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Record W2981630484 · doi:10.1093/eurheartj/ehz746.0197

P5019Derivation and validation of a new probability score in pulmonary embolism suspicion allowing safely reduction of imaging testing: PEPS (Pulmonary Embolism Probability Score)

2019· article· en· W2981630484 on OpenAlexaff
P.M. Roy, Emilie Friou, Boris Germeau, Thomas Moumneh, Delphine Douillet, Jeffrey A. Kline, Marc Righini, Grégoire Le Gal, Andréa Penaloza

Bibliographic record

VenueEuropean Heart Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicinePulmonary embolismPre- and post-test probabilityLogistic regressionInternal medicineChest painUnivariate analysisCardiologyMultivariate analysis

Abstract

fetched live from OpenAlex

Abstract Background In pulmonary embolism (PE) suspicion, several strategies based on clinical criteria and D-dimer (Dd) measurement have been developed in order to reduce resource utilization. However, they used different clinical probability (CP) assessment methods limiting their combination. Purpose To develop and validate a unique probability score integrating most of previous proposals to allow safely reduction of imaging testing. Methods 4 CP levels were previously defined in order to obtain a false negative rate <1.9%: 1) without Dd test: very low CP (PE prevalence <1.9%), 2) with Dd <1000 μg/L: low CP (<15%), 3) with Dd <500 or age x10μg/L: moderate CP (<60%) and 4) precluding PE exclusion on Dd: high CP. We used individual data from 4 prospective cohorts of suspected PE patients in Europe and America (n=11 066) for derivation and internal validation. The variables significantly associated with PE in univariate analysis were included in a multivariate logistic regression model. Points were assigned according to the regression coefficients. The score was validated in two external independent cohorts (n=1554, n=1669). Results PEPS comprised 13 variables: age <50 years (−2), age 50–64 years (−1), heart rate <80 beats/min (−1), chronic lung disease (−1), chest pain and recent dyspnea (+1), syncope (+1), male sex (+1), previous venous thromboembolism (+2), medical or orthopaedic immobilization (+2), estrogenic treatment (+2), oxygen saturation <95% (+3), unilateral lower limb pain (+3) and PE is the most likely diagnosis (+ 5). The rates of false negative and avoidable imaging tests if the PEPS strategy would have been applied were 0.6% [95% CI: 0.3–1.1] and 22.7% [20.2–25.3] in the first external validation cohort, and 0.85 [0.5–1.45] and 26.6% [23.5–29.9] in the second one. Applied retrospectively, PEPS strategy compared favourably with other strategies and combinations. Derivation Int. validation Ext. validation 1 Ext. validation 2 nPE/N % [95% CI] nPE/N % [95% CI] nPE/N % [95% CI] nPE/N % [95% CI] TOTAL 615/5588: 11.0% [10.2–11.9] 432/3726: 11.6% [10.6–12.7] 327/1546: 21.2% [19.2–23.2] 196/1669: 11.7% [10.3–13.4] Very low CP PEPS<0 16/1445: 1.1% [0.7–1.8] 16/946: 1.7% [1.0–2.7] 3/118: 2.5% [0.7–6.8] 5/347: 1.4% [0.6–3.3] Low CP 0≤PEPS<5 127/2620: 4.9% [4.1–5.7] 106/1805: 5.9% [4.9–7.1] 49/611: 8.0% [6.1–10.4] 61/647: 7.2% [5.7–9.1] Moderate CP 5≤PEPS<12 347/1334: 26.0% [23.7–28.4] 243/867: 28.0% [25.1–31.1] 206/715: 28.8% [25.6–32.2] 107/430: 24.9% [21.0–29.2] High CP 12≤PEPS 125/179:69.8% [62.8–76.1] 67/108: 62.0% [52.6–70.6] 69/102: 67.7% [58.1–76.2] 23/45: 51.1% [37.0–65.0] AUC 0.84 [0.83–0.86] 0.82 [0.80–0.84] 0.79 [0.76–0.82] 0.77 [0.74–0.80] CP: Clinical probability; PEPS: Pulmonary Embolism Probability Score. Conclusions A strategy based on the proposed score may lead to a safely substantial reduction of imaging testing. It should now be tested in an outcome interventional study.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.060
GPT teacher head0.282
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2019
Admission routes1
Has abstractyes

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