Refine
Document Type
- Article (3)
Language
- English (3)
Has Fulltext
- yes (3)
Is part of the Bibliography
- no (3)
Keywords
- ARDS (1)
- COVID-19 (1)
- Critical care (1)
- Influenza (1)
- Outcome (1)
- Parainfluenza (1)
- Pneumonia (1)
- Prognostic models (1)
- Respiratory syncytial virus (1)
- Superinfection (1)
Institute
- Medizin (3) (remove)
Aims: To analyze the relationship between exposure to chlorinated and aromatic organic solvents and malignant lymphoma in a multi-centre, population-based case-control study.
Methods: Male and female patients with malignant lymphoma (n=710) between 18 and 80 years of age were prospectively recruited in six study regions in Germany (Ludwigshafen /Upper Palatinate, Heidelberg/ Rhine-Neckar-County, Wurzburg/ Lower Frankonia, Hamburg, Bielefeld/ Gutersloh, and Munich). For each newly recruited lymphoma case, a gender, region and age-matched (+/- 1 year of birth) population control was drawn from the population registers. In a structured personal interview, we elicited a complete occupational history, including every occupational period that lasted at least one year. On the basis of job task-specific supplementary questionnaires, a trained occupational physician assessed the exposure to chlorinated hydrocarbons (trichloroethylene, tetrachloroethylene, dichloromethane, carbon tetrachloride) and aromatic hydrocarbons (benzene, toluene, xylene, styrene). Odds ratios (OR) and 95% confidence intervals (CI) were calculated using conditional logistic regression analysis, adjusted for smoking (in pack years) and alcohol consumption. To increase the statistical power, patients with specific lymphoma subentities were additionally compared with the entire control group using unconditional logistic regression analysis.
Results: We observed a statistically significant association between high exposure to chlorinated hydrocarbons and malignant lymphoma (Odds ratio = 2.1; 95% confidence interval 1.1-4.3). In the analysis of lymphoma subentities, a pronounced risk elevation was found for follicular lymphoma and marginal zone lymphoma. When specific substances were considered, the association between trichloroethylene and malignant lymphoma was of borderline statistical significance. Aromatic hydrocarbons were not significantly associated with the lymphoma diagnosis.
Conclusions: In accordance with the literature, this data point to a potential etiologic role of chlorinated hydrocarbons (particularly trichloroethylene) and malignant lymphoma. Chlorinated hydrocarbons might affect specific lymphoma subentities differentially. Our study does not support a strong association between aromatic hydrocarbons (benzene, toluene, xylene, or styrene) and the diagnosis of a malignant lymphoma.
Background: Community acquired viruses (CRVs) may cause severe disease in cancer patients. Thus, efforts should be made to diagnose CRV rapidly and manage CRV infections accordingly.
Methods: A panel of 18 clinicians from the Infectious Diseases Working Party of the German Society for Haematology and Medical Oncology have convened to assess the available literature and provide recommendations on the management of CRV infections including influenza, respiratory syncytial virus, parainfluenza virus, human metapneumovirus and adenovirus.
Results: CRV infections in cancer patients may lead to pneumonia in approximately 30% of the cases, with an associated mortality of around 25%. For diagnosis of a CRV infection, combined nasal/throat swabs or washes/aspirates give the best results and nucleic acid amplification based-techniques (NAT) should be used to detect the pathogen. Hand hygiene, contact isolation and face masks have been shown to be of benefit as general infection management. Causal treatment can be given for influenza, using a neuraminidase inhibitor, and respiratory syncytial virus, using ribavirin in addition to intravenous immunoglobulins. Ribavirin has also been used to treat parainfluenza virus and human metapneumovirus, but data are inconclusive in this setting. Cidofovir is used to treat adenovirus pneumonitis.
Conclusions: CRV infections may pose a vital threat to patients with underlying malignancy. This guideline provides information on diagnosis and treatment to improve the outcome.
Background: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.