Evidence map›Paper›PMID 32980126›Full record

ArticleGynecologic oncology2020

Factors associated with high-cost hospitalizations in elderly ovarian cancer patients.

Erica Manrriquez, Ava Mandelbaum, Esteban Aguayo, Mae Zakhour, Beth Karlan, Peyman Benharash, Joshua G Cohen

Open access · greenAbstract read
In one paragraph

Article in Gynecologic oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.3field-weighted citation impact, top 22% of its field
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

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. 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

7 authors at 2 institutions in 1 country.

Erica ManrriquezDivision of Gynecologic Oncology, Department of Obstetrics and Gynecology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, United States of America.
Ava MandelbaumCardiovascular Outcomes Research Laboratories (CORELAB), Division of Cardiac Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, United States of America.
Esteban AguayoCardiovascular Outcomes Research Laboratories (CORELAB), Division of Cardiac Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, United States of America.
Mae ZakhourDivision of Gynecologic Oncology, Department of Obstetrics and Gynecology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, United States of America.
Beth KarlanDivision of Gynecologic Oncology, Department of Obstetrics and Gynecology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, United States of America.
Peyman BenharashCardiovascular Outcomes Research Laboratories (CORELAB), Division of Cardiac Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, United States of America.
Joshua G CohenDivision of Gynecologic Oncology, Department of Obstetrics and Gynecology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, United States of America. Electronic address: jgcohen@mednet.ucla.edu.
University of California, Los Angeles · USCore Laboratories (United States) · US

Funding

UCLA CANCER EPIDEMIOLOGY TRAINING PROGRAMT32CA009142 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ZHANG, ZUO-FENG · 1986 to 2024
$6.3M
NCI NIH HHS T32 CA009142
6 · The paper itself

Abstract

objectiveTo characterize factors associated with high-cost inpatient admissions for ovarian cancer.

methodsOperative hospitalizations for ovarian cancer patients ≥65 years of age were identified using the 2010-2017 National Inpatient Sample. Admissions with high-cost were defined as those incurring ≥90th percentile of hospitalization costs each year, while the remainder were considered low-cost. Multivariable logistic regression models were developed to assess independent predictors of being in the high-cost cohort.

resultsDuring the study period, an estimated 58,454 patients met inclusion criteria. 5827 patient admissions (9.98%) were classified as high-cost. Median hospitalization cost for this high-cost group was $55,447 (interquartile range (IQR) $46,744-$74,015) compared to $16,464 (IQR $11,845-$23,286, p < 0.001) for the low-cost group. Patients with high-cost admissions were more likely to have received open (adjusted odds ratio (AOR) 2.23, 1.31-3.79) or extended (AOR 5.64, 4.79-6.66) procedures and be admitted non-electively (AOR 3.32, 2.74-4.02). Being in the top income quartile (AOR 1.77, 1.39-2.27) was also associated with high-cost. Age and hospital factors, including bed size and volume of gynecologic oncology surgery, did not affect cost group.

conclusionHigh-cost ovarian cancer admissions were three times more expensive than low-cost admissions. Fewer open and extended procedures with subsequently shorter lengths of stay may have contributed to decreasing inpatient costs over the study period. In this cohort of patients largely covered by Medicare, clinical factors outweigh socioeconomic factors as cost drivers. Understanding the relationship of disease-specific and social factors to cost will be important in informing future value-based quality improvement efforts in gynecologic cancer care.

Indexed as

Cost of IllnessAgedAged, 80 and overFemaleGeographyGynecologic Surgical ProceduresHospital CostsHumansLength of StayMedicaidMedicareOdds RatioOvarian NeoplasmsPatient AdmissionQuality ImprovementRetrospective StudiesElderly ovarian cancer patientsHigh-cost hospitalizationsNational Inpatient SampleOpen surgeryOperative admissions

Identifiers

PMID32980126
PMCPMC7771656
OpenAlexW3087789484

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.