Evidence map›Paper›PMID 33178336›Full record

ArticleTherapeutic advances in medical oncology2020

COVID-19 in cancer patients on systemic anti-cancer therapies: outcomes from the CAPITOL (COVID-19 Cancer PatIenT Outcomes in North London) cohort study.

Valerie E Crolley, Daire Hanna, Nalinie Joharatnam-Hogan, Neha Chopra, Ekin Bamac, Meera Desai, Yuk-Chun Lam, Sabiq Dipro, Ruhi Kanani, Jack Benson and 7 more

Open access · goldAbstract read
In one paragraph

Article in Therapeutic advances in medical oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 pooled it
1.3field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 20 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors at 4 institutions in 1 country.

Valerie E CrolleyNorth Middlesex University Hospital, London, UK.ORCID https://orcid.org/0000-0001-7263-4051
Daire HannaUniversity College NHS Foundation Trust, London, UK.
Nalinie Joharatnam-HoganUniversity College NHS Foundation Trust, London, UK.
Neha ChopraUniversity College NHS Foundation Trust, London, UK.
Ekin BamacUniversity College NHS Foundation Trust, London, UK.
Meera DesaiUniversity College NHS Foundation Trust, London, UK.
Yuk-Chun LamUniversity College NHS Foundation Trust, London, UK.
Sabiq DiproNorth Middlesex University Hospital, London, UK.
Ruhi KananiNorth Middlesex University Hospital, London, UK.
Jack BensonNorth Middlesex University Hospital, London, UK.
William WilsonCancer Research UK, Oxford, UK.
Thomas A FoxUniversity College NHS Foundation Trust, London, UK.
Kai-Keen ShiuUniversity College NHS Foundation Trust, London, UK.
Martin ForsterUniversity College NHS Foundation Trust, London, UK.
John BridgewaterUniversity College NHS Foundation Trust, London, UK.
Daniel HochhauserUniversity College NHS Foundation Trust, London, UK.
Khurum KhanGastrointestinal Oncology Service and Cancer of Unknown Primary Service, University College London Hospital, UCL Cancer Institute and North Middlesex University Hospital, GI Cancer Lead North London Cancer Research Network and Oncology Research Lead North Middlesex University Hospital, 250 Euston Rd, London NW1 2PG, UK North Middlesex University Hospital, London, UK UCL Cancer Institute, 72 Huntley St, Bloomsbury, London WC1E 6DD, UK.
University College London Hospitals NHS Foundation Trust · GBNorth Middlesex Hospital · GBCRUK Lung Cancer Centre of Excellence · GBLondon Cancer · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPatients with cancer are hypothesised to be at increased risk of contracting COVID-19, leading to changes in treatment pathways in those treated with systemic anti-cancer treatments (SACT). This study investigated the outcomes of patients receiving SACT to assess whether they were at greater risk of contracting COVID-19 or having more severe outcomes.

methodsData was collected from all patients receiving SACT in two cancer centres as part of CAPITOL (COVID-19 Cancer PatIenT Outcomes in North London). The primary outcome was the effect of clinical characteristics on the incidence and severity of COVID-19 infection in patients on SACT. We used univariable and multivariable models to analyse outcomes, adjusting for age, gender and comorbidities.

resultsA total of 2871 patients receiving SACT from 2 March to 31 May 2020 were analysed; 68 (2.4%) were diagnosed with COVID-19. Cancer patients receiving SACT were more likely to die if they contracted COVID-19 than those who did not [adjusted (adj.) odds ratio (OR) 9.84; 95% confidence interval (CI) 5.73-16.9]. Receiving chemotherapy increased the risk of developing COVID-19 (adj. OR 2.99; 95% CI = 1.72-5.21), with high dose chemotherapy significantly increasing risk (adj. OR 2.36, 95% CI 1.35-6.48), as did the presence of comorbidities (adj. OR 2.29; 95% CI 1.19-4.38), and having a respiratory or intrathoracic neoplasm (adj. OR 2.12; 95% CI 1.04-4.36). Receiving targeted treatment had a protective effect (adj. OR 0.53; 95% CI 0.30-0.95). Treatment intent (curative

conclusionPatients on SACT are more likely to die if they contract COVID-19. Those on chemotherapy, particularly high dose chemotherapy, are more likely to contract COVID-19, while targeted treatment appears to be protective.

Indexed as

cancerchemotherapycoronavirusCOVID-19hormone therapyimmunotherapynovel coronavirusoncologySACTSARS-CoV-2systemic anti-cancer treatmenttargeted treatmenttumour

Identifiers

PMID33178336
PMCPMC7592172
OpenAlexW3094611671

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.