Evidence map›Paper›PMID 38554626›Full record

ArticleGynecologic oncology2024

Comparing survival of older ovarian cancer patients treated with neoadjuvant chemotherapy versus primary cytoreductive surgery: Reducing bias through machine learning.

Yongmei Huang, J Alejandro Rauh-Hain, Thomas H McCoy, June Y Hou, Grace Hillyer, Jennifer S Ferris, Dawn Hershman, Jason D Wright, Alexander Melamed

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Gynecologic oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
3.8field-weighted citation impact, top 8% 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, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
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 at 3 institutions in 1 country.

Yongmei HuangColumbia University Vagelos College of Physicians and Surgeons, Department of Obstetrics and Gynecology, United States of America.
J Alejandro Rauh-HainUniversity of Texas MD Anderson Cancer Center, Division of Surgery, Department of Gynecologic Oncology and Reproductive Medicine, United States of America.
Thomas H McCoyMassachusetts General Hospital, Department of Psychiatry, United States of America.
June Y HouColumbia University Vagelos College of Physicians and Surgeons, Department of Obstetrics and Gynecology, United States of America.
Grace HillyerColumbia University Mailman School of Public Health, Department of Epidemiology, United States of America.
Jennifer S FerrisColumbia University Mailman School of Public Health, Department of Epidemiology, United States of America.
Dawn HershmanColumbia University Vagelos College of Physicians and Surgeons, Department of Medicine Columbia University Vagelos College of Physicians and Surgeons, Department of Internal Medicine, United States of America.
Jason D WrightColumbia University Vagelos College of Physicians and Surgeons, Department of Obstetrics and Gynecology, United States of America.
Alexander MelamedMassachusetts General Hospital, Vincent Department of Obstetrics and Gynecology, Meigs Division of Gynecologic Oncology, United States of America. Electronic address: alexander.melamed@mgh.harvard.edu.
Columbia University · USMassachusetts General Hospital · USThe University of Texas MD Anderson Cancer Center · US

Funding

Clinical and Translational Science AwardUL1TR001873 · NCATS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI REILLY, MUREDACH P · 2016 to 2025
$99.0M
Institutional Career Development CoreKL2TR001874 · NCATS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI GENKINGER, JEANINE M., SHIMBO, DAICHI · 2016 to 2025
$13.6M
NCATS NIH HHS KL2 TR001874NCATS NIH HHS UL1 TR001873NCI NIH HHS HHSN261201800009CNCI NIH HHS HHSN261201800009INCI NIH HHS HHSN261201800015CNCI NIH HHS HHSN261201800015INCI NIH HHS HHSN261201800032CNCI NIH HHS HHSN261201800032I
6 · The paper itself

Abstract

objectiveTo develop and evaluate a multidimensional comorbidity index (MCI) that identifies ovarian cancer patients at risk of early mortality more accurately than the Charlson Comorbidity Index (CCI) for use in health services research.

methodsWe utilized SEER-Medicare data to identify patients with stage IIIC and IV ovarian cancer, diagnosed in 2010-2015. We employed partial least squares regression, a supervised machine learning algorithm, to develop the MCI by extracting latent factors that optimally captured the variation in health insurance claims made in the year preceding cancer diagnosis, and 1-year mortality. We assessed the discrimination and calibration of the MCI for 1-year mortality and compared its performance to the commonly-used CCI. Finally, we evaluated the MCI's ability to reduce confounding in the association of neoadjuvant chemotherapy (NACT) and all-cause mortality.

resultsWe included 4723 patients in the development cohort and 933 in the validation cohort. The MCI demonstrated good discrimination for 1-year mortality (c-index: 0.75, 95% CI: 0.72-0.79), while the CCI had poor discrimination (c-index: 0.59, 95% CI: 0.56-0.63). Calibration plots showed better agreement between predicted and observed 1-year mortality risk for the MCI compared with CCI. When comparing all-cause mortality between NACT with primary cytoreductive surgery, NACT was associated with a higher hazard of death (HR: 1.13, 95% CI: 1.04-1.23) after controlling for tumor characteristics, demographic factors, and the CCI. However, when controlling for the MCI instead of the CCI, there was no longer a significant difference (HR: 1.05, 95% CI: 0.96-1.14).

conclusionsThe MCI outperformed the conventional CCI in predicting 1-year mortality, and reducing confounding due to differences in baseline health status in comparative effectiveness analysis of NACT versus primary surgery.

Indexed as

Cytoreduction Surgical ProceduresMachine LearningNeoadjuvant TherapyOvarian NeoplasmsSEER ProgramAgedAged, 80 and overBiasCarcinoma, Ovarian EpithelialChemotherapy, AdjuvantFemaleHumansMedicareNeoplasm StagingUnited StatesAll-cause mortalityMachine learningMultidimensional comorbidity indexNeoadjuvant chemotherapyPrimary cytoreductive surgery

Identifiers

PMID38554626
PMCPMC13449683
OpenAlexW4393332859

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.