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Record W2113384029 · doi:10.1200/jco.2014.55.4980

Evidence-Based Medicine for Thromboprophylaxis in Hospitalized Patients With Cancer: Why Aren't We There Yet?

2014· letter· en· W2113384029 on OpenAlexaff
Agnes Lee

Bibliographic record

VenueJournal of Clinical Oncology · 2014
Typeletter
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsBC Cancer AgencyVancouver Coastal HealthUniversity of British Columbia
Fundersnot available
KeywordsMedicineMedical prescriptionCancerIntensive care medicineDiseaseEmergency medicineVenous thromboembolismThrombosisInternal medicine

Abstract

fetched live from OpenAlex

Venous thromboembolism is a costly disease. In those afflicted, the medical, economical, and emotional impact can be overwhelming, especially in patients living with cancer. Prevention is often an effective strategy to reduce disease burden, yet historical data have shown that rates of thromboprophylaxis in hospitalized patients with cancer are dismally low. Given the vast efforts and sweeping programs directed at preventing thrombosis in hospitalized patients in recent years, including the Surgeon General’s Call to Action in 2008 and the introduction of pay-for-performance measures in medical institutions, have oncologists and other clinicians providing care to patients with cancer embraced the practice of providing thromboprophylaxis to hospitalized oncology patients? In the article by Zwicker et al, we gain the first sneak peek at the impact of these endeavors. Using a prospective, cross-sectional design, the investigators collected data from 775 patients with cancer admitted to five academic medical centers between January to June 2013 to determine the current prescription rates of pharmacologic prophylaxis and identify factors that influenced prophylaxis prescription. Overall, 51% of patients received pharmacologic prophylaxis. But if the patients with relative contraindications to anticoagulation (nearly one-third of the total sample) were excluded, 74% of the eligible patients were prescribed pharmacologic prophylaxis. This is higher than the rates reported in previous studies that extracted data from administrative databases. Multivariable analysis identified a history of prior venous thromboembolism as the strongest predictor of prophylaxis prescription and found that patients admitted with hematological malignancies or for cancer therapy were significantly less likely to receive prophylaxis. Otherwise, patient selection for prophylaxis appeared somewhat haphazard, as only 79% of those classified as having a high risk of thrombosis by the Padua Score were prescribed prophylaxis, while 63% of those considered as low risk also received prophylaxis. The authors concluded that although pharmacologic prophylaxis is frequently prescribed in hospitalized patients with cancer, this is done without regard to the presence or absence of other risk factors for venous thromboembolism. So, what does this mean to patients, clinicians, and the healthcare burden? Most importantly, does a higher prescription rate of pharmacologic prophylaxis translate to improvement in the quality of patient care in the oncology population? To answer these questions, let us review the evidence that support prophylaxis in hospitalized patients with cancer and identify the remaining gaps that must be bridged to bring about improvement in patient outcomes. To date, the only evidence available on the efficacy and safety of anticoagulant prophylaxis in hospitalized patients with cancer comes from post hoc, subgroup analyses of trials that included a small number of selected patients with cancer. A meta-analysis of the three randomized controlled trials that compared low molecular weight heparin or fondaparinux with placebo found that among the 307 patients with cancer enrolled, no statistical reduction in the overall incidence of venous thromboembolism was demonstrated with anticoagulant prophylaxis. It is difficult to know if this finding is due to a type II error (lack of statistical power), a skewed selection of low-risk patients with cancer or a true lack of efficacy using standard doses of pharmacologic prophylaxis in this hypercoagulable population. Another major barrier for clinicians in providing appropriate prophylaxis is the identification of patients who would benefit from prophylaxis. Thromboprophylaxis based on risk stratification is strongly advocated by the most recent clinical practice guidelines from both the American College of Chest Physicians (ACCP) and the American Society of Clinical Oncology (ASCO). Although individual risk factors associated with thrombosis are well established, validated risk assessment tools for estimating the overall risk of thrombosis in hospitalized patients with cancer are not yet available. The Padua Prediction Score, used in the study by Zwicker et al and recommended by the ACCP, was empirically derived and appears promising in identifying a low-risk group of patients in whom thromboprophylaxis is likely not warranted. However, the score has not undergone rigorous external validation and a recent study found that it was not predictive of in-hospital thrombosis risk in patents with sepsis. The utility of the score is also questionable in patients with cancer as it does not include known risk factors unique to patients with cancer, such as the presence metastatic disease or the use of chemotherapy. In the current ASCO guideline, only those patients who have active malignancy and are admitted with an acute medical illness or reduced mobility are recommended pharmacologic prophylaxis. Given the heterogeneity of the cancer population admitted to hospital—from those battling with sepsis to those facing the end of JOURNAL OF CLINICAL ONCOLOGY E D I T O R I A L VOLUME 32 NUMBER 17 JUNE 1

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)0.022
metaresearch head score (Gemma)0.153
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.153
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0040.004
Science and technology studies0.0010.002
Scholarly communication0.0080.008
Open science0.0030.002
Research integrity0.0070.013
Insufficient payload (model declined to judge)0.0030.001

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 head0.161
GPT teacher head0.438
Teacher spread0.277 · 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 designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations7
Published2014
Admission routes1
Has abstractyes

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