Evidence map›Paper›PMID 29480064›Full record

ArticlePulmonary circulation

Validation of claims-based algorithms for pulmonary arterial hypertension.

Ravikanth Papani, Gulshan Sharma, Amitesh Agarwal, Sean J Callahan, Winston J Chan, Yong-Fang Kuo, Yun M Shim, Andrew D Mihalek, Alexander G Duarte

Open access · goldAbstract read
In one paragraph

Article in Pulmonary circulation. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
2.6field-weighted citation impact, top 10% of its field
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

19 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.

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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

9 authors at 3 institutions in 1 country.

Ravikanth Papani1 Division of Pulmonary, Critical Care, and Sleep Medicine, University of Texas Medical Branch, Galveston, TX, USA.ORCID http://orcid.org/0000-0002-5384-9150
Gulshan Sharma1 Division of Pulmonary, Critical Care, and Sleep Medicine, University of Texas Medical Branch, Galveston, TX, USA.
Amitesh Agarwal2 Division of Pulmonary, Critical Care, and Sleep Medicine, University of Florida College of Medicine, Jacksonville, FL, USA.
Sean J Callahan3 Division of Pulmonary and Critical Care Medicine, University of Virginia School of Medicine, Charlottesville, VA, USA.
Winston J Chan4 Office of Biostatistics, Department of Preventive Medicine and Community Health, University of Texas Medical Branch, Galveston, TX, USA.
Yong-Fang Kuo4 Office of Biostatistics, Department of Preventive Medicine and Community Health, University of Texas Medical Branch, Galveston, TX, USA.
Yun M Shim3 Division of Pulmonary and Critical Care Medicine, University of Virginia School of Medicine, Charlottesville, VA, USA.
Andrew D Mihalek3 Division of Pulmonary and Critical Care Medicine, University of Virginia School of Medicine, Charlottesville, VA, USA.
Alexander G Duarte1 Division of Pulmonary, Critical Care, and Sleep Medicine, University of Texas Medical Branch, Galveston, TX, USA.
The University of Texas Medical Branch at Galveston · USUniversity of Virginia · USUniversity of Florida · US

Funding

Hyperpolarized Xenon-129 MRI: a new multi-dimensional biomarker to determine pulmonary physiologic responses to COPD therapeuticsR01HL132177 · NHLBI · UNIVERSITY OF VIRGINIA · PI QING, KUN, SHIM, YUN MICHAEL · 2017 to 2021
$3.9M
Discovery of New Anti-Inflammatory Agents to Treat COPDR01HL132287 · NHLBI · GEORGE MASON UNIVERSITY · PI NOBLE, SCHROEDER M, PAIGE, MIKELL · 2016 to 2019
$2.4M
6 · The paper itself

Abstract

Administrative claims studies do not adequately distinguish pulmonary arterial hypertension (PAH) from other forms of pulmonary hypertension (PH). Our aim is to develop and validate a set of algorithms using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes and electronic medical records (EMR), to identify patients with PAH. From January 2012 to August 2015, the EMRs of patients with ICD-9-CM codes for PH with an outpatient visit at the University of Texas Medical Branch were reviewed. Patients were divided into PAH or non-PAH groups according to EMR encounter diagnosis. Patient demographics, echocardiography, right heart catheterization (RHC) results, and PAH-specific therapies were assessed. RHC measurements were reviewed to categorize cases as hemodynamically determined PAH or not PAH. Weighted sensitivity, specificity, and positive and negative predictive values were calculated for the developed algorithms. A logistic regression analysis was conducted to determine how well the algorithms performed. External validation was performed at the University of Virginia Health System. The cohort for the development algorithms consisted of 683 patients with PH, PAH group (n = 191) and non-PAH group (n = 492). A hemodynamic diagnosis of PAH determined by RHC was recorded in the PAH (26%) and non-PAH (3%) groups. The positive predictive value for the algorithm that included ICD-9-CM and PAH-specific medications was 66.9% and sensitivity was 28.2% with a c-statistic of 0.66. The positive predictive value for the EMR-based algorithm that included ICD-9-CM, EMR encounter diagnosis, echocardiography, RHC, and PAH-specific medication was 69.4% and a c-statistic of 0.87. A validation cohort of 177 patients with PH examined from August 2015 to August 2016 using EMR-based algorithms yielded a similar positive predictive value of 62.5%. In conclusion, claims-based algorithms that included ICD-9-CM codes, EMR encounter diagnosis, echocardiography, RHC, and PAH-specific medications better-identified patients with PAH than ICD-9-CM codes alone.

Indexed as

administrative claimsidiopathic pulmonary arterial hypertensionvalidation studies

Identifiers

PMID29480064
PMCPMC5833187
OpenAlexW2789538639

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.