Evidence map›Paper›PMID 29480830›Full record

ArticleMedicine2018

A novel approach for medical research on lymphomas: A study validation of claims-based algorithms to identify incident cases.

Cécile Conte, Aurore Palmaro, Pascale Grosclaude, Laetitia Daubisse-Marliac, Fabien Despas, Maryse Lapeyre-Mestre

Abstract readComparative StudyValidation Study
In one paragraph

Article in Medicine, 2018. 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

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

1 citing paper in PubMed.

  1. 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

6 authors.

Cécile ConteLEASP-UMR 1027, Inserm-University of Toulouse Medical and Clinical Pharmacology Unit CIC 1436, Toulouse University Hospital Claudius Regaud Institute, IUCT-O, Tarn Cancer Registry, Toulouse, France.
Aurore Palmaro
Pascale Grosclaude
Laetitia Daubisse-Marliac
Fabien Despas
Maryse Lapeyre-Mestre

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The use of claims database to study lymphomas in real-life conditions is a crucial issue in the future. In this way, it is essential to develop validated algorithms for the identification of lymphomas in these databases. The aim of this study was to assess the validity of diagnosis codes in the French health insurance database to identify incident cases of lymphomas according to results of a regional cancer registry, as the gold standard.Between 2010 and 2013, incident lymphomas were identified in hospital data through 2 algorithms of selection. The results of the identification process and characteristics of incident lymphomas cases were compared with data from the Tarn Cancer Registry. Each algorithm's performance was assessed by estimating sensitivity, predictive positive value, specificity (SPE), and negative predictive value.During the period, the registry recorded 476 incident cases of lymphomas, of which 52 were Hodgkin lymphomas and 424 non-Hodgkin lymphomas. For corresponding area and period, algorithm 1 provides a number of incident cases close to the Registry, whereas algorithm 2 overestimated the number of incident cases by approximately 30%. Both algorithms were highly specific (SPE = 99.9%) but moderately sensitive. The comparative analysis illustrates that similar distribution and characteristics are observed in both sources.Given these findings, the use of claims database can be consider as a pertinent and powerful tool to conduct medico-economic or pharmacoepidemiological studies in lymphomas.

Indexed as

AlgorithmsDatabases, FactualInsurance, HealthRegistriesAdultAgedAged, 80 and overBiomedical ResearchFemaleHumansLymphomaMaleMiddle Aged

Identifiers

PMID29480830
PMCPMC5943849

What OpenQuestion holds

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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.