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Studies during the COVID-19 pandemic showed that children had heightened nasal innate immune responses compared with adults. To evaluate the role of nasal viruses and bacteria in driving these responses, we performed cytokine profiling and comprehensive, symptom-agnostic testing for respiratory viruses and bacterial pathobionts in nasopharyngeal samples from children tested for SARS-CoV-2 in 2021-22 (n = 467). Respiratory viruses and/or pathobionts were highly prevalent (82% of symptomatic and 30% asymptomatic children; 90 and 49% for children <5 years). Virus detection and load correlated with the nasal interferon response biomarker CXCL10, and the previously reported discrepancy between SARS-CoV-2 viral load and nasal interferon response was explained by viral coinfections. Bacterial pathobionts correlated with a distinct proinflammatory response with elevated IL-1β and TNF but not CXCL10. Furthermore, paired samples from healthy 1-year-olds collected 1-2 wk apart revealed frequent respiratory virus acquisition or clearance, with mucosal immunophenotype changing in parallel. These findings reveal that frequent, dynamic host-pathogen interactions drive nasal innate immune activation in children., (© 2024 Watkins et al.)

Objectives: To introduce quantum computing technologies as a tool for biomedical research and highlight future applications within healthcare, focusing on its capabilities, benefits, and limitations., Target Audience: Investigators seeking to explore quantum computing and create quantum-based applications for healthcare and biomedical research., Scope: Quantum computing requires specialized hardware, known as quantum processing units, that use quantum bits (qubits) instead of classical bits to perform computations. This article will cover (1) proposed applications where quantum computing offers advantages to classical computing in biomedicine; (2) an introduction to how quantum computers operate, tailored for biomedical researchers; (3) recent progress that has expanded access to quantum computing; and (4) challenges, opportunities, and proposed solutions to integrate quantum computing in biomedical applications., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Background: The digital transformation of medical data enables health systems to leverage real-world data from electronic health records to gain actionable insights for improving hypertension care., Methods and Results: We performed a serial cross-sectional analysis of outpatients of a large regional health system from 2010 to 2021. Hypertension was defined by systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, or recorded treatment with antihypertension medications. We evaluated 4 methods of using blood pressure measurements in the electronic health record to define hypertension. The primary outcomes were age-adjusted prevalence rates and age-adjusted control rates. Hypertension prevalence varied depending on the definition used, ranging from 36.5% to 50.9% initially and increasing over time by ≈5%, regardless of the definition used. Control rates ranged from 61.2% to 71.3% initially, increased during 2018 to 2019, and decreased during 2020 to 2021. The proportion of patients with a hypertension diagnosis ranged from 45.5% to 60.2% initially and improved during the study period. Non-Hispanic Black patients represented 25% of our regional population and consistently had higher prevalence rates, higher mean systolic and diastolic blood pressure, and lower control rates compared with other racial and ethnic groups., Conclusions: In a large regional health system, we leveraged the electronic health record to provide real-world insights. The findings largely reflected national trends but showed distinctive regional demographics and findings, with prevalence increasing, one-quarter of the patients not controlled, and marked disparities. This approach could be emulated by regional health systems seeking to improve hypertension care.

How to develop highly informative serology assays to evaluate the quality of immune protection against coronavirus disease-19 (COVID-19) has been a global pursuit over the past years. Here, a microfluidic high-plex immuno-serolomic assay is developed to simultaneously measure50 plasma or serum samples for50 soluble markers including 35proteins, 11 anti-spike/receptor binding domian (RBD) IgG antibodies spanningmajor variants, and controls. This assay demonstrates the quintuplicate test in a single run with high throughput, low sample volume, high reproducibilityand accuracy. It is applied to the measurement of 1012 blood samples including in-depth analysis of sera from 127 patients and 21 healthy donors over multiple time points, either with acute COVID infection or vaccination. The protein analysis reveals distinct immune mediator modules that exhibit a reduced degree of diversity in protein-protein cooperation in patients with hematologic malignancies or receiving B cell depletion therapy. Serological analysis identifies that COVID-infected patients with hematologic malignancies display impaired anti-RBD antibody response despite high level of anti-spike IgG, which can be associated with limited clonotype diversity and functional deficiency in B cells. These findings underscore the importance to individualize immunization strategies for these high-risk patients and provide an informative tool to monitor their responses at the systems level., (© 2023 The Authors. Small Methods published by Wiley-VCH GmbH.)

Whether SARS-CoV-2 infection and COVID-19 vaccines confer exposure-dependent ("leaky") protection against infection remains unknown. We examined the effect of prior infection, vaccination, and hybrid immunity on infection risk among residents of Connecticut correctional facilities during periods of predominant Omicron and Delta transmission. Residents with cell, cellblock, and no documented exposure to SARS-CoV-2 infected residents were matched by facility and date. During the Omicron period, prior infection, vaccination, and hybrid immunity reduced the infection risk of residents without a documented exposure (HR: 0.36 [0.25-0.54]; 0.57 [0.42-0.78]; 0.24 [0.15-0.39]; respectively) and with cellblock exposures (0.61 [0.49-0.75]; 0.69 [0.58-0.83]; 0.41 [0.31-0.55]; respectively) but not with cell exposures (0.89 [0.58-1.35]; 0.96 [0.64-1.46]; 0.80 [0.46-1.39]; respectively). Associations were similar during the Delta period and when analyses were restricted to tested residents. Although associations may not have been thoroughly adjusted due to dataset limitations, the findings suggest that prior infection and vaccination may be leaky, highlighting the potential benefits of pairing vaccination with non-pharmaceutical interventions in crowded settings., (© 2023. Springer Nature Limited.)

Background: The impact variant-specific immune evasion and waning protection have on declining coronavirus disease 2019 (COVID-19) vaccine effectiveness (VE) remains unclear. Using whole-genome sequencing (WGS), we examined the contribution these factors had on the decline that followed the introduction of the Delta variant. Furthermore, we evaluated calendar-period-based classification as a WGS alternative., Methods: We conducted a test-negative case-control study among people tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between 1 April and 24 August 2021. Variants were classified using WGS and calendar period., Results: We included 2029 cases (positive, sequenced samples) and 343 727 controls (negative tests). VE 14-89 days after second dose was significantly higher against Alpha (84.4%; 95% confidence interval [CI], 75.6%-90.0%) than Delta infection (68.9%; 95% CI, 58.0%-77.1%). The odds of Delta infection were significantly higher 90-149 than 14-89 days after second dose (P value = .003). Calendar-period-classified VE estimates approximated WGS-classified estimates; however, calendar-period-based classification was subject to misclassification (35% Alpha, 4% Delta)., Conclusions: Both waning protection and variant-specific immune evasion contributed to the lower effectiveness. While calendar-period-classified VE estimates mirrored WGS-classified estimates, our analysis highlights the need for WGS when variants are cocirculating and misclassification is likely., Competing Interests: Potential conflicts of interest. A. I. K. serves as an expert panel member for Reckitt Global Hygiene Institute, scientific advisory board member for Revelar Biotherapeutics, and a consultant for Tata Medical and Diagnostics and Regeneron Pharmaceuticals; and has received grants from Merck, Regeneron Pharmaceuticals, and Tata Medical and Diagnostics for research related to COVID-19. W. L. S. was an investigator for a research agreement, through Yale University, from the Shenzhen Center for Health Information for work to advance intelligent disease prevention and health promotion; collaborates with the National Center for Cardiovascular Diseases in Beijing; is a technical consultant to Hugo Health, a personal health information platform; cofounder of Refactor Health, an AI-augmented data management platform for health care; and has received grants from Merck and Regeneron Pharmaceutical for research related to COVID-19. J. D. H., R. C., S. M., D. F., S. H., and A. Z. are employees and shareholders of Regeneron Pharmaceuticals, Inc. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2022. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Symptomatic patients who test negative for common viruses are an important possible source of unrecognised or emerging pathogens, but metagenomic sequencing of all samples is inefficient because of the low likelihood of finding a pathogen in any given sample. We aimed to determine whether nasopharyngeal CXCL10 screening could be used as a strategy to enrich for samples containing undiagnosed viruses., Methods: In this pathogen surveillance and detection study, we measured CXCL10 concentrations from nasopharyngeal swabs from patients in the Yale New Haven health-care system, which had been tested at the Yale New Haven Hospital Clinical Virology Laboratory (New Haven, CT, USA). Patients who tested negative for a panel of respiratory viruses using multiplex PCR during Jan 23-29, 2017, or March 3-14, 2020, were included. We performed host and pathogen RNA sequencing (RNA-Seq) and analysis for viral reads on samples with CXCL10 higher than 1 ng/mL or CXCL10 testing and quantitative RT-PCR (RT-qPCR) for SARS-CoV-2. We used RNA-Seq and cytokine profiling to compare the host response to infection in samples that were virus positive (rhinovirus, seasonal coronavirus CoV-NL63, or SARS-CoV-2) and virus negative (controls)., Findings: During Jan 23-29, 2017, 359 samples were tested for ten viruses on the multiplex PCR respiratory virus panel (RVP). 251 (70%) were RVP negative. 60 (24%) of 251 samples had CXCL10 higher than 150 pg/mL and were identified for further analysis. 28 (47%) of 60 CXCL10-high samples were positive for seasonal coronaviruses. 223 (89%) of 251 samples were PCR negative for 15 viruses and, of these, CXCL10-based screening identified 32 (13%) samples for further analysis. Of these 32 samples, eight (25%) with CXCL10 concentrations higher than 1 ng/mL and sufficient RNA were selected for RNA-Seq. Microbial RNA analysis showed the presence of influenza C virus in one sample and revealed RNA reads from bacterial pathobionts in four (50%) of eight samples. Between March 3 and March 14, 2020, 375 (59%) of 641 samples tested negative for 15 viruses on the RVP. 32 (9%) of 375 samples had CXCL10 concentrations ranging from 100 pg/mL to 1000 pg/mL and four of those were positive for SARS-CoV-2. CXCL10 elevation was statistically significant, and a distinguishing feature was found in 28 (8%) of 375 SARS-CoV-2-negative samples versus all four SARS-CoV-2-positive samples (p=4·4 × 10 -5 ). Transcriptomic signatures showed an interferon response in virus-positive samples and an additional neutrophil-high hyperinflammatory signature in samples with high amounts of bacterial pathobionts. The CXCL10 cutoff for detecting a virus was 166·5 pg/mL for optimal sensitivity and 1091·0 pg/mL for specificity using a clinic-ready automated microfluidics-based immunoassay., Interpretation: These results confirm CXCL10 as a robust nasopharyngeal biomarker of viral respiratory infection and support host response-based screening followed by metagenomic sequencing of CXCL10-high samples as a practical approach to incorporate clinical samples into pathogen discovery and surveillance efforts., Funding: National Institutes of Health, the Hartwell Foundation, the Gruber Foundation, Fast Grants for COVID-19 research from the Mercatus Center, and the Huffman Family Donor Advised Fund., Competing Interests: Declaration of interests EFF is an inventor on a pending patent application (WO2019/217296 A1) and provisional patent application (63/293386). EFF and MLL are inventors on a pending patent application (WO2018/071498 A1). NDG is a paid consultant for Tempus Labs and the National Basketball Association for infectious disease genomics. WLS is a consultant for Hugo Health; cofounder of Refacto Health; and received funds from Merck, Regeneron, the Shenzen Center for Health Information, and the Beijing National Center for Cardiac Diseases. AIK received funding from Merck, Regeneron, and the Reckitt Global Hygiene Institute. All other authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Real-world data (RWD) and real-world evidence (RWE) are becoming essential tools for informing regulatory decision making in health care and offer an opportunity for all stakeholders in the healthcare ecosystem to evaluate medical products throughout their lifecycle. Although considerable interest has been given to regulatory decisions supported by RWE for treatment authorization, especially in rare diseases, less attention has been given to RWD/RWE related to in vitro diagnostic (IVD) products and clinical decision support systems (CDSS). This review examines current regulatory practices in relation to IVD product development and discusses the use of CDSS in assisting clinicians to retrieve, filter, and analyze patient data in support of complex decisions regarding diagnosis and treatment. The review then explores how utilizing RWD could augment regulatory body understanding of test performance, clinical outcomes, and benefit-risk profiles, and how RWD could be leveraged to augment CDSS and improve safety, quality, and efficiency of healthcare practices. Whereas we present examples of RWD assisting in the regulation of IVDs and CDSS, we also highlight key challenges within the current healthcare system which are impeding the potential of RWE to be fully realized. These challenges include issues such as data availability, reliability, accessibility, harmonization, and interoperability, often for reasons specific to diagnostics. Finally, we review ways that these challenges are actively being addressed and discuss how private-public collaborations and the implementation of standardized language and protocols are working toward producing more robust RWD and RWE to support regulatory decision making., (© 2022 Roche Diagnostics. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Background: The benefit of primary and booster vaccination in people who experienced a prior Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection remains unclear. The objective of this study was to estimate the effectiveness of primary (two-dose series) and booster (third dose) mRNA vaccination against Omicron (lineage BA.1) infection among people with a prior documented infection., Methods and Findings: We conducted a test-negative case-control study of reverse transcription PCRs (RT-PCRs) analyzed with the TaqPath (Thermo Fisher Scientific) assay and recorded in the Yale New Haven Health system from November 1, 2021, to April 30, 2022. Overall, 11,307 cases (positive TaqPath analyzed RT-PCRs with S-gene target failure [SGTF]) and 130,041 controls (negative TaqPath analyzed RT-PCRs) were included (median age: cases: 35 years, controls: 39 years). Among cases and controls, 5.9% and 8.1% had a documented prior infection (positive SARS-CoV-2 test record ≥90 days prior to the included test), respectively. We estimated the effectiveness of primary and booster vaccination relative to SGTF-defined Omicron (lineage BA.1) variant infection using a logistic regression adjusted for date of test, age, sex, race/ethnicity, insurance, comorbidities, social venerability index, municipality, and healthcare utilization. The effectiveness of primary vaccination 14 to 149 days after the second dose was 41.0% (95% confidence interval (CI): 14.1% to 59.4%, p 0.006) and 27.1% (95% CI: 18.7% to 34.6%, p < 0.001) for people with and without a documented prior infection, respectively. The effectiveness of booster vaccination (≥14 days after booster dose) was 47.1% (95% CI: 22.4% to 63.9%, p 0.001) and 54.1% (95% CI: 49.2% to 58.4%, p < 0.001) in people with and without a documented prior infection, respectively. To test whether booster vaccination reduced the risk of infection beyond that of the primary series, we compared the odds of infection among boosted (≥14 days after booster dose) and booster-eligible people (≥150 days after second dose). The odds ratio (OR) comparing boosted and booster-eligible people with a documented prior infection was 0.79 (95% CI: 0.54 to 1.16, p 0.222), whereas the OR comparing boosted and booster-eligible people without a documented prior infection was 0.54 (95% CI: 0.49 to 0.59, p < 0.001). This study's limitations include the risk of residual confounding, the use of data from a single system, and the reliance on TaqPath analyzed RT-PCR results., Conclusions: In this study, we observed that primary vaccination provided significant but limited protection against Omicron (lineage BA.1) infection among people with and without a documented prior infection. While booster vaccination was associated with additional protection against Omicron BA.1 infection in people without a documented prior infection, it was not found to be associated with additional protection among people with a documented prior infection. These findings support primary vaccination in people regardless of documented prior infection status but suggest that infection history may impact the relative benefit of booster doses., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: A.I.K serves as an expert panel member for Reckitt Global Hygiene Institute, scientific advisory board member for Revelar Biotherapeutics and a consultant for Tata Medical and Diagnostics and Regeneron Pharmaceuticals, and has received grants from Merck, Regeneron Pharmaceuticals and Tata Medical and Diagnostics for research related to COVID-19. W.L.S. was an investigator for a research agreement, through Yale University, from the Shenzhen Center for Health Information for work to advance intelligent disease prevention and health promotion; collaborates with the National Center for Cardiovascular Diseases in Beijing; is a technical consultant to Hugo Health, a personal health information platform, and co-founder of Refactor Health, an AI-augmented data management platform for healthcare; and has received grants from Merck and Regeneron Pharmaceutical for research related to COVID-19. Other authors declare no conflict of interest. M.O.F had past stock ownership in GlaxoSmithKline. MDTH reports a contract (to the University of Florida) from Merck, Sharp and Dohme to perform research unrelated to this project., (Copyright: © 2022 Lind et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The application of artificial intelligence (AI) for automated diagnosis of electrocardiograms (ECGs) can improve care in remote settings but is limited by the reliance on infrequently available signal-based data. We report the development of a multilabel automated diagnosis model for electrocardiographic images, more suitable for broader use. A total of 2,228,236 12-lead ECGs signals from 811 municipalities in Brazil are transformed to ECG images in varying lead conformations to train a convolutional neural network (CNN) identifying 6 physician-defined clinical labels spanning rhythm and conduction disorders, and a hidden label for gender. The image-based model performs well on a distinct test set validated by at least two cardiologists (average AUROC 0.99, AUPRC 0.86), an external validation set of 21,785 ECGs from Germany (average AUROC 0.97, AUPRC 0.73), and printed ECGs, with performance superior to signal-based models, and learning clinically relevant cues based on Grad-CAM. The model allows the application of AI to ECGs across broad settings., (© 2022. The Author(s).)

SARS-CoV-2 remdesivir resistance mutations have been generated in vitro but have not been reported in patients receiving treatment with the antiviral agent. We present a case of an immunocompromised patient with acquired B-cell deficiency who developed an indolent, protracted course of SARS-CoV-2 infection. Remdesivir therapy alleviated symptoms and produced a transient virologic response, but her course was complicated by recrudescence of high-grade viral shedding. Whole genome sequencing identified a mutation, E802D, in the nsp12 RNA-dependent RNA polymerase, which was not present in pre-treatment specimens. In vitro experiments demonstrated that the mutation conferred a ~6-fold increase in remdesivir IC 50 but resulted in a fitness cost in the absence of remdesivir. Sustained clinical and virologic response was achieved after treatment with casirivimab-imdevimab. Although the fitness cost observed in vitro may limit the risk posed by E802D, this case illustrates the importance of monitoring for remdesivir resistance and the potential benefit of combinatorial therapies in immunocompromised patients with SARS-CoV-2 infection., (© 2022. The Author(s).)

Diagnosis codes are used to study SARS-CoV2 infections and COVID-19 hospitalizations in administrative and electronic health record (EHR) data. Using EHR data (April 2020-March 2021) at the Yale-New Haven Health System and the three hospital systems of the Mayo Clinic, computable phenotype definitions based on ICD-10 diagnosis of COVID-19 (U07.1) were evaluated against positive SARS-CoV-2 PCR or antigen tests. We included 69,423 patients at Yale and 75,748 at Mayo Clinic with either a diagnosis code or a positive SARS-CoV-2 test. The precision and recall of a COVID-19 diagnosis for a positive test were 68.8% and 83.3%, respectively, at Yale, with higher precision (95%) and lower recall (63.5%) at Mayo Clinic, varying between 59.2% in Rochester to 97.3% in Arizona. For hospitalizations with a principal COVID-19 diagnosis, 94.8% at Yale and 80.5% at Mayo Clinic had an associated positive laboratory test, with secondary diagnosis of COVID-19 identifying additional patients. These patients had a twofold higher inhospital mortality than based on principal diagnosis. Standardization of coding practices is needed before the use of diagnosis codes in clinical research and epidemiological surveillance of COVID-19., (© 2022. The Author(s).)

Background: Clinical babesiosis is diagnosed, and parasite burden is determined, by microscopic inspection of a thick or thin Giemsa-stained peripheral blood smear. However, quantitative analysis by manual microscopy is subject to error. As such, methods for the automated measurement of percent parasitemia in digital microscopic images of peripheral blood smears could improve clinical accuracy, relative to the predicate method., Methods: Individual erythrocyte images were manually labeled as "parasite" or "normal" and were used to train a model for binary image classification. The best model was then used to calculate percent parasitemia from a clinical validation dataset, and values were compared to a clinical reference value. Lastly, model interpretability was examined using an integrated gradient to identify pixels most likely to influence classification decisions., Results: The precision and recall of the model during development testing were 0.92 and 1.00, respectively. In clinical validation, the model returned increasing positive signal with increasing mean reference value. However, there were 2 highly erroneous false positive values returned by the model. Further, the model incorrectly assessed 3 cases well above the clinical threshold of 10%. The integrated gradient suggested potential sources of false positives including rouleaux formations, cell boundaries, and precipitate as deterministic factors in negative erythrocyte images., Conclusions: While the model demonstrated highly accurate single cell classification and correctly assessed most slides, several false positives were highly incorrect. This project highlights the need for integrated testing of machine learning-based models, even when models in the development phase perform well., (© American Association for Clinical Chemistry 2021. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objectives: To determine the feasibility of using real-world data to assess the safety and effectiveness of two cardiac ablation catheters for the treatment of persistent atrial fibrillation and ischaemic ventricular tachycardia., Design: Retrospective cohort., Setting: Three health systems in the USA., Participants: Patients receiving ablation with the two ablation catheters of interest at any of the three health systems., Main Outcome Measures: Feasibility of identifying the medical devices and participant populations of interest as well as the duration of follow-up and positive predictive values (PPVs) for serious safety (ischaemic stroke, acute heart failure and cardiac tamponade) and effectiveness (arrhythmia-related hospitalisation) clinical outcomes of interest compared with manual chart validation by clinicians., Results: Overall, the catheter of interest for treatment of persistent atrial fibrillation was used for 4280 ablations and the catheter of interest for ischaemic ventricular tachycardia was used 1516 times across the data available within the three health systems. The duration of patient follow-up in the three health systems ranged from 91% to 97% at ≥7 days, 89% to 96% at ≥30 days, 77% to 90% at ≥6 months and 66% to 84% at ≥1 year. PPVs were 63.4% for ischaemic stroke, 96.4% for acute heart failure, 100% at one health system for cardiac tamponade and 55.7% for arrhythmia-related hospitalisation., Conclusions: It is feasible to use real-world health system data to evaluate the safety and effectiveness of cardiac ablation catheters, though evaluations must consider the implications of variation in follow-up and endpoint ascertainment among health systems., Competing Interests: Competing interests: SD receives research funding from the National Heart, Lung, and Blood Institute (NHLBI, K12HL138046) of the National Institutes of Health (NIH), from the Medical Device Innovation Consortium as part of the National Evaluation System for health Technology Coordinating Center (NESTcc), Food and Drug Administration (FDA), Greenwall Foundation, and Arnold Ventures. DJF has received educational grants from Boston Scientific, Medtronic, and Abbott; research grants from the American Heart Association, National Cardiovascular Data Registry, Boston Scientific, Abbott, Medtronic, Merit Medical, and Biosense Webster; consulting fees from Abbott and AtriCure. JGA has received consulting fees from Biosense Webster. JSR received research support through Yale University from Johnson and Johnson to develop methods of clinical trial data sharing, from the Food and Drug Administration to establish Yale-Mayo Clinic Center for Excellence in Regulatory Science and Innovation (CERSI) program (U01FD005938), from the Medical Device Innovation Consortium as part of NESTcc, from the Agency for Healthcare Research and Quality (R01HS022882), from the National Heart, Lung and Blood Institute of the National Institutes of Health (NIH) (R01HS025164, R01HL144644), and from the Laura and John Arnold Foundation to establish the Good Pharma Scorecard at Bioethics International and to establish the Collaboration for Research Integrity and Transparency (CRIT) at Yale. NDS has received research support through Mayo Clinic from the Food and Drug Administration to establish the Yale–Mayo Clinic Center for Excellence in Regulatory Science and Innovation program (U01FD005938), from the Centers of Medicare and Medicaid Innovation under the Transforming Clinical Practice Initiative, from the Agency for Healthcare Research and Quality (U19HS024075, R01HS025164, R01HS025402, R03HS025517), from the National Heart, Lung, and Blood Institute of the National Institutes of Health (R56HL130496, R01HL131535), from the National Science Foundation, and from the Patient Centered Outcomes Research Institute to develop a Clinical Data Research Network. PC is an employee of Johnson & Johnson; the manufacturer of the ThermoCool catheters, and Biosense Webster, is a Johnson & Johnson company. However, this study was a feasibility study to evaluate the suitability of the databases to conduct a study and no evaluation of the safety and effectiveness of any medical device was conducted in the study. PAN receives research funding from National Institutes of Health (NIH, including the NHLBI and the National Institute on Aging [NIA]), Agency for Healthcare Research and Quality (AHRQ), FDA, and the American Heart Association (AHA). He is a study investigator in an ablation trial sponsored by Medtronic. PAN and Mayo Clinic are involved in potential equity/royalty relationship with AliveCor. SZ is an employee of Johnson & Johnson; the manufacturer of the ThermoCool catheters and Biosense Webster, is a Johnson & Johnson company. WLS was an investigator for a research agreement, through Yale University, from the Shenzhen Center for Health Information for work to advance intelligent disease prevention and health promotion; collaborates with the National Center for Cardiovascular Diseases in Beijing; is a technical consultant to Hugo Health, a personal health information platform, and cofounder of Refactor Health, an AI-augmented data management platform for healthcare; is a consultant for Interpace Diagnostics Group, a molecular diagnostics company. In the past 36 months, JPD has received research support from Medtronic and Johnson & Johnson. His non-dependent son is an employee of Boston Scientific., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Modern artificial intelligence (AI) and machine learning (ML) methods are now capable of completing tasks with performance characteristics that are comparable to those of expert human operators. As a result, many areas throughout healthcare are incorporating these technologies, including in vitro diagnostics and, more broadly, laboratory medicine. However, there are limited literature reviews of the landscape, likely future, and challenges of the application of AI/ML in laboratory medicine., Content: In this review, we begin with a brief introduction to AI and its subfield of ML. The ensuing sections describe ML systems that are currently in clinical laboratory practice or are being proposed for such use in recent literature, ML systems that use laboratory data outside the clinical laboratory, challenges to the adoption of ML, and future opportunities for ML in laboratory medicine., Summary: AI and ML have and will continue to influence the practice and scope of laboratory medicine dramatically. This has been made possible by advancements in modern computing and the widespread digitization of health information. These technologies are being rapidly developed and described, but in comparison, their implementation thus far has been modest. To spur the implementation of reliable and sophisticated ML-based technologies, we need to establish best practices further and improve our information system and communication infrastructure. The participation of the clinical laboratory community is essential to ensure that laboratory data are sufficiently available and incorporated conscientiously into robust, safe, and clinically effective ML-supported clinical diagnostics., (© American Association for Clinical Chemistry 2021. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objective: The study sought to conduct an informatics analysis on the National Evaluation System for Health Technology Coordinating Center test case of cardiac ablation catheters and to demonstrate the role of informatics approaches in the feasibility assessment of capturing real-world data using unique device identifiers (UDIs) that are fit for purpose for label extensions for 2 cardiac ablation catheters from the electronic health records and other health information technology systems in a multicenter evaluation., Materials and Methods: We focused on data capture and transformation and data quality maturity model specified in the National Evaluation System for Health Technology Coordinating Center data quality framework. The informatics analysis included 4 elements: the use of UDIs for identifying device exposure data, the use of standardized codes for defining computable phenotypes, the use of natural language processing for capturing unstructured data elements from clinical data systems, and the use of common data models for standardizing data collection and analyses., Results: We found that, with the UDI implementation at 3 health systems, the target device exposure data could be effectively identified, particularly for brand-specific devices. Computable phenotypes for study outcomes could be defined using codes; however, ablation registries, natural language processing tools, and chart reviews were required for validating data quality of the phenotypes. The common data model implementation status varied across sites. The maturity level of the key informatics technologies was highly aligned with the data quality maturity model., Conclusions: We demonstrated that the informatics approaches can be feasibly used to capture safety and effectiveness outcomes in real-world data for use in medical device studies supporting label extensions., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Importance: Dyslipidemia, the prevalence of which historically has been low in China, is emerging as the second leading yet often unaddressed factor associated with the risk of cardiovascular diseases. However, recent national data on the prevalence, treatment, and control of dyslipidemia are lacking., Objective: To assess the prevalence, treatment, and control of dyslipidemia in community residents and the availability of lipid-lowering medications in primary care institutions in China., Design, Setting, and Participants: This cross-sectional study used data from the China-PEACE (Patient-Centered Evaluative Assessment of Cardiac Events) Million Persons Project, which enrolled 2 660 666 community residents aged 35 to 75 years from all 31 provinces in China between December 2014 and May 2019, and the China-PEACE primary health care survey of 3041 primary care institutions. Data analysis was performed from June 2019 to March 2021., Exposures: Study period., Main Outcomes and Measures: The main outcome was the prevalence of dyslipidemia, which was defined as total cholesterol greater than or equal to 240 mg/dL, low-density lipoprotein cholesterol (LDL-C) greater than or equal to 160 mg/dL, high-density lipoprotein cholesterol (HDL-C) less than 40 mg/dL, triglycerides greater than or equal to 200 mg/dL, or self-reported use of lipid-lowering medications, in accordance with the 2016 Chinese Adult Dyslipidemia Prevention Guideline., Results: This study included 2 314 538 participants with lipid measurements (1 389 322 women [60.0%]; mean [SD] age, 55.8 [9.8] years). Among them, 781 865 participants (33.8%) had dyslipidemia. Of 71 785 participants (3.2%) who had established atherosclerotic cardiovascular disease (ASCVD) and were recommended by guidelines for lipid-lowering medications regardless of LDL-C levels, 10 120 (14.1%) were treated. The overall control rate of LDL-C (≤70 mg/dL) among adults with established ASCVD was 26.6% (19 087 participants), with the control rate being 44.8% (4535 participants) among those who were treated and 23.6% (14 552 participants) among those not treated. Of 236 579 participants (10.2%) with high risk of ASCVD, 101 474 (42.9%) achieved LDL-C less than or equal to 100 mg/dL. Among participants with established ASCVD, advanced age (age 65-75 years, odds ratio [OR], 0.63; 95% CI, 0.56-0.70), female sex (OR, 0.56; 95% CI, 0.53-0.58), lower income (reference category), smoking (OR, 0.89; 95% CI, 0.85-0.94), alcohol consumption (OR, 0.87; 95% CI, 0.83-0.92), and not having diabetes (reference category) were associated with lower control of LDL-C. Among participants with high risk of ASCVD, younger age (reference category) and female sex (OR, 0.58; 95% CI, 0.56-0.59) were associated with lower control of LDL-C. Of 3041 primary care institutions surveyed, 1512 (49.7%) stocked statin and 584 (19.2%) stocked nonstatin lipid-lowering drugs. Village clinics in rural areas had the lowest statin availability., Conclusions and Relevance: These findings suggest that dyslipidemia has become a major public health problem in China and is often inadequately treated and uncontrolled. Statins were available in less than one-half of the primary care institutions. Strategies aimed at detection, prevention, and treatment are needed.

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant, Gamma, emerged in the city of Manaus in late 2020 during a large resurgence of coronavirus disease (COVID-19), and has spread throughout Brazil. The effectiveness of vaccines in settings with widespread Gamma variant transmission has not been reported., Methods: We performed a matched test-negative case-control study to estimate the effectiveness of an inactivated vaccine, CoronaVac, in healthcare workers (HCWs) in Manaus, where the Gamma variant accounted for 86% of genotyped SARS-CoV-2 samples at the peak of its epidemic. We performed an early analysis of effectiveness following administration of at least one vaccine dose and an analysis of effectiveness of the two-dose schedule. The primary outcome was symptomatic SARS-CoV-2 infection., Findings: For the early at-least-one-dose and two-dose analyses the study population was, respectively, 53,176 and 53,153 HCWs residing in Manaus and aged 18 years or older, with complete information on age, residence, and vaccination status. Among 53,153 HCWs eligible for the two-dose analysis, 47,170 (89%) received at least one dose of CoronaVac and 2,656 individuals (5%) underwent RT-PCR testing from 19 January, 2021 to 13 April, 2021. Of 3,195 RT-PCR tests, 885 (28%) were positive. 393 and 418 case-control pairs were selected for the early and two-dose analyses, respectively, matched on calendar time, age, and neighbourhood. Among those who had received both vaccine doses before the RT-PCR sample collection date, the average time from second dose to sample collection date was 14 days (IQR 7-24). In the early analysis, vaccination with at least one dose was associated with a 0.50-fold reduction (adjusted vaccine effectiveness (VE), 49.6%, 95% CI 11.3 to 71.4) in the odds of symptomatic SARS-CoV-2 infection during the period 14 days or more after receiving the first dose. However, we estimated low effectiveness (adjusted VE 36.8%, 95% CI -54.9 to 74.2) of the two-dose schedule against symptomatic SARS-CoV-2 infection during the period 14 days or more after receiving the second dose. A finding that vaccinated individuals were much more likely to be infected than unvaccinated individuals in the period 0-13 days after first dose (aOR 2.11, 95% CI 1.36-3.27) suggests that unmeasured confounding led to downward bias in the vaccine effectiveness estimate., Interpretation: Evidence from this test-negative study of the effectiveness of CoronaVac was mixed, and likely affected by bias in this setting. Administration of at least one vaccine dose showed effectiveness against symptomatic SARS-CoV-2 infection in the setting of epidemic Gamma variant transmission. However, the low estimated effectiveness of the two-dose schedule underscores the need to maintain non-pharmaceutical interventions while vaccination campaigns with CoronaVac are being implemented., Funding: Fundação Oswaldo Cruz (Fiocruz); Municipal Health Secretary of Manaus; Fundação de Vigilância em Saúde do Amazonas., Competing Interests: We declare no competing interests., (© 2021 The Authors. Published by Elsevier Ltd.)

Background: SARS-CoV-2 serologic assays are becoming increasingly available and may serve as a diagnostic aid in a multitude of settings relating to past infection status. However, there is limited literature detailing the longitudinal performance of EUA-cleared serologic assays in US populations, particularly in cohorts with a remote history of PCR-confirmed SARS-CoV-2 infection (e.g., >2 months)., Methods: We evaluated the diagnostic sensitivities and specificities of the Elecsys® Anti-SARS-CoV-2 (anti-N) and Elecsys Anti-SARS-CoV-2 S (anti-S1-RBD) assays, using 174 residual clinical samples up to 267 days post-PCR diagnosis of SARS-CoV-2 infection (n = 154) and a subset of samples obtained prior to the COVID-19 pandemic as negative controls (n = 20)., Results: The calculated diagnostic sensitivities for the anti-N and anti-S1-RBD assays were 89% and 93%, respectively. Of the 154 samples in the SARS-CoV-2-positive cohort, there were 6 discrepant results between the anti-N and anti-S1-RBD assays, 5 of which were specimens collected ≥200 days post-PCR positivity and only had detectable levels of anti-S1-RBD antibodies. When only considering specimens collected ≥100 days post-PCR positivity (n = 41), the sensitivities for the anti-N and anti-S1-RBD assays were 85% and 98%, respectively., Conclusions: The anti-S1-RBD assay demonstrated superior sensitivity at time points more remote to the PCR detection date, with 6 more specimens from the SARS-CoV-2-positive cohort detected, 5 of which were collected more than 200 days post-PCR positivity. While analytical differences and reagent lot-to-lot variability are possible, this may indicate that, in some instances, anti-S1-RBD antibodies may persist longer in vivo and may be a better target for detecting remote SARS-CoV-2 infection., (© American Association for Clinical Chemistry 2021. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

COVID-19 manifests with a wide spectrum of clinical phenotypes that are characterized by exaggerated and misdirected host immune responses 1-6 . Although pathological innate immune activation is well-documented in severe disease 1 , the effect of autoantibodies on disease progression is less well-defined. Here we use a high-throughput autoantibody discovery technique known as rapid extracellular antigen profiling 7 to screen a cohort of 194 individuals infected with SARS-CoV-2, comprising 172 patients with COVID-19 and 22 healthcare workers with mild disease or asymptomatic infection, for autoantibodies against 2,770 extracellular and secreted proteins (members of the exoproteome). We found that patients with COVID-19 exhibit marked increases in autoantibody reactivities as compared to uninfected individuals, and show a high prevalence of autoantibodies against immunomodulatory proteins (including cytokines, chemokines, complement components and cell-surface proteins). We established that these autoantibodies perturb immune function and impair virological control by inhibiting immunoreceptor signalling and by altering peripheral immune cell composition, and found that mouse surrogates of these autoantibodies increase disease severity in a mouse model of SARS-CoV-2 infection. Our analysis of autoantibodies against tissue-associated antigens revealed associations with specific clinical characteristics. Our findings suggest a pathological role for exoproteome-directed autoantibodies in COVID-19, with diverse effects on immune functionality and associations with clinical outcomes.

Recent studies have provided insights into innate and adaptive immune dynamics in coronavirus disease 2019 (COVID-19). However, the exact features of antibody responses that govern COVID-19 disease outcomes remain unclear. In this study, we analyzed humoral immune responses in 229 patients with asymptomatic, mild, moderate and severe COVID-19 over time to probe the nature of antibody responses in disease severity and mortality. We observed a correlation between anti-spike (S) immunoglobulin G (IgG) levels, length of hospitalization and clinical parameters associated with worse clinical progression. Although high anti-S IgG levels correlated with worse disease severity, such correlation was time dependent. Deceased patients did not have higher overall humoral response than discharged patients. However, they mounted a robust, yet delayed, response, measured by anti-S, anti-receptor-binding domain IgG and neutralizing antibody (NAb) levels compared to survivors. Delayed seroconversion kinetics correlated with impaired viral control in deceased patients. Finally, although sera from 85% of patients displayed some neutralization capacity during their disease course, NAb generation before 14 d of disease onset emerged as a key factor for recovery. These data indicate that COVID-19 mortality does not correlate with the cross-sectional antiviral antibody levels per se but, rather, with the delayed kinetics of NAb production., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Background: Intraoperative data may improve models predicting postoperative events. We evaluated the effect of incorporating intraoperative variables to the existing preoperative model on the predictive performance of the model for coronary artery bypass graft., Methods: We analyzed 378 572 isolated coronary artery bypass graft cases performed across 1083 centers, using the national Society of Thoracic Surgeons Adult Cardiac Surgery Database between 2014 and 2016. Outcomes were operative mortality, 5 postoperative complications, and composite representation of all events. We fitted models by logistic regression or extreme gradient boosting (XGBoost). For each modeling approach, we used preoperative only, intraoperative only, or pre+intraoperative variables. We developed 84 models with unique combinations of the 3 variable sets, 2 variable selection methods, 2 modeling approaches, and 7 outcomes. Each model was tested in 20 iterations of 70:30 stratified random splitting into development/testing samples. Model performances were evaluated on the testing dataset using the C statistic, area under the precision-recall curve, and calibration metrics, including the Brier score., Results: The mean patient age was 65.3 years, and 24.7% were women. Operative mortality, excluding intraoperative death, occurred in 1.9%. In all outcomes, models that considered pre+intraoperative variables demonstrated significantly improved Brier score and area under the precision-recall curve compared with models considering pre or intraoperative variables alone. XGBoost without external variable selection had the best C statistics, Brier score, and area under the precision-recall curve values in 4 of the 7 outcomes (mortality, renal failure, prolonged ventilation, and composite) compared with logistic regression models with or without variable selection. Based on the calibration plots, risk restratification for mortality showed that the logistic regression model underestimated the risk in 11 114 patients (9.8%) and overestimated in 12 005 patients (10.6%). In contrast, the XGBoost model underestimated the risk in 7218 patients (6.4%) and overestimated in 0 patients (0%)., Conclusions: In isolated coronary artery bypass graft, adding intraoperative variables to preoperative variables resulted in improved predictions of all 7 outcomes. Risk models based on XGBoost may provide a better prediction of adverse events to guide clinical care.

Objective: Severe acute respiratory syndrome virus (SARS-CoV-2) has infected millions of people worldwide. Our goal was to identify risk factors associated with admission and disease severity in patients with SARS-CoV-2., Design: This was an observational, retrospective study based on real-world data for 7,995 patients with SARS-CoV-2 from a clinical data repository., Setting: Yale New Haven Health (YNHH) is a five-hospital academic health system serving a diverse patient population with community and teaching facilities in both urban and suburban areas., Populations: The study included adult patients who had SARS-CoV-2 testing at YNHH between March 1 and April 30, 2020., Main Outcome and Performance Measures: Primary outcomes were admission and in-hospital mortality for patients with SARS-CoV-2 infection as determined by RT-PCR testing. We also assessed features associated with the need for respiratory support., Results: Of the 28605 patients tested for SARS-CoV-2, 7995 patients (27.9%) had an infection (median age 52.3 years) and 2154 (26.9%) of these had an associated admission (median age 66.2 years). Of admitted patients, 2152 (99.9%) had a discharge disposition at the end of the study period. Of these, 329 (15.3%) required invasive mechanical ventilation and 305 (14.2%) expired. Increased age and male sex were positively associated with admission and in-hospital mortality (median age 80.7 years), while comorbidities had a much weaker association with the risk of admission or mortality. Black race (OR 1.43, 95%CI 1.14-1.78) and Hispanic ethnicity (OR 1.81, 95%CI 1.50-2.18) were identified as risk factors for admission, but, among discharged patients, age-adjusted in-hospital mortality was not significantly different among racial and ethnic groups., Conclusions: This observational study identified, among people testing positive for SARS-CoV-2 infection, older age and male sex as the most strongly associated risks for admission and in-hospital mortality in patients with SARS-CoV-2 infection. While minority racial and ethnic groups had increased burden of disease and risk of admission, age-adjusted in-hospital mortality for discharged patients was not significantly different among racial and ethnic groups. Ongoing studies will be needed to continue to evaluate these risks, particularly in the setting of evolving treatment guidelines., Competing Interests: H.M.K. works under contract with the Centers for Medicare & Medicaid Services to support quality measurement programs; was a recipient of a research grant, through Yale, from Medtronic and the U.S. Food and Drug Administration to develop methods for post-market surveillance of medical devices; was a recipient of a research grant from Johnson & Johnson, through Yale University, to support clinical trial data sharing; was a recipient of a research agreement, through Yale University, from the Shenzhen Center for Health Information for work to advance intelligent disease prevention and health promotion; collaborates with the National Center for Cardiovascular Diseases in Beijing; receives payment from the Arnold & Porter Law Firm for work related to the Sanofi clopidogrel litigation, from the Martin Baughman Law Firm for work related to the Cook Celect IVC filter litigation, and from the Siegfried and Jensen Law Firm for work related to Vioxx litigation; chairs a Cardiac Scientific Advisory Board for UnitedHealth; was a member of the IBM Watson Health Life Sciences Board; is a member of the Advisory Board for Element Science, the Advisory Board for Facebook, and the Physician Advisory Board for Aetna; and is the co-founder of HugoHealth, a personal health information platform, and cofounder of Refactor Health, a healthcare AI-augmented data management company. W.L.S. was an investigator for a research agreement, through Yale University, from the Shenzhen Center for Health Information for work to advance intelligent disease prevention and health promotion; collaborates with the National Center for Cardiovascular Diseases in Beijing; is a technical consultant to HugoHealth, a personal health information platform, and cofounder of Refactor Health, an AI-augmented data management platform for healthcare; is a consultant for Interpace Diagnostics Group, a molecular diagnostics company. This does not alter our adherence to PLOS ONE policies on sharing data and materials. There are no patents, products in development or marketed products associated with this research to declare.

Background: The electronic health record (EHR) holds the prospect of providing more complete and timely access to clinical information for biomedical research, quality assessments, and quality improvement compared to other data sources, such as administrative claims. In this study, we sought to assess the completeness and timeliness of structured diagnoses in the EHR compared to computed diagnoses for hypertension (HTN), hyperlipidemia (HLD), and diabetes mellitus (DM)., Methods: We determined the amount of time for a structured diagnosis to be recorded in the EHR from when an equivalent diagnosis could be computed from other structured data elements, such as vital signs and laboratory results. We used EHR data for encounters from January 1, 2012 through February 10, 2019 from an academic health system. Diagnoses for HTN, HLD, and DM were computed for patients with at least two observations above threshold separated by at least 30 days, where the thresholds were outpatient blood pressure of ≥ 140/90 mmHg, any low-density lipoprotein ≥ 130 mg/dl, or any hemoglobin A1c ≥ 6.5%, respectively. The primary measure was the length of time between the computed diagnosis and the time at which a structured diagnosis could be identified within the EHR history or problem list., Results: We found that 39.8% of those with HTN, 21.6% with HLD, and 5.2% with DM did not receive a corresponding structured diagnosis recorded in the EHR. For those who received a structured diagnosis, a mean of 389, 198, and 166 days elapsed before the patient had the corresponding diagnosis of HTN, HLD, or DM, respectively, recorded in the EHR., Conclusions: We found a marked temporal delay between when a diagnosis can be computed or inferred and when an equivalent structured diagnosis is recorded within the EHR. These findings demonstrate the continued need for additional study of the EHR to avoid bias when using observational data and reinforce the need for computational approaches to identify clinical phenotypes.

There is increasing evidence that coronavirus disease 2019 (COVID-19) produces more severe symptoms and higher mortality among men than among women 1-5 . However, whether immune responses against severe acute respiratory syndrome coronavirus (SARS-CoV-2) differ between sexes, and whether such differences correlate with the sex difference in the disease course of COVID-19, is currently unknown. Here we examined sex differences in viral loads, SARS-CoV-2-specific antibody titres, plasma cytokines and blood-cell phenotyping in patients with moderate COVID-19 who had not received immunomodulatory medications. Male patients had higher plasma levels of innate immune cytokines such as IL-8 and IL-18 along with more robust induction of non-classical monocytes. By contrast, female patients had more robust T cell activation than male patients during SARS-CoV-2 infection. Notably, we found that a poor T cell response negatively correlated with patients' age and was associated with worse disease outcome in male patients, but not in female patients. By contrast, higher levels of innate immune cytokines were associated with worse disease progression in female patients, but not in male patients. These findings provide a possible explanation for the observed sex biases in COVID-19, and provide an important basis for the development of a sex-based approach to the treatment and care of male and female patients with COVID-19.

Background: Coronavirus disease 2019 (COVID-19) was formally characterized as a pandemic on March 11, 2020. Since that time, the COVID-19 pandemic has led to unprecedented demand for healthcare resources. The purpose of this study was to identify changes in laboratory test utilization in the setting of increasing local incidence of COVID-19., Methods: We performed a retrospective assessment of laboratory test order and specimen container utilization at a single, urban tertiary care medical center. Data were extracted from the laboratory information system database over a 10-week period, spanning the primordial inflection of COVID-19 incidence in our region. Total testing volumes were calculated during the first 2 and last 2 weeks of the observation period and used as reference points to examine the absolute and relative differences in test order volume between the prepandemic and COVID-19 surge periods., Results: Between February 2, 2020, and April 11, 2020, there were 873 397 tests ordered and final verified. The in-house SARS-CoV-2 PCR positivity rate for admitted patients in the last week of the observation period was 30.8%. Significant increases in workload were observed in the send-out laboratory section and for COVID-19 diagnosis (PCR) and management-related testing. Otherwise, there was a net decrease in overall demand across nearly all laboratory sections. Increases in testing were noted for tests related to COVID-19 management. Viral transport media and citrated blue top containers demonstrated increases in utilization., Conclusion: Increasing local incidence of COVID-19 had a profound impact on laboratory operations. While volume increases were seen for laboratory tests related to COVID-19 diagnostics and management, including some with limited evidence to support their use, overall testing volumes decreased substantially. During events such as COVID-19, monitoring of such patterns can help inform laboratory management, staffing, and test stewardship recommendations for managing resource and supply availability., (© American Association for Clinical Chemistry 2020. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Study Objective: The goal of this study is to create a predictive, interpretable model of early hospital respiratory failure among emergency department (ED) patients admitted with coronavirus disease 2019 (COVID-19)., Methods: This was an observational, retrospective, cohort study from a 9-ED health system of admitted adult patients with severe acute respiratory syndrome coronavirus 2 (COVID-19) and an oxygen requirement less than or equal to 6 L/min. We sought to predict respiratory failure within 24 hours of admission as defined by oxygen requirement of greater than 10 L/min by low-flow device, high-flow device, noninvasive or invasive ventilation, or death. Predictive models were compared with the Elixhauser Comorbidity Index, quick Sequential [Sepsis-related] Organ Failure Assessment, and the CURB-65 pneumonia severity score., Results: During the study period, from March 1 to April 27, 2020, 1,792 patients were admitted with COVID-19, 620 (35%) of whom had respiratory failure in the ED. Of the remaining 1,172 admitted patients, 144 (12.3%) met the composite endpoint within the first 24 hours of hospitalization. On the independent test cohort, both a novel bedside scoring system, the quick COVID-19 Severity Index (area under receiver operating characteristic curve mean 0.81 [95% confidence interval {CI} 0.73 to 0.89]), and a machine-learning model, the COVID-19 Severity Index (mean 0.76 [95% CI 0.65 to 0.86]), outperformed the Elixhauser mortality index (mean 0.61 [95% CI 0.51 to 0.70]), CURB-65 (0.50 [95% CI 0.40 to 0.60]), and quick Sequential [Sepsis-related] Organ Failure Assessment (0.59 [95% CI 0.50 to 0.68]). A low quick COVID-19 Severity Index score was associated with a less than 5% risk of respiratory decompensation in the validation cohort., Conclusion: A significant proportion of admitted COVID-19 patients progress to respiratory failure within 24 hours of admission. These events are accurately predicted with bedside respiratory examination findings within a simple scoring system., (Copyright © 2020 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.)

Recent studies have provided insights into the pathogenesis of coronavirus disease 2019 (COVID-19) 1-4 . However, the longitudinal immunological correlates of disease outcome remain unclear. Here we serially analysed immune responses in 113 patients with moderate or severe COVID-19. Immune profiling revealed an overall increase in innate cell lineages, with a concomitant reduction in T cell number. An early elevation in cytokine levels was associated with worse disease outcomes. Following an early increase in cytokines, patients with moderate COVID-19 displayed a progressive reduction in type 1 (antiviral) and type 3 (antifungal) responses. By contrast, patients with severe COVID-19 maintained these elevated responses throughout the course of the disease. Moreover, severe COVID-19 was accompanied by an increase in multiple type 2 (anti-helminths) effectors, including interleukin-5 (IL-5), IL-13, immunoglobulin E and eosinophils. Unsupervised clustering analysis identified four immune signatures, representing growth factors (A), type-2/3 cytokines (B), mixed type-1/2/3 cytokines (C), and chemokines (D) that correlated with three distinct disease trajectories. The immune profiles of patients who recovered from moderate COVID-19 were enriched in tissue reparative growth factor signature A, whereas the profiles of those with who developed severe disease had elevated levels of all four signatures. Thus, we have identified a maladapted immune response profile associated with severe COVID-19 and poor clinical outcome, as well as early immune signatures that correlate with divergent disease trajectories.

The ongoing coronavirus disease outbreak demonstrates the need for novel applications of real-time data to produce timely information about incident cases. Using health information technology (HIT) and real-world data, we sought to produce an interface that could, in near real time, identify patients presenting with suspected respiratory tract infection and enable monitoring of test results related to specific pathogens, including severe acute respiratory syndrome coronavirus 2. This tool was built upon our computational health platform, which provides access to near real-time data from disparate HIT sources across our health system. This combination of technology allowed us to rapidly prototype, iterate, and deploy a platform to support a cohesive organizational response to a rapidly evolving outbreak. Platforms that allow for agile analytics are needed to keep pace with evolving needs within the health care system., (©Wade L Schulz, Thomas J S Durant, Charles J Torre Jr, Allen L Hsiao, Harlan M Krumholz. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 28.05.2020.)

Introduction: Biomedical and translational research often relies on the evaluation of patients or specimens that meet specific clinical or laboratory criteria. The typical approach used to identify biospecimens is a manual, retrospective process that exists outside the clinical workflow. This often makes biospecimen collection cost prohibitive and prevents the collection of analytes with short stability times. Emerging data architectures offer novel approaches to enhance specimen-identification practices. To this end, we present a new tool that can be deployed in a real-time environment to automate the identification and notification of available biospecimens for biomedical research., Methods: Real-time clinical and laboratory data from Cloverleaf (Infor, NY, NY) were acquired within our computational health platform, which is built on open-source applications. Study-specific filters were developed in NiFi (Apache Software Foundation, Wakefield, MA, USA) to identify the study-appropriate specimens in real time. Specimen metadata were stored in Elasticsearch (Elastic N. V., Mountain View, CA, USA) for visualization and automated alerting., Results: Between June 2018 and December 2018, we identified 2992 unique specimens belonging to 2815 unique patients, split between two different use cases. Based on laboratory policy for specimen retention and study-specific stability requirements, secure E-mail notifications were sent to investigators to automatically notify of availability. The assessment of throughput on commodity hardware demonstrates the ability to scale to approximately 2000 results per second., Conclusion: This work demonstrates that real-world clinical data can be analyzed in real time to increase the efficiency of biospecimen identification with minimal overhead for the clinical laboratory. Future work will integrate additional data types, including the analysis of unstructured data, to enable more complex cases and biospecimen identification., Competing Interests: There are no conflicts of interest., (Copyright: © 2020 Journal of Pathology Informatics.)

Background The digital transformation of medical data provides opportunities to perform digital population health surveillance and identify people inadequately managed in usual care. We leveraged the electronic health records of a large health system to identify patients with markedly elevated blood pressure and characterize their follow-up care pattern. Methods and Results We included 373 861 patients aged 18 to 85 years, who had at least 1 outpatient encounter in the Yale New Haven Health System between January 2013 and December 2017. We described the prevalence and follow-up pattern of patients with at least 1 systolic blood pressure (SBP) ≥160 mm Hg or diastolic blood pressure (DBP) ≥100 mm Hg and patients with at least 1 SBP ≥180 mm Hg or DBP ≥120 mm Hg. Of 373 861 patients included, 56 909 (15.2%) had at least 1 SBP ≥160 mm Hg or DBP ≥100 mm Hg, and 10 476 (2.8%) had at least 1 SBP ≥180 mm Hg or DBP ≥120 mm Hg. Among patients with SBP ≥160 mm Hg or DBP ≥100 mm Hg, only 28.3% had a follow visit within 1 month (time window of follow-up recommended by the guideline) and 19.9% subsequently achieved control targets (SBP <130 mm Hg and DBP <80 mm Hg) within 6 months. Follow-up rate at 1 month and control rate at 6 months for patients with SBP ≥180 mm Hg or DBP ≥120 mm Hg was 31.9% and 17.2%. Conclusions Digital population health surveillance with an electronic health record identified a large number of patients with markedly elevated blood pressure and inadequate follow-up. Many of these patients subsequently failed to achieve control targets.

Pathogen-reduced (PR) platelets are routinely used in many countries. Some studies reported changes in platelet and red blood cell (RBC) transfusion requirements in patients who received PR platelets when compared to conventional (CONV) platelets. Over a 28-month period we retrospectively analysed platelet utilisation, RBC transfusion trends, and transfusion reaction rates data from all transfused adult patients transfused at the Yale-New Haven Hospital, New Haven, CT, USA. We determined the number of RBC and platelet components administered between 2 and 24, 48, 72 or 96 h. A total of 3767 patients received 21 907 platelet components (CONV = 8912; PR = 12 995); 1,087 patients received only CONV platelets (1578 components) and 1,466 patients received only PR platelets (2604 components). The number of subsequently transfused platelet components was slightly higher following PR platelet components (P < 0·05); however, fewer RBCs were transfused following PR platelet administration (P < 0·05). The mean time-to-next platelet component transfusion was slightly shorter following PR platelet transfusion (P = 0·002). The rate of non-septic transfusion reactions did not differ (all P > 0·05). Septic transfusion reactions (N = 5) were seen only after CONV platelet transfusions (P = 0·011). These results provide evidence for comparable clinical efficacy of PR and CONV platelets. PR platelets eliminated septic transfusion reactions without increased risk of other types of transfusions with only slight increase in platelet utilisation., (© 2019 The Authors. British Journal of Haematology published by British Society for Haematology and John Wiley & Sons Ltd.)