Evidence map›Paper›PMID 36652116›Full record

ArticleDrugs - real world outcomes2023

Validation of Algorithms to Identify Bone Metastases Using Administrative Claims Data in a Japanese Hospital.

Takahiro Hirano, Naoko Saito, Ryozo Wakabayashi, Ryohei Kuwatsuru

Abstract read
In one paragraph

Article in Drugs - real world outcomes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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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

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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

1 citing paper in PubMed.

  1. Article
4 · The record

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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

4 authors.

Takahiro HiranoClinical Study Support, Inc., Nagoya, Japan. takahiro_hirano@jp-css.com.ORCID http://orcid.org/0000-0002-3803-0809
Naoko SaitoReal-World Evidence and Data Assessment (READS), Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
Ryozo WakabayashiClinical Study Support, Inc., Nagoya, Japan.
Ryohei KuwatsuruReal-World Evidence and Data Assessment (READS), Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundValidated coding algorithms are essential to generate high-quality, real-world evidence from claims data studies.

objectiveWe aimed to evaluate the validity of the algorithms to identify patients with bone metastases using claims data from a Japanese hospital. PATIENTS AND

methodsThis study used administrative claims data and electronic medical records at Juntendo University Hospital from April 2017 to March 2019. We developed two candidate claims-based algorithms to detect bone metastases, one based on diagnosis codes alone (Algorithm 1) and the other based on the combination of diagnosis and imaging test codes (Algorithm 2). Of the patients identified by Algorithm 1, 100 patients were randomly sampled. Among these 100 patients, 88 patients met the conditions of Algorithm 2; further, 12 additional patients were randomly sampled from those identified by Algorithm 2, thus obtaining a total of 100 patients for Algorithm 2. They were evaluated for their true diagnosis using the patient chart review as the gold standard. The positive predictive value (PPV) was calculated to assess the accuracy of each algorithm.

resultsFor Algorithm 1, 82 patients were analyzed after excluding 18 patients without diagnostic imaging reports. Of these, 69 patients were true positive by chart review, resulting in a PPV of 84.1% (95% confidence interval (CI) 74.5-90.6). For Algorithm 2, 92 patients were analyzed after excluding eight patients whose diagnoses were not judged by chart review. Of these, 76 patients were confirmed positive by chart review, yielding a PPV of 82.6% (95% CI 73.4-89.1).

conclusionBoth claims-based algorithms yielded high PPVs of approximately 85%, with no improvement in PPV by adding imaging test conditions. The diagnosis code-based algorithm is sufficient and valid for identifying bone metastases in this Japanese hospital.

Identifiers

PMID36652116
PMCPMC10232691

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