Evidence map›Paper›PMID 34724703›Full record

ArticleBlood advances2022

Practice patterns and real-life outcomes for patients with acute promyelocytic leukemia in the United States.

Jan Philipp Bewersdorf, Stephanie Prozora, Nikolai A Podoltsev, Rory M Shallis, Scott F Huntington, Natalia Neparidze, Rong Wang, Amer M Zeidan, Amy J Davidoff

Abstract read
In one paragraph

Article in Blood advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. How to avoid early mortality in acute promyelocytic leukemia.Hematology. American Society of Hematology. Education Program · 2023
    Article
  8. Article
  9. Article
  10. Article
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

9 authors.

Jan Philipp BewersdorfDepartment of Internal Medicine, Yale School of Medicine and Yale Cancer Center, New Haven, CT.
Stephanie ProzoraYale Cancer Outcomes, Public Policy, and Effectiveness Research (COPPER) Center, New Haven, CT.
Nikolai A PodoltsevDepartment of Internal Medicine, Yale School of Medicine and Yale Cancer Center, New Haven, CT.ORCID 0000-0002-3657-778X
Rory M ShallisDepartment of Internal Medicine, Yale School of Medicine and Yale Cancer Center, New Haven, CT.ORCID 0000-0002-8542-2944
Scott F HuntingtonDepartment of Internal Medicine, Yale School of Medicine and Yale Cancer Center, New Haven, CT.ORCID 0000-0001-7071-6475
Natalia NeparidzeDepartment of Internal Medicine, Yale School of Medicine and Yale Cancer Center, New Haven, CT.
Rong WangYale Cancer Outcomes, Public Policy, and Effectiveness Research (COPPER) Center, New Haven, CT.
Amer M ZeidanDepartment of Internal Medicine, Yale School of Medicine and Yale Cancer Center, New Haven, CT.
Amy J DavidoffYale Cancer Outcomes, Public Policy, and Effectiveness Research (COPPER) Center, New Haven, CT.ORCID 0000-0001-8141-9249

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Yale Pathology Tissue Services Shared ResourceP30CA016359 · NCI · YALE UNIVERSITY · PI MEGAN C KING · 1985 to 2026
$85.0M
NCATS NIH HHS UL1 TR001863NCI NIH HHS P30 CA016359
6 · The paper itself

Abstract

Acute promyelocytic leukemia (APL) is associated with a favorable long-term prognosis if appropriate treatment is initiated promptly. Outcomes in clinical trials and population-based registries vary; potential explanations include a delay in treatment and lower adherence to guideline-recommended therapy in real-world practice. We used the Vizient Clinical Data Base to describe demographic characteristics, baseline clinical characteristics, and treatment patterns in patients newly diagnosed with APL during the study period of April 2017 to March 2020. Baseline white blood cell count was used to assign risk status and assess treatment concordance with National Comprehensive Cancer Network guidelines. Logistic regression models examined adjusted associations between patient, hospital, disease characteristics, and adverse outcomes (in-hospital death or discharge to hospice). Among 1464 patients with APL, 205 (14.0%) experienced an adverse outcome. A substantial subset (20.6%) of patients did not receive guideline-concordant regimens. Odds of adverse outcomes increased with failure to receive guideline-concordant treatment (odds ratio [OR], 2.31; 95% confidence interval [CI], 1.43-3.75; P = .001), high-risk disease (OR, 2.48; 95% CI, 1.53-4.00; P < .001), and increasing age (≥60 years: OR, 11.13; 95% CI, 4.55-27.22; P < .001). Higher hospital acute myeloid leukemia (AML) patient volume was associated with lower odds of adverse outcome (OR, 0.44; 95% CI, 0.20-0.99 [for ≤50 vs >200 AML patients per year]; P = .046). In conclusion, in this large database analysis, 14.0% of patients newly diagnosed with APL died or were discharged to hospice. A substantial proportion of patients did not receive guideline-concordant therapy, potentially contributing to adverse outcomes.

Indexed as

Leukemia, Myeloid, AcuteLeukemia, Promyelocytic, AcuteHospital MortalityHumansMiddle AgedOdds RatioPrognosisUnited States

Identifiers

PMID34724703
PMCPMC8791583

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.