BUDDY: molecular formula discovery via bottom-up MS/MS interrogation.

A substantial fraction of metabolic features remains undetermined in mass spectrometry (MS)-based metabolomics, and molecular formula annotation is the starting point for unraveling their chemical identities. Here we present bottom-up tandem MS (MS/MS) interrogation, a method for de novo formula annotation. Our approach prioritizes MS/MS-explainable formula candidates, implements machine-learned ranking and offers false discovery rate estimation. Compared with the mathematically exhaustive formula enumeration, our approach shrinks the formula candidate space by 42.8% on average. Method benchmarking on annotation accuracy was systematically carried out on reference MS/MS libraries and real metabolomics datasets. Applied on 155,321 recurrent unidentified spectra, our approach confidently annotated >5,000 novel molecular formulae absent from chemical databases. Beyond the level of individual metabolic features, we combined bottom-up MS/MS interrogation with global optimization to refine formula annotations while revealing peak interrelationships. This approach allowed the systematic annotation of 37 fatty acid amide molecules in human fecal data. All bioinformatics pipelines are available in a standalone software, BUDDY ( https://github.com/HuanLab/BUDDY ).

Paramounter: Direct Measurement of Universal Parameters To Process Metabolomics Data in a "White Box".

Choosing appropriate data processing parameters is critical in processing liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics data. The conventional design of experiments (DOE) approach is time-consuming and provides no intuitive explanation why the selected parameters generate the best results. After studying commonly used metabolomics data processing software, this work summarized a set of universal parameters, including mass tolerance, peak height, peak width, and instrumental shift. These universal parameters are shared among different feature extraction programs and are critical to metabolic feature extraction. We then developed Paramounter, an R program that automatically measures these universal parameters from raw LC-MS-based metabolomics data prior to metabolic feature extraction. This is made possible through novel concepts of rank-based intensity sorting, zone of interest, and many others. Paramounter also translates universal parameters to software-specific parameters for data processing in different programs. Applying Paramounter is demonstrated to provide a threefold increase in the extracted metabolites compared to using default parameters in MS-DIAL-based feature extraction. Furthermore, the comparison between Paramounter, AutoTuner, and IPO showed that Paramounter generates 3.7- and 1.6-fold more true positive features than AutoTuner and IPO, respectively. Further validation of Paramounter on 11 datasets covering different sample types, data acquisition modes, and MS vendors proved that Paramounter is a convenient and robust program. Overall, the proposed universal parameters and the development of Paramounter address a critical need in metabolomics data processing, transforming metabolomics feature extraction from a "black box" to a "white box." Paramounter is freely available on GitHub (https://github.com/HuanLab/Paramounter).

DaDIA: Hybridizing Data-Dependent and Data-Independent Acquisition Modes for Generating High-Quality Metabolomic Data.

Existing data acquisition modes such as full-scan, data-dependent (DDA), and data-independent acquisition (DIA) often present limited capabilities in capturing metabolic information in liquid chromatography-mass spectrometry (LC-MS)-based metabolomics. In this work, we proposed a novel metabolomic data acquisition workflow that combines DDA and DIA analyses to achieve better metabolomic data quality, including enhanced metabolome coverage, tandem mass spectrometry (MS2) coverage, and MS2 quality. This workflow, named data-dependent-assisted data-independent acquisition (DaDIA), performs untargeted metabolomic analysis of individual biological samples using DIA mode and the pooled quality control (QC) samples using DDA mode. This combination takes advantage of the high-feature number and MS2 spectral coverage of the DIA data and the high MS2 spectral quality of the DDA data. To analyze the heterogeneous DDA and DIA data, we further developed a computational program, DaDIA.R, to automatically extract metabolic features and perform streamlined metabolite annotation of DaDIA data set. Using human urine samples, we demonstrated that the DaDIA workflow delivers remarkably improved data quality when compared to conventional DDA or DIA metabolomics. In particular, both the number of detected features and annotated metabolites were greatly increased. Further biological demonstration using a leukemia metabolomics study also proved that the DaDIA workflow can efficiently detect and annotate around 4 times more significant metabolites than DDA workflow with broad MS2 coverage and high MS2 spectral quality for downstream statistical analysis and biological interpretation. Overall, this work represents a critical development of data acquisition mode in untargeted metabolomics, which can greatly benefit untargeted metabolomics for a wide range of biological applications.

ISFrag: De Novo Recognition of In-Source Fragments for Liquid Chromatography-Mass Spectrometry Data.

In-source fragmentation (ISF) is a naturally occurring phenomenon during electrospray ionization (ESI) in liquid chromatography-mass spectrometry (LC-MS) analysis. ISF leads to false metabolite annotation in untargeted metabolomics, prompting misinterpretation of the underlying biological mechanisms. Conventional metabolomic data cleaning mainly focuses on the annotation of adducts and isotopes, and the recognition of ISF features is mainly based on common neutral losses and the LC coelution pattern. In this work, we recognized three increasingly important patterns of ISF features, including (1) coeluting with their precursor ions, (2) being in the tandem MS (MS2) spectra of their precursor ions, and (3) sharing similar MS2 fragmentation patterns with their precursor ions. Based on these patterns, we developed an R package, ISFrag, to comprehensively recognize all possible ISF features from LC-MS data generated from full-scan, data-dependent acquisition, and data-independent acquisition modes without the assistance of common neutral loss information or MS2 spectral library. Tested using metabolite standards, we achieved a 100% correct recognition of level 1 ISF features and over 80% correct recognition for level 2 ISF features. Further application of ISFrag on untargeted metabolomics data allows us to identify ISF features that can potentially cause false metabolite annotation at an omics-scale. With the help of ISFrag, we performed a systematic investigation of how ISF features are influenced by different MS parameters, including capillary voltage, end plate offset, ion energy, and "collision energy". Our results show that while increasing energies can increase the number of real metabolic features and ISF features, the percentage of ISF features might not necessarily increase. Finally, using ISFrag, we created an ISF pathway to visualize the relationships between multiple ISF features that belong to the same precursor ion. ISFrag is freely available on GitHub (https://github.com/HuanLab/ISFrag).

EVA: Evaluation of Metabolic Feature Fidelity Using a Deep Learning Model Trained With Over 25000 Extracted Ion Chromatograms.

Extracting metabolic features from liquid chromatography-mass spectrometry (LC-MS) data relies on the recognition of extracted ion chromatogram (EIC) peak shapes using peak picking algorithms. Unfortunately, all peak picking algorithms present a significant drawback of generating a problematic number of false positives. In this work, we take advantage of deep learning technology to develop a convolutional neural network (CNN)-based program that can automatically recognize metabolic features with poor EIC shapes, which are of low feature fidelity and more likely to be false. Our CNN model was trained using 25095 EIC plots collected from 22 LC-MS-based metabolomics projects of various sample types, LC and MS conditions. Notably, we manually inspected all the EIC plots to assign good or poor EIC quality for accurate model training. The trained CNN model is embedded into a C#-based program, named EVA (short for evaluation). The EVA Windows Application is a versatile platform that can process metabolic features generated by LC-MS systems of various vendors and processed using various data processing software. Our comprehensive evaluation of EVA indicates that it achieves over 90% classification accuracy. EVA can be readily used in LC-MS-based metabolomics projects and is freely available on the Microsoft Store by searching "EVA Metabolomics".

Patient safety and quality outcomes for ED patients admitted to alternative care area inpatient beds.

BACKGROUND: Inpatient hallway beds are one solution to mitigate emergency department (ED) crowding due to boarding of admitted patients. Alternative Care Areas (AltCA) beds are located in inpatient hallways, cardiac catheterization lab, and endoscopy. We examined whether AltCA beds were associated with increased risk of patient safety and quality outcomes: transfer to Intensive Care Unit (ICU), mortality, hospital-acquired infections (HAI), falls, and 72-hour hospital readmission. METHODS: Retrospective cohort study of patients age >18 years admitted from the ED to non-ICU beds at an urban, academic hospital. AltCA bed exclusion criteria: dementia, frequent respiratory interventions, contact or airborne isolation, psychiatric admission, and inability to ambulate. The study periods were: pre-intervention 9/1/2014-3/31/2015, transition 9/1/2015-3/31/2016, and post-intervention 9/1/2016-3/31/2017. Data analysis used unadjusted and multivariable analyses which controlled for age, sex, race, ethnicity, insurance, ED triage Emergency Service Index (ESI) level, and telemetry order. RESULTS: The study included 16,801 patients, with 622 (3.7%) patients in AltCA beds. AltCA beds had younger patients than standard inpatient beds, 57.7 years and 61.7 years; fewer telemetry order, 48.4% and 59.3%; and fewer ESI level 2, 16.1% and 26.2%. AltCA beds had shorter hospital LOS than standard inpatient beds, 2.7 days and 3.4 days. AltCA beds had decreased risk of transfer to ICU -10.6 (95%CI: -18.3, -2.8) and HAI -13.4 (95%CI: -20.3, -6.5) compared to standard inpatient beds. CONCLUSION: Patients in AltCA beds did not have increased risk of patient safety and quality outcomes but rather decreased risk of transfer to ICU and HAI than standard inpatient beds.

Limits of variation, specific infectivity, and genome packaging of massively recoded poliovirus genomes.

Computer design and chemical synthesis generated viable variants of poliovirus type 1 (PV1), whose ORF (6,189 nucleotides) carried up to 1,297 "Max" mutations (excess of overrepresented synonymous codon pairs) or up to 2,104 "SD" mutations (randomly scrambled synonymous codons). "Min" variants (excess of underrepresented synonymous codon pairs) are nonviable except for P2Min, a variant temperature-sensitive at 33 and 39.5 °C. Compared with WT PV1, P2Min displayed a vastly reduced specific infectivity (si) (WT, 1 PFU/118 particles vs. P2Min, 1 PFU/35,000 particles), a phenotype that will be discussed broadly. Si of haploid PV presents cellular infectivity of a single genotype. We performed a comprehensive analysis of sequence and structures of the PV genome to determine if evolutionary conserved cis-acting packaging signal(s) were preserved after recoding. We showed that conserved synonymous sites and/or local secondary structures that might play a role in determining packaging specificity do not survive codon pair recoding. This makes it unlikely that numerous "cryptic, sequence-degenerate, dispersed RNA packaging signals mapping along the entire viral genome" [Patel N, et al. (2017) Nat Microbiol 2:17098] play the critical role in poliovirus packaging specificity. Considering all available evidence, we propose a two-step assembly strategy for +ssRNA viruses: step I, acquisition of packaging specificity, either (a) by specific recognition between capsid protein(s) and replication proteins (poliovirus), or (b) by the high affinity interaction of a single RNA packaging signal (PS) with capsid protein(s) (most +ssRNA viruses so far studied); step II, cocondensation of genome/capsid precursors in which an array of hairpin structures plays a role in virion formation.

Off the Shelf: Rapid Deployment of an Emergency Department Telemedicine Platform Using Readily Available Consumer Products.

BACKGROUND: For 20 years, telemedicine has been waiting in the wings for its time in the spotlight. The Coronavirus Disease 2019 (COVID-19) pandemic, with its emphasis on personal protective equipment (PPE) and reducing high-risk contacts, was the catalyst needed to bring telemedicine into mainstream consciousness and acceptance. OBJECTIVES: We first review some of the key factors that precipitated this abrupt alteration of the perception of telemedicine. We then detail the creation of a department-wide telemedicine network using off-the-shelf consumer products. Our goal was to very rapidly install a system that was familiar to end-users for the purpose of reducing high-risk contacts and conserving PPE. Sourcing from the consumer realm proved to be advantageous over enterprise-level equipment when these goals were desired. DISCUSSION: After a rollout of 1.5 weeks from zero to fully operational, we showed an immediate decrease in high-risk contacts and PPE use. All 80 rooms plus all triage areas in our department were outfitted with Apple iPads running Zoom. User adoption was high and telemedicine use increased from ∼17 to ∼90 instances a day, a 429% increase. We saw a decrease in high-risk contacts of about 75%, with a concomitant cost savings in PPE. CONCLUSIONS: We propose that the use of consumer products sourced from local vendors is a viable solution for telemedicine systems focusing on speed, reducing costs, and ease of deployment. Future work will focus on studying its performance characteristics vs. other systems in an evolving landscape.

Large-scale recoding of an arbovirus genome to rebalance its insect versus mammalian preference.

The protein synthesis machineries of two distinct phyla of the Animal kingdom, insects of Arthropoda and mammals of Chordata, have different preferences for how to best encode proteins. Nevertheless, arboviruses (arthropod-borne viruses) are capable of infecting both mammals and insects just like arboviruses that use insect vectors to infect plants. These organisms have evolved carefully balanced genomes that can efficiently use the translational machineries of different phyla, even if the phyla belong to different kingdoms. Using dengue virus as an example, we have undone the genome encoding balance and specifically shifted the encoding preference away from mammals. These mammalian-attenuated viruses grow to high titers in insect cells but low titers in mammalian cells, have dramatically increased LD50s in newborn mice, and induce high levels of protective antibodies. Recoded arboviruses with a bias toward phylum-specific expression could form the basis of a new generation of live attenuated vaccine candidates.

Introduction of a Horizontal and Vertical Split Flow Model of Emergency Department Patients as a Response to Overcrowding.

INTRODUCTION: ED overcrowding is an issue that is affecting every emergency department and every hospital. The inability to maintain patient flow into and out of the emergency department paralyzes the ability to provide effective and timely patient care. Many solutions have been proposed on how to mitigate the effects of ED overcrowding. Solutions involve either hospital-wide initiatives or ED-based solutions. In this article, the authors seek to describe and provide metrics for a patient flow methodology that targets ESI 3 patients in a vertical flow model. METHODS: In the Stanford Emergency Department, a vertical flow model was created from existing ED space by removing fold-down horizontal stretchers and replacing them with multiple chairs that allowed for assessment and medical management in an upright sitting position. The model was launched and sustained through frequent interdisciplinary huddles, detailed inclusion and exclusion criteria, scripted text on how to promote the flow model to patients, and close analytics of metrics. Metrics for success included patient length of stay (LOS) for those triaged to the vertical flow area compared with ESI 3 patients triaged to the traditional emergency department as a comparison group. The secondary outcome is the total number of patients seen in the vertical flow area. This was a 6-month-September 2014, to February 2015-retrospective pre- and postintervention study that examined LOS as a marker for effective launch and implementation of a vertical patient workflow model. RESULTS: The patients triaged to the vertical flow area in the study period tended to be younger than in the control period (43 years versus 52 years, P = 0.00). There was a significant decrease in our primary end point: the total LOS for ESI 3 patients triaged to the vertical flow area (270 minutes versus 384 minutes, P = 0.00). CONCLUSION: Implementation of a vertical patient flow strategy can decrease LOS for the vertical ESI 3 patients based upon the inclusion and exclusion criteria. Furthermore, this is accomplished with minimal financial investment within the physical constraints of an existing emergency department.

Lean Manufacturing Improves Emergency Department Throughput and Patient Satisfaction.

A multidisciplinary team led by nursing leadership and physicians developed a plan to meet increasing demand and improve the patient experience in the ED without expanding the department's current resources. The approach included Lean tools and engaged frontline staff and physicians. Applying Lean management principles resulted in quicker service, improved patient satisfaction, increased capacity, and reduced resource utilization. Incorporating continuous daily management is necessary for sustainment of continuous improvement activities.

Precision emergency medicine.

BACKGROUND: Precision health is a burgeoning scientific discipline that aims to incorporate individual variability in biological, behavioral, and social factors to develop personalized health solutions. To date, emergency medicine has not deeply engaged in the precision health movement. However, rapid advances in health technology, data science, and medical informatics offer new opportunities for emergency medicine to realize the promises of precision health. METHODS: In this article, we conceptualize precision emergency medicine as an emerging paradigm and identify key drivers of its implementation into current and future clinical practice. We acknowledge important obstacles to the specialty-wide adoption of precision emergency medicine and offer solutions that conceive a successful path forward. RESULTS: Precision emergency medicine is defined as the use of information and technology to deliver acute care effectively, efficiently, and authentically to individual patients and their communities. Key drivers and opportunities include leveraging human data, capitalizing on technology and digital tools, providing deliberate access to care, advancing population health, and reimagining provider education and roles. Overcoming challenges in equity, privacy, and cost is essential for success. We close with a call to action to proactively incorporate precision health into the clinical practice of emergency medicine, the training of future emergency physicians, and the research agenda of the specialty. CONCLUSIONS: Precision emergency medicine leverages new technology and data-driven artificial intelligence to advance diagnostic testing, individualize patient care plans and therapeutics, and strategically refine the convergence of the health system and the community.

Audit of data from examination image headers collected for quality assurance in the ECOG-ACRIN EA1151 tomosynthesis mammographic imaging screening trial (TMIST).

PURPOSE: A comprehensive, centrally-monitored physics quality control (QC) program was developed for the Tomosynthesis Imaging Screening Trial (TMIST), a randomized controlled trial of digital breast tomosynthesis (TM) versus digital mammography (DM) for cancer screening. As part of the program, in addition to a set of phantom-based tests, de-identified data on image acquisition and processing parameters were captured from the DICOM headers of all individual patient images in the trial. These data were analyzed to assess the potential usefulness of header data from digital mammograms and tomosynthesis images of patients for quality assurance in breast imaging. METHODS: Data were automatically extracted from the headers of all de-identified patient mammograms and tomosynthesis images in the TMIST study. Image acquisition parameters and estimated radiation doses were tracked for individual sites, systems and across system types. These parameters included (among others) kV, target/filter use, number of acquired views per examination, AEC mode, compression thickness and force and detector temperature. Consistency of manually entered study data parameters (subject ID, screening time-point) from TMIST was evaluated. Preliminary observations from the program are presented. RESULTS: We report on data from 812 651 images from 135 525 examinations acquired between October, 2017 and December, 2022. Data came from 6 system models from 3 manufacturers. There was greater variability both in the number of views used and in the estimated (proxy) doses received in DM exams compared to TM. Mean proxy doses per examination varied among manufacturers from 2.76-4.54 mGy for DM and 3-4.84 mGy for the tomosynthesis component in the TM arm with maximum examination proxy doses of 20 and 26 mGy for DM and TM respectively. Mean proxy doses per examination for the combination examination in TM (tomosynthesis plus digital mammography) varied from 6.6 to 7.6 mGy among manufacturers with a maximum of 44.5 mGy. CONCLUSIONS: Overall, modern digital mammography and tomosynthesis systems used in TMIST have operated very reliably. Doses vary considerably due to variation in the number of views per examination, thickness and fibro-glandularity of the breast, and choices in the use of synthesized versus actual 2D mammography in the TM examination. These data may also be useful in predicting equipment problems. Header information is valuable not only for automated QC, but also for cross-checking accuracy and consistency of data in a clinical study.

Quality control for digital tomosynthesis in the ECOG-ACRIN EA1151 TMIST trial.

BACKGROUND: The Tomosynthesis Mammography Imaging Screening Trial (TMIST), EA1151 conducted by the Eastern Cooperative Oncology Group (ECOG)/American College of Radiology Imaging Network (ACRIN) is a randomized clinical trial designed to assess the effectiveness for breast cancer screening of digital breast tomosynthesis (TM) compared to digital mammography (DM). Equipment from multiple vendors is being used in the study. PURPOSE: For the findings of the study to be valid and capture the true capacities of the two technology types, it is important that all equipment is operated within appropriate parameters with regard to image quality and dose. A harmonized QC program was established by a core physics team. Since there are over 120 trial sites, a centralized, automated QC program was chosen as the most practical design. This report presents results of the weekly QC testing program. A companion paper will review quality monitoring based on data from the headers of the patient images. METHODS: Study images are collected centrally after de-identification using the "TRIAD" application developed by ACR. The core physics team devised and implemented a minimal set of quality control (QC) tests to evaluate the tomosynthesis and 2D mammography systems. Weekly, monthly and annual testing is performed by the site mammography technologists with images submitted directly to the physics core. The weekly physics QC tests are described: SDNR of a low-contrast mass object, artifact spread, spatial resolution, tracking of technical factors, and in-slice noise power spectra. RESULTS: As of December 31, 2022 (5 years), 145 sites with 411 machines had submitted QC data. A total of 136 742 TMIST participant screening imaging studies had been performed. The 5th and 95th percentile mean glandular doses for a single tomosynthesis exposure to a 4.0 cm thick PMMA phantom ("standard breast phantom") were 1.24 and 1.68 mGy respectively. The largest sources of QC non-conformance were: operator error, not following the QC protocol exactly, unreported software updates and preventive maintenance activities that affected QC setpoints. Noise power spectra were measured, however, standardization of performance targets across machine types and software revisions was difficult. Nevertheless, for each machine type, test measurement results were very consistent when the protocol was followed. Deviations in test results were mostly related to software and hardware changes. CONCLUSION: Most systems performed very consistently. Although this is a harmonized program using identical phantoms and testing protocols, it is not appropriate to apply universal threshold or target metrics across the machine types because the systems have different non-linear reconstruction algorithms and image display filters. It was found to be more useful to assess pass/fail criteria in terms of relative deviations from baseline values established when a system is first characterized and after equipment is changed. Generally, systems which needed repair failed suddenly, but in retrospect, for a few cases, drops in SDNR and increases in mAs were observed prior to tube failure. TMIST is registered as NCT03233191 by Clinicaltrials.gov.

Development of a method and an assessment construct for person-centered translation of dementia public stigma scales.

Background: With the number of people with dementia dramatically increasing over time and dementia becoming a major health concern worldwide, scales have been developed to assess the stigma socially attached to this neurodegenerative disorder. There are, however, almost no available methods and assessment constructs for person-centered translation of dementia public stigma scales. Objective: To develop such a method and such an assessment construct by translating the Dementia Public Stigma Scale (DPSS) into standard written Chinese. Methods: We translated the DPSS following three major steps: (1) literal translation and mistranslation identification; (2) panel discussions of items with problematic translations; and (3) the final checking of the translated scale. Informed by the translation and adaptation process, we then developed a method for person-centered translation of dementia public stigma scales. Based on this method and our panel discussions, we finally proposed a tripartite assessment construct for quality evaluation of the translation of dementia public stigma scales. Results: Forward and backward translation did not work sufficiently in dementia public stigma scale translation. Mistranslations were induced by three major causes, including confusion caused by multiple Chinese meanings of the immediate Chinese direct translation, the lack of immediate Chinese direct translation because of varying positive/negative emotions attached to multiple translations, and the lack of culture-specific idioms in Chinese. Based on these factors, we proposed a tripartite dementia translation assessment construct. Following this assessment tool, we determined the best Chinese version that could further be tested for its psychometric properties among the public. Conclusion: A method and an assessment construct for person-centered translation of dementia public stigma scales were developed. Such a method and such an assessment construct could be followed in the translation of dementia public stigma scales and the translation evaluation of such scales.

Changes in low-acuity patient volume in an emergency department after launching a walk-in clinic.

Objective: Unscheduled low-acuity care options are on the rise and are often expected to reduce emergency department (ED) visits. We opened an ED-staffed walk-in clinic (WIC) as an alternative care location for low-acuity patients at a time when ED visits exceeded facility capacity and the impending flu season was anticipated to increase visits further, and we assessed whether low-acuity ED patient visits decreased after opening the WIC. Methods: In this retrospective cohort study, we compared patient and clinical visit characteristics of the ED and WIC patients and conducted interrupted time-series analyses to quantify the impact of the WIC on low-acuity ED patient visit volume and the trend. Results: There were 27,211 low-acuity ED visits (22.7% of total ED visits), and 7,058 patients seen in the WIC from February 26, 2018, to November 17, 2019. Low-acuity patient visits in the ED reduced significantly immediately after the WIC opened (P = 0.01). In the subsequent months, however, patient volume trended back to pre-WIC volumes such that there was no significant impact at 6, 9, or 12 months (P = 0.07). Had WIC patients been seen in the main ED, low-acuity volume would have been 27% of the total volume rather than the 22.7% that was observed. Conclusion: The WIC did not result in a sustained reduction in low-acuity patients in the main ED. However, it enabled emergency staff to see low-acuity patients in a lower resource setting during times when ED capacity was limited.

Observational study of organisational responses of 17 US hospitals over the first year of the COVID-19 pandemic.

OBJECTIVES: The COVID-19 pandemic has required significant modifications of hospital care. The objective of this study was to examine the operational approaches taken by US hospitals over time in response to the COVID-19 pandemic. DESIGN, SETTING AND PARTICIPANTS: This was a prospective observational study of 17 geographically diverse US hospitals from February 2020 to February 2021. OUTCOMES AND ANALYSIS: We identified 42 potential pandemic-related strategies and obtained week-to-week data about their use. We calculated descriptive statistics for use of each strategy and plotted percent uptake and weeks used. We assessed the relationship between strategy use and hospital type, geographic region and phase of the pandemic using generalised estimating equations (GEEs), adjusting for weekly county case counts. RESULTS: We found heterogeneity in strategy uptake over time, some of which was associated with geographic region and phase of pandemic. We identified a body of strategies that were both commonly used and sustained over time, for example, limiting staff in COVID-19 rooms and increasing telehealth capacity, as well as those that were rarely used and/or not sustained, for example, increasing hospital bed capacity. CONCLUSIONS: Hospital strategies during the COVID-19 pandemic varied in resource intensity, uptake and duration of use. Such information may be valuable to health systems during the ongoing pandemic and future ones.

JPA: Joint Metabolic Feature Extraction Increases the Depth of Chemical Coverage for LC-MS-Based Metabolomics and Exposomics.

Extracting metabolic features from liquid chromatography-mass spectrometry (LC-MS) data has been a long-standing bioinformatic challenge in untargeted metabolomics. Conventional feature extraction algorithms fail to recognize features with low signal intensities, poor chromatographic peak shapes, or those that do not fit the parameter settings. This problem also poses a challenge for MS-based exposome studies, as low-abundant metabolic or exposomic features cannot be automatically recognized from raw data. To address this data processing challenge, we developed an R package, JPA (short for Joint Metabolomic Data Processing and Annotation), to comprehensively extract metabolic features from raw LC-MS data. JPA performs feature extraction by combining a conventional peak picking algorithm and strategies for (1) recognizing features with bad peak shapes but that have tandem mass spectra (MS2) and (2) picking up features from a user-defined targeted list. The performance of JPA in global metabolomics was demonstrated using serial diluted urine samples, in which JPA was able to rescue an average of 25% of metabolic features that were missed by the conventional peak picking algorithm due to dilution. More importantly, the chromatographic peak shapes, analytical accuracy, and precision of the rescued metabolic features were all evaluated. Furthermore, owing to its sensitive feature extraction, JPA was able to achieve a limit of detection (LOD) that was up to thousands of folds lower when automatically processing metabolomics data of a serial diluted metabolite standard mixture analyzed in HILIC(-) and RP(+) modes. Finally, the performance of JPA in exposome research was validated using a mixture of 250 drugs and 255 pesticides at environmentally relevant levels. JPA detected an average of 2.3-fold more exposure compounds than conventional peak picking only.

Effectiveness, safety, and efficiency of a drive-through care model as a response to the COVID-19 testing demand in the United States.

Objectives: Here we report the clinical performance of COVID-19 curbside screening with triage to a drive-through care pathway versus main emergency department (ED) care for ambulatory COVID-19 testing during a pandemic. Patients were evaluated from cars to prevent the demand for testing from spreading COVID-19 within the hospital. Methods: We examined the effectiveness of curbside screening to identify patients who would be tested during evaluation, patient flow from screening to care team evaluation and testing, and safety of drive-through care as 7-day ED revisits and 14-day hospital admissions. We also compared main ED efficiency versus drive-through care using ED length of stay (EDLOS). Standardized mean differences (SMD) >0.20 identify statistical significance. Results: Of 5931 ED patients seen, 2788 (47.0%) were walk-in patients. Of these patients, 1111 (39.8%) screened positive for potential COVID symptoms, of whom 708 (63.7%) were triaged to drive-through care (with 96.3% tested), and 403 (36.3%) triaged to the main ED (with 90.5% tested). The 1677 (60.2%) patients who screened negative were seen in the main ED, with 440 (26.2%) tested. Curbside screening sensitivity and specificity for predicting who ultimately received testing were 70.3% and 94.5%. Compared to the main ED, drive-through patients had fewer 7-day ED revisits (3.8% vs 12.5%, SMD = 0.321), fewer 14-day hospital readmissions (4.5% vs 15.6%, SMD = 0.37), and shorter EDLOS (0.56 vs 5.12 hours, SMD = 1.48). Conclusion: Curbside screening had high sensitivity, permitting early respiratory isolation precautions for most patients tested. Low ED revisit, hospital readmissions, and EDLOS suggest drive-through care, with appropriate screening, is safe and efficient for future respiratory illness pandemics.