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Pneumonia is a leading cause of death worldwide and is a major health-care challenge in people living with HIV. Despite this, the causes of pneumonia in this population remain poorly understood. We aimed to assess the feasibility of metatranscriptomics for epidemiological surveillance of pneumonia in patients with HIV in Uganda.We performed a retrospective observational study in patients with HIV who were admitted to Mulago Hospital, Kampala, Uganda between Oct 1, 2009, and Dec 31, 2011. Inclusion criteria were age 18 years or older, HIV-positivity, and clinically diagnosed pneumonia. Exclusion criteria were contraindication to bronchoscopy or an existing diagnosis of tuberculosis. Bronchoalveolar lavage fluid was collected within 72 h of admission and a combination of RNA sequencing and Mycobacterium tuberculosis culture plus PCR were performed. The primary outcome was detection of an established or possible respiratory pathogen in the total study population.We consecutively enrolled 217 patients during the study period. A potential microbial cause for pneumonia was identified in 211 (97%) patients. At least one microorganism of established respiratory pathogenicity was identified in 113 (52%) patients, and a microbe of possible pathogenicity was identified in an additional 98 (45%). M tuberculosis was the most commonly identified established pathogen (35 [16%] patients; in whom bacterial or viral co-infections were identified in 13 [37%]). Streptococcus mitis, although not previously reported as a cause of pneumonia in patients with HIV, was the most commonly identified bacterial organism (37 [17%] patients). Haemophilus influenzae was the most commonly identified established bacterial pathogen (20 [9%] patients). Pneumocystis jirovecii was only identified in patients with a CD4 count of less than 200 cells per mL.We show the feasibility of using metatranscriptomics for epidemiologic surveillance of pneumonia by describing the spectrum of respiratory pathogens in adults with HIV in Uganda. Applying these methods to a contemporary cohort could enable broad assessment of changes in pneumonia aetiology following the emergence of SARS-CoV-2.US National Institutes of Health, Chan Zuckerberg Biohub.

To identify factors associated with telemedicine use for asthma care among children and young adults, and to describe the parent and patient experience of asthma care over telemedicine.Our mixed methods study consisted of an electronic health record analysis and a qualitative focus group analysis. We analyzed records for all patients aged 2-24 seen at UC Davis Health between March 19, 2020 and September 30, 2020 for a primary diagnosis of asthma. We performed multivariable logistic regression to quantify the relationships between patient characteristics and telemedicine use. We also conducted focus groups with parents and patients who received asthma care during the study period and used qualitative content analysis to identify themes from the transcripts.502 patients met the inclusion criteria. Telemedicine use was significantly lower among patients with a primary language other than English (OR = 0.12, 95% CI: 0.025-0.54,Alternating telemedicine with in-person visits for asthma care may result in improved access to care and reduced burdens on patients and families. Providers and researchers should work to understand the specific reasons for low telemedicine use among non-English speaking patients so that these patients receive equitable access to care.

OBJECTIVE: To compare clinical recommendations given by psychiatrists and the adherence to these recommendations by primary care physicians (PCP) following consultations conducted by asynchronous telepsychiatry (ATP) and synchronous telepsychiatry (STP). MATERIALS AND METHODS: ATP and STP consultations were compared using intermediate data from a randomized clinical trial with adult participant enrollment between April 2014 and December 2017. In both study arms, PCPs received written recommendations from the psychiatrist after each encounter. Independent clinicians reviewed PCP documentation to measure adherence to those recommendations in the 6 months following the baseline consultation. RESULTS: Medical records were reviewed for 645 psychiatrists’ consult recommendations; 344 from 61 ATP consultations and 301 from 62 STP consultations. Of those recommendations, 191 (56%) and 173 (58%) were rated fully adherent by two independent raters for ATP and STP, respectively. In a multilevel ordinal logistic regression model adjusted for recommendation type and recommended implementation timing, there was no statistically significant difference in adherence to recommendations for ATP compared with STP (adjusted odds ratio = 0.91, 95% confidence interval = 0.51–1.62). The profiles of recommendation type were comparable between ATP and STP. CONCLUSIONS: This is the first PCP adherence study comparing two forms of telemedicine. Although we did not find evidence of a difference between ATP and STP; this study supports the feasibility and acceptability of ATP and STP for the provision of collaborative psychiatric care. Clinical Trial Identifier NCT02084979.

According to a national claims database, remote retinal imaging utilization in the U.S. increased rapidly from 2011 to 2020, but insurance coverage declined, with disproportionate impact on vulnerable populations including older, Black, and lower-income patients.

Objective To compare nurse preparedness and quality of patient handoff during interfacility transfers from a pretransfer emergency department to a PICU when conducted over telemedicine versus telephone. Design Cross-sectional nurse survey linked with patient electronic medical record data using multivariable, multilevel analysis. Setting Tertiary PICU within an academic children's hospital. Participants PICU nurses who received a patient handoff between October 2017 and July 2018. Interventions None. Main results and measurements Among 239 eligible transfers, 106 surveys were completed by 55 nurses (44% survey response rate). Telemedicine was used for 30 handoffs (28%), and telephone was used for 76 handoffs (72%). Patients were comparable with respect to age, sex, race, primary spoken language, and insurance, but handoffs conducted over telemedicine involved patients with higher illness severity as measured by the Pediatric Risk of Mortality III score (4.4 vs 1.9; p = 0.05). After adjusting for Pediatric Risk of Mortality III score, survey recall time, and residual clustering by nurse, receiving nurses reported higher preparedness (measured on a five-point adjectival scale) following telemedicine handoffs compared with telephone handoffs (3.4 vs 3.1; p = 0.02). There were no statistically significant differences in both bivariable and multivariable analyses of handoff quality as measured by the Handoff Clinical Evaluation Exercise. Handoffs using telemedicine were associated with increased number of Illness severity, Patient summary, Action list, Situation awareness and contingency planning, Synthesis by receiver components (3.3 vs 2.8; p = 0.04), but this difference was not significant in the adjusted analysis (3.1 vs 2.9; p = 0.55). Conclusions Telemedicine is feasible for nurse-to-nurse handoffs of critically ill patients between pretransfer and receiving facilities and may be associated with increased perceived and objective nurse preparedness upon patient arrival. Additional research is needed to demonstrate that telemedicine during nurse handoffs improves communication, decreases preventable adverse events, and impacts family and provider satisfaction.

Objectives While hospital-hospital transfers of pediatric patients are often necessary, some pediatric transfers are potentially avoidable. Pediatric potentially avoidable transfers (PATs) represent a process with high costs and safety risks but few, if any, benefits. To better understand this issue, we described pediatric interfacility transfers with early discharges. Methods We conducted a descriptive study using electronic medical record data at a single-center over a 12-month period to examine characteristics of pediatric patients with a transfer admission source and early discharge. Among patients with early discharges, we performed descriptive statistics for PATs defined as patient transfers with a discharge home within 24 hours without receiving any specialized procedures or diagnoses. Results Of the 2,415 pediatric transfers, 31.4% were discharged home within 24 hours. Among transferred patients with early discharges, 356 patients (14.7% of total patient transfers) received no specialized procedures or diagnoses. Direct admissions were categorized as PATs 1.9-fold more frequently than transfers arriving to the emergency department. Among transferred direct admissions, PAT proportions to the neonatal intensive care unit (ICU), pediatric ICU, and non-ICU were 5.1%, 17.3%, and 27.3%, respectively. Respiratory infections, asthma, and ill-defined conditions (eg, fever, nausea with vomiting) were the most common PAT diagnoses. Conclusions Early discharges and PATs are relatively common among transferred pediatric patients. Further studies are needed to identify the etiologies and clinical impacts of PATs, with a focus on direct admissions given the high frequency of PATs among direct admissions to both the pediatric ICU and non-ICU.

PURPOSE: Pediatric readiness scores may be a useful measure of a hospital’s preparedness to care for children. However, there is limited evidence linking these scores with patient outcomes or other metrics, including the need for interfacility transfer. This study aims to determine the association of pediatric readiness scores with the odds of interfacility transfer among a cohort of non-injured children (< 18 years old) presenting to emergency departments (ED) in small rural hospitals in the state of California. METHODS: Data from the National Pediatric Readiness Project assessment were linked with the California Office of Statewide Health Planning and Development’s ED and inpatient databases to conduct a cross-sectional study of pediatric interfacility transfers. Hospitals were manually matched between these data sets. Logistic regression was performed with random intercepts for hospital and adjustment for patient-level confounders. FINDINGS: A total of 54 hospitals and 135,388 encounters met the inclusion criteria. EDs with a high pediatric readiness score (>70) had lower adjusted odds of transfer (aOR: 0.55, 95% CI: 0.33-0.93) than EDs with a low pediatric readiness score (≤ 70). The pediatric readiness section with strongest association with transfer was the “Policies, Procedures, and Protocols” section; EDs in the highest quartile had lower odds of transfer than EDs in the lowest quartile (aOR: 0.54, 95% CI: 0.31-0.91). CONCLUSIONS: Pediatric patients presenting to EDs at small rural hospitals with high pediatric readiness scores may be less likely to be transferred. Additional studies are recommended to investigate other pediatric outcomes in relation to hospital ED pediatric readiness.

Background School health systems are increasingly investing in telemedicine platforms to address acute and chronic illnesses. Asthma, the most common chronic illness in childhood, is of particular interest given its high burden on school absenteeism. Objective Conduct a systematic review evaluating impact of school-based telemedicine programs on improving asthma-related outcomes. Data Sources PubMed, Cochrane CENTRAL, CINAHL, ERIC, PsycINFO, Embase, and Google Scholar. Study Eligibility Criteria Original research, including quasi-experimental studies, without restriction on the type of telemedicine. Participants School-aged pediatric patients with asthma and their families. Interventions School-based telemedicine. Study Appraisal and Synthesis Methods Two authors independently screened each abstract, conducted full-text review, assessed study quality, and extracted information. A third author resolved disagreements. Results Of 371 articles identified, 7 were included for the review. Outcomes of interest were asthma symptom-free days, asthma symptom frequency, quality of life, health care utilization, school absences, and spirometry. Four of 7 studies reported significant increases in symptom-free days and/or decrease in symptom frequency. Five of 6 reported increases in at least one quality-of-life metric, 2 of 7 reported a decrease in at least 1 health care utilization metric, 1 of 3 showed reductions in school absences, and 1 of 2 reported improvements in spirometry measures. Limitations Variability in intervention designs and outcome measures make comparisons and quantitative analyses across studies difficult. Only 2 of 7 studies were randomized controlled trials. Conclusions and Implications of Key Findings High-quality evidence supporting the use of school-based telemedicine programs to improve patient outcomes is limited. While available evidence suggests benefit, only 2 comparative trials were identified, and the contribution of telemedicine to these studies’ results is unclear.

Objective To determine the association between potentially avoidable transfers (PATs) and emergency department (ED) pediatric readiness scores and the score's associated components. Study design This cross-sectional study linked the 2012 National Pediatric Readiness Project assessment with individual encounter data from California's statewide ED and inpatient databases during the years 2011-2013. A probabilistic linkage, followed by deterministic heuristics, linked pre-transfer and post-transfer encounters. Applying previously published definitions, a transferred child was considered a PAT if they were discharged within one day from the ED or inpatient care and had no specialized procedures. Analyses were stratified by injured and non-injured children. We compared PATs with necessary transfers using mixed-effects logistic regression models with random intercepts for hospital and adjustment for patient and hospital covariates. Results After linkage, there were 6675 injured children (27% PATs) and 18 793 non-injured children (14% PATs) who presented to 283 hospitals. In unadjusted analyses, a ten-point increase in pediatric readiness was associated with lower odds of PATs in both injured (OR: 0.93, 95% CI: 0.90-0.96) and non-injured children (OR: 0.90, 95% CI: 0.88-0.93). In adjusted analyses, a similar association was detected in injured patients (aOR: 0.92, 95% CI: 0.86-0.98) and was not detected in non-injured patients (aOR: 0.94, 95% CI 0.88-1.00). Components associated with decreased PATs included having a nurse pediatric emergency care coordinator and a quality improvement plan. Conclusions Hospital ED pediatric readiness is associated with lower odds of a PAT. Certain pediatric readiness components are modifiable risk factors that EDs could target to reduce PATs.

© The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. Lack of access to high-frequency, high-volume patient-derived data, such as mechanical ventilator waveform data, has limited the secondary use of these data for research, quality improvement, and decision support. Existing methods for collecting these data are obtrusive, require high levels of technical expertise, and are often cost-prohibitive, limiting their use and scalability for research applications. We describe here the development of an unobtrusive, open-source, scalable, and user-friendly architecture for collecting, transmitting, and storing mechanical ventilator waveform data that is generalizable to other patient care devices. The system implements a software framework that automates and enforces end-to-end data collection and transmission. A web-based data management application facilitates nontechnical end users' abilities to manage data acquisition devices, mitigates data loss and misattribution, and automates data storage. Using this integrated system, we have been able to collect ventilator waveform data from>450 patients as part of an ongoing clinical study.

Algorithmic classifiers are crucial components of clinical decision support (CDS) systems needed to advance healthcare delivery. Robust CDS systems must be derived and validated via creation of multi-reviewer adjudicated gold standard datasets. Manual annotation of physiologic data such as mechanical ventilator waveform data (VWD) can be time-consuming, and lacks methodological consistency in dataset development. To address these issues, we have created a system for annotating and adjudicating VWD called the Annotation PipeLine (APL) to optimize VWD annotation by expert reviewers. APL combines visual assessment of waveform characteristics with metadata display, enabling inclusion of quantitative thresholds into annotation decisions by reviewers. APL also includes specific features for resolving multi-reviewer disagreements and generating gold standard data sets. APL’s unique combination of methods and open source framework may accelerate the creation of CDS algorithms for ventilator management, and may serve as a model for future research into physiologic waveform annotation systems.

INTRODUCTION: Children and adolescents transferring from emergency departments (EDs) with video telemedicine programs have been associated with lower severity of illness (SOI) on admission to Pediatric Intensive Care Units (PICUs) compared to patients transferred from EDs without telemedicine programs Very little is known about the relationship between telemedicine use and SOI in the ED We hypothesize telemedicine will be used on patients with higher SOI in comparison to telephone consultations METHODS: A prospective crossover-cluster randomized controlled trial was conducted to compare telemedicine and telephone consultations conducted on critically ill children Patients age 0-14 from 15 participating EDs with established telemedicine programs were included if they were transferred to the regional PICU Patients were randomized to either telemedicine or telephone consultation based on four 6-month blocks Data was analyzed using 'intention to treat' analysis, as well as 'as treated' and 'per protocol' RePEAT (Revised Pediatric Emergency Assessment Tool) and PRISA (Pediatric Risk of Admission) scores were used to estimate SOI in the ED and Pediatric Index of Mortality (PIM) was used to assess SOI at the time of PICU admission RESULTS: This study enrolled 696 patients, 373 were admitted to the PICU Telemedicine and telephone cohorts were comparable by sex, age and race/ethnicity PIM-2/3 scores (median -4 97 telemed, -5 1 telephone, p = 0 95) and mean RePEAT scores (1 67 telemed, 1 71 telephone, p=0 61) were not statistically significant between the groups whether using the intention to treat or as treated analysis PRISA scores were similar in the intention to treat analysis, (19 5 telemed, 21 7 telephone, p=0 15) but were significantly higher in the telemedicine cohort (mean=22, SD=13 09) than in the telephone cohort (mean=19, SD=12 82), p= 0 039) in the as treated analysis CONCLUSIONS: Despite randomization of telemedicine and telephone consultations among patients presenting to 15 EDs as part of a multi-institutional trial, physicians did not adhere to telephone consultations when patients had higher SOI and instead preferentially used telemedicine More data is needed to understand the effect of telemedicine consultation on transfer rates, SOI, and optimal application in the post-COVID era

Healthcare-specific analytic software is needed to process the large volumes of streaming physiologic waveform data increasingly available from life support devices such as mechanical ventilators. Detection of clinically relevant events from these data streams will advance understanding of critical illness, enable real-time clinical decision support, and improve both clinical outcomes and patient experience. We used mechanical ventilation waveform data (VWD) as a use case to address broader issues of data access and analysis including discrimination between true events and waveform artifacts. We developed an open source data acquisition platform to acquire VWD, and a modular, multi-algorithm analytic platform (ventMAP) to enable automated detection of off-target ventilation (OTV) delivery in critically-ill patients. We tested the hypothesis that use of artifact correction logic would improve the specificity of clinical event detection without compromising sensitivity. We showed that ventMAP could accurately detect harmful forms of OTV including excessive tidal volumes and common forms of patient-ventilator asynchrony, and that artifact correction significantly improved the specificity of event detection without decreasing sensitivity. Our multi-disciplinary approach has enabled automated analysis of high-volume streaming patient waveform data for clinical and translational research, and will advance the study and management of critically ill patients requiring mechanical ventilation.

OBJECTIVES/SPECIFIC AIMS: While hospital-hospital transfers of pediatric patients is often necessary, some pediatric transfers are potentially avoidable. Pediatric potentially avoidable transfers (PAT) represent a process with high costs and safety risks but few, if any, benefits. To better understand this issue, we described pediatric inter-facility transfers with early discharges. METHODS/STUDY POPULATION: We conducted a descriptive study using electronic medical record data at a single-center over a 12-month period to examine characteristics of pediatric patients with a transfer admission source and early discharge. Among patients with early discharges, we performed descriptive statistics for PAT defined as patient transfers with a discharge home within 24 hours without receiving any specialized tests, interventions, consultations, or diagnoses. RESULTS/ANTICIPATED RESULTS: Of the 2414 pediatric transfers 31.2% were discharged home within 24 hours. Among transferred patients with early discharges, 348 patients (14.4% of total patient transfers) received no specialized tests, interventions, consultations, or diagnoses. Direct admissions were categorized as PAT 2.2-fold more frequently than transfers arriving to the emergency department. Among transferred direct admissions, PAT proportions to the neonatal intensive care unit (ICU), pediatric ICU, and non-ICU were 5.8%, 17.4%, and 27.3%, respectively. Respiratory infections, asthma, and fractures were the most common PAT diagnoses. DISCUSSION/SIGNIFICANCE OF IMPACT: Early discharges and PAT are relatively common among transferred pediatric patients. Further studies are needed to identify the etiologies and clinical impacts of PAT, with a focus on direct admissions given the high frequency of PAT among direct admissions to both the pediatric ICU and non-ICU.

OBJECTIVES/SPECIFIC AIMS: As pediatric care becomes more concentrated in large urban hospitals, smaller rural hospitals with reduced pediatric care capacity may opt to transfer pediatric patients to higher levels of care even if the patient has a condition that is manageable in a general ED. Up to 20-40% of pediatric transfers are considered avoidable, placing a burden on the patient, their family and the health care system. The aim of this study is to determine the association between pediatric readiness (as measured by the National Pediatric Readiness Project score) and risk of interfacility transfer. We hypothesized that emergency departments with higher measures of pediatric readiness would be less likely to transfer pediatric patients to another facility. METHODS/STUDY POPULATION: The most recent and complete National Pediatric Readiness Project data were from 300 California hospitals in 2012. These data include the overall pediatric readiness score, presence of inter-facility guidelines (written protocols on patients needing care not available at the hospital), presence of interfacility agreements (written agreements with other hospitals regarding transfer patients), and other variables on the facility’s capacity to care for pediatric patients. We linked these hospital data with patient encounter data from the California Office of Statewide Health Planning and Development Emergency Department (ED) Database (OSHPD) using hospital name and zip code. To be eligible for the study, the patient must have a documented ED encounter and be less than 18 years old during the encounter. A patient was considered a transfer patient if they were transferred to a separate general hospital, children’s hospital, federal health care facility, rehabilitation facility, Critical Access Hospital or psychiatric hospital. Chi-square tests and t-tests were used for descriptive statistics. For non-normal data, we used the non-parametric Kruskall-Wallis test. We also used logistic regression to compare the odds of transfer between comparison groups. Statistical analyses were conducted in the R environment version 3.4.4. RESULTS/ANTICIPATED RESULTS: In 2012, there were 2,604,723 pediatric ED encounters, of which 10,966 resulted in a transfer (0.4%). Transferred patients on average were older (15 vs. 5 years, p < 0.001) and more likely to be female (58.6% vs 46.6%, p < 0.001). The transfer group originated from hospitals with a lower median pediatric readiness score (76.0 vs 78.3, p < 0.001). Patients were less likely to be transferred if they were seen at a hospital with written guidelines with transfer protocols (OR 0.89, 95% CI 0.83—0.95, p < 0.001). Patients were more likely to be transferred if they were seen at a hospital with written interfacility agreements with other hospitals (OR 1.17, 95%CI 1.10—1.25, p < 0.001). We anticipate, that even with more sophisticated multilevel statistical models, pediatric readiness scores will remain associated with odds of transfer. DISCUSSION/SIGNIFICANCE OF IMPACT: These preliminary data suggest that hospitals with higher levels of pediatric readiness and written guidelines with transfer protocols are less likely to transfer pediatric patients. There may be actionable policy and procedural items that a hospital could enact to lower the rate of transfer patients. Future analyses will include more complex statistical modeling to adjust for confounders, will include inpatient data, and will compare the risk of potentially avoidable transfers between hospitals with varying levels of pediatric readiness.

AUTOMATED MECHANICAL VENTILATOR WAVEFORM ANALYSIS OF PATIENT-VENTILATOR ASYNCHRONY MAS , MD , Lieng , Jason Adams, MD, Brooks Kuhn, Monica BS, Jean-Pierre Delplanque, PhD , Edward Guo , Sean Peisert, PhD , Nick Anderson, PhD 1 UC Davis Medical Center, Department of Pulmonary and Critical Care Medicine 2 UC Davis School of Medicine 3 UC Davis, Department of Engineering 4 UC Davis, Department of Computer Science 5 UC Davis, Department of Pathology Figure 1: Bedside to Cloud mechanical ventilator waveform pipeline Purpose Simulation Center Mechanical ventilation is a life-saving intervention but is also associated with adverse effects including ventilator-induced lung injury (VILI). Patient-ventilator asynchrony (PVA) is thought to contribute to VILI, but the study of PVA has been hampered by limited access to the high frequency, large volume data streams produced by modern ventilators and a lack of robust PVA analytics. To address these limitations, we developed an automated pipeline for breath-by-breath analysis of mechanical ventilator waveform data. Wireless Micro- Computer Attached to Ventilator Wireless Server Secure Analytic Environment ANALYTICS: • TDAP • Python • Machine Learning • R/SAS • Tableau Critical Care Registry ASCEII Medical ICU Methods Double Trigger Ventilator Data Asynchrony Meta-Data Data Acquisition >>>>>>>>>>>>>>>>>> Storage >>>>>>>>>>>>>>>>>>>>>>> Analysis>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Figure 3: Charted tidal volumes compared to continuously recorded tidal volumes of a subject with ARDS, subsequently treated with neuromuscular blockade Cisatracurium started 8 ml/kg PBW 6 ml/kg PBW Recorded Tidal Volumes Suction Common Patient-Ventilator Asynchronies and “Artifacts” Breath Flow Ventilator Stacking Asynchrony Cough Disconnect Waveform Reconstitution Virtual Desktop Access Tidal Volume (mL) Simulated pressure and flow time series data representing normal breaths and common forms of PVA were generated on mechanical ventilators, collected unobtrusively using wireless peripheral devices, and transmitted to a networked server for storage and analysis. Two critical care physicians reviewed waveforms to generate gold standard data sets of PVA events. Rule-based algorithms were developed to quantify inspiratory and expiratory tidal volumes (TV) and identify common PVA subtypes including double trigger and delayed termination asynchrony. Data were split randomly into derivation and validation sets. Algorithm performance was compared with ventilator reported values and clinician annotation. Raw Waveform Data (CSV File) Results The mean difference between algorithm-determined and ventilator- reported TVs was 3.1% (99% CI ± 1.36%). Algorithm agreement with clinician annotation was excellent for double trigger PVA and moderate for delayed termination PVA, with Kappa statistics of 0.85 and 0.58, respectively. In the validation data set (n = 492 breaths), double trigger asynchrony was detected with an overall accuracy of 94.1%, a sensitivity of 100%, and a specificity of 92.8%. Conclusions Tidal Volume (mL) Figure 2: Derivation and validation of double trigger algorithm n=214 n=139 n=492 Rater 1 Rater 2 Algorithm Rater 1 Scan QR code for access to references via Dropbox.com Rater 2 Delayed Termination Algorithm 8 ml/kg PBW 6 ml/kg PBW Double Trigger Delayed Termination Double Trigger Sensitivity Specificity n=90 Charted Tidal Volumes n=179 Validation Breath Number Figure 4: PVA frequency pre and post cisatracurium. Severe breath stacking is defined as TVe:TVi 10 to 33%. Moderate breath stacking defined as as TVe:TVi 33 to 67%. % Asynchronous Breaths Derivation Double trigger dbl_trig A pipeline combining wireless ventilator data acquisition and rule-based signal analysis algorithms informed by the principles of bedside ventilator waveform analysis allows for accurate, automated, quantitative breath-by- breath analysis of patient-ventilator interactions. Clinical Impact Severe breath stacking bs_severe Moderate breath stacking bs_moderate Composite composite_dbl_bs Total (%) Pre-Cisatra (%) During Cisatra (%) Post-Cisatra (%) Phase of Mechanical Ventilation Improved classification and automated identification of double triggers, tidal volume, and other common types of PVA allowing more robust analysis of injurious patient-ventilator interactions Future plans include: • Expansion to all mechanically ventilated patients at UCDMC, including the emergency department • Comparison of rules-based algorithms to machine based learning algorithms • Outcomes correlation to PVA/TV metadata