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Record W3214319350 · doi:10.1182/blood-2021-149221

Impact of Race/Ethnicity on Cancer Associated Thrombosis Among Underserved Patients with Cancer

2021· article· en· W3214319350 on OpenAlexaff
Wilson Luiz da Costa, Danielle Guffey, Raka Bandyo, Courtney D. Wallace, Carolina Granada, Romil Patel, Margaret Fitzgerald, Elizabeth Y. Chiao, David García, Marc Carrier, Christopher I. Amos, Ang Li

Bibliographic record

VenueBlood · 2021
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineCancerPulmonary embolismDeep veinThrombosisIncidence (geometry)Internal medicineCumulative incidenceRetrospective cohort studyMalignancyCohort

Abstract

fetched live from OpenAlex

Abstract Introduction: Cancer-associated thrombosis (CAT) is common among patients with cancer. Risk factors for CAT include type of malignancy, advanced stage, and chemotherapy treatment, but the association of CAT with race and ethnicity remains controversial. Identifying the incidence of CAT among populations susceptible to inequalities in healthcare delivery may help delineate preventive strategies. Methods: We performed a retrospective cohort study at Harris Health System (HHS), a safety-net healthcare system that provides care for underserved minorities and uninsured patients in Houston, TX. We created an integrated database that linked consecutive patients with newly diagnosed invasive cancer with structured electronic health record (EHR) data from 2011-2020 (Figure 1). We followed patients from time of cancer diagnosis to time of first VTE, death, or loss of follow-up. VTE was defined as radiologically confirmed pulmonary embolism (PE), lower extremity deep vein thrombosis (LE-DVT), catheter-related DVT (CR-DVT), or splanchnic vein thrombosis in either inpatient or outpatient setting. We used VTE ICD9/ICD10 billing codes to assess for potential events and confirmed incident, recurrent, and historical events through medical record review. VTE occurring within 30 days prior to cancer diagnosis were considered as CAT at diagnosis. Incidence rates were assessed per 100 person-year (py) within 1 year of diagnosis and stratified by race/ethnicity, cancer type, and cancer stage. Cumulative incidence of VTE was assessed through competing risk method with death as the competing cause. Multivariable Fine-Gray competing risk models were performed to determine the effect of race/ethnicity on the risk of CAT, adjusted for age, sex, body mass index, insurance, cancer site, stage, systemic therapy, recent hospitalization, and prior history of VTE. Results: A total of 9,353 cancer patients were included in the study, where 49.3% were Hispanics, 27.6% were Non-Hispanic Blacks (NHB), 15.5% were Non-Hispanic Whites (NHW), and 7.6% were Asian/Pacific Islander (PI). Most patients (74.7%) were uninsured, 35.8% were obese, 19% had recent hospitalization, and 31.9% had stage IV disease. Overall, 832 developed CAT within 1 year, including 49.4% PE, 28.1% LE-DVT, and 17.1% CR-DVT. The median onset was 69 days (IQR 20-154), but a significant proportion (n=92) was diagnosed in the month before diagnosis. The incidence of CAT was 7.3% at 6 months and 9.6% at 1 year. The overall incidence rate was 11/100 py with a similar trend from 2011 to 2020. Figure 2 shows the variation in incidence rates for different cancer types across stages. The rate increased 2- to 10-fold from stage I to IV, reaching >40/100 py among patients with pancreatic and upper gastrointestinal cancers. Figure 3 shows the impact of race/ethnicity on the incidence of CAT, with 9.3% and 8.4% for NHW and NHB at 6 months, compared to 6.5% and 3.8% for Hispanics and Asian/PI, respectively. In the adjusted multivariable analysis, the risk of CAT remained lower in Hispanics vs. NHW (SHR 0.80 [0.65-0.97]) and Asian/PI vs. NHW (SHR 0.48 [0.32-0.73]). There was no difference in NHB vs. NHW (SHR 1.04 [0.84-1.27]). Other important covariates included history of VTE (SHR 2.29 [1.32-3.97]) and prolonged hospitalization (SHR 1.53 [CI 1.31-1.80]) in addition to staging and cancer types. Compared to no systemic therapy or adjuvant endocrine only, there was a higher incidence of VTE in patients receiving chemotherapy (SHR 1.72 [1.39-2.15]), targeted therapy (SHR 1.58 [1.12-2.23]), immunotherapy (SHR 2.13 [1.30-3.48], but not non-adjuvant endocrine therapy (SHR 0.91 [0.40-2.08]). Conclusion: In the current cancer registry-linked EHR cohort with a high proportion of Hispanics and Blacks from a large safety-net healthcare system in the US, we have observed a high incidence rate of CAT at 11/100 py. This rate is much higher than 3.9-5.8/100 py reported in contemporary cancer registries in Europe (PMID 27709226, 33171494), and likely reflects advanced disease and comorbidity at the time of diagnosis due to delayed care from the lack of health insurance. Despite adjusting for patient-, cancer-, and treatment-specific confounders, we found that Hispanics and Asian/PI had ~20% and ~50% lower rate of VTE, respectively, compared to NHW or NHB. This racial/ethnicity difference should be considered in future risk assessment models for CAT. Figure 1 Figure 1. Disclosures Carrier: Sanofi Aventis: Honoraria, Membership on an entity's Board of Directors or advisory committees; LEO Pharma: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bayer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb: Honoraria; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Aspen: Membership on an entity's Board of Directors or advisory committees; Boehringer Ingelheim: Honoraria; Servier: Honoraria.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.305
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2021
Admission routes1
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