Acute Respiratory Failure From Early Pandemic COVID-19: Noninvasive Respiratory Support vs Mechanical Ventilation.

BACKGROUND: The optimal strategy for initial respiratory support in patients with respiratory failure associated with COVID-19 is unclear, and the initial strategy may affect outcomes. RESEARCH QUESTION: Which initial respiratory support strategy is associated with improved outcomes in patients with COVID-19 with acute respiratory failure? STUDY DESIGN AND METHODS: All patients with COVID-19 requiring respiratory support and admitted to a large health care network were eligible for inclusion. We compared patients treated initially with noninvasive respiratory support (NIRS; noninvasive positive pressure ventilation by facemask or high-flow nasal oxygen) with patients treated initially with invasive mechanical ventilation (IMV). The primary outcome was time to in-hospital death analyzed using an inverse probability of treatment weighted Cox model adjusted for potential confounders. Secondary outcomes included unweighted and weighted assessments of mortality, lengths of stay (ICU and hospital), and time to intubation. RESULTS: Nearly one-half of the 2,354 patients (47%) who met inclusion criteria received IMV first, and 53% received initial NIRS. Overall, in-hospital mortality was 38% (37% for IMV and 39% for NIRS). Initial NIRS was associated with an increased hazard of death compared with initial IMV (hazard ratio, 1.42; 95% CI, 1.03-1.94), but also an increased hazard of leaving the hospital sooner that waned with time (noninvasive support by time interaction: hazard ratio, 0.97; 95% CI, 0.95-0.98). INTERPRETATION: Patients with COVID-19 with acute hypoxemic respiratory failure initially treated with NIRS showed an increased hazard of in-hospital death.

Discovery of Generalizable TBI Phenotypes Using Multivariate Time-Series Clustering.

Traumatic Brain Injury (TBI) presents a broad spectrum of clinical presentations and outcomes due to its inherent heterogeneity, leading to diverse recovery trajectories and varied therapeutic responses. While many studies have delved into TBI phenotyping for distinct patient populations, identifying TBI phenotypes that consistently generalize across various settings and populations remains a critical research gap. Our research addresses this by employing multivariate time-series clustering to unveil TBI's dynamic intricates. Utilizing a self-supervised learning-based approach to clustering multivariate time-Series data with missing values (SLAC-Time), we analyzed both the research-centric TRACK-TBI and the real-world MIMIC-IV datasets. Remarkably, the optimal hyperparameters of SLAC-Time and the ideal number of clusters remained consistent across these datasets, underscoring SLAC-Time's stability across heterogeneous datasets. Our analysis revealed three generalizable TBI phenotypes (α, ß, and γ), each exhibiting distinct non-temporal features during emergency department visits, and temporal feature profiles throughout ICU stays. Specifically, phenotype α represents mild TBI with a remarkably consistent clinical presentation. In contrast, phenotype ß signifies severe TBI with diverse clinical manifestations, and phenotype γ represents a moderate TBI profile in terms of severity and clinical diversity. Age is a significant determinant of TBI outcomes, with older cohorts recording higher mortality rates. Importantly, while certain features varied by age, the core characteristics of TBI manifestations tied to each phenotype remain consistent across diverse populations.

Impact of the COVID-19 Pandemic on Emergency Department Encounters in a Major Metropolitan Area.

BACKGROUND: The end of 2019 marked the emergence of the COVID-19 pandemic. Public avoidance of health care facilities, including the emergency department (ED), has been noted during prior pandemics. OBJECTIVE: This study described pandemic-related changes in adult and pediatric ED presentations, acuity, and hospitalization rates during the pandemic in a major metropolitan area. METHODS: The study was a cross-sectional analysis of ED visits occurring before and during the pandemic. Sites collected daily ED patient census; monthly ED patient acuity, as the Emergency Severity Index (ESI) score; and disposition. Prepandemic ED visits occurring from January 1, 2019 through December 31, 2019 were compared with ED visits occurring during the pandemic from January 1, 2020 through March 31, 2021. The change in prepandemic and pandemic ED volume was found using 7-day moving average of proportions. RESULTS: The study enrolled 83.8% of the total ED encounters. Pandemic adult and pediatric visit volume decreased to as low as 44.7% (95% CI 43.1-46.3%; p < 0.001) and 22.1% (95% CI 19.3-26.0%; p < 0.001), respectively, of prepandemic volumes. There was also a relative increase in adult and pediatric acuity (ESI level 1-3) and the admission percentage for adult (20.3% vs. 22.9%; p < 0.01) and pediatric (5.1% vs. 5.6%; p < 0.01) populations. CONCLUSIONS: Total adult and pediatric encounters were reduced significantly across a major metropolitan area. Patient acuity and hospitalization rates were relatively increased. The development of strategies for predicting ED avoidance will be important in future pandemics.

Advances in Clinical Decision Support Systems: Contributions from the 2022 Literature.

OBJECTIVE: To summarize significant research contributions published in 2022 in the field of clinical decision support (CDS) systems and select the best papers for the Decision Support section of the International Medical Informatics Association (IMIA) Yearbook 2023. METHODS: A renewed search query for identifying CDS scholarship was developed using Medical Subject Headings (MeSH) terms and related keywords. The query was executed in PubMed in January 2023. The search results were reviewed in three stages by two reviewers: title-based triaging, followed by abstract screening, and then full text review. The resulting articles were sent for external review to identity best paper candidates. RESULTS: A total of 1,939 articles related to CDS were retrieved. Of these, 11 articles were selected as candidates for best papers. The general themes of the final three best papers are (1) reducing documentation burden through in-line guidance for clinical notes, (2) clinician engagement for continuous improvement of CDS, and (3) mitigating healthcare-related carbon emissions using scalable and accessible CDS, respectively. CONCLUSION: The field of clinical decision support remains highly active and dynamic, with innovative contributions to a range of clinical domains from primary to acute care. Interoperability issues, documentation burden, clinician acceptance, and the need for effective integration into existing healthcare workflows are among the prominent challenges and areas of interest faced by CDS implementation efforts.

Barriers to Adoption of Tailored Drug-Drug Interaction Clinical Decision Support.

OBJECTIVE: Despite the benefits of the tailored drug-drug interaction (DDI) alerts and the broad dissemination strategy, the uptake of our tailored DDI alert algorithms that are enhanced with patient-specific and context-specific factors has been limited. The goal of the study was to examine barriers and health care system dynamics related to implementing tailored DDI alerts and identify the factors that would drive optimization and improvement of DDI alerts. METHODS: We employed a qualitative research approach, conducting interviews with a participant interview guide framed based on Proctor's taxonomy of implementation outcomes and informed by the Theoretical Domains Framework. Participants included pharmacists with informatics roles within hospitals, chief medical informatics officers, and associate medical informatics directors/officers. Our data analysis was informed by the technique used in grounded theory analysis, and the reporting of open coding results was based on a modified version of the Safety-Related Electronic Health Record Research Reporting Framework. RESULTS: Our analysis generated 15 barriers, and we mapped the interconnections of these barriers, which clustered around three entities (i.e., users, organizations, and technical stakeholders). Our findings revealed that misaligned interests regarding DDI alert performance and misaligned expectations regarding DDI alert optimizations among these entities within health care organizations could result in system inertia in implementing tailored DDI alerts. CONCLUSION: Health care organizations primarily determine the implementation and optimization of DDI alerts, and it is essential to identify and demonstrate value metrics that health care organizations prioritize to enable tailored DDI alert implementation. This could be achieved via a multifaceted approach, such as partnering with health care organizations that have the capacity to adopt tailored DDI alerts and identifying specialists who know users' needs, liaise with organizations and vendors, and facilitate technical stakeholders' work. In the future, researchers can adopt the systematic approach to study tailored DDI implementation problems from other system perspectives (e.g., the vendors' system).

Comparison of Force During the Endotracheal Intubation of Commercial Simulation Manikins.

Background  Medical simulation allows clinicians to safely practice the procedural skill of endotracheal intubation. Applied force to oropharyngeal structures increases the risk of patient harm, and video laryngoscopy (VL) requires less force to obtain a glottic view. It is unknown how much force is required to obtain a glottic view using commercially available simulation manikins and if variability exists. This study compares laryngoscopy force for a modified Cormack-Lehane (CL) grade I view in both normal and difficult airway scenarios between three commercially available simulation manikins. Methods Experienced clinicians (≥2 years experience) were recruited to participate from critical care, emergency medicine, and anesthesia specialties. A C-MAC size 3 VL blade was equipped with five force resistor reading (FSR) sensors (four concave surfaces, one convex), measuring resistance (Ohms) in response to applied pressure (1-100 Newtons). The study occurred in a university simulation lab. Using a randomized sequence, 49 physicians performed intubations on three manikins (Laerdal SimMan 3GPlus, Gaumard Hal S3201, CAE Apollo) in normal and difficult airway scenarios. The outcomes were sensor mean pressure, peak force, and CL grade. Summary statistics were calculated. Generalized estimating equations (GEEs) conducted for both scenarios assessed changes in pressure measured in three manikins while accounting for correlated responses of individuals assigned in random order. Paired t-test assessed for the in-manikin difference between scenarios. STATA/BE v17 (R) was used for analysis; results interpreted at type I error alpha is 0.05.  Results Participants included 49 experienced clinicians. Mean years' experience was 4(±6.6); median prior intubations were 80 (IQR 50-400). Mean individual sensor pressure varied within scenarios depending on manikin (p<0.001). Higher mean forces were used in difficult scenarios (603.4±128.9, 611.1±101.4, 467.5±72.4 FSR) than normal (462.5±121.9, 596.0±90.5, 290.6±63.2 FSR) for each manikin (p<0.001). All manikins required more peak force in the difficult scenario (p<0.03). The highest mean forces (Laerdal, CAE, difficult scenario) were associated with the higher frequency of grade 2A views (p<0.001). The Gaumard manikin was rated most realistic in terms of force required to intubate. Conclusion Commercially available high-fidelity manikins had significant variability in laryngoscopy force in both normal and difficult airway scenarios. In difficult airway scenarios, significant variability existed in CL grade between manikin brands. Experienced clinicians rated Gaumard Hal as the most realistic force applied during endotracheal intubation.

Assessment of Prediction Tasks and Time Window Selection in Temporal Modeling of Electronic Health Record Data: a Systematic Review.

Temporal electronic health record (EHR) data are often preferred for clinical prediction tasks because they offer more complete representations of a patient's pathophysiology than static data. A challenge when working with temporal EHR data is problem formulation, which includes defining the time windows of interest and the prediction task. Our objective was to conduct a systematic review that assessed the definition and reporting of concepts relevant to temporal clinical prediction tasks. We searched PubMed® and IEEE Xplore® databases for studies from January 1, 2010 applying machine learning models to EHR data for patient outcome prediction. Publications applying time-series methods were selected for further review. We identified 92 studies and summarized them by clinical context and definition and reporting of the prediction problem. For the time windows of interest, 12 studies did not discuss window lengths, 57 used a single set of window lengths, and 23 evaluated the relationship between window length and model performance. We also found that 72 studies had appropriate reporting of the prediction task. However, evaluation of prediction problem formulation for temporal EHR data was complicated by heterogeneity in assessing and reporting of these concepts. Even among studies modeling similar clinical outcomes, there were variations in terminology used to describe the prediction problem, rationale for window lengths, and determination of the outcome of interest. As temporal modeling using EHR data expands, minimal reporting standards should include time-series specific concerns to promote rigor and reproducibility in future studies and facilitate model implementation in clinical settings. Supplementary Information: The online version contains supplementary material available at 10.1007/s41666-023-00143-4.

WindowSHAP: An efficient framework for explaining time-series classifiers based on Shapley values.

Unpacking and comprehending how black-box machine learning algorithms (such as deep learning models) make decisions has been a persistent challenge for researchers and end-users. Explaining time-series predictive models is useful for clinical applications with high stakes to understand the behavior of prediction models, e.g., to determine how different variables and time points influence the clinical outcome. However, existing approaches to explain such models are frequently unique to architectures and data where the features do not have a time-varying component. In this paper, we introduce WindowSHAP, a model-agnostic framework for explaining time-series classifiers using Shapley values. We intend for WindowSHAP to mitigate the computational complexity of calculating Shapley values for long time-series data as well as improve the quality of explanations. WindowSHAP is based on partitioning a sequence into time windows. Under this framework, we present three distinct algorithms of Stationary, Sliding and Dynamic WindowSHAP, each evaluated against baseline approaches, KernelSHAP and TimeSHAP, using perturbation and sequence analyses metrics. We applied our framework to clinical time-series data from both a specialized clinical domain (Traumatic Brain Injury - TBI) as well as a broad clinical domain (critical care medicine). The experimental results demonstrate that, based on the two quantitative metrics, our framework is superior at explaining clinical time-series classifiers, while also reducing the complexity of computations. We show that for time-series data with 120 time steps (hours), merging 10 adjacent time points can reduce the CPU time of WindowSHAP by 80 % compared to KernelSHAP. We also show that our Dynamic WindowSHAP algorithm focuses more on the most important time steps and provides more understandable explanations. As a result, WindowSHAP not only accelerates the calculation of Shapley values for time-series data, but also delivers more understandable explanations with higher quality.

Managed critical care: impact of remote decision-making on patient outcomes.

OBJECTIVES: Tele-intensive care unit (tele-ICU) use has become increasingly common as an extension of bedside care for critically ill patients. The objective of this work was to illustrate the degree of tele-ICU involvement in critical care processes and evaluate the impact of tele-ICU decision-making authority. STUDY DESIGN: Previous studies examining tele-ICU impact on patient outcomes do not sufficiently account for the extent of decision-making authority between remote and bedside providers. In this study, we examine patient outcomes with respect to different levels of remote intervention. METHODS: Analysis and summary statistics were generated to characterize demographics and patient outcomes across different levels of tele-ICU intervention for 82,049 critically ill patients. Multivariate logistic regression was used to evaluate odds of mortality, readmission, and likelihood of patients being assigned to a particular remote intervention category. RESULTS: Managing (vs consulting) physician type influenced the level of remote intervention (adjusted odds ratio [AOR], 2.42). A higher level of tele-ICU intervention was a significant factor for patient mortality (AOR, 1.25). Female sex (AOR, 1.05), illness severity (AOR, 1.01), and higher tele-ICU intervention level (AOR, 1.13) increased odds of ICU readmission, whereas length of stay in number of days (AOR, 0.93) and consulting (vs managing) physician type (AOR, 0.79) decreased readmission odds. CONCLUSIONS: This study's findings suggest that higher levels of tele-ICU intervention do not negatively affect patient outcomes. Our results are a step toward understanding tele-ICU impact on patient outcomes by accounting for extent of decision-making authority, and they suggest that the level of remote intervention may reflect patient severity. Further research using more granular data is needed to better understand assignment of intervention category and how variable levels of authority affect clinical decision-making in tele-ICU settings.

A self-supervised learning-based approach to clustering multivariate time-series data with missing values (SLAC-Time): An application to TBI phenotyping.

Self-supervised learning approaches provide a promising direction for clustering multivariate time-series data. However, real-world time-series data often include missing values, and the existing approaches require imputing missing values before clustering, which may cause extensive computations and noise and result in invalid interpretations. To address these challenges, we present a Self-supervised Learning-based Approach to Clustering multivariate Time-series data with missing values (SLAC-Time). SLAC-Time is a Transformer-based clustering method that uses time-series forecasting as a proxy task for leveraging unlabeled data and learning more robust time-series representations. This method jointly learns the neural network parameters and the cluster assignments of the learned representations. It iteratively clusters the learned representations with the K-means method and then utilizes the subsequent cluster assignments as pseudo-labels to update the model parameters. To evaluate our proposed approach, we applied it to clustering and phenotyping Traumatic Brain Injury (TBI) patients in the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study. Clinical data associated with TBI patients are often measured over time and represented as time-series variables characterized by missing values and irregular time intervals. Our experiments demonstrate that SLAC-Time outperforms the baseline K-means clustering algorithm in terms of silhouette coefficient, Calinski Harabasz index, Dunn index, and Davies Bouldin index. We identified three TBI phenotypes that are distinct from one another in terms of clinically significant variables as well as clinical outcomes, including the Extended Glasgow Outcome Scale (GOSE) score, Intensive Care Unit (ICU) length of stay, and mortality rate. The experiments show that the TBI phenotypes identified by SLAC-Time can be potentially used for developing targeted clinical trials and therapeutic strategies.

Predicting Failure of Noninvasive Respiratory Support Using Deep Recurrent Learning.

BACKGROUND: Noninvasive respiratory support (NRS) is increasingly used to support patients with acute respiratory failure. However, noninvasive support failure may worsen outcomes compared to primary support with invasive mechanical ventilation. Therefore, there is a need to identify patients where NRS is failing so that treatment can be reassessed and adjusted. The objective of this study was to develop and evaluate 3 recurrent neural network (RNN) models to predict NRS failure. METHODS: This was a cross-sectional observational study to evaluate the ability of deep RNN models (long short-term memory [LSTM], gated recurrent unit [GRU]), and GRU with trainable decay) to predict failure of NRS. Data were extracted from electronic health records from all adult (≥ 18 y) patient records requiring any type of oxygen therapy or mechanical ventilation between November 1, 2013-September 30, 2020, across 46 ICUs in the Southwest United States in a single health care network. Input variables for each model included serum chloride, creatinine, albumin, breathing frequency, heart rate, SpO2 , FIO2 , arterial oxygen saturation (SaO2 ), and 2 measurements each (point-of-care and laboratory measurement) of PaO2 and partial pressure of arterial oxygen from an arterial blood gas. RESULTS: Time series data from electronic health records were available for 22,075 subjects. The highest accuracy and area under the receiver operating characteristic curve were for the LSTM model (94.04% and 0.9636, respectively). Accurate predictions were made 12 h after ICU admission, and performance remained high well in advance of NRS failure. CONCLUSIONS: RNN models using routinely collected time series data can accurately predict NRS failure well before intubation. This lead time may provide an opportunity to intervene to optimize patient outcomes.

Colchicine Drug Interaction Errors and Misunderstandings: Recommendations for Improved Evidence-Based Management.

Colchicine is useful for the prevention and treatment of gout and a variety of other disorders. It is a substrate for CYP3A4 and P-glycoprotein (P-gp), and concomitant administration with CYP3A4/P-gp inhibitors can cause life-threatening drug-drug interactions (DDIs) such as pancytopenia, multiorgan failure, and cardiac arrhythmias. Colchicine can also cause myotoxicity, and coadministration with other myotoxic drugs may increase the risk of myopathy and rhabdomyolysis. Many sources of DDI information including journal publications, product labels, and online sources have errors or misleading statements regarding which drugs interact with colchicine, as well as suboptimal recommendations for managing the DDIs to minimize patient harm. Furthermore, assessment of the clinical importance of specific colchicine DDIs can vary dramatically from one source to another. In this paper we provide an evidence-based evaluation of which drugs can be expected to interact with colchicine, and which drugs have been stated to interact with colchicine but are unlikely to do so. Based on these evaluations we suggest management options for reducing the risk of potentially severe adverse outcomes from colchicine DDIs. The common recommendation to reduce the dose of colchicine when given with CYP3A4/P-gp inhibitors is likely to result in colchicine toxicity in some patients and therapeutic failure in others. A comprehensive evaluation of the almost 100 reported cases of colchicine DDIs is included in table form in the electronic supplementary material. Colchicine is a valuable drug, but improvements in the information about colchicine DDIs are needed in order to minimize the risk of serious adverse outcomes.

A Least Absolute Shrinkage and Selection Operator-Derived Predictive Model for Postoperative Respiratory Failure in a Heterogeneous Adult Elective Surgery Patient Population.

BACKGROUND: Postoperative respiratory failure (PRF) is associated with increased hospital charges and worse patient outcomes. Reliable prediction models can help to guide postoperative planning to optimize care, to guide resource allocation, and to foster shared decision-making with patients. RESEARCH QUESTION: Can a predictive model be developed to accurately identify patients at high risk of PRF? STUDY DESIGN AND METHODS: In this single-site proof-of-concept study, we used structured query language to extract, transform, and load electronic health record data from 23,999 consecutive adult patients admitted for elective surgery (2014-2021). Our primary outcome was PRF, defined as mechanical ventilation after surgery of > 48 h. Predictors of interest included demographics, comorbidities, and intraoperative factors. We used logistic regression to build a predictive model and the least absolute shrinkage and selection operator procedure to select variables and to estimate model coefficients. We evaluated model performance using optimism-corrected area under the receiver operating curve and area under the precision-recall curve and calculated sensitivity, specificity, positive and negative predictive values, and Brier scores. RESULTS: Two hundred twenty-five patients (0.94%) demonstrated PRF. The 18-variable predictive model included: operations on the cardiovascular, nervous, digestive, urinary, or musculoskeletal system; surgical specialty orthopedic (nonspine); Medicare or Medicaid (as the primary payer); race unknown; American Society of Anesthesiologists class ≥ III; BMI of 30 to 34.9 kg/m2; anesthesia duration (per hour); net fluid at end of the operation (per liter); median intraoperative FIO2, end title CO2, heart rate, and tidal volume; and intraoperative vasopressor medications. The optimism-corrected area under the receiver operating curve was 0.835 (95% CI,0.808-0.862) and the area under the precision-recall curve was 0.156 (95% CI, 0.105-0.203). INTERPRETATION: This single-center proof-of-concept study demonstrated that a structured query language extract, transform, and load process, based on readily available patient and intraoperative variables, can be used to develop a prediction model for PRF. This PRF prediction model is scalable for multicenter research. Clinical applications include decision support to guide postoperative level of care admission and treatment decisions.

Noninvasive vs Invasive Respiratory Support for Patients with Acute Hypoxemic Respiratory Failure.

Rationale: Noninvasive respiratory support modalities are common alternatives to mechanical ventilation for patients with early acute hypoxemic respiratory failure. These modalities include noninvasive positive pressure ventilation, using either continuous or bilevel positive airway pressure, and nasal high flow using a high flow nasal cannula system. However, outcomes data historically compare noninvasive respiratory support to conventional oxygen rather than to mechanical ventilation. Objectives: The goal of this study was to compare the outcomes of in-hospital death and alive discharge in patients with acute hypoxemic respiratory failure when treated initially with noninvasive respiratory support compared to patients treated initially with invasive mechanical ventilation. Methods: We used a validated phenotyping algorithm to classify all patients with eligible International Classification of Diseases codes at a large healthcare network between January 1, 2018 and December 31, 2019 into noninvasive respiratory support and invasive mechanical ventilation cohorts. The primary outcome was time-to-in-hospital death analyzed using an inverse probability of treatment weighted Cox model adjusted for potential confounders, with estimated cumulative incidence curves. Secondary outcomes included time-to-hospital discharge alive. A secondary analysis was conducted to examine potential differences between noninvasive positive pressure ventilation and nasal high flow. Results: During the study period, 3177 patients met inclusion criteria (40% invasive mechanical ventilation, 60% noninvasive respiratory support). Initial noninvasive respiratory support was not associated with a decreased hazard of in-hospital death (HR: 0.65, 95% CI: 0.35 - 1.2), but was associated with an increased hazard of discharge alive (HR: 2.26, 95% CI: 1.92 - 2.67). In-hospital death varied between the nasal high flow (HR 3.27, 95% CI: 1.43 - 7.45) and noninvasive positive pressure ventilation (HR 0.52, 95% CI 0.25 - 1.07), but both were associated with increased likelihood of discharge alive (nasal high flow HR 2.12, 95 CI: 1.25 - 3.57; noninvasive positive pressure ventilation HR 2.29, 95% CI: 1.92 - 2.74). Conclusion: These observational data from a large healthcare network show that noninvasive respiratory support is not associated with reduced hazards of in-hospital death but is associated with hospital discharge alive. There are also potential differences between the noninvasive respiratory support modalities.

Identifying TBI Physiological States by Clustering Multivariate Clinical Time-Series Data.

Determining clinically relevant physiological states from multivariate time-series data with missing values is essential for providing appropriate treatment for acute conditions such as Traumatic Brain Injury (TBI), respiratory failure, and heart failure. Utilizing non-temporal clustering or data imputation and aggregation techniques may lead to loss of valuable information and biased analyses. In our study, we apply the SLAC-Time algorithm, an innovative self-supervision-based approach that maintains data integrity by avoiding imputation or aggregation, offering a more useful representation of acute patient states. By using SLAC-Time to cluster data in a large research dataset, we identified three distinct TBI physiological states and their specific feature profiles. We employed various clustering evaluation metrics and incorporated input from a clinical domain expert to validate and interpret the identified physiological states. Further, we discovered how specific clinical events and interventions can influence patient states and state transitions.

Computable Phenotypes for Post-acute sequelae of SARS-CoV-2: A National COVID Cohort Collaborative Analysis.

Post-acute sequelae of SARS-CoV-2 (PASC) is an increasingly recognized yet incompletely understood public health concern. Several studies have examined various ways to phenotype PASC to better characterize this heterogeneous condition. However, many gaps in PASC phenotyping research exist, including a lack of the following: 1) standardized definitions for PASC based on symptomatology; 2) generalizable and reproducible phenotyping heuristics and meta-heuristics; and 3) phenotypes based on both COVID-19 severity and symptom duration. In this study, we defined computable phenotypes (or heuristics) and meta-heuristics for PASC phenotypes based on COVID-19 severity and symptom duration. We also developed a symptom profile for PASC based on a common data standard. We identified four phenotypes based on COVID-19 severity (mild vs. moderate/severe) and duration of PASC symptoms (subacute vs. chronic). The symptoms groups with the highest frequency among phenotypes were cardiovascular and neuropsychiatric with each phenotype characterized by a different set of symptoms.

Clinical, technical, and implementation characteristics of real-world health applications using FHIR.

Objective: Understanding the current state of real-world Fast Healthcare Interoperability Resources (FHIR) applications (apps) will benefit biomedical research and clinical care and facilitate advancement of the standard. This study aimed to provide a preliminary assessment of these apps' clinical, technical, and implementation characteristics. Materials and Methods: We searched public repositories for potentially eligible FHIR apps and surveyed app implementers and other stakeholders. Results: Of the 112 apps surveyed, most focused on clinical care (74) or research (45); were implemented across multiple sites (56); and used SMART-on-FHIR (55) and FHIR version R4 (69). Apps were primarily stand-alone web-based (67) or electronic health record (EHR)-embedded (51), although 49 were not listed in an EHR app gallery. Discussion: Though limited in scope, our results show FHIR apps encompass various domains and characteristics. Conclusion: As FHIR use expands, this study-one of the first to characterize FHIR apps at large-highlights the need for systematic, comprehensive methods to assess their characteristics.

QTc Prolongation with the Use of Hydroxychloroquine and Concomitant Arrhythmogenic Medications: A Retrospective Study Using Electronic Health Records Data.

INTRODUCTION: Hydroxychloroquine can induce QT/QTc interval prolongation for some patients; however, little is known about its interactions with other QT-prolonging drugs. OBJECTIVE: The purpose of this retrospective electronic health records study was to evaluate changes in the QTc interval in patients taking hydroxychloroquine with or without concomitant QT-prolonging medications. METHODS: De-identified health records were obtained from the Cerner Health Facts® database. Variables of interest included demographics, diagnoses, clinical procedures, laboratory tests, and medications. Patients were categorized into six cohorts based on exposure to hydroxychloroquine, methotrexate, or sulfasalazine alone, or the combination of any those drugs with any concomitant drug known to prolong the QT interval. Tisdale QTc risk score was calculated for each patient cohort. Two-sample paired t-tests were used to test differences between the mean before and after QTc measurements within each group and ANOVA was used to test for significant differences across the cohort means. RESULTS: A statistically significant increase in QTc interval from the last measurement prior to concomitant exposure of 18.0 ms (95% CI 3.5-32.5; p < 0.05) was found in the hydroxychloroquine monotherapy cohort. QTc changes varied considerably across cohorts, with standard deviations ranging from 40.9 (hydroxychloroquine monotherapy) to 57.8 (hydroxychloroquine + sulfasalazine). There was no difference in QTc measurements among cohorts. The hydroxychloroquine + QTc-prolonging agent cohort had the highest average Tisdale Risk Score compared with those without concomitant exposure (p < 0.05). CONCLUSION: Our analysis of retrospective electronic health records found hydroxychloroquine to be associated with a moderate increase in the QTc interval compared with sulfasalazine or methotrexate. However, the QTc was not significantly increased with concomitant exposure to other drugs known to increase QTc interval.

Data Sharing Between Jail and Community Health Systems: Missing Links and Lessons for Re-Entry Success.

Data sharing and interoperability between jail systems and community health providers are critical for successful re-entry of incarcerated individuals into the mainstream community. Using a case study approach, we present an account of interoperability efforts between jail and community health systems in the County of Orange (California, USA), including the overall infrastructure comprising of the jail management system, jail health system, and the community health system. We also describe outcomes and lessons from the Jail to Community Re-entry Program implemented in the County of Orange, along with recommendations and common data elements required for effective care transitions from custody to community.