Maternal and Fetal Radiation-Induced Cancer Risk From Computed Tomography Pulmonary Angiography During Pregnancy: A Retrospective Cohort Study Across a Multihospital Integrated Health Care Network.

OBJECTIVE: Computed tomography pulmonary angiogram (CTPA) is important to evaluate suspected pulmonary embolism in pregnancy but has maternal/fetal radiation risks. The objective of this study was to estimate maternal and fetal radiation-induced cancer risk from CTPA during pregnancy. METHODS: Simulation modeling via the National Cancer Institute's Radiation Risk Assessment Tool was used to estimate excess cancer risks from 17 organ doses from CTPA during pregnancy, with doses determined by a radiation dose indexing monitoring system. Organ doses were obtained from a radiation dose indexing monitoring system. Maternal and fetal cancer risks per 100,000 were calculated for male and female fetuses and several maternal ages. RESULTS: The 534 CTPA examinations had top 3 maternal organ doses to the breast, lung, and stomach of 17.34, 15.53, and 9.43 mSv, respectively, with a mean uterine dose of 0.21 mSv. The total maternal excess risks of developing cancer per 100,000 were 181, 151, 121, 107, 94.5, 84, and 74.4, respectively, for a 20-, 25-, 30-, 35-, 40-, 45-, and 50-year-old woman undergoing CTPA, compared with baseline cancer risks of 41,408 for 20-year-old patients. The total fetal excess risks of developing cancer per 100,000 were 12.3 and 7.3 for female and male fetuses, respectively, when compared with baseline cancer risks of 41,227 and 48,291. DISCUSSION: Excess risk of developing cancer from CTPA was small relative to baseline cancer risk for pregnant patients and fetuses, decreased for pregnant patients with increasing maternal age, and was greater for female fetuses than male fetuses.

Ten-Year Trend in Advanced Imaging Utilization for Suspected Pulmonary Embolism in Pregnancy.

PURPOSE: Advanced imaging is essential to diagnose pulmonary embolism (PE) in pregnancy, but there are associated maternal and fetal radiation risks. The purpose of this study was to evaluate the 10-year trend in advanced imaging utilization for the evaluation of suspected PE in pregnancy. METHODS: The authors evaluated pregnant women with advanced imaging using CT pulmonary angiography (CTPA) or lung scintigraphy (LS) for evaluation of suspected PE presenting to two tertiary hospitals from 2007 to 2016. The rate of imaging was evaluated relative to positive PE rate and local pregnancy rate. positive PE was defined as a new acute PE finding on any advanced imaging within 3 days of first advanced imaging test. Local pregnancy rates were defined per 1,000 pregnancies in the county serviced by both hospitals. Chi-square testing was used to evaluate statistical significance (P < .05) in the utilization trend of advanced imaging and relative to local pregnancy rates and evaluations positive for PE. RESULTS: A total of 707 pregnant patients were identified, of whom 92.5% (n = 654) underwent CTPA and 7.5% (n = 53) underwent LS. Regression analysis showed an average increase of 5.2 advanced imaging studies per year (P < .001), with 61 and 105 studies performed in 2007 and 2016, respectively. Additionally, there was an average increase of 0.08 (P < .001) advanced imaging studies per 1,000 local pregnancies per year, doubling from 0.7 in 2007 to 1.4 in 2016 (P < .001). Finally, there was a decrease of 0.004 (P = .009) in advanced imaging positive for PE, from 3% (2 of 61) in 2007 to 0% (0 of 100) in 2016. CONCLUSIONS: Advanced imaging utilization increased by 72% over the 10-year window, driven by higher use of CTPA. Although the detection rate of PE on advanced imaging has decreased, the utilization rate among pregnant patients doubled during this period. These results highlight the need to consider the radiation risks and costs of advanced imaging in specific patient populations.

Patient and Fetal Radiation-Induced Malignancy Risk From Imaging For Evaluation of Pulmonary Embolism in Pregnancy.

BACKGROUND: Imaging for diagnosis of suspected pulmonary embolism in pregnancy presents radiation concerns for patient and fetus. OBJECTIVES: Estimate the risks of radiation-induced breast cancer and childhood leukemia from common imaging techniques for the evaluation of suspected pulmonary embolism in pregnancy. METHODS: Breast and uterine absorbed doses for various imaging techniques were input into the National Cancer Institute Radiation Risk Assessment Tool to calculate risk of breast cancer for the patient and childhood leukemia for the fetus. Absorbed doses were obtained by synthesizing data from a recent systematic review and the International Commission on Radiological Protection. Primary outcomes were the estimated excess incidences of breast cancer and childhood leukemia per 100,000 exposures. RESULTS: Baseline incidences of breast cancer for a 30-year-old woman and childhood leukemia for a male fetus were 13,341 and 939, respectively. Excess incidences of breast cancer were 0.003 and 0.275 for a single and two-view chest radiograph, respectively, 9.53 and 20.6 for low- and full-dose computed tomography pulmonary angiography (CTPA), respectively, 0.616 and 2.54 for low- and full-dose perfusion scan, respectively, and 0.732 and 2.66 for low- and full-dose ventilation perfusion scan, respectively. Excess incidences of childhood leukemia were 0.004 and 0.007 for a single and two-view chest radiograph, respectively, 0.069 and 0.490 for low- and full-dose CTPA, respectively, 0.359 and 1.47 for low- and full-dose perfusion scan, respectively, and 0.856 and 1.97 for low- and full-dose ventilation perfusion scan, respectively. CONCLUSION: Excess cancer risks for all techniques were small relative to baseline cancer risks, with CTPA techniques carrying slightly higher risk of breast cancer for the patient and ventilation perfusion techniques a higher risk of childhood leukemia.

Cost-Effectiveness and Value-of-Information Analysis Using Machine Learning-Based Metamodeling: A Case of Hepatitis C Treatment.

BACKGROUND: Metamodels can address some of the limitations of complex simulation models by formulating a mathematical relationship between input parameters and simulation model outcomes. Our objective was to develop and compare the performance of a machine learning (ML)-based metamodel against a conventional metamodeling approach in replicating the findings of a complex simulation model. METHODS: We constructed 3 ML-based metamodels using random forest, support vector regression, and artificial neural networks and a linear regression-based metamodel from a previously validated microsimulation model of the natural history hepatitis C virus (HCV) consisting of 40 input parameters. Outcomes of interest included societal costs and quality-adjusted life-years (QALYs), the incremental cost-effectiveness (ICER) of HCV treatment versus no treatment, cost-effectiveness analysis curve (CEAC), and expected value of perfect information (EVPI). We evaluated metamodel performance using root mean squared error (RMSE) and Pearson's R2 on the normalized data. RESULTS: The R2 values for the linear regression metamodel for QALYs without treatment, QALYs with treatment, societal cost without treatment, societal cost with treatment, and ICER were 0.92, 0.98, 0.85, 0.92, and 0.60, respectively. The corresponding R2 values for our ML-based metamodels were 0.96, 0.97, 0.90, 0.95, and 0.49 for support vector regression; 0.99, 0.83, 0.99, 0.99, and 0.82 for artificial neural network; and 0.99, 0.99, 0.99, 0.99, and 0.98 for random forest. Similar trends were observed for RMSE. The CEAC and EVPI curves produced by the random forest metamodel matched the results of the simulation output more closely than the linear regression metamodel. CONCLUSIONS: ML-based metamodels generally outperformed traditional linear regression metamodels at replicating results from complex simulation models, with random forest metamodels performing best. HIGHLIGHTS: Decision-analytic models are frequently used by policy makers and other stakeholders to assess the impact of new medical technologies and interventions. However, complex models can impose limitations on conducting probabilistic sensitivity analysis and value-of-information analysis, and may not be suitable for developing online decision-support tools.Metamodels, which accurately formulate a mathematical relationship between input parameters and model outcomes, can replicate complex simulation models and address the above limitation.The machine learning-based random forest model can outperform linear regression in replicating the findings of a complex simulation model. Such a metamodel can be used for conducting cost-effectiveness and value-of-information analyses or developing online decision support tools.

Comparison of Guidelines for Evaluation of Suspected Pulmonary Embolism in Pregnancy: A Cost-effectiveness Analysis.

BACKGROUND: Pulmonary embolism (PE) remains a leading cause of maternal mortality, yet diagnosis remains challenging. International diagnostic guidelines vary significantly in their recommendations, making it difficult to determine an optimal policy for evaluation. RESEARCH QUESTION: Which societal-level diagnostic guidelines for evaluation of suspected PE in pregnancy are an optimal policy in terms of its cost-effectiveness? STUDY DESIGN AND METHODS: We constructed a complex Markov decision model to evaluate the cost-effectiveness of each identified societal guidelines for diagnosis of PE in pregnancy. Our model accounted for risk stratification, empiric treatment, diagnostic testing strategies, as well as short- and long-term effects from PE, treatment with low-molecular-weight heparin, and radiation exposure from advanced imaging. We considered clinical and cost outcomes of each guideline from a US health care system perspective with a lifetime horizon. Clinical effectiveness and costs were measured in time-discounted quality-adjusted life years (QALYs) and US dollars, respectively. Strategies were compared using the incremental cost-effectiveness ratio (ICER) with a willingness-to-pay threshold of $100,000/QALY. One-way, multiway, and probabilistic sensitivity analyses were performed. RESULTS: We identified six international societal-level guidelines. Base-case analysis showed the guideline proposed by the American Thoracic Society and Society of Thoracic Radiology (ATS-STR) yielded the highest health benefits (22.90 QALYs) and was cost-effective, with an ICER of $7,808 over the guidelines proposed by the Australian Society of Thrombosis and Haemostasis and the Society of Obstetric Medicine of Australia and New Zealand (ASTH-SOMANZ). All remaining guidelines were dominated. The ATS-STR guideline-recommended strategy yielded an expected additional 2.7 QALYs/100 patients evaluated over the ASTH-SOMANZ. Conclusions were robust to sensitivity analyses, with the ATS-STR guidelines optimal in 86% of probabilistic sensitivity analysis scenarios. INTERPRETATION: The ATS-STR guidelines for diagnosis of suspected PE in pregnancy are cost-effective and generate better expected health outcomes than guidelines proposed by other medical societies.

Predictors of radiation dose for CT pulmonary angiography in pregnancy across a multihospital integrated healthcare network.

PURPOSE: There is a large range of published effective radiation dose for CTPA during pregnancy. The purpose of our study is to determine the mean effective radiation dose and predictors of mean effective radiation dose for CTPA in pregnant patients across a multihospital integrated healthcare network. METHODS: This retrospective study evaluates pregnant women who had a CTPA as the first primary advanced imaging test for evaluation of PE in a multihospital integrated healthcare network from January 2012-April 2017. Patient and CT-related data were obtained from the electronic health record and Radimetrics server (Radimetrics Inc, Bayer). DLP was recorded and effective radiation dose in mSv was determined using a conversation factor of 0.014 mSv·mGy-¹·cm-¹. Patient size was determined by water equivalent diameter. Bivariate and multivariate analysis were performed for effective radiation dose based on patient and CT factors. RESULTS: In the 534 CTPA exams, the mean effective radiation dose was 3.96 mSv. Bivariate analysis showed significant differences in radiation dose by trimester, p = 0.042: first trimester 4.52 mSv, second trimester 3.73 mSv, and third trimester 3.95 mSv. Multivariable analysis demonstrated CTPA during first trimester, increasing mAs, kVp, scan length, patient size, and use of mAs modulation, as well as decreasing pitch, to be predictive of higher effective radiation dose. CONCLUSION: Mean effective radiation dose was on the lower end of published studies. Trimester was a statistically significant predictor of effective radiation dose when accounting for known predictors of radiation dose.