Perc validating devices domain

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Its presentation can be relatively mild, sometimes even mimicking myalgia or a simple cough.This causes pulmonary embolism to be a diagnosis that is easily missed.1 2 As a result, physicians have a low threshold for suspicion and subsequent referral for further diagnostics.3 4 Referred patients will be exposed to the burden, costs, and even potential iatrogenic damage of diagnostic techniques such as spiral computed tomography or contrast nephropathy.5 However, only in a small subset (about 10-15%) of all suspected cases are emboli actually confirmed during diagnostic investigation.6Several non-invasive diagnostic prediction models have been developed for safe exclusion of pulmonary embolism and are usually followed by D-dimer testing.7 Physicians can use these models as a strategy to enhance the efficiency of the diagnostic process by precluding those patients with a low probability of pulmonary embolism from further diagnostic tests, without compromising on safety (that is, missing cases of pulmonary embolism).All retrieved papers were examined by two independent reviewers (JH, GJG) and a third independent reviewer (KGMM) in case of disagreement.Given the scope of our systematic review (see appendix box A), we assessed all diagnostic prediction models for pulmonary embolism, retrieved by our search, on their applicability in a primary care domain.Such diagnostic strategies can reduce the number of unnecessary computed tomography scans by 35%, with only 1-2% of missed cases in the group of patients with a low probability of pulmonary embolism.7In many countries, general practitioners are the first physicians to encounter patients with symptoms suggestive of pulmonary embolism.Risk stratification is valuable in deciding which patients to refer.

Only a control line will be visible if the test is negative.We firstly did a systematic review and critical appraisal of all available diagnostic models for pulmonary embolism, as recommended by guidelines on prediction models research.21 Next, the diagnostic models easily applicable in primary care were validated in the AMUSE-2 dataset—that is, a large independent prospectively constructed cohort of patients presenting to their general practitioner with complaints suggestive of pulmonary embolism.For our systematic review and critical appraisal of the existing diagnostic models for pulmonary embolism, we followed the recent methodological guidance by the Prognosis Methods Group of the Cochrane Collaboration.21 22 23 24Firstly, we framed the review question and design by using the CHARMS checklist for systematic reviews of prediction models (see appendix box A).21 We then repeated the systematic search previously performed for an aggregate meta-analysis by Lucassen et al and used the same study selection criteria.7 We searched for studies on development and validation of diagnostic prediction models published between January 2010 and October 2014.Therefore, models derived in hospital or acute care settings cannot simply be implemented in primary care.8 9 10 11 12 13 14 Reasons for this poorer performance include differences in the case mix and the prevalence of pulmonary embolism due to the unselected population, as well as differences in physicians’ experience of patients with suspected pulmonary embolism.9 10 15 16 Hence, when transferring diagnostic models or strategies across healthcare settings, evaluation of their performance in this other setting is necessary first.This form of external validation is referred to as domain or setting validation,8 10 17 or as quantification of the transportability of prediction models.13 18The recent AMUSE-2 study (Amsterdam, Maastricht, Utrecht Study on thrombo-Embolism)19 has been the first to prospectively quantify the transportability of the, perhaps best known, secondary care derived diagnostic prediction model for pulmonary embolism (that is, the Wells pulmonary embolism rule,20 combined with point of care D-dimer testing) in a primary care setting.

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