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A comparison of the predictive accuracy of three screening models for pulmonary arterial hypertension in systemic sclerosis

Overview of attention for article published in Arthritis Research & Therapy, January 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

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Title
A comparison of the predictive accuracy of three screening models for pulmonary arterial hypertension in systemic sclerosis
Published in
Arthritis Research & Therapy, January 2015
DOI 10.1186/s13075-015-0517-5
Pubmed ID
Authors

Yanjie Hao, Vivek Thakkar, Wendy Stevens, Kathleen Morrisroe, David Prior, Candice Rabusa, Peter Youssef, Eli Gabbay, Janet Roddy, Jennifer Walker, Jane Zochling, Joanne Sahhar, Peter Nash, Susan Lester, Maureen Rischmueller, Susanna M Proudman, Mandana Nikpour

Abstract

IntroductionThere is evidence that early screening for pulmonary arterial hypertension (PAH) in systemic sclerosis (SSc) improves outcomes. We compared the predictive accuracy of two recently published screening algorithms (DETECT 2013 and Australian Scleroderma Interest Group (ASIG) 2012) for SSc-associated PAH (SSc-PAH) with the commonly used European Society of Cardiology/Respiratory Society (ESC/ERS 2009) guidelines.MethodsWe included 73 consecutive SSc patients with suspected PAH undergoing right heart catheterization (RHC). The three screening models were applied to each patient. For each model, contingency table analysis was used to determine sensitivity, specificity, positive (PPV) and negative predictive values (NPV) for PAH. These properties were also evaluated in an `alternate scenario analysis¿ where the prevalence of PAH was set at 10%.ResultsRHC revealed PAH in 27 (36.9%) patients. Both DETECT and ASIG algorithms performed equally in predicting PAH with sensitivity and NPV of 100%. The ESC/ERS guidelines had sensitivity of 96.3% and NPV of only 91%, missing one case of PAH; these guidelines could not be applied to three patients who had absent tricuspid regurgitant (TR) jet. The ASIG algorithm had the highest specificity of 54.5%. With PAH prevalence set at 10%, the NPV of the models was unchanged, but the PPV dropped to less than 20%.ConclusionsIn this cohort, the DETECT and ASIG algorithms out-perform the ESC/ERS guidelines, detecting all patients with PAH. The ESC/ERS guidelines have limitations in the absence of a TR jet. Ultimately, the choice of SSc-PAH screening algorithm will also depend on cost and ease of application.

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X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 1%
Netherlands 1 1%
Italy 1 1%
Unknown 67 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 16%
Student > Postgraduate 8 11%
Student > Bachelor 7 10%
Student > Master 7 10%
Other 6 9%
Other 11 16%
Unknown 20 29%
Readers by discipline Count As %
Medicine and Dentistry 28 40%
Biochemistry, Genetics and Molecular Biology 5 7%
Nursing and Health Professions 3 4%
Agricultural and Biological Sciences 3 4%
Pharmacology, Toxicology and Pharmaceutical Science 1 1%
Other 4 6%
Unknown 26 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 January 2016.
All research outputs
#3,080,710
of 25,373,627 outputs
Outputs from Arthritis Research & Therapy
#627
of 3,381 outputs
Outputs of similar age
#41,415
of 359,972 outputs
Outputs of similar age from Arthritis Research & Therapy
#10
of 48 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,381 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.2. This one has done well, scoring higher than 81% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 359,972 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 48 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.