Pregnancy-Adapted YEARS Algorithm for Diagnosis of Suspected Pulmonary Embolism

Abstract

BACKGROUNDPulmonary embolism is one of the leading causes of maternal death in the Western world. Because of the low specificity and sensitivity of the d-dimer test, all pregnant women with suspected pulmonary embolism undergo computed tomographic (CT) pulmonary angiography or ventilation-perfusion scanning, both of which involve radiation exposure to the mother and fetus. Whether a pregnancy-adapted algorithm could be used to safely avoid diagnostic imaging in pregnant women with suspected pulmonary embolism is unknown. METHODSIn a prospective study involving pregnant women with suspected pulmonary embolism, we assessed three criteria from the YEARS algorithm (clinical signs of deep-vein thrombosis, hemoptysis, and pulmonary embolism as the most likely diagnosis) and measured the d-dimer level. Pulmonary embolism was ruled out if none of the three criteria were met and the d-dimer level was less than 1000 ng per milliliter or if one or more of the three criteria were met and the d-dimer level was less than 500 ng per milliliter. Adaptation of the YEARS algorithm for pregnant women involved compression ultrasonography for women with symptoms of deep-vein thrombosis; if the results were positive (i.e., a clot was present), CT pulmonary angiography was not performed. All patients in whom pulmonary embolism had not been ruled out underwent CT pulmonary angiography. The primary outcome was the incidence of venous thromboembolism at 3 months. The secondary outcome was the proportion of patients in whom CT pulmonary angiography was not indicated to safely rule out pulmonary embolism. RESULTSA total of 510 women were screened, of whom 12 (2.4%) were excluded. Pulmonary embolism was diagnosed in 20 patients (4.0%) at baseline. During follow-up, popliteal deep-vein thrombosis was diagnosed in 1 patient (0.21%; 95% confidence interval [CI], 0.04 to 1.2); no patient had pulmonary embolism. CT pulmonary angiography was not indicated, and thus was avoided, in 195 patients (39%; 95% CI, 35 to 44). The efficiency of the algorithm was highest during the first trimester of pregnancy and lowest during the third trimester; CT pulmonary angiography was avoided in 65% of patients who began the study in the first trimester and in 32% who began the study in the third trimester. CONCLUSIONSPulmonary embolism was safely ruled out by the pregnancy-adapted YEARS diagnostic algorithm across all trimesters of pregnancy. CT pulmonary angiography was avoided in 32 to 65% of patients. (Funded by Leiden University Medical Center and 17 other participating hospitals; Artemis Netherlands Trial Register number, NL5726.)

Could the YEARS algorithm be used to exclude pulmonary embolism during pregnancy? Data from the CT‐PE‐pregnancy study

“…These results confirm those from the recently published ARTEMIS prospective management study, and provide additional evidence that the YEARS algorithm is safe for pregnant women. 13 In non-pregnant patients, the YEARS algorithm has been shown to safely exclude PE and increase the yield of non-invasive testing as compared with the usual combination of pretest probability assessment and D-dimer testing (absolute 14% decrease in the need for CTPA). 12 In pregnant women, the need to limit radiation exposure makes it highly desirable to use algorithms that maximize the yield of non-invasive testing while preserving diagnostic exclusion safety.…”

“…12 The YEARS algorithm uses the three most predictive cri- The YEARS algorithm was recently assessed for PE diagnosis during pregnancy in the ARTEMIS study. 13 The YEARS algorithm appears to safely exclude PE in a large proportion of pregnant women with suspected PE. However, further validation is desirable prior to its implementation in clinical practice.…”

Optimization Scheme of Accurate Calculation Algorithm of Electric Charge Based on Machine Learning

“…This method is a real-time optimal control strategy by establishing a mathematical model of the relationship between input parameters and output variables to predict the trend of data changes. Based on computers, it uses digital signals for real-time monitoring, acquisition and processing, and combines other relevant technical means (such as neural networks) to analyze the process and results of problem pattern recognition [9][10]. Through the collection, analysis and processing of electricity bill data; we can judge and predict the regularity of consumer behavior.…”