Tree-informed Bayesian multi-source domain adaptation: cross-population probabilistic cause-of-death assignment using verbal autopsy.

Determining causes of deaths (CODs) occurred outside of civil registration and vital statistics systems is challenging. A technique called verbal autopsy (VA) is widely adopted to gather information on deaths in practice. A VA consists of interviewing relatives of a deceased person about symptoms of the deceased in the period leading to the death, often resulting in multivariate binary responses. While statistical methods have been devised for estimating the cause-specific mortality fractions (CSMFs) for a study population, continued expansion of VA to new populations (or "domains") necessitates approaches that recognize between-domain differences while capitalizing on potential similarities. In this article, we propose such a domain-adaptive method that integrates external between-domain similarity information encoded by a prespecified rooted weighted tree. Given a cause, we use latent class models to characterize the conditional distributions of the responses that may vary by domain. We specify a logistic stick-breaking Gaussian diffusion process prior along the tree for class mixing weights with node-specific spike-and-slab priors to pool information between the domains in a data-driven way. The posterior inference is conducted via a scalable variational Bayes algorithm. Simulation studies show that the domain adaptation enabled by the proposed method improves CSMF estimation and individual COD assignment. We also illustrate and evaluate the method using a validation dataset. The article concludes with a discussion of limitations and future directions.

Agreement between cause of death assignment by computer-coded verbal autopsy methods and physician coding of verbal autopsy interviews in South Africa.

BACKGROUND: The South African national cause of death validation (NCODV 2017/18) project collected a national sample of verbal autopsies (VA) with cause of death (COD) assignment by physician-coded VA (PCVA) and computer-coded VA (CCVA). OBJECTIVE: The performance of three CCVA algorithms (InterVA-5, InSilicoVA and Tariff 2.0) in assigning a COD was compared with PCVA (reference standard). METHODS: Seven performance metrics assessed individual and population level agreement of COD assignment by age, sex and place of death subgroups. Positive predictive value (PPV), sensitivity, overall agreement, kappa, and chance corrected concordance (CCC) assessed individual level agreement. Cause-specific mortality fraction (CSMF) accuracy and Spearman's rank correlation assessed population level agreement. RESULTS: A total of 5386 VA records were analysed. PCVA and CCVAs all identified HIV/AIDS as the leading COD. CCVA PPV and sensitivity, based on confidence intervals, were comparable except for HIV/AIDS, TB, maternal, diabetes mellitus, other cancers, and some injuries. CCVAs performed well for identifying perinatal deaths, road traffic accidents, suicide and homicide but poorly for pneumonia, other infectious diseases and renal failure. Overall agreement between CCVAs and PCVA for the top single cause (48.2-51.6) indicated comparable weak agreement between methods. Overall agreement, for the top three causes showed moderate agreement for InterVA (70.9) and InSilicoVA (73.8). Agreement based on kappa (-0.05-0.49)and CCC (0.06-0.43) was weak to none for all algorithms and groups. CCVAs had moderate to strong agreement for CSMF accuracy, with InterVA-5 highest for neonates (0.90), Tariff 2.0 highest for adults (0.89) and males (0.84), and InSilicoVA highest for females (0.88), elders (0.83) and out-of-facility deaths (0.85). Rank correlation indicated moderate agreement for adults (0.75-0.79). CONCLUSIONS: Whilst CCVAs identified HIV/AIDS as the leading COD, consistent with PCVA, there is scope for improving the algorithms for use in South Africa.

GRIK5 stimulates colon cancer growth and metastasis through cAMP/PKA/CADM3 signaling.

Glutamate receptor, ionotropic, kainate 5 (GRIK5) is a member of glutamate receptors participating, and the kainate receptor family has been proved to regulate cell proliferation and transformation. Our study aimed at exploring the role of GRIK5 in colon tumor progression. Three hundred and ninety eight colon cancer patients in The Cancer Genome Atlas Program (TCGA) data set and 26 clinical colon cancer patients were included for GRIK5 expression and prognosis analysis. GRIK5 overexpressed HCT116 and CT26 cell lines were established for cell proliferation and Transwell assay. Western blot analysis and immunostaining assay was conducted to evaluate the activation of activation of cyclic adenosine monophosphate (cAMP)/protein kinase A (PKA)/cell adhesion molecular 3 (CADM3) signaling in cell lines and tumor tissues. Subcutaneous CT26-bearing mice model was established to examine GRIK5-induced tumor growth and metastasis in vivo. Our study identified GRIK5 to be upregulated in patients with colon cancer, and higher GRIK5 levels correlated with the poor overall survival in patients. In vitro, GRIK5 overexpression markedly enhanced the proliferative properties and aggressive behaviors of colon cancer cells. Mechanistically, GRIK5 induced the activation of cAMP/PKA signaling, proceeded with augmented CADM3 expression, eventually resulting in sustained tumor growth. GRIK5 was a crucial scaffold in enabling colon cancer growth and metastasis, which offered a promising target for therapeutic manipulation of colon cancer.

In Situ Transition Layer Design Based on Ti Additive Enabling High-Performance Liquid Metal Batteries.

Liquid metal batteries (LMBs), with the merits of long lifespan and low cost, are deemed as one of the most promising energy storage technologies for large-scale energy storage applications due to the use of liquid metal electrodes and molten salt electrolytes. However, the consequent problem is that the poor wettability between graphite-based collectors and the liquid metal/alloy electrodes leads to large contact resistance, which limits the efficiency and stability of the battery. In this work, a transition layer in situ formed on a graphite-based positive electrode current collector by Ti additive is designed for the first time, which increases the wettability between the positive alloy and the current collector and improves the voltage efficiency of the Li||Sb-Sn cell from 85.6 to 88.4%. These results provide new ideas for the design of high-efficiency LMBs.

Feasibility of Quantitative Flow Ratio Virtual Stenting for Guidance of Serial Coronary Lesions Intervention.

Background Coronary physiology measurement in serial coronary lesions with multiple stenoses is challenging. Therefore, we evaluated the feasibility of Murray fractal law-based quantitative flow ratio (µQFR) virtual stenting for guidance of serial coronary lesions intervention. Methods and Results Patients who underwent elective coronary angiography and had 2 serial de novo coronary lesions of 30% to 90% diameter stenosis by visual estimation were prospectively enrolled. µQFR and fractional flow reserve (FFR) were assessed after coronary angiography. In vessels with an FFR ≤0.80, the lesion with the larger pressure gradient was considered to be the primary lesion and treated firstly, followed by FFR measurement. The second lesion was stented when FFR ≤0.80. All µQFR and predicted µQFR after stenting were calculated from diagnostic coronary angiography before interventions, with the analysts masked to the FFR data. A total of 54 patients with 61 target vessels were interrogated. Percutaneous coronary intervention was performed in 44 vessels with FFR ≤0.80. After stenting the primary lesions, 14 nonprimary lesions had FFR ≤0.80 and a second drug-eluting stent was implanted. There was excellent correlation (r=0.97, P<0.001) and good agreement (mean difference: 0.00±0.03) between baseline µQFR and FFR in identifying flow-limiting lesions. Per-vessel diagnostic accuracy of µQFR on de novo lesions was 96.7% (95% CI, 88.7%-99.6%). µQFR and FFR are highly consistent (93.2%) in identifying the primary lesion requiring revascularization. After stenting the primary lesions, per-vessel diagnostic accuracy of predicted µQFR for identifying the significance of the nonprimary lesion was 90.9%. Predicted residual µQFR with virtual stenting was higher than final FFR (mean difference: 0.05±0.06). Conclusions In vessels with serial coronary lesions, virtual stenting by µQFR can identify the primary flow-limiting lesion for revascularization.

Comparison of coronary CT angiography-based and invasive coronary angiography-based quantitative flow ratio for functional assessment of coronary stenosis: A multicenter retrospective analysis.

BACKGROUND: The aim of this study was to evaluate the diagnostic performance of coronary CT angiography (CTA)-based quantitative flow ratio (QFR), namely CT-QFR, and compare it with invasive coronary angiography (ICA)-based Murray law QFR (µQFR), using fractional flow reserve (FFR) as the reference standard. METHODS: Patients who underwent coronary CTA, ICA and pressure wire-based FFR assessment within two months were retrospectively analyzed. CT-QFR and µQFR were computed in blinded fashion and compared with FFR, all applying the same cut-off value of ≤0.80 to identify hemodynamically significant stenosis. RESULTS: Paired comparison between CT-QFR and µQFR was performed in 191 vessels from 167 patients. Average FFR was 0.81 â€‹± â€‹0.10 and 42.4% vessels had an FFR ≤0.80. CT-QFR had a slightly lower correlation with FFR compared with µQFR, although statistically non-significant (r â€‹= â€‹0.87 versus 0.90, p â€‹= â€‹0.110). The vessel-level diagnostic performance of CT-QFR was slightly lower but without statistical significance than µQFR (AUC â€‹= â€‹0.94 versus 0.97, difference: -0.03 [95%CI: -0.00-0.06], p â€‹= â€‹0.095), and substantially higher than diameter stenosis by CTA (AUC difference: 0.17 [95%CI: -0.10-0.23], p â€‹< â€‹0.001). The patient-level diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio and negative likelihood ratio for CT-QFR to identify FFR value â€‹≤ â€‹0.80 was 88%, 90%, 86%, 86%, 91%, 6.59 and 0.12, respectively. The diagnostic accuracy of CT-QFR was 84% in extensively calcified lesions, while in vessels with no or less calcification, CT-QFR showed a comparable diagnostic accuracy with µQFR (91% versus 92%, p â€‹= â€‹0.595). Intra- and inter-observer variability in CT-QFR analysis was -0.00 â€‹± â€‹0.04 and 0.00 â€‹± â€‹0.04, respectively. CONCLUSIONS: Performance in diagnosis of hemodynamically significant coronary stenosis by CT-QFR was slightly lower but without statistical significance than µQFR, and substantially higher than CTA-derived diameter stenosis. Extensively calcified lesions reduced the diagnostic accuracy of CT-QFR.

Diagnostic accuracy of CCTA-derived versus angiography-derived quantitative flow ratio (CAREER) study: a prospective study protocol.

INTRODUCTION: Coronary CT angiography (CCTA)-derived quantitative flow ratio (CT-QFR) is a novel non-invasive technology to assess the physiological significance of coronary stenoses, which enables fast and on-site computation of fractional flow reserve (FFR) from CCTA images. The objective of this investigator-initiated, prospective, single-centre clinical trial is to evaluate the diagnostic performance of CT-QFR with respect to angiography-derived QFR, using FFR as the reference standard. METHODS AND ANALYSIS: A total of 216 patients who have at least one lesion with a diameter stenosis of 30%-90% in an artery with ≥2.0 mm reference diameter will be enrolled in the study. FFR will be measured during invasive coronary angiography. CT-QFR and QFR will be assessed in two independent core laboratories in a blinded fashion. The primary endpoint is the diagnostic accuracy of CT-QFR in identifying haemodynamically significant coronary stenosis with FFR as the reference standard. The major secondary endpoint is the non-inferiority of CT-QFR compared with QFR in the patients without extensively calcified lesions. ETHICS AND DISSEMINATION: The study was approved by the Ethics Committee of Huadong Hospital Affiliated to Fudan University (2020K192). Outcomes will be disseminated through publications in peer-reviewed journals and presentations at scientific conferences. TRIAL REGISTRATION NUMBER: NCT04665817.

Impact of close interpersonal contact on COVID-19 incidence: Evidence from 1 year of mobile device data.

Close contact between people is the primary route for transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). We quantified interpersonal contact at the population level using mobile device geolocation data. We computed the frequency of contact (within 6 feet) between people in Connecticut during February 2020 to January 2021 and aggregated counts of contact events by area of residence. When incorporated into a SEIR-type model of COVID-19 transmission, the contact rate accurately predicted COVID-19 cases in Connecticut towns. Contact in Connecticut explains the initial wave of infections during March to April, the drop in cases during June to August, local outbreaks during August to September, broad statewide resurgence during September to December, and decline in January 2021. The transmission model fits COVID-19 transmission dynamics better using the contact rate than other mobility metrics. Contact rate data can help guide social distancing and testing resource allocation.

The openVA Toolkit for Verbal Autopsies.

Verbal autopsy (VA) is a survey-based tool widely used to infer cause of death (COD) in regions without complete-coverage civil registration and vital statistics systems. In such settings, many deaths happen outside of medical facilities and are not officially documented by a medical professional. VA surveys, consisting of signs and symptoms reported by a person close to the decedent, are used to infer the COD for an individual, and to estimate and monitor the COD distribution in the population. Several classification algorithms have been developed and widely used to assign causes of death using VA data. However, the incompatibility between different idiosyncratic model implementations and required data structure makes it difficult to systematically apply and compare different methods. The openVA package provides the first standardized framework for analyzing VA data that is compatible with all openly available methods and data structure. It provides an open-source, R implementation of several most widely used VA methods. It supports different data input and output formats, and customizable information about the associations between causes and symptoms. The paper discusses the relevant algorithms, their implementations in R packages under the openVA suite, and demonstrates the pipeline of model fitting, summary, comparison, and visualization in the R environment.

Prevalence of current and past COVID-19 in Ohio adults.

Purpose To estimate the prevalence of current and past COVID-19 in Ohio adults. Methods We used stratified, probability-proportionate-to-size cluster sampling. During July 2020, we enrolled 727 randomly-sampled adult English- and Spanish-speaking participants through a household survey. Participants provided nasopharyngeal swabs and blood samples to detect current and past COVID-19. We used Bayesian latent class models with multilevel regression and poststratification to calculate the adjusted prevalence of current and past COVID-19. We accounted for the potential effects of non-ignorable non-response bias. Results The estimated statewide prevalence of current COVID-19 was 0.9% (95% credible interval: 0.1%-2.0%), corresponding to ∼85,000 prevalent infections (95% credible interval: 6,300-177,000) in Ohio adults during the study period. The estimated statewide prevalence of past COVID-19 was 1.3% (95% credible interval: 0.2%-2.7%), corresponding to ∼118,000 Ohio adults (95% credible interval: 22,000-240,000). Estimates did not change meaningfully due to non-response bias. Conclusions Total COVID-19 cases in Ohio in July 2020 were approximately 3.5 times as high as diagnosed cases. The lack of broad COVID-19 screening in the United States early in the pandemic resulted in a paucity of population-representative prevalence data, limiting the ability to measure the effects of statewide control efforts.

Examinee-Examiner Network: Weakly Supervised Accurate Coronary Lumen Segmentation Using Centerline Constraint.

Accurate coronary lumen segmentation on coronary-computed tomography angiography (CCTA) images is crucial for quantification of coronary stenosis and the subsequent computation of fractional flow reserve. Many factors including difficulty in labeling coronary lumens, various morphologies in stenotic lesions, thin structures and small volume ratio with respect to the imaging field complicate the task. In this work, we fused the continuity topological information of centerlines which are easily accessible, and proposed a novel weakly supervised model, Examinee-Examiner Network (EE-Net), to overcome the challenges in automatic coronary lumen segmentation. First, the EE-Net was proposed to address the fracture in segmentation caused by stenoses by combining the semantic features of lumens and the geometric constraints of continuous topology obtained from the centerlines. Then, a Centerline Gaussian Mask Module was proposed to deal with the insensitiveness of the network to the centerlines. Subsequently, a weakly supervised learning strategy, Examinee-Examiner Learning, was proposed to handle the weakly supervised situation with few lumen labels by using our EE-Net to guide and constrain the segmentation with customized prior conditions. Finally, a general network layer, Drop Output Layer, was proposed to adapt to the class imbalance by dropping well-segmented regions and weights the classes dynamically. Extensive experiments on two different data sets demonstrated that our EE-Net has good continuity and generalization ability on coronary lumen segmentation task compared with several widely used CNNs such as 3D-UNet. The results revealed our EE-Net with great potential for achieving accurate coronary lumen segmentation in patients with coronary artery disease. Code at http://github.com/qiyaolei/Examinee-Examiner-Network.

One year of modeling and forecasting COVID-19 transmission to support policymakers in Connecticut.

To support public health policymakers in Connecticut, we developed a flexible county-structured compartmental SEIR-type model of SARS-CoV-2 transmission and COVID-19 disease progression. Our goals were to provide projections of infections, hospitalizations, and deaths, and estimates of important features of disease transmission and clinical progression. In this paper, we outline the model design, implementation and calibration, and describe how projections and estimates were used to meet the changing requirements of policymakers and officials in Connecticut from March 2020 to February 2021. The approach takes advantage of our unique access to Connecticut public health surveillance and hospital data and our direct connection to state officials and policymakers. We calibrated this model to data on deaths and hospitalizations and developed a novel measure of close interpersonal contact frequency to capture changes in transmission risk over time and used multiple local data sources to infer dynamics of time-varying model inputs. Estimated epidemiologic features of the COVID-19 epidemic in Connecticut include the effective reproduction number, cumulative incidence of infection, infection hospitalization and fatality ratios, and the case detection ratio. We conclude with a discussion of the limitations inherent in predicting uncertain epidemic trajectories and lessons learned from one year of providing COVID-19 projections in Connecticut.

Estimating seroprevalence of SARS-CoV-2 in Ohio: A Bayesian multilevel poststratification approach with multiple diagnostic tests.

Globally, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected more than 59 million people and killed more than 1.39 million. Designing and monitoring interventions to slow and stop the spread of the virus require knowledge of how many people have been and are currently infected, where they live, and how they interact. The first step is an accurate assessment of the population prevalence of past infections. There are very few population-representative prevalence studies of SARS-CoV-2 infections, and only two states in the United States-Indiana and Connecticut-have reported probability-based sample surveys that characterize statewide prevalence of SARS-CoV-2. One of the difficulties is the fact that tests to detect and characterize SARS-CoV-2 coronavirus antibodies are new, are not well characterized, and generally function poorly. During July 2020, a survey representing all adults in the state of Ohio in the United States collected serum samples and information on protective behavior related to SARS-CoV-2 and coronavirus disease 2019 (COVID-19). Several features of the survey make it difficult to estimate past prevalence: 1) a low response rate; 2) a very low number of positive cases; and 3) the fact that multiple poor-quality serological tests were used to detect SARS-CoV-2 antibodies. We describe a Bayesian approach for analyzing the biomarker data that simultaneously addresses these challenges and characterizes the potential effect of selective response. The model does not require survey sample weights; accounts for multiple imperfect antibody test results; and characterizes uncertainty related to the sample survey and the multiple imperfect, potentially correlated tests.

Impact of close interpersonal contact on COVID-19 incidence: evidence from one year of mobile device data.

Close contact between people is the primary route for transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). We sought to quantify interpersonal contact at the population-level by using anonymized mobile device geolocation data. We computed the frequency of contact (within six feet) between people in Connecticut during February 2020 - January 2021. Then we aggregated counts of contact events by area of residence to obtain an estimate of the total intensity of interpersonal contact experienced by residents of each town for each day. When incorporated into a susceptible-exposed-infective-removed (SEIR) model of COVID-19 transmission, the contact rate accurately predicted COVID-19 cases in Connecticut towns during the timespan. The pattern of contact rate in Connecticut explains the large initial wave of infections during March-April, the subsequent drop in cases during June-August, local outbreaks during August-September, broad statewide resurgence during September-December, and decline in January 2021. Contact rate data can help guide public health messaging campaigns to encourage social distancing and in the allocation of testing resources to detect or prevent emerging local outbreaks more quickly than traditional case investigation. ONE SENTENCE SUMMARY: Close interpersonal contact measured using mobile device location data explains dynamics of COVID-19 transmission in Connecticut during the first year of the pandemic.

Prevalence and Spectrum of Predisposition Genes With Germline Mutations Among Chinese Patients With Bowel Cancer.

Background: Bowel cancer is the third-most common cancer and the second leading cause of cancer-related death worldwide. Bowel cancer has a substantial hereditary component; however, additional hereditary risk factors involved in bowel cancer pathogenesis have not been systematically defined. Materials and Methods: A total of 573 patients with bowel cancer were enrolled in the present study, of whom 93.72% had colorectal cancer (CRC). Germline mutations were integrated with somatic mutation information via utilizing target next-generation sequencing. Results: Pathogenic/Likely Pathogenic (P/LP) germline alterations were identified in 47 (8.2%) patients with bowel cancer and the ratio of the number of these patients with family history was significantly higher in the P/LP group than that noted in the non-pathogenic (Non-P) group. Certain rare germline alterations were noted, such as those noted in the following genes: FANCD2, CDH1, and FLCN. A total of 32 patients (68.1%) had germline alterations in the DNA-damage repair (DDR) genes and homologous recombination (HR) accounted for the highest proportion of this subgroup. By comparing 573 patients with bowel cancer with reference controls (China_MAPs database), significant associations (p < 0.01) were observed between the incidence of bowel cancer and the presence of mutations in APC, ATM, MLH1, FANCD2, MSH3, MSH6, PMS1, and RAD51D. Somatic gene differential analysis revealed a marked difference in 18 genes and a significant difference was also noted in tumor mutation burden (TMB) between germline mutation carriers and non-germline mutation subjects (p < 0.001). In addition, TMB in DDR mutation groups indicated a dramatic difference compared with the non-DDR mutation group (p < 0.01). However, no statistically significant differences in TMB were noted among detailed DDR pathways for patients with bowel cancer, irrespective of the presence of germline mutations. Moreover, a significantly higher level (p < 0.0001) of mutation count was observed in the DDR group from The Cancer Genome Atlas (TCGA) database and the DDR and non-DDR alteration groups displayed various immune profiles. Conclusion: Chinese patients with bowel cancer exhibited a distinct spectrum of germline variants, with distinct molecular characteristics such as TMB and DDR. Furthermore, the information on somatic mutations obtained from TCGA database indicated that a deeper understanding of the interactions among DDR and immune cells would be useful to further investigate the role of DDR in bowel cancer.

Estimating causes of death where there is no medical certification: evolution and state of the art of verbal autopsy.

Over the past 70 years, significant advances have been made in determining the causes of death in populations not served by official medical certification of cause at the time of death using a technique known as Verbal Autopsy (VA). VA involves an interview of the family or caregivers of the deceased after a suitable bereavement interval about the circumstances, signs and symptoms of the deceased in the period leading to death. The VA interview data are then interpreted by physicians or, more recently, computer algorithms, to assign a probable cause of death. VA was originally developed and applied in field research settings. This paper traces the evolution of VA methods with special emphasis on the World Health Organization's (WHO)'s efforts to standardize VA instruments and methods for expanded use in routine health information and vital statistics systems in low- and middle-income countries (LMICs). These advances in VA methods are culminating this year with the release of the 2022 WHO Standard Verbal Autopsy (VA) Toolkit. This paper highlights the many contributions the late Professor Peter Byass made to the current VA standards and methods, most notably, the development of InterVA, the most commonly used automated computer algorithm for interpreting data collected in the WHO standard instruments, and the capacity building in low- and middle-income countries (LMICs) that he promoted. This paper also provides an overview of the methods used to improve the current WHO VA standards, a catalogue of the changes and improvements in the instruments, and a mapping of current applications of the WHO VA standard approach in LMICs. It also provides access to tools and guidance needed for VA implementation in Civil Registration and Vital Statistics Systems at scale.

One year of modeling and forecasting COVID-19 transmission to support policymakers in Connecticut.

To support public health policymakers in Connecticut, we developed a county-structured compartmental SEIR-type model of SARS-CoV-2 transmission and COVID-19 disease progression. Our goals were to provide projections of infections, hospitalizations, and deaths, as well as estimates of important features of disease transmission, public behavior, healthcare response, and clinical progression of disease. In this paper, we describe a transmission model developed to meet the changing requirements of public health policymakers and officials in Connecticut from March 2020 to February 2021. We outline the model design, implementation and calibration, and describe how projections and estimates were used to support decision-making in Connecticut throughout the first year of the pandemic. We calibrated this model to data on deaths and hospitalizations, developed a novel measure of close interpersonal contact frequency to capture changes in transmission risk over time and used multiple local data sources to infer dynamics of time-varying model inputs. Estimated time-varying epidemiologic features of the COVID-19 epidemic in Connecticut include the effective reproduction number, cumulative incidence of infection, infection hospitalization and fatality ratios, and the case detection ratio. We describe methodology for producing projections of epidemic evolution under uncertain future scenarios, as well as analytical tools for estimating epidemic features that are difficult to measure directly, such as cumulative incidence and the effects of non-pharmaceutical interventions. The approach takes advantage of our unique access to Connecticut public health surveillance and hospital data and our direct connection to state officials and policymakers. We conclude with a discussion of the limitations inherent in predicting uncertain epidemic trajectories and lessons learned from one year of providing COVID-19 projections in Connecticut.

One-step anatomic and function testing by cardiac CT versus second-line functional testing in symptomatic patients with coronary artery stenosis: head-to-head comparison of CT-derived fractional flow reserve and myocardial perfusion imaging.

BACKGROUND: CT-QFR is a novel coronary computed tomography angiography (CTA)-based method for on-site evaluation of patients with suspected obstructive coronary artery disease (CAD). AIMS: We aimed to compare the diagnostic performance of CT-QFR with myocardial perfusion scintigraphy (MPS) and cardiovascular magnetic resonance (CMR) as second-line tests in patients with suspected obstructive CAD after coronary CTA. METHODS: A paired analysis of CT-QFR and MPS or CMR, with an invasive FFR-based classification as reference standard was carried out. Symptomatic patients with >50% diameter stenosis on coronary CTA were randomised to MPS or CMR and referred for invasive coronary angiography. RESULTS: The rate of coronary CTA not feasible for CT-QFR analysis was 17%. Paired patient-level data were available for 118 patients in the MPS group and 113 in the CMR group. Patient-level diagnostic accuracy was better for CT-QFR than for both MPS (82.2% [95% CI: 75.2-89.2] vs 70.3% [95% CI: 62.0-78.7], p=0.029) and CMR (77.0% [95% CI: 69.1-84.9] vs 65.5% [95% CI: 56.6-74.4], p=0.047). Following a positive coronary CTA and with the intention to diagnose, CT-QFR, CMR and MPS were equally suitable as rule-in and rule-out modalities. CONCLUSIONS: The diagnostic performance of CT-QFR as a second-line test was at least similar to MPS and CMR for the evaluation of obstructive CAD in symptomatic patients presenting with ≥50% diameter stenosis on coronary CTA.

Using Bayesian Latent Gaussian Graphical Models to Infer Symptom Associations in Verbal Autopsies.

Learning dependence relationships among variables of mixed types provides insights in a variety of scientific settings and is a well-studied problem in statistics. Existing methods, however, typically rely on copious, high quality data to accurately learn associations. In this paper, we develop a method for scientific settings where learning dependence structure is essential, but data are sparse and have a high fraction of missing values. Specifically, our work is motivated by survey-based cause of death assessments known as verbal autopsies (VAs). We propose a Bayesian approach to characterize dependence relationships using a latent Gaussian graphical model that incorporates informative priors on the marginal distributions of the variables. We demonstrate such information can improve estimation of the dependence structure, especially in settings with little training data. We show that our method can be integrated into existing probabilistic cause-of-death assignment algorithms and improves model performance while recovering dependence patterns between symptoms that can inform efficient questionnaire design in future data collection.

Can the Wall Shear Stress Values of Left Internal Mammary Artery Grafts during the Perioperative Period Reflect the One-Year Patency?

PURPOSE: The left internal mammary artery (LIMA) is the preferred graft for coronary artery bypass grafting, but the reasoning for LIMA occlusion is unclear. We sought to examine whether the wall shear stress (WSS) values of LIMA grafts during the perioperative period reflected the 1-year patency by using combining computational fluid dynamics (CFD) and coronary computed tomography angiography (CCTA) images. METHODS: CCTA was performed in 233 patients with LIMA graft perioperatively and 1 year later from October 2014 to May 2017. LIMA occlusion was detected in six patients at the 1-year follow-up CCTA. Two patients were excluded due to poor imaging quality. The remaining four patients were enrolled as occlusive (OCC) group, and eight patients with patent LIMA were recruited as patent (PAT) group. The WSS values of LIMA during perioperative period were calculated. LIMA graft was artificially divided into three even segments, proximal (pLIMA), middle (mLIMA) and distal (dLIMA) segments. The independent samples t-test and the Student-Newman-Keuls test were used. RESULTS: The WSS values of dLIMA were significantly higher in the PAT group than in the OCC group (4.43 vs. 2.56, p < 0.05). The WSS values of dLIMA in the PAT group were significantly higher than pLIMA, which was absent in the OCC group. CONCLUSIONS: A higher WSS value of the distal segment of LIMA and a higher WSS value of the distal segment compared with the proximal segment of LIMA in the PAT were observed; this tendency might be helpful in predicting the 1-year patency of LIMA.