Characterization of Post-COVID-19 Definitions and Clinical Coding Practices: Longitudinal Study

BACKGROUND

Post-COVID-19 condition (colloquially known as "long COVID-19") characterized as postacute sequelae of SARS-CoV-2 has no universal clinical case definition. Recent efforts have focused on understanding long COVID-19 symptoms, and electronic health record (EHR) data provide a unique resource for understanding this condition. The introduction of the International Classification of Diseases, Tenth Revision (ICD-10) code U09.9 for "Post COVID-19 condition, unspecified" to identify patients with long COVID-19 has provided a method of evaluating this condition in EHRs; however, the accuracy of this code is unclear.

OBJECTIVE

This study aimed to characterize the utility and accuracy of the U09.9 code across 3 health care systems-the Veterans Health Administration, the Beth Israel Deaconess Medical Center, and the University of Pittsburgh Medical Center-against patients identified with long COVID-19 via a chart review by operationalizing the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC) definitions.

METHODS

Patients who were COVID-19 positive with either a U07.1 ICD-10 code or positive polymerase chain reaction test within these health care systems were identified for chart review. Among this cohort, we sampled patients based on two approaches: (1) with a U09.9 code and (2) without a U09.9 code but with a new onset long COVID-19-related ICD-10 code, which allows us to assess the sensitivity of the U09.9 code. To operationalize the long COVID-19 definition based on health agency guidelines, symptoms were grouped into a "core" cluster of 11 commonly reported symptoms among patients with long COVID-19 and an extended cluster that captured all other symptoms by disease domain. Patients having ≥2 symptoms persisting for ≥60 days that were new onset after their COVID-19 infection, with ≥1 symptom in the core cluster, were labeled as having long COVID-19 per chart review. The code's performance was compared across 3 health care systems and across different time periods of the pandemic.

RESULTS

Overall, 900 patient charts were reviewed across 3 health care systems. The prevalence of long COVID-19 among the cohort with the U09.9 ICD-10 code based on the operationalized WHO definition was between 23.2% and 62.4% across these health care systems. We also evaluated a less stringent version of the WHO definition and the CDC definition and observed an increase in the prevalence of long COVID-19 at all 3 health care systems.

CONCLUSIONS

This is one of the first studies to evaluate the U09.9 code against a clinical case definition for long COVID-19, as well as the first to apply this definition to EHR data using a chart review approach on a nationwide cohort across multiple health care systems. This chart review approach can be implemented at other EHR systems to further evaluate the utility and performance of the U09.9 code.

Contextualizing protein representations using deep learning on protein networks and single-cell data

Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across diverse biological contexts, such as tissues and cell types, remains a significant challenge for existing algorithms. We introduce Pinnacle, a flexible geometric deep learning approach that is trained on contextualized protein interaction networks to generate context-aware protein representations. Leveraging a human multi-organ single-cell transcriptomic atlas, Pinnacle provides 394,760 protein representations split across 156 cell type contexts from 24 tissues and organs. Pinnacle's contextualized representations of proteins reflect cellular and tissue organization and Pinnacle's tissue representations enable zero-shot retrieval of the tissue hierarchy. Pretrained Pinnacle's protein representations can be adapted for downstream tasks: to enhance 3D structure-based protein representations for important protein interactions in immuno-oncology (PD-1/PD-L1 and B7-1/CTLA-4) and to study the effects of drugs across cell type contexts. Pinnacle outperforms state-of-the-art, yet context-free, models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases, and can pinpoint cell type contexts that predict therapeutic targets better than context-free models (29 out of 156 cell types in rheumatoid arthritis; 13 out of 152 cell types in inflammatory bowel diseases). Pinnacle is a graph-based contextual AI model that dynamically adjusts its outputs based on biological contexts in which it operates.

Reversal of MYB-dependent suppression of MAFB expression overrides leukaemia phenotype in MLL-rearranged AML

The transcription factor MYB plays a pivotal role in haematopoietic homoeostasis and its aberrant expression is involved in the genesis and maintenance of acute myeloid leukaemia (AML). We have previously demonstrated that not all AML subtypes display the same dependency on MYB expression and that such variability is dictated by the nature of the driver mutation. However, whether this difference in MYB dependency is a general trend in AML remains to be further elucidated. Here, we investigate the role of MYB in human leukaemia by performing siRNA-mediated knock-down in cell line models of AML with different driver lesions. We show that the characteristic reduction in proliferation and the concomitant induction of myeloid differentiation that is observed in MLL-rearranged and t(8;21) leukaemias upon MYB suppression is not seen in AML cells with a complex karyotype. Transcriptome analyses revealed that MYB ablation produces consensual increase of MAFB expression in MYB-dependent cells and, interestingly, the ectopic expression of MAFB could phenocopy the effect of MYB suppression. Accordingly, in silico stratification analyses of molecular data from AML patients revealed a reciprocal relationship between MYB and MAFB expression, highlighting a novel biological interconnection between these two factors in AML and supporting new rationales of MAFB targeting in MLL-rearranged leukaemias.

Generate Analysis-Ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records With Advanced Informatic Technologies

Although randomized controlled trials (RCTs) are the gold standard for establishing the efficacy and safety of a medical treatment, real-world evidence (RWE) generated from real-world data has been vital in postapproval monitoring and is being promoted for the regulatory process of experimental therapies. An emerging source of real-world data is electronic health records (EHRs), which contain detailed information on patient care in both structured (eg, diagnosis codes) and unstructured (eg, clinical notes and images) forms. Despite the granularity of the data available in EHRs, the critical variables required to reliably assess the relationship between a treatment and clinical outcome are challenging to extract. To address this fundamental challenge and accelerate the reliable use of EHRs for RWE, we introduce an integrated data curation and modeling pipeline consisting of 4 modules that leverage recent advances in natural language processing, computational phenotyping, and causal modeling techniques with noisy data. Module 1 consists of techniques for data harmonization. We use natural language processing to recognize clinical variables from RCT design documents and map the extracted variables to EHR features with description matching and knowledge networks. Module 2 then develops techniques for cohort construction using advanced phenotyping algorithms to both identify patients with diseases of interest and define the treatment arms. Module 3 introduces methods for variable curation, including a list of existing tools to extract baseline variables from different sources (eg, codified, free text, and medical imaging) and end points of various types (eg, death, binary, temporal, and numerical). Finally, module 4 presents validation and robust modeling methods, and we propose a strategy to create gold-standard labels for EHR variables of interest to validate data curation quality and perform subsequent causal modeling for RWE. In addition to the workflow proposed in our pipeline, we also develop a reporting guideline for RWE that covers the necessary information to facilitate transparent reporting and reproducibility of results. Moreover, our pipeline is highly data driven, enhancing study data with a rich variety of publicly available information and knowledge sources. We also showcase our pipeline and provide guidance on the deployment of relevant tools by revisiting the emulation of the Clinical Outcomes of Surgical Therapy Study Group Trial on laparoscopy-assisted colectomy versus open colectomy in patients with early-stage colon cancer. We also draw on existing literature on EHR emulation of RCTs together with our own studies with the Mass General Brigham EHR.

Scalable relevance ranking algorithm via semantic similarity assessment improves efficiency of medical chart review

OBJECTIVE

Accurately assigning phenotype information to individual patients via computational phenotyping using Electronic Health Records (EHRs) has been seen as the first step towards enabling EHRs for precision medicine research. Chart review labels annotated by clinical experts, also known as "gold standard" labels, are essential for the development and validation of computational phenotyping algorithms. However, given the complexity of EHR systems, the process of chart review is both labor intensive and time consuming. We propose a fully automated algorithm, referred to as pGUESS, to rank EHR notes according to their relevance to a given phenotype. By identifying the most relevant notes, pGUESS can greatly improve the efficiency and accuracy of chart reviews.

METHOD

pGUESS uses prior guided semantic similarity to measure the informativeness of a clinical note to a given phenotype. We first select candidate clinical concepts from a pool of comprehensive medical concepts using public knowledge sources and then derive the semantic embedding vector (SEV) for a reference article (SEV_ref) and each note (SEV_note). The algorithm scores the relevance of a note as the cosine similarity between SEV_note and SEV_ref.

RESULTS

The algorithm was validated against four sets of 200 notes that were manually annotated by clinical experts to assess their informativeness to one of three disease phenotypes. pGUESS algorithm substantially outperforms existing unsupervised approaches for classifying the relevance status with respect to both accuracy and scalability across phenotypes. Averaging over the three phenotypes, the rank correlation between the algorithm ranking and gold standard label was 0.64 for pGUESS, but only 0.47 and 0.35 for the next two best performing algorithms. pGUESS is also much more computationally scalable compared to existing algorithms.

CONCLUSION

pGUESS algorithm can substantially reduce the burden of chart review and holds potential in improving the efficiency and accuracy of human annotation.

AB0624 Patients with vasculitis have a high prevalence of coronary microvascular dysfunction

Background

Vasculitides are a heterogenous group of diseases characterized by intense vessel wall inflammation, endothelial injury, and systemic inflammation. Several vasculitides are associated with high risk of cardiovascular (CV) disease, an important source of morbidity and mortality in this population. This excess CV risk is attributed both to a high burden of traditional risk factors and to inflammation, but this remains poorly studied. Indeed, inflammation is a known risk factor for CV disease and implicated in coronary microvascular dysfunction (CMD) which may precede obstructive coronary artery disease (CAD).

Objectives

We sought to assess whether vasculitis is associated with CMD in the absence of obstructive CAD.

Methods

We retrospectively identified subjects with systemic vasculitis who underwent symptom prompted rest/stress myocardial perfusion PET. Patients with an abnormal myocardial perfusion study (summed stress score ≥3) or LVEF<40% were excluded. Controls were identified from the same population and matched on age, gender and cardiovascular risk factors (CAD, hypertension, dyslipidemia, diabetes mellitus, and obesity). Coronary flow reserve (CFR), was calculated as the ratio of myocardial blood flow (ml/min/g) at peak stress compared to rest. CMD was defined as CFR <2.

Results

We studied 26 vasculitis cases and 66 matched controls. The most common vasculitides were giant cell arteritis (38%), ANCA-associated vasculitis (31%), and Takayasu’s arteritis (12%). Median (IQR) time between diagnosis and PET was 6.5 (2.9, 14.2) years. Seven (27%) cases had active vascultis at the time of PET. Cases and controls were well-matched on age, sex, and CV risk factors (Table 1). Despite a similar prevalence of CV risk factors, coronary flow reserve (reflected by CMD) was abnormal in 38% of vasculitis cases compared to 15% of controls (p=0.004). The mean [SD] CFR was 19% lower in vasculitis cases vs controls (2.11 [0.5] versus 2.6 [0.7], p=0.003).

Table 1.

The presence of coronary microvasculature dysfunction in patients with systemic vasculitis without obstructive coronary artery disease

Cohort characteristicsVasculitis (n=26)Control (n=66)P-valueAge at PET, years62 (18)61 (17)0.24Time from Vasculitis Diagnosis to PET, years (median, IQR)6.5 (2.9, 14.2)n/aFemale, n (%)18 (72%)43 (65%)0.99Vasculitis CharacteristicsLarge Vessel (e.g., giant cell arteritis, Takayasu’s), n(%)13 (50%)n/an/aMedium Vessel (e.g., polyarteritis nodosa, Kawasaki’s arteritis), n(%)2 (8%)n/an/aSmall Vessel (e.g., ANCA-associated vasculitis, Henoch-Schonlein Purpura), n(%)11 (42%)n/an/aCardiovascular Risk FactorsAt DiagnosisAt PETAt PETHypertension, n (%)12 (46%)20 (71%)47 (80%)0.47Obesity, n (%)3 (12%)2 (32%)2 (32%)0.84Diabetes, n (%)3 (12%)5 (20%)13 (20%)0.99Dyslipidemia, n (%)4 (15%)15 (58%)40 (61%)0.99Known CAD, n (%)0 (0%)1 (4%)1 (2%)0.48Imaging FindingsRest myocardial blood flow, ml/min/g1.0 (0.3)1.0 (0.3)0.8Stress myocardial blood flow, ml/min/g2.1 (0.6)2.6 (1.0)0.008Coronary Flow Reserve, ml/min/g*2.1 (0.5)2.6 (0.7)0.003Coronary Microvasculature Dysfunction** (CMD), n (%)10 (38%)11 (15%)0.004

Conclusion

Patients with systemic vasculitis, even in the absence of obstructive CAD, have a high prevalence of CMD compared with non-vasculitis patients. These differences were observed despite matching cases and controls on traditional CV risk factors, highlighting the importance of other factors, such as inflammation and vasculitis treatments on CMD and CV disease in this population. CMD is a known independent risk factor for CV mortality. Future prospective studies are needed to understand the relationship between vasculitis, systemic inflammation, and CMD.

Disclosure of Interests

None declared

Binary acronym disambiguation in clinical notes from electronic health records with an application in computational phenotyping

OBJECTIVE

The use of electronic health records (EHR) systems has grown over the past decade, and with it, the need to extract information from unstructured clinical narratives. Clinical notes, however, frequently contain acronyms with several potential senses (meanings) and traditional natural language processing (NLP) techniques cannot differentiate between these senses. In this study we introduce a semi-supervised method for binary acronym disambiguation, the task of classifying a target sense for acronyms in the clinical EHR notes.

METHODS

We developed a semi-supervised ensemble machine learning (CASEml) algorithm to automatically identify when an acronym means a target sense by leveraging semantic embeddings, visit-level text and billing information. The algorithm was validated using note data from the Veterans Affairs hospital system to classify the meaning of three acronyms: RA, MS, and MI. We compared the performance of CASEml against another standard semi-supervised method and a baseline metric selecting the most frequent acronym sense. Along with evaluating the performance of these methods for specific instances of acronyms, we evaluated the impact of acronym disambiguation on NLP-driven phenotyping of rheumatoid arthritis.

RESULTS

CASEml achieved accuracies of 0.947, 0.911, and 0.706 for RA, MS, and MI, respectively, higher than a standard baseline metric and (on average) higher than a state-of-the-art semi-supervised method. As well, we demonstrated that applying CASEml to medical notes improves the AUC of a phenotype algorithm for rheumatoid arthritis.

CONCLUSION

CASEml is a novel method that accurately disambiguates acronyms in clinical notes and has advantages over commonly used supervised and semi-supervised machine learning approaches. In addition, CASEml improves the performance of NLP tasks that rely on ambiguous acronyms, such as phenotyping.

Coronary microvascular dysfunction in patients with psoriasis

BACKGROUND

Psoriasis is a common chronic inflammatory skin disorder that is associated with excess cardiovascular risk. Inflammation is a key mediator in the onset and progression of these cardiometabolic abnormalities; however, the excess cardiovascular risk conferred by psoriatic disease remains understudied. We investigated the prevalence and severity of CMD in patients with psoriasis and determined whether CMD is a result of CV risk factors and atherosclerotic burden.

METHODS

This was a consecutive retrospective cohort study of patients with psoriasis, normal myocardial perfusion, and LV ejection fraction (EF) > 50% (N = 62) and matched controls without psoriasis (N = 112). Myocardial perfusion and myocardial flow reserve (MFR) were quantified using PET imaging. Atherosclerotic burden was determined by semi-quantitative computed tomography (CT) coronary calcium assessment.

RESULTS

The prevalence of CMD (defined as MFR < 2) was 61.3% in patients with psoriatic disease, compared to 38.4% in a matched control population (P = .004). Furthermore, patients with psoriasis had a more severe reduction in adjusted MFR (2.3 ± .81 vs 1.92 ± .65, respectively, P = .001). The degree of atherosclerotic burden, as assessed by qualitative calcium score, was similar between psoriasis and controls.

CONCLUSIONS

Patients with psoriasis without overt CAD demonstrated a high prevalence of coronary vasomotor abnormalities that are not entirely accounted for by the commonly associated coronary risk factors or the burden of atherosclerosis.

Impaired Coronary Vasodilator Reserve and Adverse Prognosis in Patients With Systemic Inflammatory Disorders

OBJECTIVES

The purpose of this study was to evaluate the prognostic value of quantitative myocardial blood flow (MBF) and myocardial flow reserve (MFR), reflecting the integrated effects of diffuse atherosclerosis and microvascular dysfunction in patients with systemic inflammatory disorders.

BACKGROUND

Rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and psoriasis (PsO) are common inflammatory conditions with excess cardiovascular (CV) risk compared to the general population. Systemic inflammation perturbs endothelial function and has been linked to coronary vasomotor dysfunction. However, the prognostic significance of this vascular dysfunction is not known.

METHODS

This was a retrospective study of patients with RA, SLE, and PsO undergoing clinically indicated rest and stress myocardial perfusion positron emission tomography (PET). Patients with an abnormal myocardial perfusion study or left ventricular dysfunction were excluded. MFR was calculated as the ratio of myocardial blood flow (MBF, ml/min/g) at peak stress compared to that at rest.

RESULTS

Among the 198 patients (median age: 65 years; 80% female), 20.7% had SLE, 31.8% had PsO, and 47.5% had RA. There were no differences in mean MFR between these conditions. Over a median follow-up of 7.8 years, there were 51 deaths and 63 major adverse cardiovascular events (MACE). Patients in the lowest tertile (MFR <1.65) had higher all-cause mortality than the highest tertile, which remained significant after adjusting for age, sex, and the pre-test clinical risk score (hazard ratio [HR]: 2.4; 95% confidence interval [CI]: 1.05 to 5.4; p = 0.038). Similarly, compared to the highest MFR tertile, those in the lowest tertile had a lower MACE-free survival after adjusting for age, sex, and the pre-test clinical risk score (HR: 3.6; 95% CI: 1.7 to 7.6; p = 0.001).

CONCLUSIONS

In patients with systemic inflammatory disorders, impaired coronary vasodilator reserve was associated with worse cardiovascular outcomes and all-cause mortality.

A Phenome-Wide Association Study of genes associated with COVID-19 severity reveals shared genetics with complex diseases in the Million Veteran Program

The study aims to determine the shared genetic architecture between COVID-19 severity with existing medical conditions using electronic health record (EHR) data. We conducted a Phenome-Wide Association Study (PheWAS) of genetic variants associated with critical illness (n=35) or hospitalization (n=42) due to severe COVID-19 using genome-wide association summary from the Host Genetics Initiative. PheWAS analysis was performed using genotype-phenotype data from the Veterans Affairs Million Veteran Program (MVP). Phenotypes were defined by International Classification of Diseases (ICD) codes mapped to clinically relevant groups using published PheWAS methods. Among 658,582 Veterans, variants associated with severe COVID-19 were tested for association across 1,559 phenotypes. Variants at the ABO locus (rs495828, rs505922) associated with the largest number of phenotypes (n_rs495828=53 and n_rs505922=59); strongest association with venous embolism, odds ratio (ORrs495828 1.33 (p=1.32 × 10^-199), and thrombosis OR_rs505922 1.33, p=2.2 × 10^-265. Among 67 respiratory conditions tested, 11 had significant associations including MUC5B locus (rs35705950) with increased risk of idiopathic fibrosing alveolitis OR 2.83, p=4.12 × 10^-191; CRHR1 (rs61667602) associated with reduced risk of pulmonary fibrosis, OR 0.84, p=2.26 × 10^-12. The TYK2 locus (rs11085727) associated with reduced risk for autoimmune conditions, e.g., psoriasis OR 0.88, p=6.48 × 10^-23, lupus OR 0.84, p=3.97 × 10^-06. PheWAS stratified by genetic ancestry demonstrated differences in genotype-phenotype associations across ancestry. LMNA (rs581342) associated with neutropenia OR 1.29 p=4.1 × 10^-13 among Veterans of African ancestry but not European. Overall, we observed a shared genetic architecture between COVID-19 severity and conditions related to underlying risk factors for severe and poor COVID-19 outcomes. Differing associations between genotype-phenotype across ancestries may inform heterogenous outcomes observed with COVID-19. Divergent associations between risk for severe COVID-19 with autoimmune inflammatory conditions both respiratory and non-respiratory highlights the shared pathways and fine balance of immune host response and autoimmunity and caution required when considering treatment targets.

Coronary Microvascular Dysfunction in Systemic Lupus Erythematosus

Background

Systemic lupus erythematosus (SLE) is a systemic autoimmune inflammatory disorder associated with premature atherosclerosis and increased cardiovascular risk. Systemic inflammation is an emerging risk factor for coronary microvascular dysfunction (CMD). We aimed to test whether CMD, defined as abnormal myocardial flow reserve (MFR) by positron emission tomography‐computed tomography, would be independently associated with SLE after adjusting for nonobstructive atherosclerotic burden and common cardiovascular risk factors.

Methods and Results

Consecutive patients with SLE who underwent symptom‐prompted stress cardiac positron emission tomography‐computed tomography were included (n=42). Obstructive coronary artery disease and systolic dysfunction were excluded. MFR was quantified by positron emission tomography‐computed tomography, and CMD was defined as MFR <2. We frequency matched patients who did not have SLE and had symptom‐prompted positron emission tomography studies on age, sex, and key cardiovascular risk factors (n=69). The attenuation correction computed tomography scans were reviewed for qualitative assessment of coronary artery calcium. Patients with SLE had a more severe reduction in global MFR compared with controls and a higher prevalence of CMD, despite a similar degree of nonobstructive atherosclerotic burden (1.91±0.5 versus 2.4±0.7, respectively, P<0.0001; CMD, 57.1% versus 33.3%, respectively, P=0.017).

Conclusions

We demonstrated that patients with SLE with cardiac symptoms without obstructive coronary artery disease have a high prevalence of coronary vasomotor abnormalities. In comparison with symptomatic matched controls, patients with SLE have a more severe reduction in MFR that is not accounted for by common cardiovascular factors or atherosclerotic burden.

Association of inflammatory disease and long-term outcomes among young adults with myocardial infarction: the Mass General Brigham YOUNG-MI Registry

AIMS

Autoimmune systemic inflammatory diseases (SIDs) are associated with an increased risk of cardiovascular (CV) disease, particularly myocardial infarction (MI). However, there are limited data on the prevalence and effects of SID among adults who experience an MI at a young age. We sought to determine the prevalence and prognostic implications of SID among adults who experienced an MI at a young age.

METHODS AND RESULTS

The YOUNG-MI registry is a retrospective cohort study from two large academic centres, which includes patients who experienced a first MI at 50 years of age or younger. SID was ascertained through physician review of the electronic medical record (EMR). Incidence of death was ascertained through the EMR and national databases. The cohort consisted of 2097 individuals, with 53 (2.5%) possessing a diagnosis of SID. Patients with SID were more likely to be female (36% vs. 19%, P = 0.004) and have hypertension (62% vs. 46%, P = 0.025). Over a median follow-up of 11.2 years, patients with SID experienced an higher risk of all-cause mortality compared with either the full cohort of non-SID patients [hazard ratio (HR) = 1.95, 95% confidence interval (CI) (1.07-3.57), P = 0.030], or a matched cohort based on age, gender, and CV risk factors [HR = 2.68, 95% CI (1.18-6.07), P = 0.018].

CONCLUSIONS

Among patients who experienced a first MI at a young age, 2.5% had evidence of SID, and these individuals had higher rates of long-term all-cause mortality. Our findings suggest that the presence of SID is associated with worse long-term survival after premature MI.

Leveraging electronic health records data to predict multiple sclerosis disease activity

Objective

No relapse risk prediction tool is currently available to guide treatment selection for multiple sclerosis (MS). Leveraging electronic health record (EHR) data readily available at the point of care, we developed a clinical tool for predicting MS relapse risk.

Methods

Using data from a clinic‐based research registry and linked EHR system between 2006 and 2016, we developed models predicting relapse events from the registry in a training set (n = 1435) and tested the model performance in an independent validation set of MS patients (n = 186). This iterative process identified prior 1‐year relapse history as a key predictor of future relapse but ascertaining relapse history through the labor‐intensive chart review is impractical. We pursued two‐stage algorithm development: (1) L₁‐regularized logistic regression (LASSO) to phenotype past 1‐year relapse status from contemporaneous EHR data, (2) LASSO to predict future 1‐year relapse risk using imputed prior 1‐year relapse status and other algorithm‐selected features.

Results

The final model, comprising age, disease duration, and imputed prior 1‐year relapse history, achieved a predictive AUC and F score of 0.707 and 0.307, respectively. The performance was significantly better than the baseline model (age, sex, race/ethnicity, and disease duration) and noninferior to a model containing actual prior 1‐year relapse history. The predicted risk probability declined with disease duration and age.

Conclusion

Our novel machine‐learning algorithm predicts 1‐year MS relapse with accuracy comparable to other clinical prediction tools and has applicability at the point of care. This EHR‐based two‐stage approach of outcome prediction may have application to neurological disease beyond MS.

[Analysis of pathogen spectrum and antimicrobial resistance of pathogens associated with hospital-acquired infections collected from 11 teaching hospitals in 2018]

Objective: To investigate the spectrum and antimicrobial resistance of major pathogens causing nosocomial infections in China, 2018. Methods: Non-duplicated nosocomial cases as well as pathogens causing bloodstream infections (BSI), hospital-acquired pneumonia (HAP) and intra-abdominal infections (IAI) from 11 teaching hospitals across China were collected. The minimum inhibitory concentrations (MICs) of clinically significant strains were determined by agar dilution method or broth microdilution method. The Clinical and Laboratory Standards Institute (CLSI) M100-S29 criteria were used for interpretation, and the WHONET-5.6 software was used in data analysis. Results: A total of 1 590 cases were collected, including 831 cases from BSI, 450 cases from HAP and 309 cases from IAI. The most prevalent pathogens causing BSI were Escherichia coli (29.2%, 243/831), Klebsiella pneumoniae (16.2%, 135/831) and Staphylococcus aureus (10.1%, 84/831); the most prevalent pathogens causing IAI were E. coli (26.2%, 81/309), Enterococcus faecium (15.5%, 48/309) and K. pneumoniae (13.3%, 41/309); while Acinetobacter baumanii (24.7%, 111/450), Pseudomonas aeruginosa (20.7%, 93/450) and K. pneumoniae (16.2%, 73/450) were dominated in HAP. All S. aureus were susceptible to tigecycline, linezolid, daptomycin and glycopeptides; 77.8% (105/135) of S. aureus strains were susceptible to ceftaroline. Methicillin-resistant S. aureus (MRSA) accounted for 29.6% (40/135) of all the S. aureus, and was lower than the accounted rate of methicillin-resistant coagulase-negative Staphylococcus (MRCNS) (83.7%, 41/49). One E. faecium strain (1.1%, 1/95) resistant to vacomycin and teicoplanin and one E. faecalis strain (2.3%, 1/43) resistant to linezolid was found. The prevalence of extended-spectrum β-lactamase (ESBL) was 56.1% (193/344) in E. coli and 22.1% (55/249) in K. pneumonia; the rate of carbapenem resistant E. coli and K. pneumonia was 4.1% (14/344) and 22.9% (57/249), respectively; the percentage of ceftazidime/avibactam resistant E. coli and K. pneumonia was 2.3% (8/344) and 2.0% (5/249), respectively; the percentage of colistin resistant E. coli and K. pneumonia was 1.5% (5/344) and 7.6% (19/249), respectively; no E. coli and K. pneumonia strains were found resistant to tigecycline. The rate of carbapenem resistant A. baumanii and P. aeruginosa were 78.9% (146/185) and 36.7% (66/180), respectively. A. baumanii showed low susceptibility to the antimicrobial agents except colistin (99.5%, 184/185) and tigecycline (91.4%, 169/185). Colistin, amikacin and ceftazidime/avibactam demonstrated high antibacterial activity against P. aeruginosa with susceptility rate of 100% (180/180), 93.3% (168/180) and 85.6% (154/180), respectively. Conclusions: Nosocomial Gram-negative pathogens show high susceptibilities to tigecycline, colistin and ceftazidime/avibactam in vitro. Antimicrobial resistance in A. baumannii is a serious problem. The prevalence of carbapenem-resistant Enterobacteriaceae has increased, which should be monitored continuously in China.

Association of inflammatory disease and long-term outcomes among young adults with myocardial infarction: the Partners YOUNG-MI registry

Background

Autoimmune systemic inflammatory diseases are associated with an increased risk of cardiovascular disease, particularly myocardial infarction (MI). However, there are limited data on the prevalence and effects of inflammatory disease among U.S. adults who experience an MI at a young age.

Purpose

We sought to determine the prevalence and prognostic value of inflammatory disease in U.S. adults who experience an MI at a young age.

Methods

The YOUNG-MI registry is a retrospective cohort study of consecutive patients who experienced a Type 1 MI at or below the age of 50 years from 2000 to 2016 at two large medical centers. A diagnosis of rheumatoid arthritis (RA), psoriasis (PsO), systemic lupus erythematosus (SLE), or inflammatory arthritis was determined through physician review of electronic medical records (EMR). Demographic information, presence of cardiovascular (CV) risk-factors, medical procedures, and medications upon discharge were also ascertained from the EMR. Incidence of death was determined using a combination of EMR and national databases. Cox proportional hazard modeling was performed on a sub-sample following Mahalanobis Distance matching on age, sex, and CV risk factors.

Results

The cohort consisted of 2097 individuals (median age 45 years, 19% female, 53% ST-elevation MI). Among these, 53 (2.5%) individuals possessed a diagnosis of systemic inflammatory disease at or before their index MI (23% SLE, 9% RA, 64% PsO, 4% inflammatory arthritis). When compared to the remainder of the cohort, patients with a diagnosis of systemic inflammatory disease were more likely to be female (36% vs 19%, p=0.004) and be diagnosed with hypertension (62% vs 46%, p=0.025). There was, however, no significant difference in the prevalence of other CV risk factors – diabetes, smoking, dyslipidemia – or a family history of premature coronary artery disease. Despite these similarities, patients with inflammatory disease were less likely to be prescribed aspirin (88% vs 95%, p=0.049) or a statin (76% vs 89%, p=0.008) upon discharge. Over a median follow-up of 11.2 years, patients with inflammatory disease experienced an increased risk of all-cause mortality when compared with the full-cohort (Figure). Compared to the matched sample (n=138), patients with systemic inflammatory disease exhibited an increased risk of all-cause mortality (HR=2.68, CI [1.18 to 6.07], p=0.018), which remained significant after multivariable adjustment for length of stay and GFR (HR=2.38, CI [1.02 to 5.54], p=0.045).

Conclusions

Among individuals who experienced an MI at a young age, approximately 2.5% had evidence of a systemic inflammatory disease at or before their MI. When compared with a population of individuals with similar cardiovascular risk profiles, those with inflammatory disease had higher rates of all-cause mortality. Our findings suggest that the presence of a systemic inflammatory disorder is independently associated with worse long-term outcomes.

Funding Acknowledgement

Type of funding source: Public grant(s) – National budget only. Main funding source(s): 1. 5T32 HL094301 NIH T32 Training Grant, “Noninvasive Cardiovascular Imaging Research Training Program”

Coronary vasomotor dysfunction is associated with worse outcomes in patients with inflammatory disease

Background

Rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and psoriasis (PsO) are common inflammatory conditions with excess cardiovascular (CV) risk compared to the general population. This excess CV risk is associated with traditional risk factors, glucocorticoid treatment, and systemic inflammation. Systemic inflammation perturbs endothelial function and has been linked to coronary vasomotor dysfunction. It is not clear if coronary vasomotor dysfunction would be associated with worse clinical outcomes in systemic autoimmune inflammatory conditions.

Purpose

We tested the hypothesis that impaired coronary flow reserve (CFR), which in the absence of flow-limiting obstructive coronary artery disease (CAD) reflects vasomotor dysfunction, among patients with SLE, RA, and PsO is associated with worse clinical outcomes.

Methods

We included patients with RA, SLE, and PsO who underwent clinically indicated rest/stress myocardial perfusion positron emission tomography (PET) at a large academic medical center from 2006 to 2019. Patients with an abnormal myocardial perfusion study (summed stress score &gt;3) or left ventricular ejection fraction &lt;40% were excluded. CFR was calculated as the ratio of myocardial blood flow (MBF, ml/min/g) at peak stress compared to the MBF at rest and adjusted for baseline heart rate and blood pressure.

Results

Among the 175 patients (median age 65.1 years, 80% female) in the cohort, 24% had SLE, 35% PsO, and 41% RA. There was no difference in mean CFR between patients with RA, SLE, or PsO. Over a median follow-up of 8.5 years after PET, there were 47 deaths. Patients in the lowest and middle tertile (CFR &lt;2.18) had a higher all-cause mortality when compared with the highest (Figure 1), and this association remained significant after adjusting for age and a composite clinical score incorporating sex, symptoms, and CV risk factors (lowest vs. highest tertile: HR 2.8; 95% confidence interval 1.2–6.5; p=0.01). CV risk factors such as diabetes, hypertension, obesity, tobacco use, and a family history of CAD were not significantly different across CFR tertiles, suggesting that inflammatory-disease specific risk factors may contribute to coronary vasomotor dysfunction.

Conclusions

In patients with systemic inflammatory disease, coronary vasomotor dysfunction was associated with worse outcomes independent of traditional CV risk factors and may have utility as a marker of CV risk among patients with inflammatory disease.

Figure 1

Funding Acknowledgement

Type of funding source: Public grant(s) – National budget only. Main funding source(s): 1. 5T32HL094301-02 NIH T32 Training Grant, “Noninvasive Cardiovascular Imaging Research Training Program”

OP0111 RHEUMATOID ARTHRITIS SEROLOGIC PHENOTYPE AT DIAGNOSIS AND SUBSEQUENT RISK FOR PNEUMONIA IDENTIFIED USING MACHINE LEARNING APPROACHES

Background:

Patients with rheumatoid arthritis (RA) are at increased risk of serious infections, with considerable excess morbidity and mortality after pneumonia. RA-related autoantibodies such as anti-cyclic citrullinated peptide (CCP) and rheumatoid factor (RF) may be generated at inflamed pulmonary mucosa prior to clinical RA onset. Therefore, patients with seropositive RA may be at increased risk for pneumonia after RA diagnosis due to subclinical pulmonary injury.

Objectives:

We investigated whether seropositive RA was associated with increased pneumonia risk compared to seronegative RA.

Methods:

We performed a retrospective cohort study among RA patients seen at a health care system in Boston, MA. RA patients were identified using a previously validated electronic health record (EHR) algorithm incorporating billing codes, natural language processing (NLP) of notes, medications, and laboratory results at 97% specificity1. We constructed an incident RA cohort using NLP for the index date of initial mention of RA. All patients were required to have both CCP and RF data from clinical care to determine serologic RA phenotype. We used semi-supervised machine learning approaches to identify pneumonia using billing codes and terms extracted using NLP, with the Centers for Disease Control definition of pneumonia from medical record review as a gold standard. The area under the receiver operating curve (AUROC) for this billing code+NLP pneumonia algorithm was 0.94 compared to the standard rule-based pneumonia algorithm (billing code on inpatient discharge) AUROC of 0.86 (p<0.001). Smoking status was extracted using NLP methods. Other covariates, including a previous validated weighted RA multimorbidity score2, were determined using structured EHR data. We used Cox regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for pneumonia adjusting for potential confounders.

Results:

We analyzed a total of 4,110 patients with incident RA and both CCP/RF data available. Mean age at index date was 53.0 years (SD 14.8), 77.2% were female, and 79.8% were CCP+ or RF+. During 32,248 patient-years of follow-up (mean 7.8 years/patient), we identified 240 pneumonia cases. Patients with seropositive RA had a HR of 1.99 (95%CI 1.30-3.01, Table) for pneumonia compared to patients with seronegative RA, adjusted for age, sex, smoking, index year, ESR level, glucocorticoid use, DMARD use, and weighted RA multimorbidity score. While CCP+ RA (HR 1.91, 95%CI 1.23-2.97) and RF+ RA (HR 2.07, 95%CI 1.35-3.16) had increased pneumonia risk compared to seronegative RA, the CCP+RF- RA subgroup had no association with pneumonia (HR 0.67, 95%CI 0.23-1.93).

Conclusion:

Patients with incident seropositive RA, particularly RF+ RA, had increased risk for pneumonia throughout the RA disease course that was not explained by measured confounders including smoking status, multimorbidity, medications, and ESR level. Further studies should investigate how RF+ may predispose RA patients to later develop pneumonia after clinical RA diagnosis.

References:

[1]Liao KP, Cai T, Gainer V, et al. Electronic medical records for discovery research in rheumatoid arthritis. Arthritis Care Res. 2010;62(8):1120–1127.

[2]Radner H, Yoshida K, Mjaavatten MD, et al. Development of a multimorbidity index: Impact on quality of life using a rheumatoid arthritis cohort. Semin Arthritis Rheum. 2015;45(2):167–173.

Disclosure of Interests:

Jeffrey Sparks Consultant of: Bristol-Myers Squibb, Optum, Janssen, Gilead, Weixing Huang: None declared, Bing Lu: None declared, Sicong Huang: None declared, Andrew Cagan: None declared, Vivian Gainer: None declared, Sean Finan: None declared, Guergana Savova: None declared, Daniel Solomon Grant/research support from: Funding from Abbvie and Amgen unrelated to this work, Elizabeth Karlson: None declared, Katherine Liao: None declared

Pithecellobium clypearia extract enriched in gallic acid and luteolin has antibacterial activity against MRSA and reduces resistance to erythromycin, ceftriaxone sodium and levofloxacin

AIMS

Antibiotic adjuvants can give a second life to the antibiotics to which bacteria are highly resistant. We evaluated the antimicrobial effects of extracts from Pithecellobium clypearia against methicillin-resistant Staphylococcus aureus (MRSA) and also the potential for synergy with several antibiotics.

METHODS AND RESULTS

For this study, four extracts from P. clypearia were tested on MRSA using the broth microdilution method for activity assessment. The ethyl acetate fraction (S20b) had the strongest antibacterial activity against MRSA among the fractions tested. In all, 14 compounds such as gallic acid and luteolin in S20b were analysed by UFLC-Q-TOF-MS/MS. S20b combined with erythromycin showed synergy effects against MRSA and combined with ceftriaxone sodium and levofloxacin showed additive effects against MRSA. Electron microscopy showed that extract S20b damaged the MRSA cell wall and K⁺ efflux measurements indicated that extract S20b increased cell membrane permeability. Moreover, S20b suppression of PBP2a expression was assessed by Western blot. Furthermore, an in vivo study was used to investigate the therapeutic potential of S20b based on a mouse pneumonia model.

CONCLUSIONS

The in vitro study results have shown that S20b not only inhibits MRSA growth directly but also reduces the resistance of MRSA to the evaluated antibacterial agents. Based on the in vivo study, it can be concluded that S20b can treat pneumonia in the mouse model.

SIGNIFICANCE AND IMPACT OF THE STUDY

This study is the first research to demonstrate that S20b can inhibit MRSA growth and reduce drug resistance of clinical isolates to antibiotics. S20b has the potential to be used as a therapeutic agent against MRSA and treatment for MRSA pneumonia.

Large-Scale Simultaneous Testing of Cross-Covariance Matrices with Applications to PheWAS

Motivated by applications in phenome-wide association studies (PheWAS), we consider in this paper simultaneous testing of columns of high-dimensional cross-covariance matrices and develop a multiple testing procedure with theoretical guarantees. It is shown that the proposed testing procedure maintains a desired false discovery rate (FDR) and false discovery proportion (FDP) under mild regularity conditions. We also provide results on the magnitudes of the signals that can be detected with high power. Simulation studies demonstrate that the proposed procedure can be substantially more powerful than existing FDR controlling procedures in the presence of correlation of unknown structure. The proposed multiple testing procedure is applied to a PheWAS of two auto-immune genetic markers using a rheumatoid arthritis patient cohort constructed from the electronic medical records of Partners Healthcare System.

Automated grouping of medical codes via multiview banded spectral clustering

OBJECTIVE

With its increasingly widespread adoption, electronic health records (EHR) have enabled phenotypic information extraction at an unprecedented granularity and scale. However, often a medical concept (e.g. diagnosis, prescription, symptom) is described in various synonyms across different EHR systems, hindering data integration for signal enhancement and complicating dimensionality reduction for knowledge discovery. Despite existing ontologies and hierarchies, tremendous human effort is needed for curation and maintenance - a process that is both unscalable and susceptible to subjective biases. This paper aims to develop a data-driven approach to automate grouping medical terms into clinically relevant concepts by combining multiple up-to-date data sources in an unbiased manner.

METHODS

We present a novel data-driven grouping approach - multi-view banded spectral clustering (mvBSC) combining summary data from multiple healthcare systems. The proposed method consists of a banding step that leverages the prior knowledge from the existing coding hierarchy, and a combining step that performs spectral clustering on an optimally weighted matrix.

RESULTS

We apply the proposed method to group ICD-9 and ICD-10-CM codes together by integrating data from two healthcare systems. We show grouping results and hierarchies for 13 representative disease categories. Individual grouping qualities were evaluated using normalized mutual information, adjusted Rand index, and F1-measure, and were found to consistently exhibit great similarity to the existing manual grouping counterpart. The resulting ICD groupings also enjoy comparable interpretability and are well aligned with the current ICD hierarchy.

CONCLUSION

The proposed approach, by systematically leveraging multiple data sources, is able to overcome bias while maximizing consensus to achieve generalizability. It has the advantage of being efficient, scalable, and adaptive to the evolving human knowledge reflected in the data, showing a significant step toward automating medical knowledge integration.

Disease Activity, Antineutrophil Cytoplasmic Antibody Type, and Lipid Levels in Antineutrophil Cytoplasmic Antibody-Associated Vasculitis

OBJECTIVE

Patients with antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) have an elevated risk of cardiovascular disease (CVD). This study was undertaken to develop a clearer understanding of the association between changes in disease activity and lipid levels in AAV, which may inform CVD risk stratification in this population.

METHODS

Lipid levels were assessed in stored serum samples (obtained at baseline and month 6) from the Rituximab for ANCA-Associated Vasculitis (RAVE) trial, which randomized patients to receive either rituximab or cyclophosphamide followed by azathioprine. Paired t-tests and multivariable linear regression were used to assess changes in lipid levels.

RESULTS

Of the 142 patients with serum samples available, the mean ± SD age was 52.3 ± 14.7 years, 72 (51%) were male, 95 (67%) were proteinase 3 (PR3)-ANCA positive, 72 (51%) had received a new diagnosis of AAV, and 75 (53%) were treated with rituximab. Several lipid levels increased between baseline and month 6, including total cholesterol (+12.4 mg/dl [95% confidence interval (95% CI) +7.1, +21.0]), low-density lipoprotein (+10.3 mg/dl [95% CI +6.1, +17.1]), and apolipoprotein B (+3.5 mg/dl [95% CI +1.0, +8.3]). These changes were observed among newly diagnosed and PR3-ANCA-positive patients but not among those with relapsing disease or myeloperoxidase-ANCA-positive patients. There was no difference in change in lipid levels between rituximab-treated patients and cyclophosphamide-treated patients. Changes in lipid levels correlated with changes in erythrocyte sedimentation rate but not with other inflammatory markers or glucocorticoid exposure.

CONCLUSION

Lipid levels increased during remission induction among patients with newly diagnosed AAV and those who were PR3-ANCA positive. Disease activity and ANCA type should be considered when assessing lipid profiles to stratify CVD risk in patients with AAV.

Automated feature selection of predictors in electronic medical records data

The use of Electronic Health Records (EHR) for translational research can be challenging due to difficulty in extracting accurate disease phenotype data. Historically, EHR algorithms for annotating phenotypes have been either rule-based or trained with billing codes and gold standard labels curated via labor intensive medical chart review. These simplistic algorithms tend to have unpredictable portability across institutions and low accuracy for many disease phenotypes due to imprecise billing codes. Recently, more sophisticated machine learning algorithms have been developed to improve the robustness and accuracy of EHR phenotyping algorithms. These algorithms are typically trained via supervised learning, relating gold standard labels to a wide range of candidate features including billing codes, procedure codes, medication prescriptions and relevant clinical concepts extracted from narrative notes via Natural Language Processing (NLP). However, due to the time intensiveness of gold standard labeling, the size of the training set is often insufficient to build a generalizable algorithm with the large number of candidate features extracted from EHR. To reduce the number of candidate predictors and in turn improve model performance, we present an automated feature selection method based entirely on unlabeled observations. The proposed method generates a comprehensive surrogate for the underlying phenotype with an unsupervised clustering of disease status based on several highly predictive features such as diagnosis codes and mentions of the disease in text fields available in the entire set of EHR data. A sparse regression model is then built with the estimated outcomes and remaining covariates to identify those features most informative of the phenotype of interest. Relying on the results of Li and Duan (1989), we demonstrate that variable selection for the underlying phenotype model can be achieved by fitting the surrogate-based model. We explore the performance of our methods in numerical simulations and present the results of a prediction model for Rheumatoid Arthritis (RA) built on a large EHR data mart from the Partners Health System consisting of billing codes and NLP terms. Empirical results suggest that our procedure reduces the number of gold-standard labels necessary for phenotyping thereby harnessing the automated power of EHR data and improving efficiency.

Feature extraction for phenotyping from semantic and knowledge resources

OBJECTIVE

Phenotyping algorithms can efficiently and accurately identify patients with a specific disease phenotype and construct electronic health records (EHR)-based cohorts for subsequent clinical or genomic studies. Previous studies have introduced unsupervised EHR-based feature selection methods that yielded algorithms with high accuracy. However, those selection methods still require expert intervention to tweak the parameter settings according to the EHR data distribution for each phenotype. To further accelerate the development of phenotyping algorithms, we propose a fully automated and robust unsupervised feature selection method that leverages only publicly available medical knowledge sources, instead of EHR data.

METHODS

SEmantics-Driven Feature Extraction (SEDFE) collects medical concepts from online knowledge sources as candidate features and gives them vector-form distributional semantic representations derived with neural word embedding and the Unified Medical Language System Metathesaurus. A number of features that are semantically closest and that sufficiently characterize the target phenotype are determined by a linear decomposition criterion and are selected for the final classification algorithm.

RESULTS

SEDFE was compared with the EHR-based SAFE algorithm and domain experts on feature selection for the classification of five phenotypes including coronary artery disease, rheumatoid arthritis, Crohn's disease, ulcerative colitis, and pediatric pulmonary arterial hypertension using both supervised and unsupervised approaches. Algorithms yielded by SEDFE achieved comparable accuracy to those yielded by SAFE and expert-curated features. SEDFE is also robust to the input semantic vectors.

CONCLUSION

SEDFE attains satisfying performance in unsupervised feature selection for EHR phenotyping. Both fully automated and EHR-independent, this method promises efficiency and accuracy in developing algorithms for high-throughput phenotyping.

Saccharomyces cerevisiae Hog1 MAP kinase pathway is activated in response to honokiol exposure

AIM

The goal of the study was to investigate the cellular tolerance mechanism in response to honokiol exposure.

METHODS AND RESULTS

The broth microdilution method was employed to test the sensitivity of different Saccharomyces cerevisiae strains to honokiol. Intracellular levels of reactive oxygen species (ROSs) were determined by DCFH-DA staining. The phosphorylation of Hog1 was evaluated by Western blot analysis. The mRNA expressions of genes involved in the Ras-cyclic AMP (cAMP) pathway were analysed by real-time reverse transcription polymerase chain reaction. We found that the sod1▵ mutant was hypersensitive to honokiol and produced more ROS compared with wild-type and sod2▵ cells. Hog1 was phosphorylated in response to honokiol exposure and deletion of HOG1 increased the sensitivity to honokiol. The expressions of genes involved in the Ras-cAMP pathway were down-regulated after honokiol exposure; exogenous cAMP significantly reduced the phosphorylation of Hog1, although the level was higher than the control level.

CONCLUSIONS

In addition to SOD1, the Ras-cAMP cascade and Hog1 MAP kinase pathway is essential for protecting against honokiol-induced oxidative stress.

SIGNIFICANCE AND IMPACT OF THE STUDY

Our results provide insight into the understanding of the action mechanism of honokiol.

Transethnic meta-analysis identifies GSDMA and PRDM1 as susceptibility genes to systemic sclerosis

OBJECTIVES

Systemic sclerosis (SSc) is an autoimmune disease characterised by skin and systemic fibrosis culminating in organ damage. Previous genetic studies including genome-wide association studies (GWAS) have identified 12 susceptibility loci satisfying genome-wide significance. Transethnic meta-analyses have successfully expanded the list of susceptibility genes and deepened biological insights for other autoimmune diseases.

METHODS

We performed transethnic meta-analysis of GWAS in the Japanese and European populations, followed by a two-staged replication study comprising a total of 4436 cases and 14 751 controls. Associations between significant single nuclear polymorphisms (SNPs) and neighbouring genes were evaluated. Enrichment analysis of H3K4Me3, a representative histone mark for active promoter was conducted with an expanded list of SSc susceptibility genes.

RESULTS

We identified two significant SNP in two loci, GSDMA and PRDM1, both of which are related to immune functions and associated with other autoimmune diseases (p=1.4×10^-10 and 6.6×10^-10, respectively). GSDMA also showed a significant association with limited cutaneous SSc. We also replicated the associations of previously reported loci including a non-GWAS locus, TNFAIP3. PRDM1 encodes BLIMP1, a transcription factor regulating T-cell proliferation and plasma cell differentiation. The top SNP in GSDMA was a missense variant and correlated with gene expression of neighbouring genes, and this could explain the association in this locus. We found different human leukocyte antigen (HLA) association patterns between the two populations. Enrichment analysis suggested the importance of CD4-naïve primary T cell.

CONCLUSIONS

GSDMA and PRDM1 are associated with SSc. These findings provide enhanced insight into the genetic and biological basis of SSc.

Simultaneous detection of 45 fusion genes in leukemia by dual-color fluorescence real-time PCR

INTRODUCTION

Detection of recurrent genetic abnormalities is of great significance for a refined diagnosis and assessment of prognosis in leukemia. Conventional nested reverse transcription PCR is labor intensive and time-consuming.

METHODS

We have developed a novel dual-color TaqMan probe-based real-time PCR method for the simultaneous screening of 45 fusion transcripts in 12 parallel reactions. The method was tested and validated with cell lines carrying known fusion transcripts and patient samples.

RESULTS

A multiplex real-time PCR method was successfully developed for rapid detection of 45 fusion genes and validated for 15 of the more commonly detected fusion genes. Intra-assay reproducibility assessed for the most frequent rearrangements ranged from 0.41% to 0.74% for the coefficient of variation (CV) of cycle threshold (Ct) and the interassay reproducibility ranged from 1.62% to 2.83% in five separate experiments. The lowest detection limit for the translocations tested ranged between 1 : 16 000 and 1 : 32 000. Validation of the method with 213 patient samples showed 100% specificity and excellent consistence with conventional nested RT-PCR.

CONCLUSION

Overall, we believe that this method is easily applicable, cost-effective, and clinically useful for a rapid screening of fusion genes in the initial diagnostic phase of leukemia. Its use can also be extended to the monitoring of minimal residual disease.

In utero exposure of high-dose di-n-butyl phthalate resulted in opposite effects on testicular cell apoptosis in late embryonic and pubertal male rat offspring

Objective:

To investigate the effects of in utero exposure to high-dose di- n-butyl phthalate (DBP) on testicular cell apoptosis in late embryonic and pubertal male rat offspring.

Methods:

Twenty pregnant Sprague-Dawley (SD) rats were divided into two groups. During gestation day (GD) 12 to GD 19, control group was given 1 ml day−1 of olive oil and experimental group was given DBP 500 mg kg−1 day−1 by gavage. On GD 19.5 and postnatal day (PND) 45, the testes were removed. Morphological analysis of the testes was observed by transmission electron microscopy and hematoxylin and eosin (H&E) staining. Testicular cell apoptosis was detected by terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL). The expression of Bcl-2, Bax, and p53 was presented by immunohistochemistry (IHC) and western blot. Data of the two groups was compared using independent samples t-test and Mann–Whitney test by SPSS 20.0.

Results:

H&E staining showed that spermatogenetic cells were significantly decreased in DBP exposed pubertal rat testis. The apoptosis index of testes in DBP-treated group was significantly lower on GD 19.5 but higher on PND 45 than that of the controls ( p < 0.01). IHC and western blot revealed significantly increased expression of Bcl-2 in GD 19.5 rat testis and Bax and p53 in PND 45 rat testis after DBP exposure, compared with the control ( p < 0.05).

Conclusion:

In utero exposure of high-dose DBP resulted in opposite effects on testicular cell apoptosis in late embryonic and pubertal rat offspring. The overexpression of Bcl-2, Bax, and p53 might be related to the occurrence of abnormal apoptosis and finally produce male infertility.

Development of EST-SSR markers in the relict tree Davidia involucrata (Davidiaceae) using transcriptome sequencing

Davidia involucrata, reputed to be a "living fossil" in the plant kingdom, is a relict tree endemic to China. Extant natural populations are diminishing due to anthropogenic disturbance. In order to understand its ability to survive in a range of climatic conditions and to design conservation strategies for this endangered species, we developed genic simple sequence repeats (SSRs) from mRNA transcripts. In total, 142,950 contigs were assembled. Of these, 30,411 genic SSR loci were discovered and 12,208 primer pairs were designed. Dinucleotides were the most common (77.31%) followed by trinucleotides (16.44%). Thirteen randomly selected primers were synthesized and validated using 24 individuals of D. involucrata. The markers displayed high polymorphism with the number of alleles per locus ranging from 3 to 12 and the observed and expected heterozygosities ranging from 0.083 to 1.0 and 0.102 to 0.69, respectively. This large expressed sequence tag dataset and the novel SSR markers will be key tools in comparative studies that may reveal the adaptive evolution, population structure, and resolve the genetic diversity in this endangered species.

Development of expressed sequence tag-based microsatellite markers for the critically endangered Isoëtes sinensis (Isoetaceae) based on transcriptome analysis

Isoëtes sinensis is a critically endangered quillwort. To facilitate studies on the conservation genetics of this species, we developed expressed sequence tag-simple sequence repeat (EST-SSR) markers. A total of 50,063 unigenes were predicted by transcriptome sequencing, 5294 (10.6%) of which significantly matched 3011 Gene Ontology annotations and 2363 were assigned to Kyoto Encyclopedia of Genes and Genomes metabolic pathways. Most of these (2297) were involved in metabolism. A total of 1982 SSR motifs were identified, with trinucleotides being the dominant repeat motif, and 1438 (72.6%) SSR primers were designed. Eighteen randomly selected primer pairs were used to genotype 24 I. sinensis accessions, which confirmed the suitability of these novel markers for molecular studies of I. sinensis. The heterozygosity index value ranged between 0.0799 and 0.9106, while the Shannon-Wiener diversity index value ranged between 0.1732 and 2.5589. The EST-SSRs reported in this study are linked to genic sequences, and are therefore ideal for investigating the evolutionary history of I. sinensis. These markers, together with the large EST dataset generated in this study, will greatly facilitate conservation genetic studies of I. sinensis.

Meta-analysis of cardiovascular toxicity risks in cancer patients on selected targeted agents

PURPOSE

The purpose was to estimate the risk and severity of cardiovascular toxicities associated with selected targeted agents.

METHODS

We searched English-language literature for randomized clinical trials published between January 1, 2000 and November 30, 2013 of targeted cancer therapy drugs approved by the FDA by November 2010. One hundred ten studies were eligible. Using meta-analytic methods, we calculated the relative risks of several cardiovascular toxicities [congestive heart failure (CHF), decreased left ventricular ejection fraction (DLVEF), myocardial infarction (MI), arrhythmia, and hypertension (HTN)], adjusting for sample size using the inverse-variance technique. For each targeted agent and side effect, we calculated the number needed to harm.

RESULTS

Regarding CHF, trastuzumab showed significantly greater risk of all-grade and high-grade CHF. There was significant increased risk of all-grade DLVEF with sorafenib, sunitinib, and trastuzumab and high-grade DLVEF with bevacizumab and trastuzumab. Sorafenib was associated with significant increased all-grade risk of MI based on one study. None was associated with high-grade risk of MI or increased risk of arrhythmia. Bevacizumab, sorafenib, and sunitinib had significant increased risk of all-grade and high-grade HTN.

CONCLUSIONS

Several of the targeted agents were significantly associated with increased risk of specific cardiovascular toxicities, CHF, DLVEF, and HTN. Several had significant increased risk for high-grade cardiovascular toxicities (CHF, DLVEF, and HTN). Patients receiving such therapy should be closely monitored for these toxicities and early and aggressive treatment should occur. However, clinical experience has demonstrated that some of these toxicities may be reversible and due to secondary effects.