Evidence map›Paper›PMID 33826018›Full record

ArticleThe international journal of cardiovascular imaging2021

Detection of severe pulmonary hypertension based on computed tomography pulmonary angiography.

Yoav Granot, Zach Rozenbaum, Tomer Ziv-Baran, Shlomo Berliner, Sharon Z Adam, Yan Topilsky, Galit Aviram

Abstract read
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In one paragraph

Article in The international journal of cardiovascular imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 23% 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

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.

  1. Pooled it
  2. 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

7 authors at 2 institutions in 2 countries.

Yoav GranotDepartment of Cardiology, Tel Aviv Medical Center, Tel Aviv, Affiliated to the Sackler School of Medicine, Tel Aviv University, 6 Weizmann Street, 6423906, Tel Aviv, Israel. yoavgran@gmail.com.ORCID http://orcid.org/0000-0002-3863-4493
Zach RozenbaumDepartment of Cardiology, Montefiore Medical Center, Bronx, NY, USA.
Tomer Ziv-BaranDepartment of Epidemiology and Preventive Medicine, School of Public Health, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
Shlomo BerlinerDepartment of Internal Medicine, Tel Aviv Medical Center, Tel Aviv, Affiliated to the Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
Sharon Z AdamDepartment of Radiology, Tel Aviv Medical Center, Tel Aviv, Affiliated to the Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
Yan TopilskyDepartment of Cardiology, Tel Aviv Medical Center, Tel Aviv, Affiliated to the Sackler School of Medicine, Tel Aviv University, 6 Weizmann Street, 6423906, Tel Aviv, Israel.
Galit AviramDepartment of Radiology, Tel Aviv Medical Center, Tel Aviv, Affiliated to the Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
Tel Aviv University · ILMontefiore Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pulmonary hypertension (PH) is often diagnosed late in the disease course. As many patients may undergo computed tomography pulmonary angiography (CTPA) for exclusion of pulmonary embolism (PE), we aimed to create a model that can detect the existence of PH and grade its severity. Consecutive patients who underwent CTPA which was negative for PE, and echocardiography study within 24 h, were included. The CT parameters evaluated to assess PH were: the diameters of the main pulmonary artery (MPA), ascending aorta (AA), calculation of each heart chamber volume, and the severity of reflux of contrast material. Randomly, 70% of patients were included in the model creation group, and 30% were used to validate the model. The final study group included 740 patients, 268 male patients, median age 72 years. 374 patients (51%) had PH, of them 94 (13%) had severe PH on the echocardiography. Right atrium (RA) and Left atrium (LA) volume indices were the strongest parameter to indicate PH (area under the curve, AUC = 0.738 and 0.736, respectively), while Right ventricle (RV) and RA volume indices were the strongest parameter to identify severe PH (AUC = 0.735 and 0.715, respectively) with MPA diameter being the least influential indicator (AUC = 0.623). Using the patients age, gender, and multiple CTPA parameters, we created a model for predicting the existence of severe PH. After validation, the model demonstrated 91% sensitivity and a negative predictive value of 97%. Applying our models, CTPA can be used to identify severe PH immediately after the completion of CTPA exam.

Indexed as

Hypertension, PulmonaryPulmonary EmbolismAngiographyComputed Tomography AngiographyHumansInfant, NewbornMalePredictive Value of TestsPulmonary ArteryRetrospective StudiesTomography, X-Ray ComputedCTPAPulmonary hypertensionVolumetric analysis

Identifiers

PMID33826018
OpenAlexW3148485747

What OpenQuestion holds

Textmetadata
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.