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- Blixt, L., et al.
(författare)
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Covid-19 in patients with chronic lymphocytic leukemia : clinical outcome and B- and T-cell immunity during 13 months in consecutive patients
- 2022
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Ingår i: Leukemia. - : Springer Nature. - 0887-6924 .- 1476-5551. ; 36:2, s. 476-481
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Tidskriftsartikel (refereegranskat)abstract
- We studied clinical and immunological outcome of Covid-19 in consecutive CLL patients from a well-defined area during month 1–13 of the pandemic. Sixty patients (median age 71 y, range 43–97) were identified. Median CIRS was eight (4–20). Patients had indolent CLL (n = 38), had completed (n = 12) or ongoing therapy (n = 10). Forty-six patients (77%) were hospitalized due to severe Covid-19 and 11 were admitted to ICU. Severe Covid-19 was equally distributed across subgroups irrespective of age, gender, BMI, CLL status except CIRS (p < 0.05). Fourteen patients (23%) died; age ≥75 y was the only significant risk factor (p < 0.05, multivariate analysis with limited power). Comparing month 1–6 vs 7–13 of the pandemic, deaths were numerically reduced from 32% to 18%, ICU admission from 37% to 15% whereas hospitalizations remained frequent (86% vs 71%). Seroconversion occurred in 33/40 patients (82%) and anti-SARS-CoV-2 antibodies were detectable at six and 12 months in 17/22 and 8/11 patients, respectively. Most (13/17) had neutralizing antibodies and 19/28 had antibodies in saliva. SARS-CoV-2-specific T-cells (ELISpot) were detected in 14/17 patients. Covid-19 continued to result in high admission even among consecutive and young early- stage CLL patients. A robust and durable B and/or T cell immunity was observed in most convalescents.
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| 3. |
- Castro Dopico, Xaquin, et al.
(författare)
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Probabilistic classification of anti-SARS-CoV-2 antibody responses improves seroprevalence estimates
- 2022
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Ingår i: Clinical & Translational Immunology (CTI). - : John Wiley & Sons. - 2050-0068. ; 11:3
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: Population-level measures of seropositivity are critical for understanding the epidemiology of an emerging pathogen, yet most antibody tests apply a strict cutoff for seropositivity that is not learnt in a data-driven manner, leading to uncertainty when classifying low-titer responses. To improve upon this, we evaluated cutoff-independent methods for their ability to assign likelihood of SARS-CoV-2 seropositivity to individual samples. Methods: Using robust ELISAs based on SARS-CoV-2 spike (S) and the receptor-binding domain (RBD), we profiled antibody responses in a group of SARS-CoV-2 PCR+ individuals (n = 138). Using these data, we trained probabilistic learners to assign likelihood of seropositivity to test samples of unknown serostatus (n = 5100), identifying a support vector machines-linear discriminant analysis learner (SVM-LDA) suited for this purpose. Results: In the training data from confirmed ancestral SARS-CoV-2 infections, 99% of participants had detectable anti-S and -RBD IgG in the circulation, with titers differing > 1000-fold between persons. In data of otherwise healthy individuals, 7.2% (n = 367) of samples were of uncertain serostatus, with values in the range of 3-6SD from the mean of pre-pandemic negative controls (n = 595). In contrast, SVM-LDA classified 6.4% (n = 328) of test samples as having a high likelihood (> 99% chance) of past infection, 4.5% (n = 230) to have a 50–99% likelihood, and 4.0% (n = 203) to have a 10–49% likelihood. As different probabilistic approaches were more consistent with each other than conventional SD-based methods, such tools allow for more statistically-sound seropositivity estimates in large cohorts. Conclusion: Probabilistic antibody testing frameworks can improve seropositivity estimates in populations with large titer variability.
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