Geriatric risk factors for serious COVID-19 outcomes among older adults with cancer: a cohort study from the COVID-19 and Cancer Consortium
Summary
Background
Older age is associated with poorer outcomes of SARS-CoV-2 infection, although the heterogeneity of ageing results in some older adults being at greater risk than others. The objective of this study was to quantify the association of a novel geriatric risk index, comprising age, modified Charlson comorbidity index, and Eastern Cooperative Oncology Group performance status, with COVID-19 severity and 30-day mortality among older adults with cancer.
Methods
In this cohort study, we enrolled patients aged 60 years and older with a current or previous cancer diagnosis (excluding those with non-invasive cancers and premalignant or non-malignant conditions) and a current or previous laboratory-confirmed COVID-19 diagnosis who reported to the COVID-19 and Cancer Consortium (CCC19) multinational, multicentre, registry between March 17, 2020, and June 6, 2021. Patients were also excluded for unknown age, missing data resulting in unknown geriatric risk measure, inadequate data quality, or incomplete follow-up resulting in unknown COVID-19 severity. The exposure of interest was the CCC19 geriatric risk index. The primary outcome was COVID-19 severity and the secondary outcome was 30-day all-cause mortality; both were assessed in the full dataset. Adjusted odds ratios (ORs) and 95% CIs were estimated from ordinal and binary logistic regression models.
Findings
5671 patients with cancer and COVID-19 were included in the analysis. Median follow-up time was 56 days (IQR 22–120), and median age was 72 years (IQR 66–79). The CCC19 geriatric risk index identified 2365 (41·7%) patients as standard risk, 2217 (39·1%) patients as intermediate risk, and 1089 (19·2%) as high risk. 36 (0·6%) patients were excluded due to non-calculable geriatric risk index. Compared with standard-risk patients, high-risk patients had significantly higher COVID-19 severity (adjusted OR 7·24; 95% CI 6·20–8·45). 920 (16·2%) of 5671 patients died within 30 days of a COVID-19 diagnosis, including 161 (6·8%) of 2365 standard-risk patients, 409 (18·5%) of 2217 intermediate-risk patients, and 350 (32·1%) of 1089 high-risk patients. High-risk patients had higher adjusted odds of 30-day mortality (adjusted OR 10·7; 95% CI 8·54–13·5) than standard-risk patients.
Interpretation
The CCC19 geriatric risk index was strongly associated with COVID-19 severity and 30-day mortality. Our CCC19 geriatric risk index, based on readily available clinical factors, might provide clinicians with an easy-to-use risk stratification method to identify older adults most at risk for severe COVID-19 as well as mortality.
Funding
US National Institutes of Health National Cancer Institute Cancer Center.
Introduction
Individuals with cancer who develop COVID-19 are at risk for more severe outcomes than those without cancer.
Among those with cancer, age also increases the risk of adverse outcomes of infection.
,
,
Evidence before this study
We searched PubMed for studies published from inception up to Oct 28, 2021, using the key terms “COVID-19” in combination with “cancer”, “frailty”, and “comorbidities”. Systematic reviews showed the increased risk of adverse outcomes of COVID-19 among people of older age, with cancer, comorbidities, and who were frail. Studies identifying older adults at greatest risk for adverse outcomes and mortality after SARS-CoV-2 infection are scarce.
Added value of this study
Among patients aged 60 years or older reported to the COVID-19 and Cancer Consortium, patients categorised as high risk—using a novel geriatric risk index comprising age, performance status, and comorbidities—had significantly higher COVID-19 severity, including hospitalisation, need for intensive care and mechanical ventilation, and death, within 30 days due to any cause than patients categorised as standard risk or intermediate risk.
Implications of all the available evidence
Patients with cancer identified as high risk are at higher risk for severe complications due to COVID-19 than patients categorised as standard risk or intermediate risk, highlighting the need for continued protective strategies for this vulnerable population.
The Cancer and Aging Research Group and the International Society of Geriatric Oncology released statements on older people with cancer, but noted that these statements were based on clinical consensus, not robust evidence.
,
As a result, there is an urgent need to determine the effect of COVID-19 in older adults with cancer and identify those most vulnerable for adverse outcomes. The primary objective of this study was to determine whether a measure of geriatric risk—combining age, comorbidities, and performance status—could capture risk of severe clinical outcomes among older patients with cancer and COVID-19. We also sought to describe the presentation, complications, and effect of COVID-19 on subsequent cancer care among older adults with cancer.
Methods
Study design and participants
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The CCC19 registry is accruing deidentified data, from contributing institutions (appendix pp 1–5), on patients aged 18 years or older with a current or past history of haematological malignancy or invasive solid tumour who have either a laboratory-confirmed SARS-CoV-2 infection or a presumptive diagnosis of COVID-19. Contributing institutions in the consortium independently identify patients and report data through the online REDCap data collection survey instruments developed by CCC19, described in detail previously.
The mechanism of data collection can be retrospective (after the acute course of COVID-19) or concurrent, at the discretion of the respondent, and can be for outpatients or hospitalised patients.
,
Patients with non-invasive cancers and premalignant or non-malignant conditions were excluded. Patients were also excluded due to unknown age, missing data resulting in unknown geriatric risk measure, inadequate data quality (quality score ≥5 according to our previously published metric
), or incomplete follow-up resulting in unknown COVID-19 severity (figure 1). The full CCC19 data dictionary is available online. No protected health information, as defined by the Health Insurance Portability and Accountability Act of 1996 is collected by this centralised registry, which was considered exempt from institutional review board review (VUMC institutional review board 200467) and was approved by local institutional review boards at participating sites per institutional policy, according to the principles of the Declaration of Helsinki. This study is registered on ClinicalTrials.gov, NCT04354701, and is ongoing.
Figure 1CONSORT diagram and cohort assembly
CCC19=COVID-19 and Cancer Consortium. *In-situ malignancy, precursor haematologic condition, benign haematologic condition, non-melanoma skin cancer, non-invasive malignancy, false-positive SARS-CoV-2 test, or non-CCC19 site. †Quality score ≥5.
Procedures
Patients considered to have typical presentations reported one or more typical symptom, as classified by the US Centers for Disease Control and Prevention: fever (subjective, >100·4°F, or >38°C), cough, dyspnoea, myalgia, arthralgia, headache, anosmia, ageusia, sore throat, rhinorrhoea, nausea, vomiting, diarrhoea, and abdominal pain.
Patients considered to have atypical presentations reported one or more atypical symptoms only: fatigue, altered mental state, abdominal discomfort, conjunctivitis, and all other symptoms not previously categorised as typical. COVID-19 severity at presentation was categorised as follows: mild if no hospitalisation indicated, moderate if hospitalisation indicated, and severe if intensive care unit (ICU) admission indicated. Presentation of infection was categorised on the basis of the level of care medically indicated for COVID-19 as reported in the registry, rather than only on the level of care administered, to more accurately reflect the true severity of disease than categorisation on the basis of patient preference or institutional policies in the setting of resource limitation.
The CCC19 registry uses data generally available from electronic health records and was not specifically designed to examine frailty or the population of older adults. As such, no comprehensive geriatric assessment data or specific data encompassing most extant approaches to operationalising frailty (ie, Clinical Frailty Scale) are collected. Of the available registry variables that can be associated with ageing, performance status and comorbidities were the most complete and a priori expected to be prognostic, on the basis of existing literature. However, rather than simply examining each variable, we sought to aggregate these ageing-associated variables into a single index to provide a clinically useful measure. Adapting the IMWG measure into the CCC19 geriatric risk index allowed us to use a previously published approach to combining these variables. The calculation for CCC19 geriatric risk index was based on age, Charlson comorbidity index (CCI; modified to exclude cancer diagnosis as a comorbidity, not age-adjusted)
, and Eastern Cooperative Oncology Group (ECOG)
performance status as follows: age (≤75 years, 0 points; 76–80 years, 1 point; >80 years, 2 points); CCI (zero, 0 points; one or two, 1 point; more than two, 2 points); and ECOG performance status (zero, 0 points; one, 1 point; two or more, 2 points). ECOG performance status was determined by the health-care professionals abstracting from the patient’s chart, and they were instructed to enter the ECOG performance status at the time closest to COVID-19 diagnosis, or to note that no ECOG performance status was recorded within 3 months before COVID-19 diagnosis if applicable. Based on the sum of these data patients were categorised as standard risk (0 or 1 point), intermediate risk (2 or 3 points), or high risk (4–6 points). To maximise use of the available data, all patients had a geriatric risk point total calculated; patients with unknown ECOG performance status were initially categorised separately on the basis of the available age and comorbidity data and denoted as belonging to at least that category, recognising that their level of geriatric risk might be underestimated. For simplicity of clinical application and given similarity to the next lowest groups, these at least categories were consolidated (eg, at least standard risk was consolidated with standard risk). We analysed the non-consolidated measure in a sensitivity analysis. Variables used to define the CCC19 geriatric risk measure are summarised in the appendix (p 6).
Covariates
Covariates were defined a priori and were sex, race and ethnicity, smoking status, obesity, dementia, malignancy type (solid cancer or haematological neoplasm), cancer status (remission or no evidence of disease vs active or measurable disease, with active or measurable disease defined as responding to therapy, stable, or progressing), anti-cancer therapy within 3 months before COVID-19 diagnosis (any systemic therapy, radiotherapy, and excluding surgery), country of patient residence (USA or outside USA), and month of COVID-19 diagnosis (January to April, 2020; May to August, 2020; September to December, 2020; January to April, 2021; and May to June, 2021).
Outcomes
These outcomes were assessed over the patient’s total follow-up period. The secondary outcome was death from any cause within 30 days of COVID-19 diagnosis. Primary and secondary outcomes were assessed in the full dataset.
Predefined exploratory outcomes included symptoms and severity of COVID-19 at presentation, as well as receipt of anti-COVID-19 treatments (including remdesivir, hydroxychloroquine, and corticosteroids), major clinical complications (including cardiovascular, pulmonary, and gastrointestinal complications and acute kidney injury), and COVID-19 effect on subsequent cancer care (ie, therapy modifications).
Statistical analysis
Standard descriptive statistics were used to summarise baseline demographic and clinical characteristics, symptoms and severity of COVID-19 at presentation, rates of anti-COVID-19 treatments, outcomes, clinical complications, and anti-cancer therapy modifications among geriatric risk subgroups. Intersections of COVID-19 symptoms were summarised overall and among patients aged 80 years or older. Multiple imputation (ten iterations; missingness rates were
Multivariable models that included all covariates (listed above) plus age, modified CCI, ECOG performance status, and dementia were fit to each imputed dataset, with the shrinkage penalty estimated from the first two datasets via cross-validation and averaged. Covariates with non-zero coefficients in all imputed datasets were retained; country of patient residence and month of COVID-19 diagnosis were included as design variables. Coefficients and SEs from unadjusted and adjusted models, variance inflation factors, and clinical judgement were used to assess model stability. Exploratory analyses with smoothing splines were used to determine the association of age (as a continuous variable) with outcomes, which appeared linear. All other covariates were categorical. The relative importance of each variable was quantified using its proportion of the model’s χ2 statistic, obtained via analysis of variance. Analyses were done using R (version 4.0.2), including the Hmisc, rms, ordinalNet, and glmnet extension packages.
Role of the funding source
The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.
Results
Table 1COVID-19 symptoms, severity, treatments, outcomes, and complications among older adults with cancer, stratified by geriatric risk index
Data are n/N (%). Cells with less than five patients were masked (ie, <5) to minimise the risk of re-identification as per CCC19 policy. Number of patients does not include those with missing data. CCC19=COVID-19 and Cancer Consortium.
Table 2Demographic and clinical characteristics at COVID-19 diagnosis of older adults with cancer, stratified by geriatric risk index
Data are n (%) or median (IQR). The missing data or unknown category indicates either missingness due to non-response to optional survey questions or a response of unknown; an unknown category was provided for all survey questions.
Figure 2Distribution of the primary ordinal COVID-19 severity outcome in older adults with cancer, stratified by COVID-19 and Cancer Consortium geriatric risk index
Figure 3Association of COVID-19 and Cancer Consortium geriatric risk index with COVID-19 severity and 30-day all-cause mortality among older adults with cancer
Model for COVID-19 severity adjusted for sex, race and ethnicity, smoking status, obesity, type of malignancy, cancer status, recent (within 3 months) anti-cancer therapies (cytotoxic chemotherapy, targeted therapy, endocrine therapy, immunotherapy, locoregional therapy), country of patient residence, and month of COVID-19 diagnosis; all variance inflation factors <1·6. Model for 30-day all-cause mortality adjusted for sex, race and ethnicity, smoking status, obesity, type of malignancy, cancer status, recent (within 3 months) anti-cancer therapies (cytotoxic chemotherapy, endocrine therapy, immunotherapy, locoregional therapy), country of patient residence, and month of COVID-19 diagnosis; all variance inflation factors <1·7. OR=odds ratio. *ORs relative to standard-risk patients.
Figure 4Presenting symptoms of COVID-19 among older adults with cancer
UpSet plot indicating intersection between symptoms in older adults with cancer in a matrix layout. The bar graph in the lower left corner depicts symptom-level distribution across each category (typical or atypical). Each row in the dot graph represents a symptom classification; solid dots represent each symptom part of the intersecting sets. The centre bar graph depicts the number of symptoms in each intersection.
Discussion
,
,
,
can effectively risk stratify this vulnerable patient population.
However, operationalising frailty requires specific data, such as comprehensive geriatric assessment or objective measures that are rarely available in routine clinical practice. Thus, the applicability of frailty in real-world clinical care is limited. The CCC19 geriatric risk index, in contrast, uses information readily available from routine oncology care (age, comorbidities, and ECOG performance status) to identify a group with an extremely high risk for severe outcomes of COVID-19, lending credence to our approach of combining known risk factors into a single geriatric risk index.
,
,
Given concerns about vaccine efficacy among older adults and those with cancer, vaccination is only one part of a broader mitigation strategy for this extremely vulnerable population, which must also include public health measures: social distancing, mask wearing, and aggressive vaccination of household contacts. Unfortunately, these public health measures have their own serious adverse consequences, as older adults with or without cancer are at risk of poor outcomes from social isolation, loneliness, physical deconditioning, and loss of autonomy, which all increase the risk of depression and anxiety.
,
,
In our study, altered mental state was the sixth most common symptom present at diagnosis. Of note, our study probably underestimated the prevalence of delirium, because it is under-recognised and under-reported in clinical practice. In a study that used a standardised method for identifying acute confusion as a proxy for delirium in patients with COVID-19, the prevalence of delirium was 34·6% (45 of 130 patients) in the subgroup of frail older adults.
Similarly, although adjusted analyses were done, observational studies are subject to selection bias and unmeasured confounding. Additionally, we used a simplified geriatric risk measure adapted from a measure validated in myeloma patients.
This measure does not fully approximate the existing approaches to operationalising frailty, such as the phenotypic frailty approach, which requires assessment of grip strength or gait speed
and which are rarely obtained in routine practice and are not included in the CCC19 registry. Another key limitation is that the IMWG score was validated in patients with haematologic malignancies, and our cohort was primarily a cohort of patients with solid tumours; however, the proportion of patients with solid tumours versus haematologic malignancies in each risk group was similar, lending face validity to its application in this study. Another limitation is possible misclassification of ECOG in older adults when compared with geriatric assessment, as performance scores have been shown repeatedly to underestimate the level of vulnerability in older adults, as even individuals with good performance scores might have impairments in functional status and other important domains.
,
Consequently, the CCC19 geriatric risk index might not identify at-risk patients who have ageing-associated vulnerabilities other than impaired performance scores or comorbidities. Lastly, the CCC19 geriatric risk index will require external validation. Despite these limitations, our large study generates important data and sets the foundation for further investigation in this large vulnerable population.
Older adults with cancer should be prioritised for vaccination roll-out.
It is unknown how modifications of anti-cancer therapy affect cancer control, and whether functional decline due to COVID-19 affects subsequent ability to tolerate cancer therapy in a group who might already have been at high risk for toxicity of therapy.
Patients with high-risk geriatric profiles present with more severe initial presentations and are at greater risk for death and other adverse sequelae of COVID-19 than those with standard-risk profiles. Our CCC19 geriatric risk index, based on readily available clinical factors, might provide clinicians with an easy-to-use risk stratification method to identify older adults most at risk for severe COVID-19 as well as mortality.
Contributors
AE, JLW, NMK, OAP, and TMW conceived and designed the study and reviewed and edited the manuscript. AE, JLW, and TMW did the formal analysis and methodology, and wrote the original draft. BF and CHe designed the study, did the formal analysis, visualisation, and methodology, wrote the original draft, and reviewed and edited the manuscript. SM did project administration and coordination and reviewed and edited the manuscript. KV-L did the data analysis (for treatment change) and reviewed and edited the manuscript. SMR, EW-B, RPR, MAT, AD, DRR, ARK, LT, RCL, CS, RE, GB, AK, and DPS designed the study and reviewed and edited the manuscript. ZB, AC, JCl, JCr, BD, CRF, SLF, PGri, PGro, SGul, SGup, CHw, HK, SJK, EJK, CL, RRM, AN, NAP, MP, ALS, AS, JAS, CTS, SW, NW, and TMW-D contributed substantial patient cases to the CCC19 and were responsible for reading and editing the manuscript. BF, CHe, and JLW accessed and verified the data. BF, CHe, and JLW had full access to data in the study. All authors had final responsibility for the decision to submit for publication.
Data sharing
Declaration of interests
The following authors declare competing interests not related to the current work: ZB reports grants from Genentech/imCORE, non-financial support from Bristol Myers Squibb, and personal fees from UpToDate. AE reports salary support from the Canadian Institute of Health Research, the Detweiler Travelling Fellowship (Royal College of Physicians and Surgeons of Canada), and the Henry R Shibata Fellowship (Cedar’s Cancer Foundation). CRF reports grants from Merck Foundation, NCCN/Pfizer, and National Cancer Institute, and other support from National Cancer Institute and the Patient-Centered Outcomes Research Institute. PGri reports personal fees and non-financial support from AstraZeneca, personal fees from Astellas Pharma, personal fees from Bayer, grants and personal fees from Bristol Myers Squibb, grants and non-financial support from Clovis Oncology, personal fees from Dyania Health, grants and personal fees from EMD Serono, personal fees from Exelixis, personal fees from Foundation Medicine, personal fees from Genentech/Roche, personal fees from Genzyme, grants and personal fees from GlaxoSmithKline, personal fees from Guardant Health, grants and personal fees from Immunomedics/Gilead, personal fees from Infinity Pharmaceuticals, personal fees from Janssen, grants and personal fees from Merck, grants and personal fees from Mirati Therapeutics, grants and personal fees from Pfizer, grants and personal fees from QED Therapeutics, personal fees from Regeneron Pharmaceuticals, personal fees from Seattle Genetics, personal fees from 4D Pharma, personal fees from UroGen, grants from Bavarian Nordic, and grants from Debiopharm. SGup reports grants and personal fees from Bristol Myers Squibb, personal fees from Merck, Janssen, Seattle Genetis, EMD Sorono, and Pfizer, and grants from Astellas and BMS. CHw reports grants from Merck, Bayer, and AstraZeneca, and personal fees from Tempus and EMD Sorono, and other support from Johnson and Johnson. AK reports other support from TESARO, Fibrogen, Geistlich Pharma, Astellas Pharma, Rafael Pharmaceuticals, and Novocure. ARK reports other support from Merck and Sanofi, personal fees from OncLive, grants from ASCO Conquer Cancer Foundation, and grants from Bladder Cancer Advocacy Network. HK reports position on advisory board of Sanofi Genzyme NSCLC Northeast. NMK reports personal fees from BMS, Janssen, Seattle Genetics, Celldex, Sandoz, Invitae, Beyond Spring, Spectrum G1 Therapeutics, and Total Health, and grants from Amgen, Jazz Therapeutics, G1 Therapeutics, and Samsung. CL reports grants from imCORE/Genentech. RRM reports serving on advisory board or as a consultant for Astrazeneca, Aveo, Bayer, BMS, Caris, Dendreon, Exelixis, Janssen, Merck, Myovant, Novartis, Pfizer, Sanofi, Sorrento Therapeutics, and Tempus. RRM received institutional research funding from Pfizer, Bayer, Tempus. SM reports personal fees from National Geographic. OAP reports grants from National Institutes of Health (NIH) and Agency for Healthcare Research and Quality, and personal fees from International Consulting Associates. NAP reports personal fees from AstraZeneca, Merck, Pfizer, Eli Lilly, Genentech, BMS, Amgen, Inivata, G1 Therapeutics, Xencor, Mirati, Janssen, Boehringer Ingelheim, and Sanofi-Genzyme-Regeneron. RPR reports grants from BMS and Janssen, and personal fees from BMS, Janssen, Dova, and Inari. MAT reports personal fees from VIA Oncology, GSK, and Adaptive Advisory Board, other support from Syapse, UpToDate, Takeda, Celgene, Doximity, AbbVie, BMS, CRAB CTC, Denovo, Hoosier Research Network, Lilly, LynxBio, Strata Oncology, and TG Therapeutics. JLW reports personal fees from Roche, Westat, Flatiron Health, Melax Tech, and IBM Watson Health, other support from HemOnc and Janssen, and grants from AACR. TMW reports personal fees from Carevive, and personal fees from Sanofi, and Seattle Genetics. TMW-D reports grants from BMS, Merck, Janssen, and GSK/Tesaro, personal fees from Exicure, Shattuck Labs, Merck, Caris Life Science, and SITC, and other support from High Enroll. EW-B reports grants from Pfizer Global Medical Grants, personal fees from Astellas, Aveo Oncology Bristol Myers Squibb, Exelixis, and Janssen, and other support from Immunomedics and Nektar. The following authors declare competing interests during the conduct of the study: SM reports grants and other support from National Cancer Institute and from the International Association for the Study of Lung Cancer. DPS reports grants from American Cancer Society and Hope Foundation for Cancer Research and from NIH. LT reports grants from NIH. JLW reports grants from NIH. All other authors declare no competing interests.
Acknowledgments
We thank all members of the CCC19 steering committee: Toni Choueiri, Petros Grivas, Gilberto Lopes, Corrie Painter, Solange Peters, Brian Rini, Dimpy Shah, Mike Thompson, Dimitrios Farmakiotis, Narjust Duma, and Jeremy Warner for their invaluable guidance of the CCC19 consortium. We also thank Gary Lyman for important contributions to the study design. The CCC19 Research Coordinating Center (CHe, SM, BF, JLW, and KV-L) is supported by the NIH National Cancer Institute (NCI) Cancer Center Support Grants P30 CA068485. AE is supported by the Canadian Institute of Health Research, the Detweiler Travelling Fellowship, and the Henry R Shibata Fellowship. AS was supported in part by the Beatriz and Samuel Seaver Foundation, the Memorial Sloan Kettering Cancer and Aging Program, and NIH NCI Cancer Center Support Grant P30 CA008748. CRF was supported by T32 CA 236621 and P30 CA 046592. OAP was supported by P01 AG027296 and R01 AG053307. PGri was supported by P30CA015704–45. REDCap is developed and supported by Vanderbilt Institute for Clinical and Translational Research grant support (UL1 TR000445 from NCATS/NIH).
Supplementary Material
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Published: February 14, 2022
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- Elucidating the effect of geriatric parameters on COVID-19 outcomes for older adults with cancer
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A diagnosis of active cancer is associated with a higher risk of severe COVID-19 than no diagnosis.1 Increasing age and comorbid conditions are also independently associated with severe COVID-19.2 Therefore, older adults with cancer are disproportionately disadvantaged during the pandemic (although many studies to support this statement were done before the availability of effective COVID-19 vaccines). In this context, COVID-19 might challenge the equal and evidence-based management of older adults with cancer.
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