Evidence map›Paper›PMID 28781884›Full record

ArticleCanadian journal of kidney health and disease2016

Positive Predictive Values of International Classification of Diseases, 10th Revision Coding Algorithms to Identify Patients With Autosomal Dominant Polycystic Kidney Disease.

Vinusha Kalatharan, York Pei, Kristin K Clemens, Rebecca K McTavish, Stephanie N Dixon, Matthew Rochon, Danielle M Nash, Arsh Jain, Sisira Sarma, Andrew Zaleski and 2 more

Abstract read
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In one paragraph

Article in Canadian journal of kidney health and disease, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 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

12 authors.

Vinusha KalatharanDepartment of Epidemiology & Biostatistics, Western University, London, Ontario, Canada.
York PeiDivision of Nephrology, University Health Network, University of Toronto, Ontario, Canada.
Kristin K ClemensDepartment of Epidemiology & Biostatistics, Western University, London, Ontario, Canada.
Rebecca K McTavishDepartment of Epidemiology & Biostatistics, Western University, London, Ontario, Canada.
Stephanie N DixonDepartment of Epidemiology & Biostatistics, Western University, London, Ontario, Canada.
Matthew RochonDepartment of Radiology, Western University, London, Ontario, Canada.
Danielle M NashInstitute for Clinical Evaluative Sciences, London, Ontario, Canada.
Arsh JainDivision of Nephrology, Department of Medicine, Western University, London, Ontario, Canada.
Sisira SarmaDepartment of Epidemiology & Biostatistics, Western University, London, Ontario, Canada.
Andrew ZaleskiDepartment of Radiology, Western University, London, Ontario, Canada.
Andrea LumDepartment of Radiology, Western University, London, Ontario, Canada.
Amit X GargDepartment of Epidemiology & Biostatistics, Western University, London, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInternational Classification of Diseases, 10th Revision codes (ICD-10) for autosomal dominant polycystic kidney disease (ADPKD) is used within several administrative health care databases. It is unknown whether these codes identify patients who meet strict clinical criteria for ADPKD.

objectiveThe objective of this study is (1) to determine whether different ICD-10 coding algorithms identify adult patients who meet strict clinical criteria for ADPKD as assessed through medical chart review and (2) to assess the number of patients identified with different ADPKD coding algorithms in Ontario.

designValidation study of health care database codes, and prevalence.

settingOntario, Canada. PATIENTS: For the chart review, 201 adult patients with hospital encounters between April 1, 2002, and March 31, 2014, assigned either ICD-10 codes Q61.2 or Q61.3. MEASUREMENTS: This study measured positive predictive value of the ICD-10 coding algorithms and the number of Ontarians identified with different coding algorithms.

methodsWe manually reviewed a random sample of medical charts in London, Ontario, Canada, and determined whether or not ADPKD was present according to strict clinical criteria.

resultsThe presence of either ICD-10 code Q61.2 or Q61.3 in a hospital encounter had a positive predictive value of 85% (95% confidence interval [CI], 79%-89%) and identified 2981 Ontarians (0.02% of the Ontario adult population). The presence of ICD-10 code Q61.2 in a hospital encounter had a positive predictive value of 97% (95% CI, 86%-100%) and identified 394 adults in Ontario (0.003% of the Ontario adult population). LIMITATIONS: (1) We could not calculate other measures of validity; (2) the coding algorithms do not identify patients without hospital encounters; and (3) coding practices may differ between hospitals.

conclusionsMost patients with ICD-10 code Q61.2 or Q61.3 assigned during their hospital encounters have ADPKD according to the clinical criteria. These codes can be used to assemble cohorts of adult patients with ADPKD and hospital encounters.

Indexed as

administrative datapolycystic kidney diseasepositive predictive valuevalidation studyvalidity

Identifiers

PMID28781884
PMCPMC5518965

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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.