mellitus hospitalized with COVID-19. Therefore, these cardiac biomarkers may be used for prognostication of COVID-19 patients.

COVID-19 In-Hospital Mortality in People with Diabetes Is Driven by Comorbidities and Age-Propensity Score-Matched Analysis of Austrian National Public Health Institute Data.

BACKGROUND: It is a matter of debate whether diabetes alone or its associated comorbidities are responsible for severe COVID-19 outcomes. This study assessed the impact of diabetes on intensive care unit (ICU) admission and in-hospital mortality in hospitalized COVID-19 patients. METHODS: A retrospective analysis was performed on a countrywide cohort of 40,632 COVID-19 patients hospitalized between March 2020 and March 2021. Data were provided by the Austrian data platform. The association of diabetes with outcomes was assessed using unmatched and propensity-score matched (PSM) logistic regression. RESULTS: 12.2% of patients had diabetes, 14.5% were admitted to the ICU, and 16.2% died in the hospital. Unmatched logistic regression analysis showed a significant association of diabetes (odds ratio [OR]: 1.24, 95% confidence interval [CI]: 1.15-1.34, p < 0.001) with in-hospital mortality, whereas PSM analysis showed no significant association of diabetes with in-hospital mortality (OR: 1.08, 95%CI: 0.97-1.19, p = 0.146). Diabetes was associated with higher odds of ICU admissions in both unmatched (OR: 1.36, 95%CI: 1.25-1.47, p < 0.001) and PSM analysis (OR: 1.15, 95%CI: 1.04-1.28, p = 0.009). CONCLUSIONS: People with diabetes were more likely to be admitted to ICU compared to those without diabetes. However, advanced age and comorbidities rather than diabetes itself were associated with increased in-hospital mortality in COVID-19 patients.

COVID-19 fatality prediction in people with diabetes and prediabetes using a simple score upon hospital admission.

AIM: To assess predictors of in-hospital mortality in people with prediabetes and diabetes hospitalized for COVID-19 infection and to develop a risk score for identifying those at the greatest risk of a fatal outcome. MATERIALS AND METHODS: A combined prospective and retrospective, multicentre, cohort study was conducted at 10 sites in Austria in 247 people with diabetes or newly diagnosed prediabetes who were hospitalized with COVID-19. The primary outcome was in-hospital mortality and the predictor variables upon admission included clinical data, co-morbidities of diabetes or laboratory data. Logistic regression analyses were performed to identify significant predictors and to develop a risk score for in-hospital mortality. RESULTS: The mean age of people hospitalized (n = 238) for COVID-19 was 71.1 ± 12.9 years, 63.6% were males, 75.6% had type 2 diabetes, 4.6% had type 1 diabetes and 19.8% had prediabetes. The mean duration of hospital stay was 18 ± 16 days, 23.9% required ventilation therapy and 24.4% died in the hospital. The mortality rate in people with diabetes was numerically higher (26.7%) compared with those with prediabetes (14.9%) but without statistical significance (P = .128). A score including age, arterial occlusive disease, C-reactive protein, estimated glomerular filtration rate and aspartate aminotransferase levels at admission predicted in-hospital mortality with a C-statistic of 0.889 (95% CI: 0.837-0.941) and calibration of 1.000 (P = .909). CONCLUSIONS: The in-hospital mortality for COVID-19 was high in people with diabetes but not significantly different to the risk in people with prediabetes. A risk score using five routinely available patient variables showed excellent predictive performance for assessing in-hospital mortality.

Evaluation of the EDI enzyme linked immunosorbent assays for the detection of SARS-CoV-2 IgM and IgG antibodies in human plasma.

BACKGROUND: Besides SARS-CoV-2 RT-PCR testing, serological testing is emerging as additional option in COVID-19 diagnostics. Aim of this study was to evaluate novel immunoassays for detection of SARS-CoV-2 antibodies in human plasma. METHODS: Using EDITM Novel Coronavirus COVID-19 Enzyme Linked Immunosorbent Assays (ELISAs), we measured SARS-CoV-2 IgM and IgG antibodies in 64 SARS-CoV-2 RT-PCR confirmed COVID-19 patients with serial blood samples (n = 104) collected at different time points from symptom onset. Blood samples from 200 healthy blood donors and 256 intensive care unit (ICU) patients collected before the COVID-19 outbreak were also used. RESULTS: The positivity rates in the COVID-19 patients were 5.9% for IgM and 2.9% for IgG ≤ 5 days after symptom onset; Between day 5 and day 10 the positivity rates were 37.1% for IgM and 37.1% for IgG and rose to 76.4% for IgM and 82.4% for IgG after > 10-15 days. After 15-22 days the "true" positivity rates were 94.4% for IgM and 100% for IgG. The "false" positivity rates were 0.5% for IgM and 1.0% for IgG in the healthy blood donors, 1.6% for IgM and 1.2% for IgG in ICU patients. CONCLUSIONS: This study shows high "true" vs. low "false" positivity rates for the EDITM SARS-CoV-2 IgM and IgG ELISAs.

Comparison of the Elecsys® Anti-SARS-CoV-2 immunoassay with the EDI™ enzyme linked immunosorbent assays for the detection of SARS-CoV-2 antibodies in human plasma.

BACKGROUND: Here, we report on a head-to-head comparison of the fully-automated Elecsys® Anti-SARS-CoV-2 immunoassay with the EDITM enzyme linked immunosorbent assays (ELISA) for the detection of SARS-CoV-2 antibodies in human plasma. METHODS: SARS-CoV-2 antibodies were measured with the Elecsys® assay and the EDITM ELISAs (IgM and IgG) in 64 SARS-CoV-2 RT-PCR confirmed COVID-19 patients with serial blood samples (n = 104) collected at different time points from symptom onset. Blood samples from 200 healthy blood donors and 256 intensive care unit (ICU) patients collected before the COVID-19 outbreak were also used. RESULTS: In COVID-19 patients, the percentage of positive results rose with time from symptom onset, peaking to positivity rates after 15-22 days of 100% for the Elecsys® assay, of 94% for the EDITM IgM-ELISA and of 100% for the EDITM IgG ELISA. In the 104 blood samples, the agreement between positive/negative classifications of the Elecsys® assay and the EDITM ELISAs (IgM or IgG) was 90%. The false positivity rates in the healthy blood donors and the ICU patients were < 1% for the Elecsys® assay and < 3% for the EDITM ELISAs. CONCLUSIONS: Our results indicate a high sensitivity and specificity for the Elecsys® assay and an acceptable agreement with the EDITM ELISAs.