Decon2LS: An open-source software package for automated processing and visualization of high resolution mass spectrometry data

Abstract Background Data generated from liquid chromatography coupled to high-resolution mass spectrometry (LC-MS)-based studies of a biological sample can contain large amounts of biologically significant information in the form of proteins, peptides, and metabolites. Interpreting this data involves inferring the masses and abundances of biomolecules injected into the instrument. Because of the inherent complexity of mass spectral patterns produced by these biomolecules, the analysis is significantly enhanced by using visualization capabilities to inspect and confirm results. In this paper we describe Decon2LS, an open-source software package for automated processing and visualization of high-resolution MS data. Drawing extensively on algorithms developed over the last ten years for ICR2LS, Decon2LS packages the algorithms as a rich set of modular, reusable processing classes for performing diverse functions such as reading raw data, routine peak finding, theoretical isotope distribution modelling, and deisotoping. Because the source code is openly available, these functionalities can now be used to build derivative applications in relatively fast manner. In addition, Decon2LS provides an extensive set of visualization tools, such as high performance chart controls. Results With a variety of options that include peak processing, deisotoping, isotope composition, etc, Decon2LS supports processing of multiple raw data formats. Deisotoping can be performed on an individual scan, an individual dataset, or on multiple datasets using batch processing. Other processing options include creating a two dimensional view of mass and liquid chromatography (LC) elution time features, generating spectrum files for tandem MS data, creating total intensity chromatograms, and visualizing theoretical peptide profiles. Application of Decon2LS to deisotope different datasets obtained across different instruments yielded a high number of features that can be used to identify and quantify peptides in the biological sample. Conclusion Decon2LS is an efficient software package for discovering and visualizing features in proteomics studies that require automated interpretation of mass spectra. Besides being easy to use, fast, and reliable, Decon2LS is also open-source, which allows developers in the proteomics and bioinformatics communities to reuse and refine the algorithms to meet individual needs. Decon2LS source code, installer, and tutorials may be downloaded free of charge at http://http:/ncrr.pnl.gov/software/

Spatial and Functional Relationships Among Pol V-Associated Loci, Pol IV-Dependent siRNAs, and Cytosine Methylation in the \u3cem\u3eArabidopsis\u3c/em\u3e Epigenome

Multisubunit RNA polymerases IV and V (Pols IV and V) mediate RNA-directed DNA methylation and transcriptional silencing of retrotransposons and heterochromatic repeats in plants. We identified genomic sites of Pol V occupancy in parallel with siRNA deep sequencing and methylcytosine mapping, comparing wild-type plants with mutants defective for Pol IV, Pol V, or both Pols IV and V. Approximately 60% of Pol V-associated regions encompass regions of 24-nucleotide (nt) siRNA complementarity and cytosine methylation, consistent with cytosine methylation being guided by base-pairing of Pol IV-dependent siRNAs with Pol V transcripts. However, 27% of Pol V peaks do not overlap sites of 24-nt siRNA biogenesis or cytosine methylation, indicating that Pol V alone does not specify sites of cytosine methylation. Surprisingly, the number of methylated CHH motifs, a hallmark of RNA-directed de novo methylation, is similar in wild-type plants and Pol IV or Pol V mutants. In the mutants, methylation is lost at 50%–60% of the CHH sites that are methylated in the wild type but is gained at new CHH positions, primarily in pericentromeric regions. These results indicate that Pol IV and Pol V are not required for cytosine methyltransferase activity but shape the epigenome by guiding CHH methylation to specific genomic sites

Machine Learning for Predicting Critical Events Among Hospitalized Children

Importance: Unrecognized deterioration among hospitalized children is associated with a high risk of mortality and morbidity. The current approach to pediatric risk stratification is fragmented, as each hospital unit (emergency, ward, or intensive care) uses different tools for predicting specific outcomes. Objective: To develop a machine learning model for the early detection of deterioration across all units, thereby enabling a unified risk assessment throughout the patient’s hospital stay. Design, Setting, and Participants: This retrospective cohort study used data from pediatric (age Main Outcomes and Measures: The primary outcome was critical events, defined as invasive mechanical ventilation, administration of vasoactive medications, or death within 12 hours of an observation. Results: The cohort included 135 621 patients (mean [SD] age, 7 [6] years; 60 376 [44.5%] female). Patient age, hospital unit, vital signs, laboratory results, and prior comorbidities were used to derive a regression-based model, an extreme gradient-boosted machine (XGB) model, and 2 deep learning models. Data from 2 hospitals were used as a derivation cohort, while patients in the third hospital constituted the hold-out external test cohort. The XGB model was the best-performing machine learning model, outperforming 2 existing ward-focused models in terms of discrimination (C statistic: XGB, 0.86; ward-focused models, 0.82 [P  Conclusions and Relevance: This retrospective cohort study describes the development of a novel hospitalwide model for continuously predicting the risk of critical events through the entirety of a child’s stay. The model facilitated a unified framework for risk assessment in a pediatric hospital.</p

Alcohol Withdrawal Severity Measures for Identifying Patients Requiring High-Intensity Care

OBJECTIVES: Alcohol withdrawal syndrome (AWS) may progress to require high-intensity care. Approaches to identify hospitalized patients with AWS who received higher level of care have not been previously examined. This study aimed to examine the utility of Clinical Institute Withdrawal Assessment Alcohol Revised (CIWA-Ar) for alcohol scale scores and medication doses for alcohol withdrawal management in identifying patients who received high-intensity care. DESIGN: A multicenter observational cohort study of hospitalized adults with alcohol withdrawal. SETTING: University of Chicago Medical Center and University of Wisconsin Hospital. PATIENTS: Inpatient encounters between November 2008 and February 2022 with a CIWA-Ar score greater than 0 and benzodiazepine or barbiturate administered within the first 24 hours. The primary composite outcome was patients who progressed to high-intensity care (intermediate care or ICU). INTERVENTIONS: None. MAIN RESULTS: Among the 8742 patients included in the study, 37.5% (n = 3280) progressed to high-intensity care. The odds ratio for the composite outcome increased above 1.0 when the CIWA-Ar score was 24. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) at this threshold were 0.12 (95% CI, 0.11–0.13), 0.95 (95% CI, 0.94–0.95), 0.58 (95% CI, 0.54–0.61), and 0.64 (95% CI, 0.63–0.65), respectively. The OR increased above 1.0 at a 24-hour lorazepam milligram equivalent dose cutoff of 15 mg. The sensitivity, specificity, PPV, and NPV at this threshold were 0.16 (95% CI, 0.14–0.17), 0.96 (95% CI, 0.95–0.96), 0.68 (95% CI, 0.65–0.72), and 0.65 (95% CI, 0.64–0.66), respectively. CONCLUSIONS: Neither CIWA-Ar scores nor medication dose cutoff points were effective measures for identifying patients with alcohol withdrawal who received high-intensity care. Research studies for examining outcomes in patients who deteriorate with AWS will require better methods for cohort identification

Abstract TMP38: Using Natural Language Processing To Investigate Diagnostic Error In Acute Stroke

Introduction: Diagnostic error occurs in approximately 10% of acute stroke (AS) presentations. The diagnostic process includes history, physical examination, and test performance and interpretation. However, critical information for diagnosis is contained in unstructured clinical notes. Hypothesis: We hypothesized that natural language processing (NLP) can identify features in unstructured clinical notes associated with potential diagnostic error during ED “catch and release” (CR) encounters prior to AS admissions. Methods: Using a retrospective case-control design and ICD-10 codes, we identified index emergency department (ED) admissions with a diagnosis of first-time stroke (cases) and age and sex-matched gastroenteritis (controls) who had an ED CR encounter in prior 30 days. Notes were processed using cTAKES to identify concept unique identifiers (CUI) among clinical narratives from the CR encounters. Regression analysis was utilized to determine CUI terms from the CR encounter that were associated with stroke cases compared to controls. These CUI terms were grouped by clinical experts into 3 aspects of the diagnostic process: history (e.g., risk factors, medications, symptoms), neurologic examination (e.g., mental status exam, cranial nerves, pronator drift), and tests (e.g., labs, CT, MRI). Results: In an analytic cohort of 319 stroke cases and 319 gastroenteritis controls, a non-cerebrovascular neurologic diagnosis at the CR encounter was noted in 20.2% of cases versus 6.0% in controls (P&lt;0.01). We identified 120 terms at the CR encounter associated with stroke (OR &gt;2.0 and p&lt;0.05). Grouped by themes, tests accounted for 50 (41.7%), examination for 37 (30.1%), and history for 33 (27.5%) terms. Terms related to neurologic examination had the highest median OR (median OR 6.7, IQR 2.7-11.5) followed by history (median OR 3.8, IQR 3.2-4.9) and tests (median OR 3.5, IQR 2.8-4.6). Conclusions: Neurologic presentations to the ED preceded 20% of stroke cases suggesting some of these may represent missed diagnoses for minor stroke and TIA. NLP may be a useful surveillance approach to identify neurologic symptoms, deficits, and tests present at CR encounters and trigger interventions to reduce diagnostic error prior to stroke. </jats:p

A Prediction Model for Bacteremia and Transfer to Intensive Care in Pediatric and Adolescent Cancer Patients With Febrile Neutropenia

Abstract Objectives:Febrile neutropenia (FN) is a common condition in children receiving chemotherapy. Our goal in this study was to develop a model for predicting blood stream infection (BSI) and transfer to intensive care (TIC) at time of presentation in pediatric cancer patients with FN. Methods: We conducted an observational cohort analysis of pediatric and adolescent cancer patients younger than 24 years admitted for fever and chemotherapy-induced neutropenia over a 7-year period. We excluded stem cell transplant recipients who developed FN after transplant and febrile non-neutropenic episodes. The primary outcome was onset of BSI, as determined by positive blood culture within 7 days of onset of FN. The secondary outcome was transfer to intensive care (TIC) within 14 days of FN onset. Predictor variables include demographics, clinical, and laboratory measures on initial presentation for FN. Data were divided into independent derivation (2009-2015) and prospective validation (2015-2016) cohorts. Prediction models were built for both outcomes using logistic regression and random forest and compared with Hakim model. Performance was assessed using area under the receiver operating characteristic curve (AUC) metrics. Results: A total of 505 FN episodes (FNEs) were identified in 230 patients. BSI was diagnosed in 106 (21%) and TIC occurred in 56 (10.6%) episodes. The most common oncologic diagnosis with FN was acute lymphoblastic leukemia (ALL), and the highest rate of BSI was in patients with AML. Patients who had BSI had higher maximum temperature, higher rates of prior BSI and higher incidence of hypotension compared with patients who did not have BSI. FN patients who were transferred to the intensive care (TIC) had higher temperature and higher incidence of hypotension at presentation compared to FN patients who didn’t have TIC. We compared 3 models: (1) random forest (2) logistic regression and (3) Hakim model. The areas under the curve for BSI prediction were (0.79, 0.65, and 0.64, P &lt; 0.05) for models 1,2, and 3, respectively. And for TIC prediction were (0.88, 0.76, and 0.65, P &lt; 0.05) respectively. The random forest model demonstrated higher accuracy in predicting BSI and TIC and showed a negative predictive value (NPV) of 0.91 and 0.97 for BSI and TIC respectively at the best cutoff point as determined by Youden’s Index. Likelihood ratios (LRs) (post-test probability) for RF model have potential utility of identifying low risk for BSI and TIC (0.24 and 0.12) and high-risk patients (3.5 and 6.8) respectively. Conclusions: Our prediction model has a good diagnostic performance in clinical practices for both BSI and TIC in FN patients at the time of presentation. The model can be used to identify a group of individuals at low risk for BSI who may benefit from early discharge and reduce length of stay, also it can identify FN patients at high risk of complications who might benefit from more intensive therapies at presentation.</jats:p

Multiple Organ Dysfunction Interactions in Critically Ill Children

IntroductionMultiple organ dysfunction (MOD) is a common pathway to morbidity and death in critically ill children. Defining organ dysfunction is challenging, as we lack a complete understanding of the complex pathobiology. Current pediatric organ dysfunction criteria assign the same diagnostic value—the same “weight”— to each organ system. While each organ dysfunction in isolation contributes to the outcome, there are likely complex interactions between multiple failing organs that are not simply additive.ObjectiveDetermine whether certain combinations of organ system dysfunctions have a significant interaction associated with higher risk of morbidity or mortality in critically ill children.MethodsWe conducted a retrospective observational cohort study of critically ill children at two large academic medical centers from 2010 and 2018. Patients were included in the study if they had at least two organ dysfunctions by day 3 of PICU admission based on the Pediatric Organ Dysfunction Information Update Mandate (PODIUM) criteria. Mortality was described as absolute number of deaths and mortality rate. Combinations of two pediatric organ dysfunctions were analyzed with interaction terms as independent variables and mortality or persistent MOD as the dependent variable in logistic regression models.ResultsOverall, 7,897 patients met inclusion criteria and 446 patients (5.6%) died. The organ dysfunction interactions that were significantly associated with the highest absolute number of deaths were cardiovascular + endocrinologic, cardiovascular + neurologic, and cardiovascular + respiratory. Additionally, the interactions associated with the highest mortality rates were liver + cardiovascular, respiratory + hematologic, and respiratory + renal. Among patients with persistent MOD, the most common organ dysfunctions with significant interaction terms were neurologic + respiratory, hematologic + immunologic, and endocrinologic + respiratory. Further analysis using classification and regression trees (CART) demonstrated that the absence of respiratory and liver dysfunction was associated with the lowest likelihood of mortality.Implications and Future DirectionsCertain combinations of organ dysfunctions are associated with a higher risk of persistent MOD or death. Notably, the three most common organ dysfunction interactions were associated with 75% of the mortality in our cohort. Critically ill children with MOD presenting with these combinations of organ dysfunctions warrant further study.</jats:sec

Designing a Data Dashboard for Health Departments and Overdose Fatality Review Teams

Introduction Overdose Fatality Review (OFR) teams within health departments rely on data from multiple isolated sources to study the opioid crisis. Recently, we linked electronic health record data with state agency data to form a regional Substance Misuse Data Commons (SMDC) using privacy-preserving record linkage. Our goal in this study was to use human factors design principles to design a data dashboard for OFR teams using the linked datasets that will overcome current barriers within OFR workflows. Methods We utilized NASA task load surveys, semi-structured interviews, and design sessions with end users to identify data needs for an optimal dashboard design. We assessed current workloads, data collection processes, and desired future state. We subsequently performed iterative design sessions for the generation and evaluation of low-fidelity prototypes. To overcome issues with privacy and security, we used synthetic data in a cloud-based platform to represent the SMDC for simulation.  Results Eleven OFR organizers participated. Pre-dashboard surveys on existing workflow showed high mental workload associated with data aggregation and case review, identifying a need for more accessible, comprehensive data. In our low-fidelity dashboard demo with synthetic data, iterative design adjustments were made in data visualization, storyline organizations, and theme-based data aggregation across pre-hospital and hospital data. Conclusions We refined the data dashboard prototype into a high-fidelity version, set for further usability and human factors evaluation. We addressed privacy and security concerns through synthetic data use while the real-world data is maintained in a HIPAA-secure Azure cloud environment with access for approved users