Evidence map›Paper›PMID 37550677›Full record

Trial reportBMC pulmonary medicine2023

PECSS: Pulmonary Embolism Comprehensive Screening Score to safely rule out pulmonary embolism among suspected patients presenting to emergency department.

Luojia Tang, Yundi Hu, Dong Pan, Chun Yang, Cheng Tang, Yunchuan Huang, Jianyong Gu, Min Min, Xiaolei Lin, Chaoyang Tong

Open access · goldAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in BMC pulmonary medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.8field-weighted citation impact, top 27% 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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
  3. 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

10 authors at 4 institutions in 1 country.

Luojia Tang *Emergency Department of Zhongshan Hospital, Fudan University, Shanghai, China.
Yundi Hu *School of Data Science, Fudan University, Shanghai, China.
Dong PanDepartment of Information and Intelligence Development of Zhongshan Hospital, Fudan University, Shanghai, China.
Chun YangDepartment of Information and Intelligence Development of Zhongshan Hospital, Fudan University, Shanghai, China.
Cheng TangEmergency Department of Zhongshan Hospital, Fudan University, Shanghai, China.
Yunchuan HuangEmergency Department of Zhongshan Hospital, Fudan University, Shanghai, China.
Jianyong GuEmergency Department of Zhongshan Hospital, Fudan University, Shanghai, China.
Min MinEmergency Department of Zhongshan Hospital, Fudan University, Shanghai, China.
Xiaolei LinSchool of Data Science, Fudan University, Shanghai, China. xiaoleilin@fudan.edu.cn.
Chaoyang TongEmergency Department of Zhongshan Hospital, Fudan University, Shanghai, China. tong.chaoyang@zs-hospital.sh.cn.
Fudan University · CNSun Yat-sen University · CNThe First Affiliated Hospital, Sun Yat-sen University · CNZhongshan Hospital · CN

Funding

Science and Technology of Shanghai Committee 21MC1930400
6 · The paper itself

Abstract

backgroundPulmonary embolism is a severe cardiovascular disease and can be life-threatening if left untreated. However, the detection rate of pulmonary embolism using existing pretest probability scores remained relatively low and clinical rule out often relied on excessive use of computed tomographic pulmonary angiography.

methodsWe retrospectively collected data from pulmonary embolism suspected patients in Zhongshan Hospital from July 2018 to October 2022. Pulmonary embolism diagnosis and severity grades were confirmed by computed tomographic pulmonary angiography. Patients were randomly divided into derivation and validation set. To construct the Pulmonary Embolism Comprehensive Screening Score (PECSS), we first screened for candidate clinical predictors using univariate logistic regression models. These predictors were then included in a searching algorithm with indicators of Wells score, where a series of points were assigned to each predictor. Optimal D-Dimer cutoff values were investigated and incorporated with PECSS to rule out pulmonary embolism.

resultsIn addition to Wells score, PECSS identified seven clinical predictors (anhelation, abnormal blood pressure, in critical condition when admitted, age > 65 years and high levels of pro-BNP, CRP and UA,) strongly associated with pulmonary embolism. Patients can be safely ruled out of pulmonary embolism if PECSS ≤ 4, or if 4 < PECSS ≤ 6 and D-Dimer ≤ 2.5 mg/L. Comparing with Wells approach, PECSS achieved lower failure rates across all pulmonary embolism severity grades. These findings were validated in the held-out validation set.

conclusionsCompared to Wells score, PECSS approaches achieved lower failure rates and better compromise between sensitivity and specificity. Calculation of PECSS is easy and all predictors are readily available upon emergency department admission, making it widely applicable in clinical settings. TRAIL REGISTRATION: The study was retrospectively registered (No. CJ0647) and approved by Human Genetic Resources in China in April 2022. Ethical approval was received from the Medical Ethics Committee of Zhongshan Hospital (NO.B2021-839R).

Indexed as

Computed Tomography AngiographyPulmonary EmbolismAgedAngiographyEmergency Service, HospitalFibrin Fibrinogen Degradation ProductsHumansTomography, X-Ray ComputedFibrin Fibrinogen Degradation ProductsD-DimerEmergency DepartmentPretest probabilityPulmonary EmbolismScreening Score

Identifiers

PMID37550677
PMCPMC10408070
OpenAlexW4385638170

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.