Evidence map›Paper›PMID 25631308›Full record

ArticleBMJ open2015

A validation study of a new classification algorithm to identify rheumatoid arthritis using administrative health databases: case-control and cohort diagnostic accuracy studies. Results from the RECord linkage On Rheumatic Diseases study of the Italian Society for Rheumatology.

Greta Carrara, Carlo A Scirè, Antonella Zambon, Marco A Cimmino, Carlo Cerra, Marta Caprioli, Giovanni Cagnotto, Federica Nicotra, Andrea Arfè, Simona Migliazza and 3 more

Abstract readValidation Study
In one paragraph

Article in BMJ open, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 2 pooled it
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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

25 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  18. CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2019
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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

13 authors.

Greta CarraraEpidemiology Unit, Italian Society for Rheumatology, Milan, Italy.
Carlo A ScirèEpidemiology Unit, Italian Society for Rheumatology, Milan, Italy.
Antonella ZambonDepartment of Statistics and Quantitative Methods, Section of Biostatistics, Epidemiology and Public Health, University of Milano-Bicocca, Milan, Italy.
Marco A CimminoResearch Laboratory and Academic Division of Clinical Rheumatology, Department of Internal Medicine, University of Genova, Genoa, Italy.
Carlo CerraInformation System and Management Control, Local Health Authority (ASL), Pavia, Italy.
Marta CaprioliDepartment of Medicine, Istituto Clinico Beato Matteo, Vigevano, Italy.
Giovanni CagnottoDepartment of Rheumatology, IRCCS San Matteo Foundation, Pavia, Italy.
Federica NicotraDepartment of Statistics and Quantitative Methods, Section of Biostatistics, Epidemiology and Public Health, University of Milano-Bicocca, Milan, Italy.
Andrea ArfèDepartment of Statistics and Quantitative Methods, Section of Biostatistics, Epidemiology and Public Health, University of Milano-Bicocca, Milan, Italy.
Simona MigliazzaInformation System and Management Control, Local Health Authority (ASL), Pavia, Italy.
Giovanni CorraoDepartment of Statistics and Quantitative Methods, Section of Biostatistics, Epidemiology and Public Health, University of Milano-Bicocca, Milan, Italy.
Giovanni MinisolaDivision of Rheumatology, San Camillo Hospital, Rome, Italy.
Carlomaurizio MontecuccoDepartment of Rheumatology, IRCCS San Matteo Foundation, Pavia, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo develop and validate a new algorithm to identify patients with rheumatoid arthritis (RA) and estimate disease prevalence using administrative health databases (AHDs) of the Italian Lombardy region.

designCase-control and cohort diagnostic accuracy study.

methodsIn a randomly selected sample of 827 patients drawn from a tertiary rheumatology centre (training set), clinically validated diagnoses were linked to administrative data including diagnostic codes and drug prescriptions. An algorithm in steps of decreasing specificity was developed and its accuracy assessed calculating sensitivity/specificity, positive predictive value (PPV)/negative predictive value, with corresponding CIs. The algorithm was applied to two validating sets: 106 patients from a secondary rheumatology centre and 6087 participants from the primary care. Alternative algorithms were developed to increase PPV at population level. Crude and adjusted prevalence estimates taking into account algorithm misclassification rates were obtained for the Lombardy region.

resultsThe algorithms included: RA certification by a rheumatologist, certification for other autoimmune diseases by specialists, RA code in the hospital discharge form, prescription of disease-modifying antirheumatic drugs and oral glucocorticoids. In the training set, a four-step algorithm identified clinically diagnosed RA cases with a sensitivity of 96.3 (95% CI 93.6 to 98.2) and a specificity of 90.3 (87.4 to 92.7). Both external validations showed highly consistent results. More specific algorithms achieved >80% PPV at the population level. The crude RA prevalence in Lombardy was 0.52%, and estimates adjusted for misclassification ranged from 0.31% (95% CI 0.14% to 0.42%) to 0.37% (0.25% to 0.47%).

conclusionsAHDs are valuable tools for the identification of RA cases at the population level, and allow estimation of disease prevalence and to select retrospective cohorts.

Indexed as

AlgorithmsAdultAgedAged, 80 and overArthritis, RheumatoidCase-Control StudiesCohort StudiesDatabases, FactualFemaleHumansItalyMaleMedical Record LinkageMedical Records Systems, ComputerizedMiddle AgedPrevalenceEPIDEMIOLOGYPUBLIC HEALTH

Identifiers

PMID25631308
PMCPMC4316439

What OpenQuestion holds

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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.