Ultralow-Dose Dynamic Expiratory CT and Repeated Imaging Enhance Evaluation for Tracheomalacia.

OBJECTIVE: This study aims to determine if a novel imaging protocol (ultralow-dose dynamic expiratory computed tomography [CT] with repeated imaging) identifies tracheomalacia (TM) more reliably than traditional dynamic tracheal CT. METHODS: We performed a retrospective evaluation of 184 consecutive ultralow-dose dynamic CTs for TM during 2017. The protocol obtains images during 1 inspiration and 2 forced expirations. Tracheal narrowing during both expirations (airway narrowing [percentage] during first dynamic expiration CT [DE1], airway narrowing [percentage] during second dynamic expiration CT [DE2]) was reported as a percentage of inspiratory area. We identified maximum narrowing of each patient's sequence (maximum narrowing [percentage] on either dynamic expiration CT [DEmax] = greatest narrowing of DE1 or DE2) and compared DE1, DE2, and DEmax in individual studies and between patients. Outcomes included frequency of TM, tracheal narrowing, and severity. Reliability was assessed by comparing tracheal area narrowing and TM grade. RESULTS: There was significantly more airway narrowing using 2 expiratory image acquisitions. Average DEmax tracheal area was 12% narrower than DE1 alone and 21% worse than DE2 alone (both P < 0.001). Using DEmax, TM was diagnosed 35% more often than DE1 alone and 31% more often than DE2 alone ( P < 0.001). DEmax identified more severe distribution of TM compared with DE1 or DE2 alone ( P < 0.001). Reliability between DE1 and DE2 was good for tracheal narrowing and moderate for TM grade. The mean effective radiation dose was 2.41 millisievert (mSv) for routine inspiration CT and 0.07 mSv for each dynamic expiration CT (total effective radiation, 2.55 mSv). CONCLUSIONS: Dynamic expiration CT with 2 expiratory image acquisitions enhanced evaluation of TM, minimally increased radiation dose, and should be considered as a noninvasive screening option.

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.

Characteristics of Suspected COVID-19 Discharged Emergency Department Patients Who Returned During the First Wave.

INTRODUCTION: Limited information exists on patients with suspected coronavirus disease 2019 (COVID-19) who return to the emergency department (ED) during the first wave. In this study we aimed to identify predictors of ED return within 72 hours for patients with suspected COVID-19. METHODS: Incorporating data from 14 EDs within an integrated healthcare network in the New York metropolitan region from March 2-April 27, 2020, we analyzed this data on predictors for a return ED visit-including demographics, comorbidities, vital signs, and laboratory results. RESULTS: In total, 18,599 patients were included in the study. The median age was 46 years old [interquartile range 34-58]), 50.74% were female, and 49.26% were male. Overall, 532 (2.86%) returned to the ED within 72 hours, and 95.49% were admitted at the return visit. Of those tested for COVID-19, 59.24% (4704/7941) tested positive. Patients with chief complaints of "fever" or "flu" or a history of diabetes or renal disease were more likely to return at 72 hours. Risk of return increased with persistently abnormal temperature (odds ratio [OR] 2.43, 95% CI 1.8-3.2), respiratory rate (2.17, 95% CI 1.6-3.0), and chest radiograph (OR 2.54, 95% CI 2.0-3.2). Abnormally high neutrophil counts, low platelet counts, high bicarbonate values, and high aspartate aminotransferase levels were associated with a higher rate of return. Risk of return decreased when discharged on antibiotics (OR 0.12, 95% CI 0.0-0.3) or corticosteroids (OR 0.12, 95% CI 0.0-0.9). CONCLUSION: The low overall return rate of patients during the first COVID-19 wave indicates that physicians' clinical decision-making successfully identified those acceptable for discharge.

The association of clinically relevant variables with chest radiograph lung disease burden quantified in real-time by radiologists upon initial presentation in individuals hospitalized with COVID-19.

OBJECTIVES: We aimed to correlate lung disease burden on presentation chest radiographs (CXR), quantified at the time of study interpretation, with clinical presentation in patients hospitalized with coronavirus disease 2019 (COVID-19). MATERIAL AND METHODS: This retrospective cross-sectional study included 5833 consecutive adult patients, aged 18 and older, hospitalized with a diagnosis of COVID-19 with a CXR quantified in real-time while hospitalized in 1 of 12 acute care hospitals across a multihospital integrated healthcare network between March 24, 2020, and May 22, 2020. Lung disease burden was quantified in real-time by 118 radiologists on 5833 CXR at the time of exam interpretation with each lung annotated by the degree of lung opacity as clear (0%), mild (1-33%), moderate (34-66%), or severe (67-100%). CXR findings were classified as (1) clear versus disease, (2) unilateral versus bilateral, (3) symmetric versus asymmetric, or (4) not severe versus severe. Lung disease burden was characterized on initial presentation by patient demographics, co-morbidities, vital signs, and lab results with chi-square used for univariate analysis and logistic regression for multivariable analysis. RESULTS: Patients with severe lung disease were more likely to have oxygen impairment, an elevated respiratory rate, low albumin, high lactate dehydrogenase, and high ferritin compared to non-severe lung disease. A lack of opacities in COVID-19 was associated with a low estimated glomerular filtration rate, hypernatremia, and hypoglycemia. CONCLUSIONS: COVID-19 lung disease burden quantified in real-time on presentation CXR was characterized by demographics, comorbidities, emergency severity index, Charlson Comorbidity Index, vital signs, and lab results on 5833 patients. This novel approach to real-time quantified chest radiograph lung disease burden by radiologists needs further research to understand how this information can be incorporated to improve clinical care for pulmonary-related diseases.. An absence of opacities in COVID-19 may be associated with poor oral intake and a prerenal state as evidenced by the association of clear CXRs with a low eGFR, hypernatremia, and hypoglycemia.

A predictive model for lung cancer screening nonadherence in a community setting health-care network.

BACKGROUND: Lung cancer screening (LCS) decreases lung cancer mortality. However, its benefit may be limited by nonadherence to screening. Although factors associated with LCS nonadherence have been identified, to the best of our knowledge, no predictive models have been developed to predict LCS nonadherence. The purpose of this study was to develop a predictive model leveraging a machine learning model to predict LCS nonadherence risk. METHODS: A retrospective cohort of patients who enrolled in our LCS program between 2015 and 2018 was used to develop a model to predict the risk of nonadherence to annual LCS after the baseline examination. Clinical and demographic data were used to fit logistic regression, random forest, and gradient-boosting models that were internally validated on the basis of accuracy and area under the receiver operating curve. RESULTS: A total of 1875 individuals with baseline LCS were included in the analysis, with 1264 (67.4%) as nonadherent. Nonadherence was defined on the basis of baseline chest computed tomography (CT) findings. Clinical and demographic predictors were used on the basis of availability and statistical significance. The gradient-boosting model had the highest area under the receiver operating curve (0.89, 95% confidence interval = 0.87 to 0.90), with a mean accuracy of 0.82. Referral specialty, insurance type, and baseline Lung CT Screening Reporting & Data System (LungRADS) score were the best predictors of nonadherence to LCS. CONCLUSIONS: We developed a machine learning model using readily available clinical and demographic data to predict LCS nonadherence with high accuracy and discrimination. After further prospective validation, this model can be used to identify patients for interventions to improve LCS adherence and decrease lung cancer burden.

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.

A radiographic, deep transfer learning framework, adapted to estimate lung opacities from chest x-rays.

Chest radiographs (CXRs) are the most widely available radiographic imaging modality used to detect respiratory diseases that result in lung opacities. CXR reports often use non-standardized language that result in subjective, qualitative, and non-reproducible opacity estimates. Our goal was to develop a robust deep transfer learning framework and adapt it to estimate the degree of lung opacity from CXRs. Following CXR data selection based on exclusion criteria, segmentation schemes were used for ROI (Region Of Interest) extraction, and all combinations of segmentation, data balancing, and classification methods were tested to pick the top performing models. Multifold cross validation was used to determine the best model from the initial selected top models, based on appropriate performance metrics, as well as a novel Macro-Averaged Heatmap Concordance Score (MA HCS). Performance of the best model is compared against that of expert physician annotators, and heatmaps were produced. Finally, model performance sensitivity analysis across patient populations of interest was performed. The proposed framework was adapted to the specific use case of estimation of degree of CXR lung opacity using ordinal multiclass classification. Acquired between March 24, 2020, and May 22, 2020, 38,365 prospectively annotated CXRs from 17,418 patients were used. We tested three neural network architectures (ResNet-50, VGG-16, and ChexNet), three segmentation schemes (no segmentation, lung segmentation, and lateral segmentation based on spine detection), and three data balancing strategies (undersampling, double-stage sampling, and synthetic minority oversampling) using 38,079 CXR images for training, and validation with 286 images as the out-of-the-box dataset that underwent expert radiologist adjudication. Based on the results of these experiments, the ResNet-50 model with undersampling and no ROI segmentation is recommended for lung opacity classification, based on optimal values for the MAE metric and HCS (Heatmap Concordance Score). The degree of agreement between the opacity scores predicted by this model with respect to the two sets of radiologist scores (OR or Original Reader and OOBTR or Out Of Box Reader) in terms of performance metrics is superior to the inter-radiologist opacity score agreement.

Utilization of a Guideline-recommended Imaging Paradigm for Pregnant Patients With Suspicion of Pulmonary Embolism.

PURPOSE: A dose reduction imaging paradigm utilizing chest x-ray (CXR) to triage between computed tomography pulmonary angiography (CTPA) and lung scintigraphy (LS) was introduced in 2001 and adopted in 2012 by the American Thoracic Society/Society of Thoracic Radiology (ATS) guideline for the evaluation of pulmonary embolism in pregnancy. We aimed to assess the utilization of this imaging paradigm preadoption and postadoption by the ATS guideline, and identify factors associated with its utilization. MATERIALS AND METHODS: This retrospective cohort study evaluated consecutive pregnant patients who received CTPA or LS for the evaluation of pulmonary embolism in pregnancy at 2 tertiary hospitals between September 2008 and March 2017, excluding 2012 for guideline release washout. Utilization of the imaging paradigm was defined per patient by the use of CXR before advanced imaging, with CTPA performed following positive CXR and LS performed following negative CXR. Multivariate analyses were performed to assess factors associated with utilization of the imaging paradigm. P <0.05 is considered significant. RESULTS: Overall, 9.8% (63/643) of studies utilized the dose reduction imaging paradigm, 13.3% (34/256) before the guidelines, and 7.5% (29/387) after. Multivariable analysis showed that the dose reduction imaging paradigm utilization was higher for inpatients (odds ratio [OR]: 4.5) and outpatients (OR: 3.1) relative to the emergency department patients, and lower for second (OR: 0.3) and third (OR: 0.2) trimester patients, without significant differences by study priority, patient age, or patient race. CONCLUSIONS: Guideline-recommended dose reduction imaging paradigm utilization was low, and decreased after guideline publication. Utilization varied by patient setting and trimester, which are potential targets for interventions to improve guideline compliance.

Development and validation of self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients.

Clinical prognostic models can assist patient care decisions. However, their performance can drift over time and location, necessitating model monitoring and updating. Despite rapid and significant changes during the pandemic, prognostic models for COVID-19 patients do not currently account for these drifts. We develop a framework for continuously monitoring and updating prognostic models and apply it to predict 28-day survival in COVID-19 patients. We use demographic, laboratory, and clinical data from electronic health records of 34912 hospitalized COVID-19 patients from March 2020 until May 2022 and compare three modeling methods. Model calibration performance drift is immediately detected with minor fluctuations in discrimination. The overall calibration on the prospective validation cohort is significantly improved when comparing the dynamically updated models against their static counterparts. Our findings suggest that, using this framework, models remain accurate and well-calibrated across various waves, variants, race and sex and yield positive net-benefits.

Comparison of Chest Radiograph Impressions for Diagnosing Pneumonia: Accounting for Categories of Language Certainty.

OBJECTIVES: Uncertain language in chest radiograph (CXR) reports for the diagnosis of pneumonia is prevalent. The purpose of this study is to validate an a priori stratification of CXR results for diagnosing pneumonia based on language of certainty. DESIGN: Retrospective chart review. SETTING AND PARTICIPANTS: CXR reports of 2,411 patient visits ≥ 18 years, admitted to medicine, who received a CXR and noncontrast chest CT within 48 hours of emergency department registration at two large academic hospitals (tertiary and quaternary care) were reviewed. METHODS: The CXR and CT report impressions were categorized as negative, uncertain, or positive. Uncertain CXRs were further stratified into four categories based on language modifiers for the degree of pneumonia certainty. Comparison of CXR and CT results were determined using χ2 test; a P value of less than .0033 was considered significant to account for multiple comparisons. RESULTS: CXR reports for the diagnosis of pneumonia revealed the following distribution: 61% negative, 32% uncertain, and 7% positive; CT reports were 55% negative, 22% uncertain, and 23% positive for the diagnosis of pneumonia. There were significant differences between CXR categories compared with CT categories for diagnosis of pneumonia (P < .001). Negative CXR results were not significantly different than the uncertain category with the most uncertain language (P = .030) but were significantly different from all other uncertain categories and positive CXR results (each P < .001). Positive CXR results were not significantly different than the least uncertain category (most certain language) (P = .130) but were significantly different from all other categories (each P < .001). CONCLUSIONS AND IMPLICATIONS: Language used in CXR reports to diagnose pneumonia exists in categories of varying certainty and should be considered when evaluating patients for pneumonia.

Algorithmic Approach to the Diagnosis of Organizing Pneumonia: A Correlation of Clinical, Radiologic, and Pathologic Features.

Organizing pneumonia (OP), characterized histopathologically by patchy filling of alveoli and bronchioles by loose plugs of connective tissue, may be seen in a variety of conditions. These include but are not limited to after an infection, drug reactions, radiation therapy, and collagen vascular diseases. When a specific cause is responsible for this entity, it is referred to as "secondary OP." When an extensive search fails to reveal a cause, it is referred to as "cryptogenic OP" (previously called "bronchiolitis obliterans with OP"), which is a clinical, radiologic, and pathologic entity classified as an interstitial lung disease. The clinical presentation of OP often mimics that of other disorders, such as infection and cancer, which can result in a delay in diagnosis and inappropriate management of the underlying disease. The radiographic presentation of OP is polymorphous but often has subpleural consolidations with air bronchograms or solitary or multiple nodules, which can wax and wane. Diagnosis of OP sometimes requires histopathologic confirmation and exclusion of other possible causes. Treatment usually requires a prolonged steroid course, and disease relapse is common. The aim of this article is to summarize the clinical, radiographic, and histologic presentations of this disease and to provide a practical diagnostic algorithmic approach incorporating clinical history and characteristic imaging patterns.

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.

Factors Associated With Lung Cancer Screening Adherence Among Patients With Negative Baseline CT Results in a Community Health Care Setting.

PURPOSE: Lung cancer screening (LCS) decreases lung cancer mortality; however, that reduction depends upon screening adherence. The purpose of this study was to determine factors associated with adherence rate for LCS among patients with negative baseline CT results in a multi-integrated health care network. METHODS: A retrospective analysis was conducted among patients with negative baseline CT results in a multi-integrated health care network LCS program between January 2015 and January 2020. The two outcomes were adherence for the first and second subsequent LCS studies. Negative baseline result was defined as a Lung CT Screening Reporting and Data System score 0, 1, or 2. Adherence was defined as undergoing a follow-up study within 11 to 15 months of a prior scan. Multivariable logistic regression was used to determine significant predictors of adherence, adjusting for patient demographics, median household income (on the basis of geocoding ZIP codes from the US Census Bureau), smoking history, screening sites, and provider specialty. RESULTS: A total of 30.7% (512 of 1,668) and 16.3% (270 of 1,660) of patients were adherent for the first two annual subsequent screens, respectively. First-year adherence was higher among former smokers and varied by site and provider specialty. Second-year adherence was higher among former smokers and varied by site, provider specialty, and pack-years smoked. CONCLUSIONS: Adherence to LCS in a multihospital integrated health care network was poor and even lower at year 2. The identified factors associated with adherence may serve as targets to increase LCS adherence and decrease lung cancer mortality.

CT pulmonary angiography in pregnancy: Specific conversion factors to estimate effective radiation dose from dose length product: A retrospective cross-sectional study across a multi-hospital integrated healthcare network.

PURPOSE: Effective dose describes radiation-related cancer risk from CT scans and is estimated using a readily available conversion factor (k-factor), which varies by body part and study type. To purpose of this study is to determine the specific k-factor for CTPA in pregnant patients and its predictive factors. METHODS: This retrospective cross-sectional study evaluates CTPA in pregnancy across a multihospital integrated healthcare network from January 2012 to April 2017. Patient and CTPA-related data were obtained from the electronic health record and a radiation dose index monitoring system. Each patient's effective dose was determined by patient-specific Monte-Carlo simulation with Cristy phantoms and divided by patient dose-length-product to determine the k-factor. K-factor for pregnant patients was compared to the k-factor for adults of standard physique with a one-sample t-test. Bivariate and multivariable analyses were performed for patient and CT predictors of k-factor. RESULTS: A total of 534 patients were included. The mean k-factor for all patients was 0.0249 (mSv·mGy-1·cm-1), 78% greater than k-factor of 0.014 (p < 0.001) suggested for the general adult population. Multivariable analysis demonstrated lower k-factors with increasing pitch (p = 0.0002), patient size (p < 0.001), and scan length (p < 0.0001). The 120 kVp (p < 0.001) and 140 kVp (p = 0.0028) analyses showed a larger k-factor than 80 and 100 kVp studies combined. CONCLUSIONS: Specific k-factor for CTPA in pregnant patients is greater than the previously used generic chest CT k-factor and should be used to estimate the effective dose for CTPA exams in pregnancy.

External validation of the IMPROVE-DD risk assessment model for venous thromboembolism among inpatients with COVID-19.

There is a need to discriminate which COVID-19 inpatients are at higher risk for venous thromboembolism (VTE) to inform prophylaxis strategies. The IMPROVE-DD VTE risk assessment model (RAM) has previously demonstrated good discrimination in non-COVID populations. We aimed to externally validate the IMPROVE-DD VTE RAM in medical patients hospitalized with COVID-19. This retrospective cohort study evaluated the IMPROVE-DD VTE RAM in adult patients with COVID-19 admitted to one of thirteen Northwell Health hospitals in the New York metropolitan area between March 1, 2020 and April 27, 2020. VTE was defined as new-onset symptomatic deep venous thrombosis or pulmonary embolism. To assess the predictive value of the RAM, the receiver operating characteristic (ROC) curve was plotted and the area under the curve (AUC) was calculated. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Of 9407 patients who met study criteria, 274 patients developed VTE with a prevalence of 2.91%. The VTE rate was 0.41% for IMPROVE-DD score 0-1 (low risk), 1.21% for score 2-3 (moderate risk), and 5.30% for score ≥ 4 (high risk). Approximately 45.7% of patients were classified as high VTE risk, 33.3% moderate risk, and 21.0% low risk. Discrimination of low versus moderate-high VTE risk demonstrated sensitivity 0.971, specificity 0.215, PPV 0.036, and NPV 0.996. ROC AUC was 0.703. In this external validation study, the IMPROVE-DD VTE RAM demonstrated very good discrimination to identify hospitalized COVID-19 patients at low, moderate, and high VTE risk.

Sex Differences in Coronary Arterial Calcification in Symptomatic Patients.

Despite the increasing use of Coronary Artery Calcium (CAC) scoring for cardiovascular risk stratification in asymptomatic patients, the gender differences in CAC among symptomatic patients have not been well evaluated. We analyzed patients presenting to the emergency department (ED) with chest pain suggesting possible coronary artery disease (CAD) who received coronary computed tomography angiography (CCTA). Ordinal logistic regression was used to determine the odds ratio for the association of traditional cardiovascular risk factors and CAC. Patients with a CAC score ≥ 100 were followed for cardiovascular events or changes in medical management. Our cohort included 542 individuals (263 male, 279 female). Ordinal logistic regression model showed that among traditional cardiovascular risk factors, male sex had the highest odds ratio (OR) of 3.04 (p < 0.001, 95% CI [2.01, 4.59]) for the presence of CAC. Also, males had more diffuse distribution of coronary atherosclerosis (p=0.01). Subgroup analysis revealed that obesity was a bigger risk factor in male patients (OR 2.16), while smoking showed the greatest effect (OR 4.27) on CAC in women. Of patients who had CAC > 100 with an average follow-up of 346 days, there was an increase in both aspirin and statin use, yet significant sex differences were observed especially in patients with non-obstructive lesions on CCTA. Among male patients with non-obstructive lesions, 68.2% were on aspirin and 86.4% were on statin therapy after the CCTA compared to 27.3% and 45.5% respectively in their female counterparts. In conclusion, sex not only is the most powerful predictor for higher CAC among traditional cardiovascular risk factors in symptomatic patients but also influences the contribution of various traditional risk factors to elevated CAC. Furthermore, the discovery of CAD led to the initiation of medical therapy in male patients more frequently than in female patients, even after adjusting for the degree of luminal stenosis detected on coronary CT angiography.

Incidence of Venous Thromboembolism and Mortality in Patients with Initial Presentation of COVID-19.

Venous thromboembolism (VTE) has emerged as an important issue in patients with COVID-19. The purpose of this study is to identify the incidence of VTE and mortality in COVID-19 patients initially presenting to a large health system. Our retrospective study included adult patients (excluding patients presenting with obstetric/gynecologic conditions) across a multihospital health system in the New York Metropolitan Region from March 1-April 27, 2020. VTE and mortality rates within 8 h of assessment were described. In 10,871 adults with COVID-19, 118 patients (1.09%) were diagnosed with symptomatic VTE (101 pulmonary embolism, 17 deep vein thrombosis events) and 28 patients (0.26%) died during initial assessment. Among these 146 patients, 64.4% were males, 56.8% were 60 years or older, 15.1% had a BMI > 35, and 11.6% were admitted to the intensive care unit. Comorbidities included hypertension (46.6%), diabetes (24.7%), hyperlipidemia (14.4%), chronic lung disease (12.3%), coronary artery disease (11.0%), and prior VTE (7.5%). Key medications included corticosteroids (22.6%), statins (21.2%), antiplatelets (20.6%), and anticoagulants (20.6%). Highest D-Dimer was greater than six times the upper limit of normal in 51.4%. Statin and antiplatelet use were associated with decreased VTE or mortality (each p < 0.01). In COVID-19 patients who initially presented to a large multihospital health system, the overall symptomatic VTE and mortality rate was over 1.0%. Statin and antiplatelet use were associated with decreased VTE or mortality. The potential benefits of antithrombotics in high risk COVID-19 patients during the pre-hospitalization period deserves study.

Validation of the IMPROVE-DD risk assessment model for venous thromboembolism among hospitalized patients with COVID-19.

BACKGROUND: Antithrombotic guidance statements for hospitalized patients with coronavirus disease 2019 (COVID-19) suggest a universal thromboprophylactic strategy with potential to escalate doses in high-risk patients. To date, no clear approach exists to discriminate patients at high risk for venous thromboembolism (VTE). OBJECTIVES: The objective of this study is to externally validate the IMPROVE-DD risk assessment model (RAM) for VTE in a large cohort of hospitalized patients with COVID-19 within a multihospital health system. METHODS: This retrospective cohort study evaluated the IMPROVE-DD RAM on adult inpatients with COVID-19 hospitalized between March 1, 2020, and April 27, 2020. Diagnosis of VTE was defined by new acute deep venous thrombosis or pulmonary embolism by Radiology Department imaging or point-of-care ultrasound. The receiver operating characteristic (ROC) curve was plotted and area under the curve (AUC) calculated. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated using standard methods. RESULTS: A total of 9407 patients were included, with a VTE prevalence of 2.9%. The VTE rate was 0.4% for IMPROVE-DD score 0-1 (low risk), 1.3% for score 2-3 (moderate risk), and 5.3% for score ≥ 4 (high risk). Approximately 45% of the total population scored high VTE risk, while 21% scored low VTE risk. IMPROVE-DD discrimination of low versus medium/high risk showed sensitivity of 0.971, specificity of 0.218, PPV of 0.036, and NPV of 0.996. ROC AUC was 0.702. CONCLUSIONS: The IMPROVE-DD VTE RAM demonstrated very good discrimination to identify hospitalized patients with COVID-19 as low, moderate, and high VTE risk in this large external validation study with potential to individualize thromboprophylactic strategies.

Prevalence and Predictors of Venous Thromboembolism or Mortality in Hospitalized COVID-19 Patients.

BACKGROUND: We aimed to identify the prevalence and predictors of venous thromboembolism (VTE) or mortality in hospitalized coronavirus disease 2019 (COVID-19) patients. METHODS: A retrospective cohort study of hospitalized adult patients admitted to an integrated health care network in the New York metropolitan region between March 1, 2020 and April 27, 2020. The final analysis included 9,407 patients with an overall VTE rate of 2.9% (2.4% in the medical ward and 4.9% in the intensive care unit [ICU]) and a VTE or mortality rate of 26.1%. Most patients received prophylactic-dose thromboprophylaxis. Multivariable analysis showed significantly reduced VTE or mortality with Black race, history of hypertension, angiotensin converting enzyme/angiotensin receptor blocker use, and initial prophylactic anticoagulation. It also showed significantly increased VTE or mortality with age 60 years or greater, Charlson Comorbidity Index (CCI) of 3 or greater, patients on Medicare, history of heart failure, history of cerebrovascular disease, body mass index greater than 35, steroid use, antirheumatologic medication use, hydroxychloroquine use, maximum D-dimer four times or greater than the upper limit of normal (ULN), ICU level of care, increasing creatinine, and decreasing platelet counts. CONCLUSION: In our large cohort of hospitalized COVID-19 patients, the overall in-hospital VTE rate was 2.9% (4.9% in the ICU) and a VTE or mortality rate of 26.1%. Key predictors of VTE or mortality included advanced age, increasing CCI, history of cardiovascular disease, ICU level of care, and elevated maximum D-dimer with a cutoff at least four times the ULN. Use of prophylactic-dose anticoagulation but not treatment-dose anticoagulation was associated with reduced VTE or mortality.