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Background and Objective: Vaccination against SARS-CoV-2 in immunocompromised patients with hematologic malignancies (HM) is crucial to reduce the severity of COVID-19. Despite vaccination efforts, over a third of HM patients remain unresponsive, increasing their risk of severe breakthrough infections. This study aims to leverage machine learning's adaptability to COVID-19 dynamics, efficiently selecting patient-specific features to enhance predictions and improve healthcare strategies. Highlighting the complex COVID-hematology connection, the focus is on interpretable machine learning to provide valuable insights to clinicians and biologists., Methods: The study evaluated a dataset with 1166 patients with hematological diseases. The output was the achievement or non-achievement of a serological response after full COVID-19 vaccination. Various machine learning methods were applied, with the best model selected based on metrics such as the Area Under the Curve (AUC), Sensitivity, Specificity, and Matthew Correlation Coefficient (MCC). Individual SHAP values were obtained for the best model, and Principal Component Analysis (PCA) was applied to these values. The patient profiles were then analyzed within identified clusters., Results: Support vector machine (SVM) emerged as the best-performing model. PCA applied to SVM-derived SHAP values resulted in four perfectly separated clusters. These clusters are characterized by the proportion of patients that generate antibodies (PPGA). Cluster 1, with the second-highest PPGA (69.91%), included patients with aggressive diseases and factors contributing to increased immunodeficiency. Cluster 2 had the lowest PPGA (33.3%), but the small sample size limited conclusive findings. Cluster 3, representing the majority of the population, exhibited a high rate of antibody generation (84.39%) and a better prognosis compared to cluster 1. Cluster 4, with a PPGA of 66.33%, included patients with B-cell non-Hodgkin's lymphoma on corticosteroid therapy., Conclusions: The methodology successfully identified four separate patient clusters using Machine Learning and Explainable AI (XAI). We then analyzed each cluster based on the percentage of HM patients who generated antibodies after COVID-19 vaccination. The study suggests the methodology's potential applicability to other diseases, highlighting the importance of interpretable ML in healthcare research and decision-making., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

The Japanese Society of Hematology performed an observational cross-sectional study to clarify the morbidity, prognosis, and prognostic factors in patients with COVID-19 with hematological diseases (HDs) in Japan. The study included patients with HDs who enrolled in our epidemiological survey and had a COVID-19 diagnosis and a verified outcome of up to 2 months. The primary endpoints were characteristics and short-term prognosis of COVID-19 in patients with HDs. A total of 367 patients from 68 institutes were enrolled over 1 year, and the collected data were analyzed. The median follow-up among survivors was 73 days (range, 1-639 days). The 60-day overall survival (OS) rate was 86.6%. In the multivariate analysis, albumin ≤ 3.3 g/dL and a need for oxygen were independently associated with inferior 60-day OS rates (hazard ratio [HR] 4.026, 95% confidence interval (CI) 1.954-8.294 and HR 14.55, 95% CI 3.378-62.64, respectively), whereas 60-day survival was significantly greater in patients with benign rather than malignant disease (HR 0.095, 95% CI 0.012-0.750). Together, these data suggest that intensive treatment may be necessary for patients with COVID-19 with malignant HDs who have low albumin levels and require oxygen at the time of diagnosis., (© 2024. The Author(s).)

Diagnosis of SARS-CoV-2 infections is mostly based on the nasopharyngeal swabs (NPS). However, this collection is invasive and uncomfortable, especially for children and patients with coagulopathies, whose NPS collection often causes bleeding. Thus, the aim of this study was to evaluate the usefulness and accuracy of saliva for the diagnosis of COVID-19 in patients presenting bleeding disorders. Samples of NPS, oropharyngeal swabs (OPS), and saliva were collected simultaneously from 1159 hospitalized patients with hematological diseases and from 524 healthcare workers, both symptomatic and asymptomatic for SARS-CoV-2. All samples were evaluated for SARS-CoV-2 by qRT-PCR. SARS-CoV-2 was detected in NPS, OPS and saliva from 16.9%, 14.4% and 15.6% individuals, respectively. Tests in saliva showed sensitivity, specificity, and overall agreement of 73.3%, 96.9% and 92.7% (=0.74), respectively. Salivary tests had good accuracy (AUC = 0.7) for discriminating negative and positive qRT-PCR for SARS-CoV-2. Higher sensitivity was observed in symptomatic than in non-symptomatic patients, as well as in healthy subjects than in patients with hematological disease, in both OPS and saliva. The mean viral load in NPS was significantly higher than in OPS and in saliva samples ( p < 0.001). Saliva is a good diagnostic tool to detect SARS-CoV-2, especially among patients symptomatic for COVID-19, and is a valuable specimen for mass screening of hospitalized patients with hematological diseases, especially for those that with bleeding disorders.

Preventing COVID-19 infection in pediatric patients with hematological disease and cancer is important. Thus, patients and those around them should avoid crowds, wear masks, and perform hand hygiene. It is necessary to try to prevent nosocomial infections during hospitalization. Moreover, determining COVID-19 complications and the condition of patients with hematological disease and cancer, and determining a personalized treatment strategy that prioritizes the treatment of high-risk conditions is necessary when suffering from COVID-19. However, another problem is when the patient refrains from visiting a medical institution due to the fear of contracting COVID-19 infection and becomes seriously ill. Furthermore, the lack of medical resources has affected the treatment of pediatric patients with hematological disease and cancer (e.g., delays and changes in treatment). Thus, appropriate information needs to be provided to children and their parents so that preventive measures can be taken.

Background: The effects of vaccination are modified by hematological and autoimmune diseases and/or treatment. Anti-SARS-CoV-2 mRNA vaccine contains polyethylene glycol (PEG), it is largely unknown whether PEG influences the effects of vaccination or induces a humoral response. This study examined whether anti-PEG antibodies before vaccination (pre-existing) influenced the acquisition of SARS-CoV-2 antibodies and evaluated the relationship between the development of anti-SARS-CoV-2 antibodies and anti-PEG antibodies after SARS-CoV-2 vaccination in hematological and autoimmune diseases. Methods: Anti-SARS-CoV-2 antibody IgG, anti-PEG IgG, and IgM titers were evaluated in patients with hematological and autoimmune diseases after the second dose of BNT162B2. Anti-PEG IgG and IgM titers were also measured before vaccination to examine changes after vaccination and the relationship with vaccine efficacy. Results: In patients with hematological (n = 182) and autoimmune diseases (n = 96), anti-SARS-CoV-2 and anti-PEG antibody titers were evaluated after a median of 33 days from 2nd vaccination. The median anti-SARS-CoV-2 antibody titers were 1901 AU/mL and 3832 AU/mL in patients with hematological and autoimmune disease, respectively. Multiple regression analysis showed that age and days from 2nd vaccination were negatively associated with anti-SARS-CoV-2 antibody titers. Anti-CD20 antibody treatment was negatively correlated with anti-SARS-CoV-2 antibody titers in hematological disease, and C-reactive protein (CRP) was positively correlated with anti-SARS-CoV-2 antibody titers in autoimmune disease. Baseline anti-PEG antibody titers were significantly higher in patients with autoimmune disease but were not correlated with anti-SARS-CoV-2 antibody titers. Patients with increased anti-PEG IgG acquired higher anti-SARS-CoV-2 antibody titers in patients with autoimmune disease. Conclusions: Anti-SARS-CoV-2 antibody acquisition was suboptimal in patients with hematological disease, but both anti-SARS-CoV-2 antibody and anti-PEG IgG were acquired in patients with autoimmune disease, reflecting robust humoral immune response. Pre-existing anti-PEG antibody titers did not affect anti-SARS-CoV-2 antibody acquisition.

Hematology patients are extremely vulnerable to COVID-19 infection due to the immunosuppression arising from the direct effect of the disease and the medicines administered. Our purpose is to analyze the results of the patients that both have a hematological disease and receive treatment for COVID-19 infection in our hospital. Four hundred COVID-19 positive patients that received inpatient treatment between March 12, 2020 and October 1, 2020 in our center and got a diagnosis by using real -time polymerase chain reaction (RT-PCR) test were scanned retrospectively. Eighty one patients were included in the study. Nineteen patients had a hematological disease; 62 had a chronic disease but didn't have a hematological disease. We found that the group with hematological disease had a high level of ferritin (p= 0.0001). While the use of steroids in COVID-19 treatments is more frequent in the group with hematological disease (p= 0.01), the use of LMWH (low molecular weight heparin) is more frequent in the group with no hematological diseases (p= 0.02). Intensive care treatment and mechanical ventilatory support were required more for the patients with hematological disease than the others (p= 0.03. p= 0.008). While the mortality rate is 42.1% in the patients with hematological disease, it is 9.7% in the patients with chronic disease (p= 0.003). In cox regression analysis, the study found that hematological diseases (HR: 4.02, 95% CI: 1.7-1844.5, p= 0.02), cardiac diseases (HR: 2.28, 95% CI: 1.2-77.9, p= 0.03), and intensive care treatment (HR: 4.60, 95% CI: 3.1-3115.0, p= 0.009) are significant risk factors. Hematological patients infected with COVID-19 have a more severe and mortal clinical manifestation than the patients with other chronical disease. [ABSTRACT FROM AUTHOR]