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Numerous navigation applications rely on data from global navigation satellite systems (GNSS), even though their accuracy is compromised in urban areas, posing a significant challenge, particularly for precise autonomous car localization. Extensive research has focused on enhancing localization accuracy by integrating various sensor types to address this issue. This paper introduces a novel approach for car localization, leveraging image features that correspond with highly detailed semantic 3D building models. The core concept involves augmenting positioning accuracy by incorporating prior geometric and semantic knowledge into calculations. The work assesses outcomes using Level of Detail 2 (LoD2) and Level of Detail 3 (LoD3) models, analyzing whether facade-enriched models yield superior accuracy. This comprehensive analysis encompasses diverse methods, including off-the-shelf feature matching and deep learning, facilitating thorough discussion. Our experiments corroborate that LoD3 enables detecting up to 69% more features than using LoD2 models. We believe that this study will contribute to the research of enhancing positioning accuracy in GNSS-denied urban canyons. It also shows a practical application of under-explored LoD3 building models on map-based car positioning.

Thermal point clouds integrate thermal radiation and laser point clouds effectively. However, the semantic information for the interpretation of building thermal point clouds can hardly be precisely inferred. Transferring the semantics encapsulated in 3D building models at Level of Detail (LoD)3 has a potential to fill this gap. In this work, we propose a workflow enriching thermal point clouds with the geo-position and semantics of LoD3 building models, which utilizes features of both modalities: model point clouds are generated from LoD3 models, and thermal point clouds are co-registered by coarse-to-fine registration. The proposed method can automatically co-register the point clouds from different sources and enrich the thermal point cloud in facade-detailed semantics. The enriched thermal point cloud supports thermal analysis and can facilitate the development of currently scarce deep learning models operating directly on thermal point clouds.

Semantic 3D building models are widely available and used in numerous applications. Such 3D building models display rich semantics but no façade openings, chiefly owing to their aerial acquisition techniques. Hence, refining models’ façades using dense, street-level, terrestrial point clouds seems a promising strategy. In this paper, we propose a method of combining visibility analysis and neural networks for enriching 3D models with window and door features. In the method, occupancy voxels are fused with classified point clouds, which provides semantics to voxels. Voxels are also used to identify conflicts between laser observations and 3D models. The semantic voxels and conflicts are combined in a Bayesian network to classify and delineate façade openings, which are reconstructed using a 3D model library. Unaffected building semantics is preserved while the updated one is added, thereby upgrading the building model to LoD3. Moreover, Bayesian network results are back-projected onto point clouds to improve points’ classification accuracy. We tested our method on a municipal CityGML LoD2 repository and the open point cloud datasets: TUM-MLS-2016 and TUM-FAÇADE. Validation results revealed that the method improves the accuracy of point cloud semantic segmentation and upgrades buildings with façade elements. The method can be applied to enhance the accuracy of urban simulations and facilitate the development of semantic segmentation algorithms.

Point clouds are widely regarded as one of the best dataset types for urban mapping purposes. Hence, point cloud datasets are commonly investigated as benchmark types for various urban interpretation methods. Yet, few researchers have addressed the use of point cloud benchmarks for façade segmentation. Robust façade segmentation is becoming a key factor in various applications ranging from simulating autonomous driving functions to preserving cultural heritage. In this work, we present a method of enriching existing point cloud datasets with façade-related classes that have been designed to facilitate façade segmentation testing. We propose how to efficiently extend existing datasets and comprehensively assess their potential for façade segmentation. We use the method to create the TUM-FAÇADE dataset, which extends the capabilities of TUM-MLS-2016. Not only can TUM-FAÇADE facilitate the development of point-cloud-based façade segmentation tasks, but our procedure can also be applied to enrich further datasets.

Throughout the years, semantic 3D city models have been created to depict 3D spatial phenomenon. Recently, an increasing number of mobile laser scanning (MLS) units yield terrestrial point clouds at an unprecedented level. Both dataset types often depict the same 3D spatial phenomenon differently, thus their fusion should increase the quality of the captured 3D spatial phenomenon. Yet, each dataset has modality-dependent uncertainties that hinder their immediate fusion. Therefore, we present a method for fusing MLS point clouds with semantic 3D building models while considering uncertainty issues. Specifically, we show MLS point clouds coregistration with semantic 3D building models based on expert confidence in evaluated metadata quantified by confidence interval (CI). This step leads to the dynamic adjustment of the CI, which is used to delineate matching bounds for both datasets. Both coregistration and matching steps serve as priors for a Bayesian network (BayNet) that performs application-dependent identity estimation. The BayNet propagates uncertainties and beliefs throughout the process to estimate end probabilities for confirmed, unmodeled, and other city objects. We conducted promising preliminary experiments on urban MLS and CityGML datasets. Our strategy sets up a framework for the fusion of MLS point clouds and semantic 3D building models. This framework aids the challenging parallel usage of such datasets in applications such as façade refinement or change detection. To further support this process, we open-sourced our implementation.

Nowadays, the number of connected devices providing unstructured data is rapidly rising. These devices acquire data with a temporal and spatial resolution at an unprecedented level creating an influx of geoinformation which, however, lacks semantic information. Simultaneously, structured datasets like semantic 3D city models are widely available and assure rich semantics and high global accuracy but are represented by rather coarse geometries. While the mentioned downsides curb the usability of these data types for nowadays’ applications, the fusion of both shall maximize their potential. Since testing and developing automated driving functions stands at the forefront of the challenges, we propose a pipeline fusing structured (CityGML and HD Map datasets) and unstructured datasets (MLS point clouds) to maximize their advantages in the automatic 3D road space models reconstruction domain. The pipeline is a parameterized end-to-end solution that integrates segmentation, reconstruction, and modeling tasks while ensuring geometric and semantic validity of models. Firstly, the segmentation of point clouds is supported by the transfer of semantics from a structured to an unstructured dataset. The distinction between horizontal- and vertical-like point cloud subsets enforces a further segmentation or an immediate refinement while only adequately depicted models by point clouds are allowed. Then, based on the classified and filtered point clouds the input 3D model geometries are refined. Building upon the refinement, the semantic enrichment of the 3D models is presented. The deployment of a simulation engine for automated driving research and a city model database tool underlines the versatility of possible application areas.

Background: There is a lack of information about health problems after returning from tropical countries among travelers from Poland. The aim was to create characteristics of diseases imported to Poland from tropical regions and to determine the changes in the travel trends., Method: This retrospective study is based on medical records of 2391 Polish patients >18 years old, hospitalized between 2006 and 2016 after returning from tropical areas. The analysis covered purpose, duration and travel destination, and health problems related to the travel. 1098 patients (short travel, <1 month, n = 345 vs long travel, >6 months, n = 753) were selected for further analysis., Results: The most frequently visited region was Sub-Saharan Africa. Tourists dominated among short-term and missionaries among long-term travelers. The most popular health problems in both groups were digestive system disease and febrile diseases. Diarrhoea of undetermined aetiology, dengue fever, malaria, fever of unknown aetiology and infectious mononucleosis were more likely to occur among short-term travelers whereas blastocystosis, giardiasis, schistosomiasis among long-term travelers. In the group of long-term travelers 363/753 (47,8%) were diagnosed with an infectious or parasitic disease in relation to a trip to a country with a hot climate., Conclusions: Tropical diseases occur among Polish travelers so they should be taken into account in the context of prophylaxis when preparing for travel and in the diagnosis of diseases that occur after returning from a tropical zone. This first analysis of disease incidence among Polish travelers indicates a strong need for more research in this area., Competing Interests: Declaration of competing interest Authors declare no conflict of interest, (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Cytokine release syndrome (CRS), also known as cytokine storm, is one of the most consequential adverse effects of chimeric antigen receptor therapies that have shown otherwise promising results in cancer treatment. When emerging, CRS could be identified by the analysis of specific cytokine and chemokine profiles that tend to exhibit similarities across patients. In this paper, we exploit these similarities using machine learning algorithms and set out to pioneer a meta-review informed method for the identification of CRS based on specific cytokine peak concentrations and evidence from previous clinical studies. To this end we also address a widespread challenge of the applicability of machine learning in general: reduced training data availability. We do so by augmenting available (but often insufficient) patient cytokine concentrations with statistical knowledge extracted from domain literature. We argue that such methods could support clinicians in analyzing suspect cytokine profiles by matching them against the said CRS knowledge from past clinical studies, with the ultimate aim of swift CRS diagnosis. We evaluate our proposed methods under several design choices, achieving performance of more than 90% in terms of CRS identification accuracy, and showing that many of our choices outperform a purely data-driven alternative. During evaluation with real-world CRS clinical data, we emphasize the potential of our proposed method of producing interpretable results, in addition to being effective in identifying the onset of cytokine storm., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Oskar Wysocki, Donal Landers, Andre Freitas reports financial support was provided by AstraZeneca PLC. Magdalena Wysocka, Holly Butterworth, Fiona Thistlethwaite reports financial support was provided by UK Research and Innovation. Alex Bogatu reports financial support was provided by Horizon 2020 European Innovation Council Fast Track to Innovation. Elaine Kilgour reports financial support was provided by Cancer Research UK. Manon Pillai, Jennifer Allison reports administrative support was provided by The Christie NHS Foundation Trust. Fiona Thistlethwaite reports a relationship with Zenzium Ltd that includes: funding grants., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: There is an increasing interest in the use of Deep Learning (DL) based methods as a supporting analytical framework in oncology. However, most direct applications of DL will deliver models with limited transparency and explainability, which constrain their deployment in biomedical settings., Methods: This systematic review discusses DL models used to support inference in cancer biology with a particular emphasis on multi-omics analysis. It focuses on how existing models address the need for better dialogue with prior knowledge, biological plausibility and interpretability, fundamental properties in the biomedical domain. For this, we retrieved and analyzed 42 studies focusing on emerging architectural and methodological advances, the encoding of biological domain knowledge and the integration of explainability methods., Results: We discuss the recent evolutionary arch of DL models in the direction of integrating prior biological relational and network knowledge to support better generalisation (e.g. pathways or Protein-Protein-Interaction networks) and interpretability. This represents a fundamental functional shift towards models which can integrate mechanistic and statistical inference aspects. We introduce a concept of bio-centric interpretability and according to its taxonomy, we discuss representational methodologies for the integration of domain prior knowledge in such models., Conclusions: The paper provides a critical outlook into contemporary methods for explainability and interpretability used in DL for cancer. The analysis points in the direction of a convergence between encoding prior knowledge and improved interpretability. We introduce bio-centric interpretability which is an important step towards formalisation of biological interpretability of DL models and developing methods that are less problem- or application-specific., (© 2023. The Author(s).)

Patients with cancer have been shown to have increased risk of COVID-19 severity. We previously built and validated the COVID-19 Risk in Oncology Evaluation Tool (CORONET) to predict the likely severity of COVID-19 in patients with active cancer who present to hospital. We assessed the differences in presentation and outcomes of patients with cancer and COVID-19, depending on the wave of the pandemic. We examined differences in features at presentation and outcomes in patients worldwide, depending on the waves of the pandemic: wave 1 D614G (n = 1430), wave 2 Alpha (n = 475), and wave 4 Omicron variant (n = 63, UK and Spain only). The performance of CORONET was evaluated on 258, 48, and 54 patients for each wave, respectively. We found that mortality rates were reduced in subsequent waves. The majority of patients were vaccinated in wave 4, and 94% were treated with steroids if they required oxygen. The stages of cancer and the median ages of patients significantly differed, but features associated with worse COVID-19 outcomes remained predictive and did not differ between waves. The CORONET tool performed well in all waves, with scores in an area under the curve (AUC) of >0.72. We concluded that patients with cancer who present to hospital with COVID-19 have similar features of severity, which remain discriminatory despite differences in variants and vaccination status. Survival improved following the first wave of the pandemic, which may be associated with vaccination and the increased steroid use in those patients requiring oxygen. The CORONET model demonstrated good performance, independent of the SARS-CoV-2 variants.

Purpose: Patients with cancer are at increased risk of severe COVID-19 disease, but have heterogeneous presentations and outcomes. Decision-making tools for hospital admission, severity prediction, and increased monitoring for early intervention are critical. We sought to identify features of COVID-19 disease in patients with cancer predicting severe disease and build a decision support online tool, COVID-19 Risk in Oncology Evaluation Tool (CORONET)., Methods: Patients with active cancer (stage I-IV) and laboratory-confirmed COVID-19 disease presenting to hospitals worldwide were included. Discharge (within 24 hours), admission (≥ 24 hours inpatient), oxygen (O 2 ) requirement, and death were combined in a 0-3 point severity scale. Association of features with outcomes were investigated using Lasso regression and Random Forest combined with Shapley Additive Explanations. The CORONET model was then examined in the entire cohort to build an online CORONET decision support tool. Admission and severe disease thresholds were established through pragmatically defined cost functions. Finally, the CORONET model was validated on an external cohort., Results: The model development data set comprised 920 patients, with median age 70 (range 5-99) years, 56% males, 44% females, and 81% solid versus 19% hematologic cancers. In derivation, Random Forest demonstrated superior performance over Lasso with lower mean squared error (0.801 v 0.807) and was selected for development. During validation (n = 282 patients), the performance of CORONET varied depending on the country cohort. CORONET cutoffs for admission and mortality of 1.0 and 2.3 were established. The CORONET decision support tool recommended admission for 95% of patients eventually requiring oxygen and 97% of those who died (94% and 98% in validation, respectively). The specificity for mortality prediction was 92% and 83% in derivation and validation, respectively. Shapley Additive Explanations revealed that National Early Warning Score 2, C-reactive protein, and albumin were the most important features contributing to COVID-19 severity prediction in patients with cancer at time of hospital presentation., Conclusion: CORONET, a decision support tool validated in health care systems worldwide, can aid admission decisions and predict COVID-19 severity in patients with cancer., Competing Interests: Rebecca J. LeeSpeakers' Bureau: AstraZenecaResearch Funding: Bristol Myers Squibb (Inst), AstraZeneca/MedImmune (Inst) Rohan ShottonHonoraria: ServierTravel, Accommodations, Expenses: Servier Laurence AlbigesConsulting or Advisory Role: Bristol Myers Squibb (Inst), Ipsen (Inst), Roche (Inst), Novartis (Inst), Pfizer (Inst), Astellas Pharma (Inst), Merck (Inst), MSD (Inst), AstraZeneca (Inst), Janssen (Inst), Eisai (Inst), Corvus Pharmaceuticals (Inst), Bellerophon Therapeutics (Inst)Research Funding: Bristol Myers Squibb (Inst)Travel, Accommodations, Expenses: BMS, MSD Dirk ArnoldEmployment: Asklepios KlinikenHonoraria: Bayer, Merck Serono, Roche/Genentech, Servier, Bristol Myers Squibb, Merck Sharp and Dome, AstraZeneca, Amgen, Boston Scientific, Pierre Fabre, IpsenConsulting or Advisory Role: Bayer, Merck Serono, Biocompatibles, Terumo, Bristol Myers Squibb, MSD Oncology, AstraZenecaResearch Funding: Roche/Genentech (Inst), Sanofi (Inst), Oncolytics (Inst)Travel, Accommodations, Expenses: Boston ScientificUncompensated Relationships: ESMO Council, ESMO Journals (Ann Oncol, ESMO Open), German Society for Hematology and Medical Oncology, German Cancer Society, European Organisation for Research and Treatment of Cancer (EORTC) Kathryn BanfillStock and Other Ownership Interests: Roche (I)Honoraria: AstraZeneca Mark BaxterHonoraria: IpsenTravel, Accommodations, Expenses: Ipsen Fabrice BarlesiHonoraria: Genentech/Roche, Pfizer, Pierre Fabre, AstraZeneca, Bristol Myers Squibb, Boehringer Ingelheim, Lilly, Novartis, Merck Serono, MSD Oncology, Takeda, Bayer, Seattle Genetics, Mirati TherapeuticsConsulting or Advisory Role: Roche/Genentech, Pfizer, Novartis, Pierre Fabre, Bristol Myers Squibb, AstraZeneca/MedImmune, Boehringer Ingelheim, Lilly, Merck Serono, MSD Oncology, Takeda, Bayer, Mirati TherapeuticsResearch Funding: Roche/Genentech (Inst), AstraZeneca/MedImmune (Inst), Bristol Myers Squibb (Inst), Pierre Fabre (Inst), AbbVie (Inst), Amgen (Inst), Bayer (Inst), Boehringer Ingelheim (Inst), Eisai (Inst), Lilly (Inst), Ipsen (Inst), Innate Pharma (Inst), Novartis (Inst), Merck Serono (Inst), MSD Oncology (Inst), Pfizer (Inst), Sanofi/Aventis (Inst), Takeda (Inst)Travel, Accommodations, Expenses: Roche/Genentech, Bristol Myers Squibb, AstraZeneca/MedImmune, MSD Oncology Benjamin BesseResearch Funding: AstraZeneca (Inst), Pfizer (Inst), Lilly (Inst), Onxeo (Inst), Inivata (Inst), AbbVie (Inst), Amgen (Inst), Blueprint Medicines (Inst), Celgene (Inst), GlaxoSmithKline (Inst), Sanofi (Inst), Takeda (Inst), Cristal Therapeutics (Inst), Daiichi Sankyo (Inst), Janssen Oncology (Inst), OSE Immunotherapeutics (Inst), BeiGene (Inst), Boehringer Ingelheim (Inst), Roche/Genentech (Inst), Tolero Pharmaceuticals (Inst), 4D Pharma (Inst), Aptitude Health (Inst), Cergentis (Inst), Chugai Pharma (Inst), Genzyme (Inst), Ipsen (Inst), Turning Point Therapeutics (Inst), Eisai (Inst) Antonio CallesHonoraria: AstraZeneca, Boehringer Ingelheim, Pfizer, Roche/Genentech, Lilly, Novartis, Merck Sharp & Dohme, Bristol Myers Squibb, Amgen, Bayer, Takeda, Sanofi/RegeneronConsulting or Advisory Role: Boehringer Ingelheim, Roche/Genentech, Lilly, Merck Sharp & Dohme, Novartis, Pfizer, Bristol Myers Squibb, Takeda, Igen Biotech, Sanofi/RegeneronResearch Funding: MSD Oncology Ellen CopsonHonoraria: Roche, Pfizer, AstraZeneca, Lilly, NovartisConsulting or Advisory Role: Lilly, NanoString Technologies, Pfizer, Sanofi/Aventis, RocheSpeakers' Bureau: Roche, Pfizer, AstraZeneca, Sanofi/AventisResearch Funding: SECA, AstraZeneca (Inst)Travel, Accommodations, Expenses: Roche, AstraZeneca Adina E. CroitoruConsulting or Advisory Role: Ipsen, Pfizer, MSD OncologyResearch Funding: Bristol Myers Squibb (Inst), Amgen (Inst), Astellas Pharma (Inst), Exelixis (Inst), Merck (Inst), Merck KGaA (Inst)Travel, Accommodations, Expenses: Merck, ServierUncompensated Relationships: Lilly, Roche, Bayer, Pfizer, Sanofi Leonie EastlakeTravel, Accommodations, Expenses: Servier Paul FitzpatrickStock and Other Ownership Interests: AstraZeneca/MedImmuneResearch Funding: AstraZeneca/MedImmune (Inst)Other Relationship: Pistoia Alliance, EHDEN IMI project Henrik FrederiksenResearch Funding: AbbVie (Inst), Gilead Sciences (Inst), Sanofi (Inst) Hannah FrostResearch Funding: AstraZeneca (Inst) Sarju GanatraExpert Testimony: Haymarket Medical EducationTravel, Accommodations, Expenses: Haymarket Medical Education Andreas GlenthøjHonoraria: Novo NordiskConsulting or Advisory Role: Novartis, Celgene/Bristol Myers Squibb, Bluebird Bio, Sanofi, Novo Nordisk, AgiosResearch Funding: Sanofi, Saniona A/STravel, Accommodations, Expenses: Agios Fabio GomesHonoraria: AstraZeneca, Merck Serono, Roche Donna M. GrahamConsulting or Advisory Role: Clinigen GroupSpeakers' Bureau: Cancer Drug Development ForumResearch Funding: Pfizer (Inst) Kevin HarringtonHonoraria: Arch Oncology (Inst), AstraZeneca (Inst), BMS (Inst), Boehringer Ingelheim (Inst), Merck Serono (Inst), MSD (Inst), Oncolys BioPharma (Inst), Pfizer (Inst), Replimune (Inst), Inzen Therapeutics (Inst), Codiak Biosciences (Inst)Consulting or Advisory Role: Arch Oncology (Inst), AstraZeneca (Inst), BMS (Inst), Boehringer Ingelheim (Inst), Merck Serono (Inst), MSD (Inst), Oncolys BioPharma (Inst), Replimune (Inst), Inzen Therapeutics (Inst)Speakers' Bureau: BMS (Inst), Merck Serono (Inst), MSD (Inst)Research Funding: AstraZeneca (Inst), Merck Sharp & Dohme (Inst), Replimune (Inst), Boehringer Ingelheim (Inst) Lasse H. JakobsenHonoraria: Takeda, Roche Khurum KhanHonoraria: ServierConsulting or Advisory Role: Bayer Health Christophe MassardConsulting or Advisory Role: Amgen, Astellas Pharma, AstraZeneca, Bayer, BeiGene, BMS, Celgene, Debiopharm Group, Genentech/Roche, Ipsen, Janssen, Lilly, MSD, Novartis, Pfizer, Sanofi, ORION, Taiho Pharmaceutical, Blueprint Medicines, Innate Pharma, PharmaMar, Faron Pharmaceuticals Olivier MichielinConsulting or Advisory Role: Bristol Myers Squibb, MSD, Novartis, Roche, Amgen, Pierre FabreResearch Funding: MSD, Bristol Myers Squibb, NeraCare GmbHExpert Testimony: Bristol Myers SquibbTravel, Accommodations, Expenses: Bristol Myers Squibb, MSD Anne C. MosenthalHonoraria: Springer Nature Berta ObispoHonoraria: Sanofi, Lilly, Angelini, LEO Pharma, RoviConsulting or Advisory Role: Rovi George PentheroudakisHonoraria: Roche, Amgen, Bristol Myers Squibb, MSD, MerckConsulting or Advisory Role: Roche, AmgenResearch Funding: Roche (Inst), Amgen (Inst), Bristol Myers Squibb (Inst), Merck (Inst), AstraZeneca (Inst), Novartis (Inst), Pfizer (Inst)Travel, Accommodations, Expenses: Sanofi, MSD, Roche, Amgen, BMS Solange PetersHonoraria: Roche (Inst), Bristol Myers Squibb (Inst), Novartis (Inst), Pfizer (Inst), MSD (Inst), AstraZeneca (Inst), Takeda (Inst), Illumina (Inst), Medscape (Inst), Prime Oncology (Inst), RMEI Medical Education (Inst), Research to Practice (Inst), PER (Inst), Imedex (Inst), ecancer (Inst)Consulting or Advisory Role: Roche/Genentech (Inst), Novartis (Inst), Bristol Myers Squibb (Inst), Pfizer (Inst), MSD (Inst), Amgen (Inst), AstraZeneca (Inst), Janssen (Inst), Regeneron (Inst), Merck Serono (Inst), Boehringer Ingelheim (Inst), Takeda (Inst), Lilly (Inst), AbbVie (Inst), Bayer (Inst), Biocartis (Inst), Debiopharm Group (Inst), Illumina (Inst), PharmaMar (Inst), Sanofi (Inst), Seattle Genetics (Inst), Blueprint Medicines (Inst), Daiichi Sankyo (Inst), Incyte (Inst), Bioinvent (Inst), Clovis Oncology (Inst), Vaccibody (Inst), Phosplatin Therapeutics (Inst), Foundation Medicine (Inst)Research Funding: Roche (Inst), BMS (Inst), MSD (Inst), Amgen (Inst), Lilly (Inst), AstraZeneca (Inst), Pfizer (Inst), Illumina (Inst), Merck Serono (Inst), Novartis (Inst), Biodesix (Inst), Boehringer Ingelheim (Inst), Iovance Biotherapeutics (Inst), Phosplatin Therapeutics (Inst)Travel, Accommodations, Expenses: Roche, Bristol Myers Squibb, MSD, Sanofi, IncyteUncompensated Relationships: Journal of Thoracic Oncology, ESMO, European Thoracic Oncology Platform (ETOP), Annals of Oncology (I) Kimberly Rieger-ChristResearch Funding: Veracyte, Ravel, Grail, Exact Sciences, Nucleix Timothy RobinsonTravel, Accommodations, Expenses: Daiichi Sankyo/Lilly Emanuela RomanoConsulting or Advisory Role: AstraZeneca/MedImmune (Inst), Bristol Myers Squibb (Inst), Roche/Genentech (Inst)Research Funding: Bristol Myers Squibb, AmgenTravel, Accommodations, Expenses: AstraZeneca/MedImmune, Bristol Myers Squibb, Roche Michael RoweHonoraria: MSDSpeakers' Bureau: ServierTravel, Accommodations, Expenses: Astellas Pharma Anne ThomasConsulting or Advisory Role: BMSSpeakers' Bureau: Bristol Myers SquibbExpert Testimony: BMS Lance TurtleSpeakers' Bureau: Eisai (Inst) David ViñalSpeakers' Bureau: ServierTravel, Accommodations, Expenses: Merck Caroline WilsonConsulting or Advisory Role: Roche, Pfizer Carlo PalmieriHonoraria: PfizerConsulting or Advisory Role: Pfizer, Daiichi-Sankyo, Lilly, Novartis, Seattle GeneticsResearch Funding: Pfizer, Daiichi-SankyoTravel, Accommodations, Expenses: Roche Donal LandersEmployment: AstraZeneca, AthenexLeadership: DeLondra OncologyStock and Other Ownership Interests: DeLondra OncologyResearch Funding: AstraZeneca (Inst) Timothy CooksleyHonoraria: Bristol Myers Squibb Foundation Caroline DiveConsulting or Advisory Role: Biocartis, Merck, AstraZeneca, GRAIL, Boehringer IngelheimResearch Funding: AstraZeneca, Astex Pharmaceuticals, Bioven, Amgen, Carrick Therapeutics, Merck, Taiho Oncology, GlaxoSmithKline, Bayer, Boehringer Ingelheim, Roche, BMS, Novartis, Celgene, Epigene Therapeutics, Angle, Menarini, Clearbridge Biomedics, Thermo Fisher Scientific, NeoMed André FreitasResearch Funding: AstraZeneca (Inst) Anne C. ArmstrongStock and Other Ownership Interests: AstraZeneca (I)Consulting or Advisory Role: Gilead Sciences, MSDResearch Funding: AstraZeneca/MedImmune (Inst)Travel, Accommodations, Expenses: Gilead Sciences, MSD OncologyNo other potential conflicts of interest were reported.

Objectives: COVID-19 is a heterogeneous disease, and many reports have described variations in demographic, biochemical and clinical features at presentation influencing overall hospital mortality. However, there is little information regarding longitudinal changes in laboratory prognostic variables in relation to disease progression in hospitalised patients with COVID-19., Design and Setting: This retrospective observational report describes disease progression from symptom onset, to admission to hospital, clinical response and discharge/death among patients with COVID-19 at a tertiary centre in South East England., Participants: Six hundred and fifty-one patients treated for SARS-CoV-2 between March and September 2020 were included in this analysis. Ethical approval was obtained from the HRA Specific Review Board (REC 20/HRA/2986) for waiver of informed consent., Results: The majority of patients presented within 1 week of symptom onset. The lowest risk patients had low mortality (1/45, 2%), and most were discharged within 1 week after admission (30/45, 67%). The highest risk patients, as determined by the 4C mortality score predictor, had high mortality (27/29, 93%), with most dying within 1 week after admission (22/29, 76%). Consistent with previous reports, most patients presented with high levels of C reactive protein (CRP) (67% of patients >50 mg/L), D-dimer (98%>upper limit of normal (ULN)), ferritin (65%>ULN), lactate dehydrogenase (90%>ULN) and low lymphocyte counts (81%

Objectives: Dexamethasone has now been incorporated into the standard of care for COVID-19 hospital patients. However, larger intensive care unit studies have failed to show discernible improvements in mortality in the recent wave. We aimed to investigate the impacts of these factors on disease outcomes in a UK hospital study., Methods: This retrospective observational study reports patient characteristics, interventions and outcomes in COVID-19 patients from a UK teaching hospital; cohort 1, pre 16th June-2020 (pre-dexamethasone); cohort 2, 17th June to 30th November-2020 (post-dexamethasone, pre-VOC 202,012/01 as dominant strain); cohort 3, 1st December-2020 to 3rd March-2021 (during establishment of VOC202012/01 as the dominant strain)., Results: Dexamethasone treatment was more common in cohorts 2 and 3 (42.7% and 51.6%) compared with cohort 1 (2.5%). After adjusting for risk, odds of death within 28 days were 2-fold lower in cohort 2 vs 1 (OR:0.47,[0.27,0.79],p = 0.006). Mortality was higher cohort 3 vs 2 (20% vs 14%); but not significantly different to cohort 1 (OR: 0.86,[0.64, 1.15],p = 0.308)., Conclusions: The real world finding of lower mortality following dexamethasone supports the published trial evidence and highlights ongoing need for research with introduction of new treatments and ongoing concern over new COVID-19 variants., (Copyright © 2021. Published by Elsevier B.V.)

Background: Cancer patients are at increased risk of death from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Cancer and its treatment affect many haematological and biochemical parameters, therefore we analysed these prior to and during coronavirus disease 2019 (COVID-19) and correlated them with outcome., Patients and Methods: Consecutive patients with cancer testing positive for SARS-CoV-2 in centres throughout the United Kingdom were identified and entered into a database following local governance approval. Clinical and longitudinal laboratory data were extracted from patient records. Data were analysed using Mann-Whitney U test, Fisher's exact test, Wilcoxon signed rank test, logistic regression, or linear regression for outcomes. Hierarchical clustering of heatmaps was performed using Ward's method., Results: In total, 302 patients were included in three cohorts: Manchester (n = 67), Liverpool (n = 62), and UK (n = 173). In the entire cohort (N = 302), median age was 69 (range 19-93 years), including 163 males and 139 females; of these, 216 were diagnosed with a solid tumour and 86 with a haematological cancer. Preinfection lymphopaenia, neutropaenia and lactate dehydrogenase (LDH) were not associated with oxygen requirement (O 2 ) or death. Lymphocyte count (P < 0.001), platelet count (P = 0.03), LDH (P < 0.0001) and albumin (P < 0.0001) significantly changed from preinfection to during infection. High rather than low neutrophils at day 0 (P = 0.007), higher maximal neutrophils during COVID-19 (P = 0.026) and higher neutrophil-to-lymphocyte ratio (NLR; P = 0.01) were associated with death. In multivariable analysis, age (P = 0.002), haematological cancer (P = 0.034), C-reactive protein (P = 0.004), NLR (P = 0.036) and albumin (P = 0.02) at day 0 were significant predictors of death. In the Manchester/Liverpool cohort 30 patients have restarted therapy following COVID-19, with no additional complications requiring readmission., Conclusion: Preinfection biochemical/haematological parameters were not associated with worse outcome in cancer patients. Restarting treatment following COVID-19 was not associated with additional complications. Neutropaenia due to cancer/treatment is not associated with COVID-19 mortality. Cancer therapy, particularly in patients with solid tumours, need not be delayed or omitted due to concerns that treatment itself increases COVID-19 severity., Competing Interests: Disclosure RJL speaker fees BMS and Astrazeneca, MR honoraria from Astellas Pharma, speaker fees MSD and Servier. CW consultancy and speaker fees Pfizer, Amgen, Novartis, AA conference fee Merck, spouse shares in Astrazeneca. TR financial support to attend educational workshops from Amgen and Daiichi-Sankyo. JT is now working at Astra Zeneca. CD, outside of this scope of work, has received research funding from AstraZeneca, Astex Pharmaceuticals, Bioven, Amgen, Carrick Therapeutics, Merck AG, Taiho Oncology, Clearbridge Biomedics, Angle PLC, Menarini Diagnostics, GSK, Bayer, Boehringer Ingelheim, Roche, BMS, Novartis, Celgene, Thermofisher. CD is on advisory boards for, and has received consultancy fees/honoraria from, AstraZeneca, Biocartis and Merck KGaA. The remaining authors have no conflicts of interest to declare., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)