Incidence and severity of pulmonary embolism in COVID-19 infection: Ancestral, Alpha, Delta, and Omicron variants

Little information is available regarding incidence and severity of pulmonary embolism (PE) across the periods of ancestral strain, Alpha, Delta, and Omicron variants. The aim of this study is to investigate the incidence and severity of PE over the dominant periods of ancestral strain and Alpha, Delta, and Omicron variants. We hypothesized that the incidence and the severity by proximity of PE in patients with the newer variants and vaccination would be decreased compared with those in ancestral and earlier variants. Patients with COVID-19 diagnosis between March 2020 and February 2022 and computed tomography pulmonary angiogram performed within a 6-week window around the diagnosis (-2 to +4 weeks) were studied retrospectively. The primary endpoints were the associations of the incidence and location of PE with the ancestral strain and each variant. Of the 720 coronavirus disease 2019 patients with computed tomography pulmonary angiogram (58.6 ± 17.2 years; 374 females), PE was diagnosed among 42/358 (12%) during the ancestral strain period, 5/60 (8%) during the Alpha variant period, 16/152 (11%) during the Delta variant period, and 13/150 (9%) during the Omicron variant period. The most proximal PE (ancestral strain vs variants) was located in the main/lobar arteries (31% vs 6%-40%), in the segmental arteries (52% vs 60%-75%), and in the subsegmental arteries (17% vs 0%-19%). There was no significant difference in both the incidence and location of PE across the periods, confirmed by multivariable logistic regression models. In summary, the incidence and severity of PE did not significantly differ across the periods of ancestral strain and Alpha, Delta, and Omicron variants.

Listen Before You Auscultate: An Active-Learning Approach to Bedside Cardiac Assessment

Introduction

Bedside cardiac assessment (BCA) is deficient across a spectrum of noncardiology trainees. Learners not taught BCA well may become instructors who do not teach well, creating a self-perpetuating problem. To improve BCA teaching and learning, we developed a high-quality, patient-centered curriculum for medicine clerkship students that could be flexibly implemented and accessible to other health professions learners.

Methods

With a constructivist perspective, we aligned learning goals, activities, and assessments. The curriculum used a "listen before you auscultate" framework, capturing patient history as context for a six-step, systematic approach. In the flipped classroom, short videos and practice questions preceded two 1-hour class activities that integrated diagnostic reasoning, pathophysiology, physical diagnosis, and reflection. Activities included case discussions, jugular venous pressure evaluation, heart sound competitions, and simulated conversations with patients. Two hundred sixty-eight students at four US and international medical schools participated. We incorporated feedback, performed thematic analysis, and assessed learners' confidence and knowledge.

Results

Low posttest data capture limited quantitative results. Students reported increased confidence in BCA ability. Knowledge increased in both BCA and control groups. Thematic analysis suggested instructional design strategies were effective and peer encounters, skills practice, and encounters with educators were meaningful.

Discussion

The curriculum supported active learning of day-to-day clinical competencies and promoted professional identity formation alongside BCA ability. Feedback and increased confidence on the late-clerkship posttest suggested durable learning. We recommend approaches to confirm this and other elements of knowledge, skill acquisition, or behaviors and are surveying impacts on professional identity formation-related constructs.

Severe COVID-19 pneumonia leads to post-COVID-19 lung abnormalities on follow-up CT scans

Purpose

To investigate the association of the maximal severity of pneumonia on CT scans obtained within 6-week of diagnosis with the subsequent development of post-COVID-19 lung abnormalities (Co-LA).

Methods

COVID-19 patients diagnosed at our hospital between March 2020 and September 2021 were studied retrospectively. The patients were included if they had (1) at least one chest CT scan available within 6-week of diagnosis; and (2) at least one follow-up chest CT scan available ≥ 6 months after diagnosis, which were evaluated by two independent radiologists. Pneumonia Severity Categories were assigned on CT at diagnosis according to the CT patterns of pneumonia and extent as: 1) no pneumonia (Estimated Extent, 0%); 2) non-extensive pneumonia (GGO and OP, <40%); and 3) extensive pneumonia (extensive OP and DAD, >40%). Co-LA on follow-up CT scans, categorized using a 3-point Co-LA Score (0, No Co-LA; 1, Indeterminate Co-LA; and 2, Co-LA).

Results

Out of 132 patients, 42 patients (32%) developed Co-LA on their follow-up CT scans 6-24 months post diagnosis. The severity of COVID-19 pneumonia was associated with Co-LA: In 47 patients with extensive pneumonia, 33 patients (70%) developed Co-LA, of whom 18 (55%) developed fibrotic Co-LA. In 52 with non-extensive pneumonia, 9 (17%) developed Co-LA: In 33 with no pneumonia, none (0%) developed Co-LA.

Conclusions

Higher severity of pneumonia at diagnosis was associated with the increased risk of development of Co-LA after 6-24 months of SARS-CoV-2 infection.

Traction Bronchiectasis/Bronchiolectasis on CT Scans in Relationship to Clinical Outcomes and Mortality: The COPDGene Study

Background The clinical impact of interstitial lung abnormalities (ILAs) on poor prognosis has been reported in many studies, but risk stratification in ILA will contribute to clinical practice. Purpose To investigate the association of traction bronchiectasis/bronchiolectasis index (TBI) with mortality and clinical outcomes in individuals with ILA by using the COPDGene cohort. Materials and Methods This study was a secondary analysis of prospectively collected data. Chest CT scans of participants with ILA for traction bronchiectasis/bronchiolectasis were evaluated and outcomes were compared with participants without ILA from the COPDGene study (January 2008 to June 2011). TBI was classified as follows: TBI-0, ILA without traction bronchiectasis/bronchiolectasis; TBI-1, ILA with bronchiolectasis but without bronchiectasis or architectural distortion; TBI-2, ILA with mild to moderate traction bronchiectasis; and TBI-3, ILA with severe traction bronchiectasis and/or honeycombing. Clinical outcomes and overall survival were compared among the TBI groups and the non-ILA group by using multivariable linear regression model and Cox proportional hazards model, respectively. Results Overall, 5295 participants (median age, 59 years; IQR, 52-66 years; 2779 men) were included, and 582 participants with ILA and 4713 participants without ILA were identified. TBI groups were associated with poorer clinical outcomes such as quality of life scores in the multivariable linear regression model (TBI-0: coefficient, 3.2 [95% CI: 0.6, 5.7; P = .01]; TBI-1: coefficient, 3.3 [95% CI: 1.1, 5.6; P = .003]; TBI-2: coefficient, 7.6 [95% CI: 4.0, 11; P < .001]; TBI-3: coefficient, 32 [95% CI: 17, 48; P < .001]). The multivariable Cox model demonstrated that ILA without traction bronchiectasis (TBI-0-1) and with traction bronchiectasis (TBI-2-3) were associated with shorter overall survival (TBI-0-1: hazard ratio [HR], 1.4 [95% CI: 1.0, 1.9; P = .049]; TBI-2-3: HR, 3.8 [95% CI: 2.6, 5.6; P < .001]). Conclusion Traction bronchiectasis/bronchiolectasis was associated with poorer clinical outcomes compared with the group without interstitial lung abnormalities; TBI-2 and 3 were associated with shorter survival. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Lee and Im in this issue.

Analytics to monitor local impact of the Protecting Access to Medicare Act's imaging clinical decision support requirements

OBJECTIVE

This study aimed is to: (1) extend the Integrating the Biology and the Bedside (i2b2) data and application models to include medical imaging appropriate use criteria, enabling it to serve as a platform to monitor local impact of the Protecting Access to Medicare Act's (PAMA) imaging clinical decision support (CDS) requirements, and (2) validate the i2b2 extension using data from the Medicare Imaging Demonstration (MID) CDS implementation.

MATERIALS AND METHODS

This study provided a reference implementation and assessed its validity and reliability using data from the MID, the federal government's predecessor to PAMA's imaging CDS program. The Star Schema was extended to describe the interactions of imaging ordering providers with the CDS. New ontologies were added to enable mapping medical imaging appropriateness data to i2b2 schema. z-Ratio for testing the significance of the difference between 2 independent proportions was utilized.

RESULTS

The reference implementation used 26 327 orders for imaging examinations which were persisted to the modified i2b2 schema. As an illustration of the analytical capabilities of the Web Client, we report that 331/1192 or 28.1% of imaging orders were deemed appropriate by the CDS system at the end of the intervention period (September 2013), an increase from 162/1223 or 13.2% for the first month of the baseline period, December 2011 (P = .0212), consistent with previous studies.

CONCLUSIONS

The i2b2 platform can be extended to monitor local impact of PAMA's appropriateness of imaging ordering CDS requirements.

Assessing clinical utility of machine learning and artificial intelligence approaches to analyze speech recordings in multiple sclerosis: A pilot study

BACKGROUND

An early diagnosis together with an accurate disease progression monitoring of multiple sclerosis is an important component of successful disease management. Prior studies have established that multiple sclerosis is correlated with speech discrepancies. Early research using objective acoustic measurements has discovered measurable dysarthria.

METHOD

The objective was to determine the potential clinical utility of machine learning and deep learning/AI approaches for the aiding of diagnosis, biomarker extraction and progression monitoring of multiple sclerosis using speech recordings. A corpus of 65 MS-positive and 66 healthy individuals reading the same text aloud was used for targeted acoustic feature extraction utilizing automatic phoneme segmentation. A series of binary classification models was trained, tuned, and evaluated regarding their Accuracy and area-under-the-curve.

RESULTS

The Random Forest model performed best, achieving an Accuracy of 0.82 on the validation dataset and an area-under-the-curve of 0.76 across 5 k-fold cycles on the training dataset. 5 out of 7 acoustic features were statistically significant.

CONCLUSION

Machine learning and artificial intelligence in automatic analyses of voice recordings for aiding multiple sclerosis diagnosis and progression tracking seems promising. Further clinical validation of these methods and their mapping onto multiple sclerosis progression is needed, as well as a validating utility for English-speaking populations.

A Case-control Study of Major Genetic Predisposition Risk Alleles in Developing DDD in the Northeast US Population: Effects of Gene-gene Interactions

STUDY DESIGN

A case-control study of risk alleles for degenerative disc disease (DDD) using magnetic resonance (MR) imaging for phenotyping.

OBJECTIVE

We aim to provide the first statistically adequately powered study of the relationship between the presence of common risk alleles and occurrence of DDD in Eastern US population.

SUMMARY OF BACKGROUND DATA

Many genetic predisposing factors have been identified in elevating the risk of DDD, including common variants in VDR, COL1A1, AGC1, COL9A2/3 genes.

METHODS

We utilized the Mass General Brigham (MGB) Biobank in which subjects' Medical Record is linked with genotyped data from single-nucleotide polymorphism (SNP) arrays. Subjects with lumbosacral spine MR imaging studies were used to construct the Cases cohort; the Biobank's Controls cohort was used as the Control cohort. Odds ratios (OR) and False-discovery-rate (FDR) q values from multiple-hypotheses-testing corrections were used to assess the likelihood of DDD given occurrence of the listed DDD risk alleles.

RESULTS

Four-hundred-fourteen subjects (mean age = 64, range = 27 to 94) were Cases and 925 Controls (mean age = 46, range = 21-61). A systematic search has identified 25 SNPs in 18 genes in the SNP arrays. At univariate level, rs1544410 in VDR was significantly associated with DDD for male subjects (odds ratio [OR] = 0.594, P = 0.011). After adjustment for all significant variants and demographics, three predictor variables had a significant association with the outcome, age (OR = 1.130, q < 0.0001), rs143383 (OR = 1.951, q = 0.056), and rs3737821 (OR = 2.701, q = 0.069). A novel variant-to-variant correlation rs143383:rs763110 had a significant adjusted OR = 7.933, q = 0.070).

CONCLUSION

In this large-scale study of common variants' correlation with the presence of DDD in the Northeast United States, we have found a novel and significant variant-to-variant interaction to be associated with the risk of developing DDD, corroborating and necessitating the inclusion of gene-gene interactions in predictive risk model development for DDD.Level of Evidence: 4.

Progression of traction bronchiectasis/bronchiolectasis in interstitial lung abnormalities is associated with increased all-cause mortality: Age Gene/Environment Susceptibility-Reykjavik Study

PURPOSE

The aim of this study is to assess the role of traction bronchiectasis/bronchiolectasis and its progression as a predictor for early fibrosis in interstitial lung abnormalities (ILA).

METHODS

Three hundred twenty-seven ILA participants out of 5764 in the Age, Gene/Environment Susceptibility (AGES)-Reykjavik Study who had undergone chest CT twice with an interval of approximately five-years were enrolled in this study. Traction bronchiectasis/bronchiolectasis index (TBI) was classified on a four-point scale: 0, ILA without traction bronchiectasis/bronchiolectasis; 1, ILA with bronchiolectasis but without bronchiectasis or architectural distortion; 2, ILA with mild to moderate traction bronchiectasis; 3, ILA and severe traction bronchiectasis and/or honeycombing. Traction bronchiectasis (TB) progression was classified on a five-point scale: 1, Improved; 2, Probably improved; 3, No change; 4, Probably progressed; 5, Progressed. Overall survival (OS) among participants with different TB Progression Score and between the TB progression group and No TB progression group was also investigated. Hazard radio (HR) was estimated with Cox proportional hazards model.

RESULTS

The higher the TBI at baseline, the higher TB Progression Score (P < 0.001). All five participants with TBI = 3 at baseline progressed; 46 (90 %) of 51 participants with TBI = 2 progressed. TB progression was also associated with shorter OS with statistically significant difference (adjusted HR = 1.68, P < 0.001).

CONCLUSION

TB progression was visualized on chest CT frequently and clearly. It has the potential to be the predictor for poorer prognosis of ILA.

One size does not fit all: Factors associated with increased frequency of radiation overexposure alerts based on fixed-alert thresholds

OBJECTIVES

Quantify the expected rate of CT radiation dose alerts for three body regions using accepted radiation dose benchmarks and assess key determinants of alert frequency.

METHODS

This IRB-approved retrospective cohort study evaluated consecutive CT examinations performed between July and December 2013 within an academic medical system. CTDI_vol x-ray tube output metrics were compared to the body-region-specific benchmark levels, Achievable Doses (AD), Diagnostic Reference Levels (DRL), and Dose Notification Values (DNV). A logistic regression model for the simulated alerts was fit as a function of the independent predictors: scanner, body region, gender, weight, and age.

RESULTS

For 17,000 exams, the proportion of events triggering alerts increased with patient weight. Significant covariates were scanner, body region, patient weight and patient age (all p < 0.0001). Odds of alert generation for the AD, DRL, and DNV benchmarks increased by 7.6%, 6.6% and 2.9% per kilogram, respectively, and by 0.8%, 1.1% and -2.7% per year of age (all p < 0.0001). Compared to the most highly optimized scanner, odds of alert generation varied by a factor of 595 for AD, 1126 for DRL, and 13 for DNV.

CONCLUSION

Alert frequency was significantly correlated with weight, age, body region and scanner. Controllable factors include scanner functionality and associated protocol optimization. Patient factors driving alert frequency are predominantly weight, and to a lesser degree, age. Size-agnostic fixed dose thresholds can frequently produce false positive alerts in appropriately performed exams of large patients, while missing opportunities to identify outlier scans of higher-than-expected dose in small patients.

Interstitial lung abnormalities in patients with stage I non-small cell lung cancer are associated with shorter overall survival: the Boston lung cancer study

BACKGROUND

Interstitial lung abnormalities (ILA) can be detected on computed tomography (CT) in lung cancer patients and have an association with mortality in advanced non-small cell lung cancer (NSCLC) patients. The aim of this study is to demonstrate the significance of ILA for mortality in patients with stage I NSCLC using Boston Lung Cancer Study cohort.

METHODS

Two hundred and thirty-one patients with stage I NSCLC from 2000 to 2011 were investigated in this retrospective study (median age, 69 years; 93 males, 138 females). ILA was scored on baseline CT scans prior to treatment using a 3-point scale (0 = no evidence of ILA, 1 = equivocal for ILA, 2 = ILA) by a sequential reading method. ILA score 2 was considered the presence of ILA. The difference of overall survival (OS) for patients with different ILA scores were tested via log-rank test and multivariate Cox proportional hazards models were used to estimate hazard ratios (HRs) including ILA score, age, sex, smoking status, and treatment as the confounding variables.

RESULTS

ILA was present in 22 out of 231 patients (9.5%) with stage I NSCLC. The presence of ILA was associated with shorter OS (patients with ILA score 2, median 3.85 years [95% confidence interval (CI): 3.36 - not reached (NR)]; patients with ILA score 0 or 1, median 10.16 years [95%CI: 8.65 - NR]; P <  0.0001). In a Cox proportional hazards model, the presence of ILA remained significant for increased risk for death (HR = 2.88, P = 0.005) after adjusting for age, sex, smoking and treatment.

CONCLUSIONS

ILA was detected on CT in 9.5% of patients with stage I NSCLC. The presence of ILA was significantly associated with a shorter OS and could be an imaging marker of shorter survival in stage I NSCLC.

Determination of latent dimensionality in international trade flow

Currently, high-dimensional data is ubiquitous in data science, which necessitates the development of techniques to decompose and interpret such multidimensional (aka tensor) datasets. Finding a low dimensional representation of the data, that is, its inherent structure, is one of the approaches that can serve to understand the dynamics of low dimensional latent features hidden in the data. Moreover, decomposition methods with non-negative constraints are shown to extract more insightful factors. Nonnegative RESCAL is one such technique, particularly well suited to analyze self-relational data, such as dynamic networks found in international trade flows. Particularly, non-negative RESCAL computes a low dimensional tensor representation by finding the latent space containing multiple modalities. Furthermore, estimating the dimensionality of this latent space is crucial for extracting meaningful latent features. Here, to determine the dimensionality of the latent space with non-negative RESCAL, we propose a latent dimension determination method which is based on clustering of the solutions of multiple realizations of non-negative RESCAL decompositions. We demonstrate the performance of our model selection method on synthetic data. We then apply our method to decompose a network of international trade flows data from International Monetary Fund and shows that with a correct latent dimension determination, the resulting features are able to capture relevant empirical facts from economic literature.

Traction Bronchiectasis/Bronchiolectasis is Associated with Interstitial Lung Abnormality Mortality

PURPOSE

To investigate if the presence and severity of traction bronchiectasis/bronchiolectasis are associated with poorer survival in subjects with ILA.

METHOD

The study included 3,594 subjects (378 subjects with ILA and 3,216 subjects without ILA) in AGES-Reykjavik Study. Chest CT scans of 378 subjects with ILA were evaluated for traction bronchiectasis/bronchiolectasis, defined as dilatation of bronchi/bronchioles within areas demonstrating ILA. Traction bronchiectasis/bronchiolectasis Index (TBI) was assigned as: TBI = 0, ILA without traction bronchiectasis/bronchiolectasis: TBI = 1, ILA with bronchiolectasis but without bronchiectasis or architectural distortion: TBI = 2, ILA with mild to moderate traction bronchiectasis: TBI = 3, ILA and severe traction bronchiectasis and/or honeycombing. Overall survival (OS) was compared among the subjects in different TBI groups and those without ILA.

RESULTS

The median OS was 12.93 years (95%CI; 12.67 - 13.43) in the subjects without ILA; 11.95 years (10.03 - not reached) in TBI-0 group; 8.52 years (7.57 - 9.30) in TBI-1 group; 7.63 years (6.09 - 9.10) in TBI-2 group; 5.40 years (1.85 - 5.98) in TBI-3 group. The multivariable Cox models demonstrated significantly shorter OS of TBI-1, TBI-2, and TBI-3 groups compared to subjects without ILA (P < 0.0001), whereas TBI-0 group had no significant OS difference compared to subjects without ILA, after adjusting for age, sex, and smoking status.

CONCLUSIONS

The presence and severity of traction bronchiectasis/bronchiolectasis are associated with shorter survival. The traction bronchiectasis/bronchiolectasis is an important contributor to increased mortality among subjects with ILA.

Concordance between gene expression in peripheral whole blood and colonic tissue in children with inflammatory bowel disease

Background

Presenting features of inflammatory bowel disease (IBD) are non-specific. We hypothesized that mRNA profiles could (1) identify genes and pathways involved in disease pathogenesis; (2) identify a molecular signature that differentiates IBD from other conditions; (3) provide insight into systemic and colon-specific dysregulation through study of the concordance of the gene expression.

Methods

Children (8–18 years) were prospectively recruited at the time of diagnostic colonoscopy for possible IBD. We used transcriptome-wide mRNA profiling to study gene expression in colon biopsies and paired whole blood samples. Using blood mRNA measurements, we fit a regression model for disease state prediction that was validated in an independent test set of adult subjects (GSE3365).

Results

Ninety-eight children were recruited [39 Crohn’s disease, 18 ulcerative colitis, 2 IBDU, 39 non-IBD]. There were 1,118 significantly differentially (IBD vs non-IBD) expressed genes in colon tissue, and 880 in blood. The direction of relative change in expression was concordant for 106/112 genes differentially expressed in both tissue types. The regression model from the blood mRNA measurements distinguished IBD vs non-IBD disease status in the independent test set with 80% accuracy using only 6 genes. The overlap of 5 immune and metabolic pathways in the two tissue types was significant (p<0.001).

Conclusions

Blood and colon tissue from patients with IBD share a common transcriptional profile dominated by immune and metabolic pathways. Our results suggest that peripheral blood expression levels of as few as 6 genes (IL7R, UBB, TXNIP, S100A8, ALAS2, and SLC2A3) may distinguish patients with IBD from non-IBD.

Comparing Artificial Intelligence Approaches to Retrieve Clinical Reports Documenting Implantable Devices Posing MRI Safety Risks

OBJECTIVE

Assess sensitivity, specificity, and accuracy of two approaches to identify patients with implantable devices that pose safety risks for MRI-an expert-derived approach and an ontology-derived natural language processing (NLP). Determine the proportion of clinical data that identify these implantable devices.

METHODS

This Institutional Review Board-approved retrospective study was performed at a 793-bed academic hospital. The expert-derived approach used an open-source software with a list of curated terms to query for implantable devices posing high safety risk ("MRI-Red") in patients undergoing MRI. The ontology-derived approach used an NLP system with terms mapped to Systematized Nomenclature of Medicine-Clinical Terms. Queries were performed in three clinical data types-25,000 radiology reports, 174,769 emergency department (ED) notes, and 41,085 other clinical reports (eg, cardiology, operating room, physician notes, radiology reports, pathology reports, patient letters). Sensitivity, specificity, and accuracy of both methods against manual review of a randomly sampled 465 reports were assessed and tested for significant differences between expert-derived and ontology-derived approaches using t test.

RESULTS

Accuracy, sensitivity, and specificity of expert-versus ontology-derived approaches were similar (0.83 versus 0.91, P = .080; 0.88 versus 0.96, P = .178; 0.82 versus 0.92, P = .110). The proportion of radiology reports, ED notes, and other clinical reports retrieved containing implantable devices with high safety risks for MRI ranged from 1.47% to 1.88%.

DISCUSSION

Artificial intelligence approaches such as expert-driven NLP and ontology-driven NLP have similar accuracy in identifying patients with implantable devices that pose high safety risks for MRI.

Detection of unwarranted CT radiation exposure from patient and imaging protocol meta-data using regularized regression

BACKGROUND

Variability in radiation exposure from CT scans can be appropriate and driven by patient features such as body habitus. Quantitative analysis may be performed to discover instances of unwarranted radiation exposure and to reduce the probability of such occurrences in future patient visits. No universal process to perform identification of outliers is widely available, and access to expertise and resources is variable.

OBJECTIVE

The goal of this study is to develop an automated outlier detection procedure to identify all scans with an unanticipated high radiation exposure, given the characteristics of the patient and the type of the exam.

MATERIALS AND METHODS

This Institutional Review Board-approved retrospective cohort study was conducted from June 30, 2012 - December 31, 2013 in a quaternary academic medical center. The de-identified dataset contained 28 fields for 189,959 CT exams. We applied the variable selection method Least Absolute Shrinkage and Selection Operator (LASSO) to select important variables for predicting CT radiation dose. We then employed a regression approach that is robust to outliers, to learn from data a predictive model of CT radiation doses given important variables identified by LASSO. Patient visits whose predicted radiation dose was statistically different from the radiation dose actually received were identified as outliers.

RESULTS

Our methodology identified 1% of CT exams as outliers. The top-5 predictors discovered by LASSO and strongly correlated with radiation dose were Tube Current, kVp, Weight, Width of collimator, and Reference milliampere-seconds. A human expert validation of the outlier detection algorithm has yielded specificity of 0.85 [95% CI 0.78-0.92] and sensitivity of 0.91 [95% CI 0.85-0.97] (PPV = 0.84, NPV = 0.92). These values substantially outperform alternative methods we tested (F1 score 0.88 for our method against 0.51 for the alternatives).

CONCLUSION

The study developed and tested a novel, automated method for processing CT scanner meta-data to identify CT exams where patients received an unwarranted amount of radiation. Radiation safety and protocol review committees may use this technique to uncover systemic issues and reduce future incidents.

Can Automated Retrieval of Data from Emergency Department Physician Notes Enhance the Imaging Order Entry Process?

BACKGROUND

When a paucity of clinical information is communicated from ordering physicians to radiologists at the time of radiology order entry, suboptimal imaging interpretations and patient care may result.

OBJECTIVES

Compare documentation of relevant clinical information in electronic health record (EHR) provider note to computed tomography (CT) order requisition, prior to ordering of head CT for emergency department (ED) patients presenting with headache.

METHODS

In this institutional review board-approved retrospective observational study performed between April 1, 2013 and September 30, 2014 at an adult quaternary academic hospital, we reviewed data from 666 consecutive ED encounters for patients with headaches who received head CT. The primary outcome was the number of concept unique identifiers (CUIs) relating to headache extracted via ontology-based natural language processing from the history of present illness (HPI) section in ED notes compared with the number of concepts obtained from the imaging order requisition.

RESULTS

Our analysis was conducted on cases where the HPI note section was completed prior to image order entry, which occurred in 23.1% (154/666) of encounters. For these 154 encounters, the number of CUIs specific to headache per note extracted from the HPI (median = 3, interquartile range [IQR]: 2-4) was significantly greater than the number of CUIs per encounter obtained from the imaging order requisition (median = 1, IQR: 1-2; Wilcoxon signed rank p < 0.0001). Extracted concepts from notes were distinct from order requisition indications in 92.9% (143/154) of cases.

CONCLUSION

EHR provider notes are a valuable source of relevant clinical information at the time of imaging test ordering. Automated extraction of clinical information from notes to prepopulate imaging order requisitions may improve communication between ordering physicians and radiologists, enhance efficiency of ordering process by reducing redundant data entry, and may help improve clinical relevance of clinical decision support at the time of order entry, potentially reducing provider burnout from extraneous alerts.

Quantifying the white blood cell transcriptome as an accessible window to the multiorgan transcriptome

Motivation: We investigate and quantify the generalizability of the white blood cell (WBC) transcriptome to the general, multiorgan transcriptome. We use data from the NCBI's Gene Expression Omnibus (GEO) public repository to define two datasets for comparison, WBC and OO (Other Organ) sets.

Results: Comprehensive pair-wise correlation and expression level profiles are calculated for both datasets (with sizes of 81 and 1463, respectively). We have used mapping and ranking across the Gene Ontology (GO) categories to quantify similarity between the two sets. GO mappings of the most correlated and highly expressed genes from the two datasets tightly match, with the notable exceptions of components of the ribosome, cell adhesion and immune response. That is, 10 877 or 48.8% of all measured genes do not change >10% of rank range between WBC and OO; only 878 (3.9%) change rank >50%. Two trans-tissue gene lists are defined, the most changing and the least changing genes in expression rank. We also provide a general, quantitative measure of the probability of expression rank and correlation profile in the OO system given the expression rank and correlation profile in the WBC dataset.

Contact:vvaltchinov@partners.org

Supplementary information:Supplementary data are available at Bioinformatics online.