Ruling out pulmonary embolism across different healthcare settings: A systematic review and individual patient data meta-analysis

Geert Jan Geersing, Toshihiko Takada, Frederikus A. Klok, Harry R. Büller, D. Mark Courtney, Yonathan Freund, Javier Galipienzo, Gregoire Le Gal, Waleed Ghanima, Jeffrey A. Kline, Menno V. Huisman, Karel G.M. Moons, Arnaud Perrier, Sameer Parpia, Helia Robert-Ebadi, Marc Righini, Pierre Marie Roy, Maarten van Smeden, Milou A.M. Stals, Philip S. WellsKerstin de Wit, Noémie Kraaijpoel, Nick van Es

Research output: Contribution to journalArticlepeer-review

Abstract

Background The challenging clinical dilemma of detecting pulmonary embolism : (PE) in suspected patients is encountered in a variety of healthcare settings. We hypothesized that the optimal diagnostic approach to detect these patients in terms of safety and efficiency depends on underlying PE prevalence, case mix, and physician experience, overall reflected by the type of setting where patients are initially assessed. The objective of this study was to assess the capability of ruling out PE by available diagnostic strategies across all possible settings. Methods and findings We performed a literature search (MEDLINE) followed by an individual patient data (IPD) meta-analysis (MA; 23 studies), including patients from self-referral emergency care (n = 12,612), primary healthcare clinics (n = 3,174), referred secondary care (n = 17,052), and hospitalized or nursing home patients (n = 2,410). Multilevel logistic regression was performed to evaluate diagnostic performance of the Wells and revised Geneva rules, both using fixed and adapted D-dimer thresholds to age or pretest probability (PTP), for the YEARS algorithm and for the Pulmonary Embolism Rule-out Criteria (PERC). All strategies were tested separately in each healthcare setting. Following studies done in this field, the primary diagnostic metrices estimated from the models were the “failure rate” of each strategy-i.e., the proportion of missed PE among patients categorized as “PE excluded” and “efficiency”-defined as the proportion of patients categorized as “PE excluded” among all patients. In self-referral emergency care, the PERC algorithm excludes PE in 21% of suspected patients at a failure rate of 1.12% (95% confidence interval [CI] 0.74 to 1.70), whereas this increases to 6.01% (4.09 to 8.75) in referred patients to secondary care at an efficiency of 10%. In patients from primary healthcare and those referred to secondary care, strategies adjusting D-dimer to PTP are the most efficient (range: 43% to 62%) at a failure rate ranging between 0.25% and 3.06%, with higher failure rates observed in patients referred to secondary care. For this latter setting, strategies adjusting D-dimer to age are associated with a lower failure rate ranging between 0.65% and 0.81%, yet are also less efficient (range: 33% and 35%). For all strategies, failure rates are highest in hospitalized or nursing home patients, ranging between 1.68% and 5.13%, at an efficiency ranging between 15% and 30%. The main limitation of the primary analyses was that the diagnostic performance of each strategy was compared in different sets of studies since the availability of items used in each diagnostic strategy differed across included studies; however, sensitivity analyses suggested that the findings were robust. Conclusions The capability of safely and efficiently ruling out PE of available diagnostic strategies differs for different healthcare settings. The findings of this IPD MA help in determining the optimum diagnostic strategies for ruling out PE per healthcare setting, balancing the trade-off between failure rate and efficiency of each strategy.

Original languageEnglish (US)
Article numbere1003905
JournalPLoS Medicine
Volume19
Issue number1
DOIs
StatePublished - Jan 2022
Externally publishedYes

ASJC Scopus subject areas

  • Medicine(all)

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