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BACKGROUND: Noninvasive respiratory support modalities are common alternatives to mechanical ventilation in acute hypoxemic respiratory failure. However, studies historically compare noninvasive respiratory support to conventional oxygen rather than mechanical ventilation. In this study, we compared outcomes in patients with acute hypoxemic respiratory failure treated initially with noninvasive respiratory support to patients treated initially with invasive mechanical ventilation. METHODS: This is a retrospective observational cohort study between January 1, 2018 and December 31, 2019 at a large healthcare network in the United States. We used a validated phenotyping algorithm to classify adult patients (≥18 years) with eligible International Classification of Diseases codes into two cohorts: those treated initially with noninvasive respiratory support or those treated invasive mechanical ventilation only. 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 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). CONCLUSIONS: These data show that noninvasive respiratory support is not associated with reduced hazards of in-hospital death but is associated with hospital discharge alive.

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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.

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Background: Telemedicine offers opportunity for robust diagnoses recommendations to support healthcare providers intra-consultation in a way that does not limit providers ability to explore diagnostic codes and make the most appropriate selection for each consultation. Objective: The objective of this work was to develop a recommendation system for ICD-10 coding using multiclass sequence classification and deep learning. The recommendations are intended to support telemedicine clinicians in making timely and appropriate diagnosis selections. The recommendations allow clinicians to find and select the best diagnosis code much quicker and without leaving the telemedicine platform to search codes and code descriptions. Methods: We developed an LSTM model for multi-class text sequence classification to make diagnosis recommendations. The LSTM recommender used text-based symptoms, complaints, and consultation request reasons as model inputs. Data were extracted from a live telemedicine platform which spans general medicine, dermatology, and mental health clinical specialties. A popularity-based model was used for baseline comparison. Results: Using over 2.8 MM telemedicine consultations during 2021 and 2022, our LSTM recommender average accuracy was 31.7%. LSTM recommender average coverage in the top 20 recommended diagnoses was 85.8% with an average personalization score of 0.87. Conclusions: LSTM multi-class sequence classification recommends diagnoses specific to individual consultations, is retrainable on regular intervals, and could improve diagnoses recommendations such that providers require less time and resources searching for diagnosis codes. In addition, the LSTM recommender is robust enough to make recommendations across clinical specialties such as general medicine, dermatology, and mental health.

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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.

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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.

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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.

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Acute respiratory failure is a common reason for ICU admission and imposes significant strain on patients and the healthcare system. Noninvasive positive-pressure ventilation and high-flow nasal oxygen are increasingly used as an alternative to invasive mechanical ventilation to treat acute respiratory failure. As such, there is a need to accurately cohort patients using large, routinely collected, clinical data to better understand utilization patterns and patient outcomes. The primary objective of this retrospective observational study was to externally validate our computable phenotyping algorithm for patients with acute respiratory failure requiring various sequences of respiratory support in real-world data from a large healthcare delivery network. DESIGN: This is a cross-sectional observational study to validate our algorithm for phenotyping acute respiratory patients by method of respiratory support. We randomly selected 5% (n = 4,319) from each phenotype for manual validation. We calculated the algorithm performance and generated summary statistics for each phenotype and a priori defined clinical subgroups. SETTING: Data were extracted from a clinical data warehouse containing electronic health record data from 46 ICUs in the southwest United States. PATIENTS: All adult (≥ 18 yr) patient records requiring any type of oxygen therapy or mechanical ventilation between November 1, 2013, and September 30, 2020, were extracted for the study. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Micro- and macroaveraged multiclass specificities of the algorithm were 0.902 and 0.896, respectively. Sensitivity and specificity of phenotypes individually were greater than 0.90 for all phenotypes except for those patients extubated from invasive to noninvasive ventilation. We successfully created clinical subgroups of common illnesses requiring ventilatory support and provide high-level comparison of outcomes. CONCLUSIONS: The electronic phenotyping algorithm is robust and provides a necessary tool for retrospective research for characterizing patients with acute respiratory failure across modalities of respiratory support.

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Purpose: The goal of this study was to compare noninvasive respiratory support to invasive mechanical ventilation as the initial respiratory support in COVID-19 patients with acute hypoxemic respiratory failure. Methods: All patients admitted to a large healthcare network with acute hypoxemic respiratory failure associated with COVID-19 and requiring respiratory support were eligible for inclusion. We compared patients treated initially with noninvasive respiratory support (noninvasive positive pressure ventilation by facemask or high flow nasal oxygen) with patients treated initially with invasive mechanical ventilation. 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 (intensive care unit and hospital) and time-to-intubation. Results: Over the study period, 2354 patients met inclusion criteria. Nearly half (47%) received invasive mechanical ventilation first and 53% received initial noninvasive respiratory support. There was an overall 38% in-hospital mortality (37% for invasive mechanical ventilation and 39% for noninvasive respiratory support). Initial noninvasive respiratory support was associated with an increased hazard of death compared to initial invasive mechanical ventilation (HR: 1.61, p < 0.0001, 95% CI: 1.33 - 1.94). However, patients on initial noninvasive respiratory support also experienced an increased hazard of leaving the hospital sooner, but the hazard ratio waned with time (HR: 0.97, p < 0.0001, 95% CI: 0.96 - 0.98). Conclusion: These data show that the COVID-19 patients with acute hypoxemic respiratory failure initially treated with noninvasive respiratory support had an increased hazard of in-hospital death.

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The COVID-19 pandemic introduced unique challenges for treating acute respiratory failure patients and highlighted the need for reliable phenotyping of patients using retrospective electronic health record data. In this study, we applied a rule-based phenotyping algorithm to classify COVID-19 patients requiring ventilatory support. We analyzed patient outcomes of the different phenotypes based on type and sequence of ventilation therapy. Invasive mechanical ventilation, noninvasive positive pressure ventilation, and high flow nasal insufflation were three therapies used to phenotype patients leading to a total of seven subgroups; patients treated with a single therapy (3), patients treated with either form of noninvasive ventilation and subsequently requiring intubation (2), and patients initially intubated and then weaned onto a noninvasive therapy (2). In addition to summary statistics for each phenotype, we highlight data quality challenges and importance of mapping to standard terminologies. This work illustrates potential impact of accurate phenotyping on patient-level and system-level outcomes including appropriate resource allocation under resource constrained circumstances.

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BACKGROUND: Heart failure is a leading cause of mortality and morbidity worldwide. Acute heart failure, broadly defined as rapid onset of new or worsening signs and symptoms of heart failure, often requires hospitalization and admission to the intensive care unit (ICU). This acute condition is highly heterogeneous and less well-understood as compared to chronic heart failure. The ICU, through detailed and continuously monitored patient data, provides an opportunity to retrospectively analyze decompensation and heart failure to evaluate physiological states and patient outcomes. OBJECTIVE: The goal of this study is to examine the prevalence of cardiovascular risk factors among those admitted to ICUs and to evaluate combinations of clinical features that are predictive of decompensation events, such as the onset of acute heart failure, using machine learning techniques. To accomplish this objective, we leveraged tele-ICU data from over 200 hospitals across the United States. METHODS: We evaluated the feasibility of predicting decompensation soon after ICU admission for 26,534 patients admitted without a history of heart failure with specific heart failure risk factors (ie, coronary artery disease, hypertension, and myocardial infarction) and 96,350 patients admitted without risk factors using remotely monitored laboratory, vital signs, and discrete physiological measurements. Multivariate logistic regression and random forest models were applied to predict decompensation and highlight important features from combinations of model inputs from dissimilar data. RESULTS: The most prevalent risk factor in our data set was hypertension, although most patients diagnosed with heart failure were admitted to the ICU without a risk factor. The highest heart failure prediction accuracy was 0.951, and the highest area under the receiver operating characteristic curve was 0.9503 with random forest and combined vital signs, laboratory values, and discrete physiological measurements. Random forest feature importance also highlighted combinations of several discrete physiological features and laboratory measures as most indicative of decompensation. Timeline analysis of aggregate vital signs revealed a point of diminishing returns where additional vital signs data did not continue to improve results. CONCLUSIONS: Heart failure risk factors are common in tele-ICU data, although most patients that are diagnosed with heart failure later in an ICU stay presented without risk factors making a prediction of decompensation critical. Decompensation was predicted with reasonable accuracy using tele-ICU data, and optimal data extraction for time series vital signs data was identified near a 200-minute window size. Overall, results suggest combinations of laboratory measurements and vital signs are viable for early and continuous prediction of patient decompensation.

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BACKGROUND: Acute respiratory failure is generally treated with invasive mechanical ventilation or noninvasive respiratory support strategies. The efficacies of the various strategies are not fully understood. There is a need for accurate therapy-based phenotyping for secondary analyses of electronic health record data to answer research questions regarding respiratory management and outcomes with each strategy. OBJECTIVE: The objective of this study was to address knowledge gaps related to ventilation therapy strategies across diverse patient populations by developing an algorithm for accurate identification of patients with acute respiratory failure. To accomplish this objective, our goal was to develop rule-based computable phenotypes for patients with acute respiratory failure using remotely monitored intensive care unit (tele-ICU) data. This approach permits analyses by ventilation strategy across broad patient populations of interest with the ability to sub-phenotype as research questions require. METHODS: Tele-ICU data from ≥200 hospitals were used to create a rule-based algorithm for phenotyping patients with acute respiratory failure, defined as an adult patient requiring invasive mechanical ventilation or a noninvasive strategy. The dataset spans a wide range of hospitals and ICU types across all US regions. Structured clinical data, including ventilation therapy start and stop times, medication records, and nurse and respiratory therapy charts, were used to define clinical phenotypes. All adult patients of any diagnoses with record of ventilation therapy were included. Patients were categorized by ventilation type, and analysis of event sequences using record timestamps defined each phenotype. Manual validation was performed on 5% of patients in each phenotype. RESULTS: We developed 7 phenotypes: (0) invasive mechanical ventilation, (1) noninvasive positive-pressure ventilation, (2) high-flow nasal insufflation, (3) noninvasive positive-pressure ventilation subsequently requiring intubation, (4) high-flow nasal insufflation subsequently requiring intubation, (5) invasive mechanical ventilation with extubation to noninvasive positive-pressure ventilation, and (6) invasive mechanical ventilation with extubation to high-flow nasal insufflation. A total of 27,734 patients met our phenotype criteria and were categorized into these ventilation subgroups. Manual validation of a random selection of 5% of records from each phenotype resulted in a total accuracy of 88% and a precision and recall of 0.8789 and 0.8785, respectively, across all phenotypes. Individual phenotype validation showed that the algorithm categorizes patients particularly well but has challenges with patients that require ≥2 management strategies. CONCLUSIONS: Our proposed computable phenotyping algorithm for patients with acute respiratory failure effectively identifies patients for therapy-focused research regardless of admission diagnosis or comorbidities and allows for management strategy comparisons across populations of interest.

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BACKGROUND: Many intensive care units (ICUs) utilize telemedicine in response to an expanding critical care patient population, off-hours coverage, and intensivist shortages, particularly in rural facilities. Advances in digital health technologies, among other reasons, have led to the integration of active, well-networked critical care telemedicine (tele-ICU) systems across the United States, which in turn, provide the ability to generate large-scale remote monitoring data from critically ill patients. OBJECTIVE: The objective of this study was to explore opportunities and challenges of utilizing multisite, multimodal data acquired through critical care telemedicine. Using a publicly available tele-ICU, or electronic ICU (eICU), database, we illustrated the quality and potential uses of remote monitoring data, including cohort discovery for secondary research. METHODS: Exploratory analyses were performed on the eICU Collaborative Research Database that includes deidentified clinical data collected from adult patients admitted to ICUs between 2014 and 2015. Patient and ICU characteristics, top admission diagnoses, and predictions from clinical scoring systems were extracted and analyzed. Additionally, a case study on respiratory failure patients was conducted to demonstrate research prospects using tele-ICU data. RESULTS: The eICU database spans more than 200 hospitals and over 139,000 ICU patients across the United States with wide-ranging clinical data and diagnoses. Although mixed medical-surgical ICU was the most common critical care setting, patients with cardiovascular conditions accounted for more than 20% of ICU stays, and those with neurological or respiratory illness accounted for nearly 15% of ICU unit stays. The case study on respiratory failure patients showed that cohort discovery using the eICU database can be highly specific, albeit potentially limiting in terms of data provenance and sparsity for certain types of clinical questions. CONCLUSIONS: Large-scale remote monitoring data sources, such as the eICU database, have a strong potential to advance the role of critical care telemedicine by serving as a testbed for secondary research as well as for developing and testing tools, including predictive and prescriptive analytical solutions and decision support systems. The resulting tools will also inform coordination of care for critically ill patients, intensivist coverage, and the overall process of critical care telemedicine.

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Clinical scoring systems have been developed for many specific applications, yet they remain underutilized for common reasons such as model inaccuracy and difficulty of use. For intensive care units specifically, the Acute Physiology and Chronic Health Evaluation (APACHE) score is used as a decision-making tool and hospital efficacy measure. In an attempt to alleviate the general underlying limitations of scoring instruments and demonstrate the utility of readily available medical databases, machine learning techniques were used to evaluate APACHE IV and IVa prediction measures in an open-source, teleICU research database. The teleICU database allowed for large-scale evaluation of APACHE IV and IVa predictions by comparing predicted values to the actual, recorded patient outcomes along with preliminary exploration of new predictive models for patient mortality and length of stay in both the hospital and the ICU. An increase in performance was observed in the newly developed models trained on the APACHE input variables highlighting avenues of future research and illustrating the utility of teleICU databases for model development and evaluation.

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