Evidence map›Paper›PMID 33720842›Full record

ArticleJMIR medical informatics2021

Characterizing the Anticancer Treatment Trajectory and Pattern in Patients Receiving Chemotherapy for Cancer Using Harmonized Observational Databases: Retrospective Study.

Hokyun Jeon, Seng Chan You, Seok Yun Kang, Seung In Seo, Jeremy L Warner, Rimma Belenkaya, Rae Woong Park

Open access · goldAbstract read
In one paragraph

Article in JMIR medical informatics, 2021. 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
0.8field-weighted citation impact, top 26% 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.

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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 5 institutions in 2 countries.

Hokyun Jeon *Department of Biomedical Sciences, Ajou University School of Medicine, Suwon, Gyeonggi-do, Republic of Korea.ORCID https://orcid.org/0000-0002-6220-4207
Seng Chan You *Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Gyeonggi-do, Republic of Korea.ORCID https://orcid.org/0000-0002-5052-6399
Seok Yun KangDepartment of Hematology-Oncology, Ajou University School of Medicine, Suwon, Gyeonggi-do, Republic of Korea.ORCID https://orcid.org/0000-0002-4995-9912
Seung In SeoDepartment of Internal Medicine, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0003-4417-0135
Jeremy L WarnerDivision of Hematology and Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States.ORCID https://orcid.org/0000-0002-2851-7242
Rimma BelenkayaDepartment of Health Informatics, Memorial Sloan Kettering Cancer Center, New York, NY, United States.ORCID https://orcid.org/0000-0001-7318-1541
Rae Woong ParkDepartment of Biomedical Sciences, Ajou University School of Medicine, Suwon, Gyeonggi-do, Republic of Korea.ORCID https://orcid.org/0000-0003-4989-3287
Ajou University · KRHallym University · KRMemorial Sloan Kettering Cancer Center · USVanderbilt University Medical Center · USYonsei University · KR

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
Cancer Deep Phenotyping from Electronic Medical RecordsU24CA248010 · NCI · BOSTON CHILDREN'S HOSPITAL · PI HARRY S HOCHHEISER, GUERGANA K. SAVOVA · 2020 to 2026
$6.1M
Advancing Cancer Pharmacoepidemiology Research Through EHRs and InformaticsU24CA194215 · NCI · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI TAO, CUI, YANG, PING · 2016 to 2020
$3.3M
Network Analysis for a Data-Driven Approach to Cancer CareU01CA231840 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI WARNER, JEREMY LYLE · 2018 to 2020
$1.2M
NCI NIH HHS P30 CA008748NCI NIH HHS U01 CA231840NCI NIH HHS U24 CA194215NCI NIH HHS U24 CA248010
6 · The paper itself

Abstract

backgroundAccurate and rapid clinical decisions based on real-world evidence are essential for patients with cancer. However, the complexity of chemotherapy regimens for cancer impedes retrospective research that uses observational health databases.

objectiveThe aim of this study is to compare the anticancer treatment trajectories and patterns of clinical events according to regimen type using the chemotherapy episodes determined by an algorithm.

methodsWe developed an algorithm to extract the regimen-level abstracted chemotherapy episodes from medication records in a conventional Observational Medical Outcomes Partnership (OMOP) common data model (CDM) database. The algorithm was validated on the Ajou University School Of Medicine (AUSOM) database by manual review of clinical notes. Using the algorithm, we extracted episodes of chemotherapy from patients in the EHR database and the claims database. We also developed an application software for visualizing the chemotherapy treatment patterns based on the treatment episodes in the OMOP-CDM database. Using this software, we generated the trends in the types of regimen used in the institutions, the patterns of the iterative chemotherapy use, and the trajectories of cancer treatment in two EHR-based OMOP-CDM databases. As a pilot study, the time of onset of chemotherapy-induced neutropenia according to regimen was measured using the AUSOM database. The anticancer treatment trajectories for patients with COVID-19 were also visualized based on the nationwide claims database.

resultsWe generated 178,360 treatment episodes for patients with colorectal, breast, and lung cancer for 85 different regimens. The algorithm precisely identified the type of chemotherapy regimen in 400 patients (average positive predictive value >98%). The trends in the use of routine clinical chemotherapy regimens from 2008-2018 were identified for 8236 patients. For a total of 12 regimens (those administered to the largest proportion of patients), the number of repeated treatments was concordant with the protocols for standard chemotherapy regimens for certain cases. In addition, the anticancer treatment trajectories for 8315 patients were shown, including 62 patients with COVID-19. A comparative analysis of neutropenia showed that its onset in colorectal cancer regimens tended to cluster between days 9-15, whereas it tended to cluster between days 2-8 for certain regimens for breast cancer or lung cancer.

conclusionsWe propose a method for generating chemotherapy episodes for introduction into the oncology extension module of the OMOP-CDM databases. These proof-of-concept studies demonstrated the usability, scalability, and interoperability of the proposed framework through a distributed research network.

Indexed as

algorithmantineoplastic combined chemotherapy protocolscancerchemotherapydatabaseelectronic health recordinteroperabilitypatternretrospectivescalability

Identifiers

PMID33720842
PMCPMC8058693
OpenAlexW3125571263

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.