Evidence map›Paper›PMID 36944932›Full record

ArticleBMC health services research2023

Validation of algorithms to identify colorectal cancer patients from administrative claims data of a Japanese hospital.

Takahiro Hirano, Makiko Negishi, Yoshiki Kuwatsuru, Masafumi Arai, Ryozo Wakabayashi, Naoko Saito, Ryohei Kuwatsuru

Full text readValidation Study
In one paragraph

Article in BMC health services research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

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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.

Takahiro HiranoClinical Study Support, Inc., Daiei Bldg., 2F, 1-11-20 Nishiki, Naka-ku, Nagoya, 460-0003, Japan. takahiro_hirano@jp-css.com.
Makiko NegishiReal-World Evidence and Data Assessment (READS), Graduate School of Medicine, Juntendo University, Tokyo, Japan.
Yoshiki KuwatsuruReal-World Evidence and Data Assessment (READS), Graduate School of Medicine, Juntendo University, Tokyo, Japan.
Masafumi AraiReal-World Evidence and Data Assessment (READS), Graduate School of Medicine, Juntendo University, Tokyo, Japan.
Ryozo WakabayashiClinical Study Support, Inc., Daiei Bldg., 2F, 1-11-20 Nishiki, Naka-ku, Nagoya, 460-0003, Japan.
Naoko SaitoReal-World Evidence and Data Assessment (READS), Graduate School of Medicine, Juntendo University, Tokyo, Japan.
Ryohei KuwatsuruReal-World Evidence and Data Assessment (READS), Graduate School of Medicine, Juntendo University, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAdministrative claims data are a valuable source for clinical studies; however, the use of validated algorithms to identify patients is essential to minimize bias. We evaluated the validity of diagnostic coding algorithms for identifying patients with colorectal cancer from a hospital's administrative claims data.

methodsThis validation study used administrative claims data from a Japanese university hospital between April 2017 and March 2019. We developed diagnostic coding algorithms, basically based on the International Classification of Disease (ICD) 10th codes of C18-20 and Japanese disease codes, to identify patients with colorectal cancer. For random samples of patients identified using our algorithms, case ascertainment was performed using chart review as the gold standard. The positive predictive value (PPV) was calculated to evaluate the accuracy of the algorithms.

resultsOf 249 random samples of patients identified as having colorectal cancer by our coding algorithms, 215 were confirmed cases, yielding a PPV of 86.3% (95% confidence interval [CI], 81.5-90.1%). When the diagnostic codes were restricted to site-specific (right colon, left colon, transverse colon, or rectum) cancer codes, 94 of the 100 random samples were true cases of colorectal cancer. Consequently, the PPV increased to 94.0% (95% CI, 87.2-97.4%).

conclusionOur diagnostic coding algorithms based on ICD-10 codes and Japanese disease codes were highly accurate in detecting patients with colorectal cancer from this hospital's claims data. The exclusive use of site-specific cancer codes further improved the PPV from 86.3 to 94.0%, suggesting their desirability in identifying these patients more precisely.

Indexed as

Colorectal NeoplasmsEast Asian PeopleAlgorithmsDatabases, FactualHospitals, UniversityHumansInternational Classification of DiseasesPredictive Value of TestsAdministrative claims dataColorectal cancerDiagnostic codesJapanPositive predictive valueValidation

Identifiers

PMID36944932
PMCPMC10029250

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