Characteristics and Outcomes of Over 300,000 Patients with COVID-19 and History of Cancer in the United States and Spain

Abstract

Background:

We described the demographics, cancer subtypes, comorbidities, and outcomes of patients with a history of cancer and coronavirus disease 2019 (COVID-19). Second, we compared patients hospitalized with COVID-19 to patients diagnosed with COVID-19 and patients hospitalized with influenza.

Methods:

We conducted a cohort study using eight routinely collected health care databases from Spain and the United States, standardized to the Observational Medical Outcome Partnership common data model. Three cohorts of patients with a history of cancer were included: (i) diagnosed with COVID-19, (ii) hospitalized with COVID-19, and (iii) hospitalized with influenza in 2017 to 2018. Patients were followed from index date to 30 days or death. We reported demographics, cancer subtypes, comorbidities, and 30-day outcomes.

Results:

We included 366,050 and 119,597 patients diagnosed and hospitalized with COVID-19, respectively. Prostate and breast cancers were the most frequent cancers (range: 5%–18% and 1%–14% in the diagnosed cohort, respectively). Hematologic malignancies were also frequent, with non-Hodgkin's lymphoma being among the five most common cancer subtypes in the diagnosed cohort. Overall, patients were aged above 65 years and had multiple comorbidities. Occurrence of death ranged from 2% to 14% and from 6% to 26% in the diagnosed and hospitalized COVID-19 cohorts, respectively. Patients hospitalized with influenza (n = 67,743) had a similar distribution of cancer subtypes, sex, age, and comorbidities but lower occurrence of adverse events.

Conclusions:

Patients with a history of cancer and COVID-19 had multiple comorbidities and a high occurrence of COVID-19-related events. Hematologic malignancies were frequent.

Impact:

This study provides epidemiologic characteristics that can inform clinical care and etiologic studies.

SciScore for 10.1101/2021.01.12.21249672: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Institutional Review Board Statement	IRB: All the data partners obtained Institutional Review Board (IRB) approval or exemption to conduct this study.
Randomization	not detected.
Blinding	not detected.
Power Analysis	not detected.
Sex as a biological variable	12 Data from the US included Electronic Health Records (EHR) from the hospital setting: CU-AMC-HDC, CUIMC, Optum-EHR,13 and VA-OMOP (93% male, mostly veterans); as well as claims data: HealthVerity and IQVIA-OpenClaims.

Table 2: Resources

No key resources detected.

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

However, this study also has several limitations. First, we were not able to provide detailed cancer information, such as the year of cancer diagnosis or stage of tumour at diagnosis; nor identify patients with active cancer treatment, and we were not able to characterize patients stratified by cancer types due to small sample sizes. Secondly, although we included patients with a clinical COVID-19 diagnosis to reduce selection bias due to testing restrictions during the first months of the pandemic, we cannot exclude that we have incurred some false positives. Additionally, we did not have information on the cause of death and reported all-cause death as an outcome. Third, the differences found in the COVID-19/seasonal influenza comparison may have been influenced by temporal changes in clinical practice standards and coding. Further, the use of influenza vaccination among high-risk population groups likely contributed to the observed low proportion of adverse events among influenza patients. Finally, we found heterogeneous results across databases, which hinders the interpretation of our findings. Heterogeneity across data sources is a known phenomenon when using real-world data that reflects the existence of different coding practices, observation period, healthcare settings and populations. Indeed, the fact that the percentages of several cancer types were very low in some databases might be explained by differences in coding practices across data sources (i.e. some codes ...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
No protocol registration statement was detected.

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