Dataset Information

Derivation and validation of a prognostic model for pulmonary embolism.

ABSTRACT:

Rationale

An objective and simple prognostic model for patients with pulmonary embolism could be helpful in guiding initial intensity of treatment.

Objectives

To develop a clinical prediction rule that accurately classifies patients with pulmonary embolism into categories of increasing risk of mortality and other adverse medical outcomes.

Methods

We randomly allocated 15,531 inpatient discharges with pulmonary embolism from 186 Pennsylvania hospitals to derivation (67%) and internal validation (33%) samples. We derived our prediction rule using logistic regression with 30-day mortality as the primary outcome, and patient demographic and clinical data routinely available at presentation as potential predictor variables. We externally validated the rule in 221 inpatients with pulmonary embolism from Switzerland and France.

Measurements

We compared mortality and nonfatal adverse medical outcomes across the derivation and two validation samples.

Main results

The prediction rule is based on 11 simple patient characteristics that were independently associated with mortality and stratifies patients with pulmonary embolism into five severity classes, with 30-day mortality rates of 0-1.6% in class I, 1.7-3.5% in class II, 3.2-7.1% in class III, 4.0-11.4% in class IV, and 10.0-24.5% in class V across the derivation and validation samples. Inpatient death and nonfatal complications were ConclusionsOur rule accurately classifies patients with pulmonary embolism into classes of increasing risk of mortality and other adverse medical outcomes. Further validation of the rule is important before its implementation as a decision aid to guide the initial management of patients with pulmonary embolism.

SUBMITTER: Aujesky D

PROVIDER: S-EPMC2718410 | biostudies-literature | 2005 Oct

REPOSITORIES: biostudies-literature

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Publications

Derivation and validation of a prognostic model for pulmonary embolism.

Aujesky Drahomir D Obrosky D Scott DS Stone Roslyn A RA Auble Thomas E TE Perrier Arnaud A Cornuz Jacques J Roy Pierre-Marie PM Fine Michael J MJ

American journal of respiratory and critical care medicine 20050714 8

<h4>Rationale</h4>An objective and simple prognostic model for patients with pulmonary embolism could be helpful in guiding initial intensity of treatment.<h4>Objectives</h4>To develop a clinical prediction rule that accurately classifies patients with pulmonary embolism into categories of increasing risk of mortality and other adverse medical outcomes.<h4>Methods</h4>We randomly allocated 15,531 inpatient discharges with pulmonary embolism from 186 Pennsylvania hospitals to derivation (67%) and ...[more]

PMID: 16020800

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Project description:ImportanceIn patients with suspected pulmonary embolism (PE), overuse of diagnostic imaging is an important point of concern.ObjectiveTo derive and validate a 4-level pretest probability rule (4-Level Pulmonary Embolism Clinical Probability Score [4PEPS]) that makes it possible to rule out PE solely on clinical criteria and optimized D-dimer measurement to safely decrease imaging testing for suspected PE.Design, setting, and participantsThis study included consecutive outpatients suspected of having PE from US and European emergency departments. Individual data from 3 merged management studies (n = 11 114; overall prevalence of PE, 11%) were used for the derivation cohort and internal validation cohort. The external validation cohorts were taken from 2 independent studies, the first with a high PE prevalence (n = 1548; prevalence, 21.5%) and the second with a moderate PE prevalence (n = 1669; prevalence, 11.7%). A prior definition of pretest probability target values to achieve a posttest probability less than 2% was used on the basis of the negative likelihood ratios of D-dimer. Data were collected from January 2003 to April 2016, and data were analyzed from June 2018 to August 2019.Main outcomes and measuresThe rate of PE diagnosed during the initial workup or during follow-up and the rate of imaging testing.ResultsOf the 5588 patients in the derivation cohort, 3441 (61.8%) were female, and the mean (SD) age was 52 (18.5) years. The 4PEPS comprises 13 clinical variables scored from -2 to 5. It results in the following strategy: (1) very low probability of PE if 4PEPS is less than 0: PE ruled out without testing; (2) low probability of PE if 4PEPS is 0 to 5: PE ruled out if D-dimer level is less than 1.0 μg/mL; (3) moderate probability of PE if 4PEPS is 6 to 12: PE ruled out if D-dimer level is less than the age-adjusted cutoff value; (4) high probability of PE if 4PEPS is greater than 12: PE ruled out by imaging without preceding D-dimer test. In the first and the second external validation cohorts, the area under the receiver operator characteristic curves were 0.79 (95% CI, 0.76 to 0.82) and 0.78 (95% CI, 0.74 to 0.81), respectively. The false-negative testing rates if the 4PEPS strategy had been applied were 0.71% (95% CI, 0.37 to 1.23) and 0.89% (95% CI, 0.53 to 1.49), respectively. The absolute reductions in imaging testing were -22% (95% CI, -26 to -19) and -19% (95% CI, -22 to -16) in the first and second external validation cohorts, respectively. The 4PEPS strategy compared favorably with all recent strategies in terms of imaging testing.Conclusions and relevanceThe 4PEPS strategy may lead to a substantial and safe reduction in imaging testing for patients with suspected PE. It should now be tested in a formal outcome study.

Project description:BackgroundPrognostic assessment is important for the management of patients with acute pulmonary embolism (APE). Pulmonary Embolism Severity Index (PESI) and simple PESI (sPESI) are new emerged prognostic assessment tools for APE. The aim of this meta-analysis is to assess the accuracy of the PESI and the sPESI to predict prognostic outcomes (all-cause and PE-related mortality, serious adverse events) in APE patients, and compare between these two PESIs.MethodsMEDLINE and EMBASE database were searched up to June 2012 using the terms "Pulmonary Embolism Severity Index" and "pulmonary embolism". Summary odds ratio (OR) with 95% confidence intervals (CIs) for prognostic outcomes in low risk PESI versus high risk PESI were calculated. Summary receiver operating characteristic curve (SROC) used to estimate overall predicting accuracies of prognostic outcomes.ResultsTwenty-one studies were included in this meta-analysis. The results showed low-risk PESI was significantly associated with lower all-cause mortality (OR 0.13; 95% CI 0.12 to 0.15), PE-related mortality (OR 0.09; 95% CI 0.05 to 0.17) and serious adverse events (OR 0.34; 95% CI 0.29 to 0.41), with no homogeneity across studies. In sPESI subgroup, the OR of all-cause mortality, PE-related mortality, and serious adverse events was 0.10 (95% CI 0.08 to 0.14), 0.09 (95% CI 0.03 to 0.26) and 0.40 (95% CI 0.31 to 0.51), respectively; while in PESI subgroup, the OR was 0.14 (95% CI 0.13 to 0.16), 0.09 (95% CI 0.04 to 0.21), and 0.30 (95% CI 0.23 to 0.38), respectively. For accuracy analysis, the pooled sensitivity, the pooled specificity, and the overall weighted AUC for PESI predicting all-cause mortality was 0.909 (95% CI: 0.900 to 0.916), 0.411 (95% CI: 0.407 to 0.415), and 0.7853±0.0058, respectively; for PE-related mortality, it was 0.953 (95% CI: 0.913 to 0.978), 0.374 (95% CI: 0.360 to 0.388), and 0.8218±0.0349, respectively; for serious adverse events, it was 0.821 (95% CI: 0.795 to 0.845), 0.389 (95% CI: 0.384 to 0.394), and 0.6809±0.0208, respectively. In sPESI subgroup, the AUC for predicting all-cause mortality, PE-related mortality, and serious adverse events was 0.7920±0.0117, 0.8317±0.0547, and 0.6454±0.0197, respectively. In PESI subgroup, the AUC was 0.7856±0.0075, 0.8158±0.0451, and 0.6609±0.0252, respectively.ConclusionsPESI has discriminative power to predict the short-term death and adverse outcome events in patients with acute pulmonary embolism, the PESI and the sPESI have similar accuracy, while sPESI is easier to use. However, the calibration for predicting prognosis can't be calculated from this meta-analysis, some prospective studies for accessing PESI predicting calibration can be recommended.

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Dataset Information

Derivation and validation of a prognostic model for pulmonary embolism.

Rationale

Objectives

Methods

Measurements

Main results

Publications

Derivation and validation of a prognostic model for pulmonary embolism.

Similar Datasets

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