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Clinical prediction models have been widely acknowledged as informative tools providing evidence-based support for clinical decision making. However, prediction models are often underused in clinical practice due to many reasons including missing information upon real-time risk calculation in electronic health records (EHR) system. Existing literature to address this challenge focuses on statistical comparison of various approaches while overlooking the feasibility of their implementation in EHR. In this article, we propose a novel and feasible submodel approach to address this challenge for prediction models developed using the model approximation (also termed "preconditioning") method. The proposed submodel coefficients are equivalent to the corresponding original prediction model coefficients plus a correction factor. Comprehensive simulations were conducted to assess the performance of the proposed method and compared with the existing "one-step-sweep" approach as well as the imputation approach. In general, the simulation results show the preconditioning-based submodel approach is robust to various heterogeneity scenarios and is comparable to the imputation-based approach, while the "one-step-sweep" approach is less robust under certain heterogeneity scenarios. The proposed method was applied to facilitate real-time implementation of a prediction model to identify emergency department patients with acute heart failure who can be safely discharged home., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

The p K a 's of acids are known to depend on the ionic strength of an electrolyte solution, an effect that qualitatively results from electrostatic interactions of the acid and conjugate base with proximal ions. Here, we explore an intramolecular variation on this theme in which a carboxylic acid group is tethered to a pyridinium cationic charge center with increasingly long alkyl linkages in the series Py + -(CH 2 ) n -COOH, with n = 1-7. The effective acidities of the carboxylic acid group in the isolated cations are determined by recording the red-shifts in the frequencies of the acid OH stretches upon attachment to D 2 and N 2 molecules, measured by using cryogenic ion spectroscopy. The short chains indeed lead to substantial increases in acidity, with the effect falling off in the range of n = 4-5. The response of the CO stretch on the acid head group is surprisingly strong and, in contrast to the expected red shift of the OH, displays a blue shift as the chain length is reduced. Electronic structure calculations recover these trends and indicate that the proximal cationic charge center acts to draw electron density away from the acid C═O bond. This acts to blue-shift the CO stretch while weakening the C-C bond that attaches the head group to the chain.

Here we analyze cryogenic ion vibrational spectra of tagged protonated formic acid (PFA) with electronic structure and anharmonic vibrational calculations to establish the isomers generated by electrospray ionization (ESI) followed by buffer gas cooling to ∼25 K. Two isomers are identified (the trans form (E,Z) and the cis form (E,E)) and generated in comparable abundance despite the fact that the calculated E,E structure lies 6.40 kJ mol -1 above the E,Z form. A large (∼60 kJ mol -1 ) barrier separates them such that the E,E form can be kinetically trapped upon cooling in the ion trap. The anticooperativity between the H-bonds of the OH groups is explored by measuring the shift in the D 2 -bound OH fundamental when a second D 2 is attached. Both isomers are observed in the N 2 -tagged counterparts, displaying the expected red-shifted OH bands. These results indicate that ESI generates both isomers and both must be considered when analyzing cluster spectra based on the PFA core ion.

Background: The Vanderbilt Clinical Informatics Center (VCLIC) is based in the Department of Biomedical Informatics (DBMI) and operates across Vanderbilt University Medical Center (VUMC) and Vanderbilt University (VU) with a goal of enabling and supporting clinical informatics research and practice. VCLIC supports several types of applied clinical informatics teaching, including teaching of students in courses, professional education for staff and faculty throughout VUMC, and workshops and conferences that are open to the public., Objectives: In this paper, we provide a detailed accounting of our center and institution's methods of educating and training faculty, staff, students, and trainees from across the academic institution and health system on clinical informatics topics, including formal training programs and informal applied learning sessions., Methods: Through a host of informal learning events, such as workshops, seminars, conference-style events, bite-size instructive videos, and hackathons, as well as several formal education programs, such as the Clinical Informatics Graduate Course, Master's in Applied Clinical Informatics, Medical Student Integrated Science Course, Graduate Medical Education Elective, and Fellowship in Clinical Informatics, VCLIC and VUMC provide opportunities for faculty, students, trainees, and even staff to engage with Clinical Informatics topics and learn related skills., Results: The described programs have trained hundreds of participants from across the academic and clinical enterprises. Of the VCLIC-held events, the majority of attendees indicated through surveys that they were satisfied, with the average satisfaction score being 4.63/5, and all events averaging a satisfaction score of greater than 4. Across the 20 events VCLIC has held, our largest audiences are DBMI, HealthIT operational staff, and students from the medical and nursing schools., Conclusions: VCLIC has created and delivered a successful suite of formal and informal educational events and programs to disseminate clinical informatics knowledge and skills to learners across the academic institution and healthcare system., Competing Interests: The authors declare that they have no conflict of interest., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

Background:  Over the past 30 years, the American Medical Informatics Association (AMIA) has played a pivotal role in fostering a collaborative community for professionals in biomedical and health informatics. As an interdisciplinary association, AMIA brings together individuals with clinical, research, and computer expertise and emphasizes the use of data to enhance biomedical research and clinical work. The need for a recognition program within AMIA, acknowledging applied informatics skills by members, led to the establishment of the Fellows of AMIA (FAMIA) Recognition Program in 2018., Objectives:  To outline the evolution of the FAMIA program and shed light on its origins, development, and impact. This report explores factors that led to the establishment of FAMIA, considerations affecting its development, and the objectives FAMIA seeks to achieve within the broader context of AMIA., Methods:  The development of FAMIA is examined through a historical lens, encompassing key milestones, discussions, and decisions that shaped the program. Insights into the formation of FAMIA were gathered through discussions within AMIA membership and leadership, including proposals, board-level discussions, and the involvement of key stakeholders. Additionally, the report outlines criteria for FAMIA eligibility and the pathways available for recognition, namely the Certification Pathway and the Long-Term Experience Pathway., Results:  The FAMIA program has inducted five classes, totaling 602 fellows. An overview of disciplines, roles, and application pathways for FAMIA members is provided. A comparative analysis with other fellow recognition programs in related fields showcases the unique features and contributions of FAMIA in acknowledging applied informatics., Conclusion:  Now in its sixth year, FAMIA acknowledges the growing influence of applied informatics within health information professionals, recognizing individuals with experience, training, and a commitment to the highest level of applied informatics and the science associated with it., Competing Interests: The authors have no conflict of interests to disclose regarding this study., (Thieme. All rights reserved.)

Objective: This study aims to investigate the feasibility of using Large Language Models (LLMs) to engage with patients at the time they are drafting a question to their healthcare providers, and generate pertinent follow-up questions that the patient can answer before sending their message, with the goal of ensuring that their healthcare provider receives all the information they need to safely and accurately answer the patient's question, eliminating back-and-forth messaging, and the associated delays and frustrations., Methods: We collected a dataset of patient messages sent between January 1, 2022 to March 7, 2023 at Vanderbilt University Medical Center. Two internal medicine physicians identified 7 common scenarios. We used 3 LLMs to generate follow-up questions: (1) Comprehensive LLM Artificial Intelligence Responder (CLAIR): a locally fine-tuned LLM, (2) GPT4 with a simple prompt, and (3) GPT4 with a complex prompt. Five physicians rated them with the actual follow-ups written by healthcare providers on clarity, completeness, conciseness, and utility., Results: For five scenarios, our CLAIR model had the best performance. The GPT4 model received higher scores for utility and completeness but lower scores for clarity and conciseness. CLAIR generated follow-up questions with similar clarity and conciseness as the actual follow-ups written by healthcare providers, with higher utility than healthcare providers and GPT4, and lower completeness than GPT4, but better than healthcare providers., Conclusion: LLMs can generate follow-up patient messages designed to clarify a medical question that compares favorably to those generated by healthcare providers., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Background: Despite growing interest in patient-reported outcome measures to track the progression of Crohn's disease, frameworks to apply these questionnaires in the preoperative setting are lacking. Using the Short Inflammatory Bowel Disease Questionnaire (sIBDQ), this study aimed to describe the interpretable quality of life thresholds and examine potential associations with future bowel resection in Crohn's disease., Methods: Adult patients with Crohn's disease completing an sIBDQ at a clinic visit between 2020 and 2022 were eligible. A stoplight framework was adopted for sIBDQ scores, including a "Resection Red" zone suggesting poor quality of life that may benefit from discussions about surgery as well as a "Nonoperative Green" zone. Thresholds were identified with both anchor- and distribution-based methods using receiver operating characteristic curve analysis and subgroup percentile scores, respectively. To quantify associations between sIBDQ scores and subsequent bowel resection, multivariable logistic regression models were fit with covariates of age, sex assigned at birth, body mass index, medications, disease pattern and location, resection history, and the Harvey Bradshaw Index. The incremental discriminatory value of the sIBDQ beyond clinical factors was assessed through the area under the receiver operating characteristics curve (AUC) with an internal validation through bootstrap resampling., Results: Of the 2003 included patients, 102 underwent Crohn's-related bowel resection. The sIBDQ Nonoperative Green zone threshold ranged from 61 to 64 and the Resection Red zone from 36 to 38. When adjusting for clinical covariates, a worse sIBDQ score was associated with greater odds of subsequent 90-day bowel resection when considered as a 1-point (odds ratio [OR] [95% CI], 1.05 [1.03-1.07]) or 5-point change (OR [95% CI], 1.27 [1.14-1.41]). Inclusion of the sIBDQ modestly improved discriminative performance (AUC [95% CI], 0.85 [0.85-0.86]) relative to models that included only demographics (0.57 [0.57-0.58]) or demographics with clinical covariates (0.83 [0.83-0.84])., Conclusion: In the decision-making process for bowel resection, disease-specific patient-reported outcome measures may be useful to identify patients with Crohn's disease with poor quality of life and promote a shared understanding of personalized burden., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024 Society for Surgery of the Alimentary Tract. Published by Elsevier Inc. All rights reserved.)

Objective: To develop and validate a predictive model for postpartum hemorrhage that can be deployed in clinical care using automated, real-time electronic health record (EHR) data and to compare performance of the model with a nationally published risk prediction tool., Methods: A multivariable logistic regression model was developed from retrospective EHR data from 21,108 patients delivering at a quaternary medical center between January 1, 2018, and April 30, 2022. Deliveries were divided into derivation and validation sets based on an 80/20 split by date of delivery. Postpartum hemorrhage was defined as blood loss of 1,000 mL or more in addition to postpartum transfusion of 1 or more units of packed red blood cells. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC) and was compared with a postpartum hemorrhage risk assessment tool published by the CMQCC (California Maternal Quality Care Collaborative). The model was then programmed into the EHR and again validated with prospectively collected data from 928 patients between November 7, 2023, and January 31, 2024., Results: Postpartum hemorrhage occurred in 235 of 16,862 patients (1.4%) in the derivation cohort. The predictive model included 21 risk factors and demonstrated an AUC of 0.81 (95% CI, 0.79-0.84) and calibration slope of 1.0 (Brier score 0.013). During external temporal validation, the model maintained discrimination (AUC 0.80, 95% CI, 0.72-0.84) and calibration (calibration slope 0.95, Brier score 0.014). This was superior to the CMQCC tool (AUC 0.69 [95% CI, 0.67-0.70], P <.001). The model maintained performance in prospective, automated data collected with the predictive model in real time (AUC 0.82 [95% CI, 0.73-0.91])., Conclusion: We created and temporally validated a postpartum hemorrhage prediction model, demonstrated its superior performance over a commonly used risk prediction tool, successfully coded the model into the EHR, and prospectively validated the model using risk factor data collected in real time. Future work should evaluate external generalizability and effects on patient outcomes; to facilitate this work, we have included the model coefficients and examples of EHR integration in the article., Competing Interests: Financial Disclosure The authors did not report any potential conflicts of interest., (Copyright © 2024 by the American College of Obstetricians and Gynecologists. Published by Wolters Kluwer Health, Inc. All rights reserved.)

In this work, we report the infrared spectrum of the pyrene anion, measured using messenger tagging with up to three Ar atoms. We assign the spectrum using density functional theory and vibrational perturbation theory. We discuss our results in the context of computed and experimental spectra from the literature as well as recent observations from astronomical sources, addressing the question of whether polycyclic aromatic hydrocarbon anions could contribute to the strong infrared emission bands at 3.29 μm from carbon-rich regions of space.

An IR-vacuum ultraviolet (VUV) ion-dip spectroscopy method is utilized to examine the IR spectrum of acetaldehyde oxide (CH3CHOO) in the overtone CH stretch (2νCH) spectral region. IR activation creates a depletion of the ground state population that reduces the VUV photoionization signal on the parent mass channel. IR activation of the more stable and populated syn-CH3CHOO conformer results in rapid unimolecular decay to OH + vinoxy products and makes the most significant contribution to the observed spectrum. The resultant IR-VUV ion-dip spectrum of CH3CHOO is similar to that obtained previously for syn-CH3CHOO using IR action spectroscopy with UV laser-induced fluorescence detection of OH products. The prominent IR features at 5984 and 6081 cm-1 are also observed using UV + VUV photoionization of OH products. Complementary theoretical calculations utilizing a general implementation of second-order vibrational perturbation theory provide new insights on the vibrational transitions that give rise to the experimental spectrum in the overtone CH stretch region. The introduction of physically motivated small shifts of the harmonic frequencies yields remarkably improved agreement between experiment and theory in the overtone CH stretch region. The prominent features are assigned as highly mixed states with contributions from two quanta of CH stretch and/or a combination of CH stretch with an overtone in mode 4. The generality of this approach is demonstrated by applying it to three different levels of electronic structure theory/basis sets, all of which provide spectra that are virtually indistinguishable despite showing large deviations prior to introducing the shifts to the harmonic frequencies., (© 2024 Author(s). Published under an exclusive license by AIP Publishing.)

Objective: This study aimed to develop and assess the performance of fine-tuned large language models for generating responses to patient messages sent via an electronic health record patient portal., Materials and Methods: Utilizing a dataset of messages and responses extracted from the patient portal at a large academic medical center, we developed a model (CLAIR-Short) based on a pre-trained large language model (LLaMA-65B). In addition, we used the OpenAI API to update physician responses from an open-source dataset into a format with informative paragraphs that offered patient education while emphasizing empathy and professionalism. By combining with this dataset, we further fine-tuned our model (CLAIR-Long). To evaluate fine-tuned models, we used 10 representative patient portal questions in primary care to generate responses. We asked primary care physicians to review generated responses from our models and ChatGPT and rated them for empathy, responsiveness, accuracy, and usefulness., Results: The dataset consisted of 499 794 pairs of patient messages and corresponding responses from the patient portal, with 5000 patient messages and ChatGPT-updated responses from an online platform. Four primary care physicians participated in the survey. CLAIR-Short exhibited the ability to generate concise responses similar to provider's responses. CLAIR-Long responses provided increased patient educational content compared to CLAIR-Short and were rated similarly to ChatGPT's responses, receiving positive evaluations for responsiveness, empathy, and accuracy, while receiving a neutral rating for usefulness., Conclusion: This subjective analysis suggests that leveraging large language models to generate responses to patient messages demonstrates significant potential in facilitating communication between patients and healthcare providers., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objectives: To evaluate the capability of using generative artificial intelligence (AI) in summarizing alert comments and to determine if the AI-generated summary could be used to improve clinical decision support (CDS) alerts., Materials and Methods: We extracted user comments to alerts generated from September 1, 2022 to September 1, 2023 at Vanderbilt University Medical Center. For a subset of 8 alerts, comment summaries were generated independently by 2 physicians and then separately by GPT-4. We surveyed 5 CDS experts to rate the human-generated and AI-generated summaries on a scale from 1 (strongly disagree) to 5 (strongly agree) for the 4 metrics: clarity, completeness, accuracy, and usefulness., Results: Five CDS experts participated in the survey. A total of 16 human-generated summaries and 8 AI-generated summaries were assessed. Among the top 8 rated summaries, five were generated by GPT-4. AI-generated summaries demonstrated high levels of clarity, accuracy, and usefulness, similar to the human-generated summaries. Moreover, AI-generated summaries exhibited significantly higher completeness and usefulness compared to the human-generated summaries (AI: 3.4 ± 1.2, human: 2.7 ± 1.2, P = .001)., Conclusion: End-user comments provide clinicians' immediate feedback to CDS alerts and can serve as a direct and valuable data resource for improving CDS delivery. Traditionally, these comments may not be considered in the CDS review process due to their unstructured nature, large volume, and the presence of redundant or irrelevant content. Our study demonstrates that GPT-4 is capable of distilling these comments into summaries characterized by high clarity, accuracy, and completeness. AI-generated summaries are equivalent and potentially better than human-generated summaries. These AI-generated summaries could provide CDS experts with a novel means of reviewing user comments to rapidly optimize CDS alerts both online and offline., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Ion receptors are molecular hosts that bind ionic guests, often with great selectivity. The interplay of solvation and ion binding in anion host-guest complexes in solution governs the binding efficiency and selectivity of such ion receptors. To gain molecular-level insight into the intrinsic binding properties of octamethyl calix[4]pyrrole (omC4P) host molecules with halide guest ions, we performed cryogenic ion vibrational spectroscopy (CIVS) of omC4P in complexes with fluoride, chloride, and bromide ions. We interpret the spectra using density functional theory, describing the infrared spectra of these complexes with both harmonic and anharmonic second-order vibrational perturbation theory (VPT2) calculations. The NH stretching modes of the pyrrole moieties serve as sensitive probes of the ion binding properties, as their frequencies encode the ion-receptor interactions. While scaled harmonic spectra reproduce the experimental NH stretching modes of the chloride and bromide complexes in broad strokes, the high proton affinity of fluoride introduces strong anharmonic effects. As a result, the spectrum of F - ·omC4P is not even qualitatively captured by harmonic calculations, but it is recovered very well by VPT2 calculations. In addition, the VPT2 calculations recover the intricate coupling of the NH stretching modes with overtones and combination bands of CH stretching and NH bending modes and with low-frequency vibrations of the omC4P macrocycle, which are apparent for all of the halide ion complexes investigated here. A comparison of the CIVS spectra with infrared spectra of solutions of the same ion-receptor complexes in d 3 -acetonitrile and d 6 -acetone shows how ion solvation changes the ion-receptor interactions for the different halide ions.

Background: Numerous pressure injury prediction models have been developed using electronic health record data, yet hospital-acquired pressure injuries (HAPIs) are increasing, which demonstrates the critical challenge of implementing these models in routine care., Objective: To help bridge the gap between development and implementation, we sought to create a model that was feasible, broadly applicable, dynamic, actionable, and rigorously validated and then compare its performance to usual care (ie, the Braden scale)., Methods: We extracted electronic health record data from 197,991 adult hospital admissions with 51 candidate features. For risk prediction and feature selection, we used logistic regression with a least absolute shrinkage and selection operator (LASSO) approach. To compare the model with usual care, we used the area under the receiver operating curve (AUC), Brier score, slope, intercept, and integrated calibration index. The model was validated using a temporally staggered cohort., Results: A total of 5458 HAPIs were identified between January 2018 and July 2022. We determined 22 features were necessary to achieve a parsimonious and highly accurate model. The top 5 features included tracheostomy, edema, central line, first albumin measure, and age. Our model achieved higher discrimination than the Braden scale (AUC 0.897, 95% CI 0.893-0.901 vs AUC 0.798, 95% CI 0.791-0.803)., Conclusions: We developed and validated an accurate prediction model for HAPIs that surpassed the standard-of-care risk assessment and fulfilled necessary elements for implementation. Future work includes a pragmatic randomized trial to assess whether our model improves patient outcomes., (© Thomas J Reese, Henry J Domenico, Antonio Hernandez, Daniel W Byrne, Ryan P Moore, Jessica B Williams, Brian J Douthit, Elise Russo, Allison B McCoy, Catherine H Ivory, Bryan D Steitz, Adam Wright. Originally published in JMIR Medical Informatics (https://medinform.jmir.org).)

Objective: To develop and evaluate a data-driven process to generate suggestions for improving alert criteria using explainable artificial intelligence (XAI) approaches., Methods: We extracted data on alerts generated from January 1, 2019 to December 31, 2020, at Vanderbilt University Medical Center. We developed machine learning models to predict user responses to alerts. We applied XAI techniques to generate global explanations and local explanations. We evaluated the generated suggestions by comparing with alert's historical change logs and stakeholder interviews. Suggestions that either matched (or partially matched) changes already made to the alert or were considered clinically correct were classified as helpful., Results: The final dataset included 2 991 823 firings with 2689 features. Among the 5 machine learning models, the LightGBM model achieved the highest Area under the ROC Curve: 0.919 [0.918, 0.920]. We identified 96 helpful suggestions. A total of 278 807 firings (9.3%) could have been eliminated. Some of the suggestions also revealed workflow and education issues., Conclusion: We developed a data-driven process to generate suggestions for improving alert criteria using XAI techniques. Our approach could identify improvements regarding clinical decision support (CDS) that might be overlooked or delayed in manual reviews. It also unveils a secondary purpose for the XAI: to improve quality by discovering scenarios where CDS alerts are not accepted due to workflow, education, or staffing issues., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objective: Positive antinuclear antibodies (ANAs) cause diagnostic dilemmas for clinicians. Currently, no tools exist to help clinicians interpret the significance of a positive ANA in individuals without diagnosed autoimmune diseases. We developed and validated a risk model to predict risk of developing autoimmune disease in positive ANA individuals., Methods: Using a de-identified electronic health record (EHR), we randomly chart reviewed 2,000 positive ANA individuals to determine if a systemic autoimmune disease was diagnosed by a rheumatologist. A priori , we considered demographics, billing codes for autoimmune disease-related symptoms, and laboratory values as variables for the risk model. We performed logistic regression and machine learning models using training and validation samples., Results: We assembled training (n = 1030) and validation (n = 449) sets. Positive ANA individuals who were younger, female, had a higher titer ANA, higher platelet count, disease-specific autoantibodies, and more billing codes related to symptoms of autoimmune diseases were all more likely to develop autoimmune diseases. The most important variables included having a disease-specific autoantibody, number of billing codes for autoimmune disease-related symptoms, and platelet count. In the logistic regression model, AUC was 0.83 (95% CI 0.79-0.86) in the training set and 0.75 (95% CI 0.68-0.81) in the validation set., Conclusion: We developed and validated a risk model that predicts risk for developing systemic autoimmune diseases and can be deployed easily within the EHR. The model can risk stratify positive ANA individuals to ensure high-risk individuals receive urgent rheumatology referrals while reassuring low-risk individuals and reducing unnecessary referrals., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Barnado, Moore, Domenico, Green, Camai, Suh, Han, Walker, Anderson, Caruth, Katta, McCoy and Byrne.)

Badger's rule-like correlations between OH stretching frequencies and intensities and the OH bond length are used to develop a spectral mapping procedure for studies of pure and protonated water clusters. This approach utilizes the vibrationally averaged OH bond lengths, which were obtained from diffusion Monte Carlo simulations that were performed using the general potential developed by Yu and Bowman. Good agreement is achieved between the spectra obtained using this approach and previously reported spectra for H + (H 2 O) n clusters, with n = 3, 4, and 5, as well as their perdeuterated analogues. The analysis of the spectra obtained by this spectral mapping approach supports previous work that assigned the spectrum of H + (H 2 O) 6 to a mixture of Eigen and Zundel-like structures. Analysis of the calculated spectra also suggests a reassignment of the frequency of one of the transitions that involves the OH stretching vibration of the OH bonds in the hydronium core in the Eigen-like structure of H + (H 2 O) 6 from 1917 cm -1 to roughly 2100 cm -1 . For D + (D 2 O) 6 , comparison of the measured spectrum to those obtained by using the spectral mapping approach suggests that the carrier of the measured spectrum is one or more of the isomers of D + (D 2 O) 6 that contain a four-membered ring and two flanking water molecules. While there are several candidate structures, the two flanking water molecules most likely form a chain that is bound to the hydronium core.

Collision-induced dissociation (CID) of small, protonated peptides leads to the formation of b-type fragment ions that can occur with several structural motifs driven by different covalent intramolecular bonding arrangements. Here, we characterize the so-called "oxazolone" and "macrocycle" b n ion structures that occur upon CID of oligoglycine peptides (G n ) ions ( n = 2-6). This is determined by acquiring the vibrational band patterns of the cryogenically cooled, D 2 -tagged b n ions obtained using isomer-selective, two-color IR-IR photobleaching and analyzing them with predicted (DFT) harmonic spectra for the candidate structures. Both oxazolone and macrocyclic isomers are formed by b 4 , whereas only oxazolone species are created for b 2 and b 3 and the macrocycle is created for b 5 . As such, n = 4 corresponds to the minimum size where both Oxa and MC forms are present.

Background: Early detection of clinical deterioration among hospitalized patients is a clinical priority for patient safety and quality of care. Current automated approaches for identifying these patients perform poorly at identifying imminent events., Objective: Develop a machine learning algorithm using pager messages sent between clinical team members to predict imminent clinical deterioration., Design: We conducted a large observational study using long short-term memory machine learning models on the content and frequency of clinical pages., Participants: We included all hospitalizations between January 1, 2018 and December 31, 2020 at Vanderbilt University Medical Center that included at least one page message to physicians. Exclusion criteria included patients receiving palliative care, hospitalizations with a planned intensive care stay, and hospitalizations in the top 2% longest length of stay., Main Measures: Model classification performance to identify in-hospital cardiac arrest, transfer to intensive care, or Rapid Response activation in the next 3-, 6-, and 12-hours. We compared model performance against three common early warning scores: Modified Early Warning Score, National Early Warning Score, and the Epic Deterioration Index., Key Results: There were 87,783 patients (mean [SD] age 54.0 [18.8] years; 45,835 [52.2%] women) who experienced 136,778 hospitalizations. 6214 hospitalized patients experienced a deterioration event. The machine learning model accurately identified 62% of deterioration events within 3-hours prior to the event and 47% of events within 12-hours. Across each time horizon, the model surpassed performance of the best early warning score including area under the receiver operating characteristic curve at 6-hours (0.856 vs. 0.781), sensitivity at 6-hours (0.590 vs. 0.505), specificity at 6-hours (0.900 vs. 0.878), and F-score at 6-hours (0.291 vs. 0.220)., Conclusions: Machine learning applied to the content and frequency of clinical pages improves prediction of imminent deterioration. Using clinical pages to monitor patient acuity supports improved detection of imminent deterioration without requiring changes to clinical workflow or nursing documentation., (© 2023. The Author(s).)