A common data model for the standardization of intensive care unit medication features.

Objective: Common data models provide a standard means of describing data for artificial intelligence (AI) applications, but this process has never been undertaken for medications used in the intensive care unit (ICU). We sought to develop a common data model (CDM) for ICU medications to standardize the medication features needed to support future ICU AI efforts. Materials and Methods: A 9-member, multi-professional team of ICU clinicians and AI experts conducted a 5-round modified Delphi process employing conference calls, web-based communication, and electronic surveys to define the most important medication features for AI efforts. Candidate ICU medication features were generated through group discussion and then independently scored by each team member based on relevance to ICU clinical decision-making and feasibility for collection and coding. A key consideration was to ensure the final ontology both distinguished unique medications and met Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles. Results: Using a list of 889 ICU medications, the team initially generated 106 different medication features, and 71 were ranked as being core features for the CDM. Through this process, 106 medication features were assigned to 2 key feature domains: drug product-related (n = 43) and clinical practice-related (n = 63). Each feature included a standardized definition and suggested response values housed in the electronic data library. This CDM for ICU medications is available online. Conclusion: The CDM for ICU medications represents an important first step for the research community focused on exploring how AI can improve patient outcomes and will require ongoing engagement and refinement.

Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction.

INTRODUCTION: Intravenous (IV) medications are a fundamental cause of fluid overload (FO) in the intensive care unit (ICU); however, the association between IV medication use (including volume), administration timing, and FO occurrence remains unclear. METHODS: This retrospective cohort study included consecutive adults admitted to an ICU ≥72 hours with available fluid balance data. FO was defined as a positive fluid balance ≥7% of admission body weight within 72 hours of ICU admission. After reviewing medication administration record (MAR) data in three-hour periods, IV medication exposure was categorized into clusters using principal component analysis (PCA) and Restricted Boltzmann Machine (RBM). Medication regimens of patients with and without FO were compared within clusters to assess for temporal clusters associated with FO using the Wilcoxon rank sum test. Exploratory analyses of the medication cluster most associated with FO for medications frequently appearing and used in the first 24 hours was conducted. RESULTS: FO occurred in 127/927 (13.7%) of the patients enrolled. Patients received a median (IQR) of 31 (13-65) discrete IV medication administrations over the 72-hour period. Across all 47,803 IV medication administrations, ten unique IV medication clusters were identified with 121-130 medications in each cluster. Among the ten clusters, cluster 7 had the greatest association with FO; the mean number of cluster 7 medications received was significantly greater in patients in the FO cohort compared to patients who did not experience FO (25.6 vs.10.9. p<0.0001). 51 of the 127 medications in cluster 7 (40.2%) appeared in > 5 separate 3-hour periods during the 72-hour study window. The most common cluster 7 medications included continuous infusions, antibiotics, and sedatives/analgesics. Addition of cluster 7 medications to a prediction model with APACHE II score and receipt of diuretics improved the ability for the model to predict fluid overload (AUROC 5.65, p =0.0004). CONCLUSIONS: Using ML approaches, a unique IV medication cluster was strongly associated with FO. Incorporation of this cluster improved the ability to predict development of fluid overload in ICU patients compared with traditional prediction models. This method may be further developed into real-time clinical applications to improve early detection of adverse outcomes.

The Delphi Delirium Management Algorithms. A practical tool for clinicians, the result of a modified Delphi expert consensus approach.

Delirium is common in hospitalised patients, and there is currently no specific treatment. Identifying and treating underlying somatic causes of delirium is the first priority once delirium is diagnosed. Several international guidelines provide clinicians with an evidence-based approach to screening, diagnosis and symptomatic treatment. However, current guidelines do not offer a structured approach to identification of underlying causes. A panel of 37 internationally recognised delirium experts from diverse medical backgrounds worked together in a modified Delphi approach via an online platform. Consensus was reached after five voting rounds. The final product of this project is a set of three delirium management algorithms (the Delirium Delphi Algorithms), one for ward patients, one for patients after cardiac surgery and one for patients in the intensive care unit.

Clinical Impact of the Implementation Strategies Used to Apply the 2013 Pain, Agitation/Sedation, Delirium or 2018 Pain, Agitation/Sedation, Delirium, Immobility, Sleep Disruption Guideline Recommendations: A Systematic Review and Meta-Analysis.

OBJECTIVES: To summarize the effectiveness of implementation strategies for ICU execution of recommendations from the 2013 Pain, Agitation/Sedation, Delirium (PAD) or 2018 PAD, Immobility, Sleep Disruption (PADIS) guidelines. DATA SOURCES: PubMed, CINAHL, Scopus, and Web of Science were searched from January 2012 to August 2023. The protocol was registered with PROSPERO (CRD42020175268). STUDY SELECTION: Articles were included if: 1) design was randomized or cohort, 2) adult population evaluated, 3) employed recommendations from greater than or equal to two PAD/PADIS domains, and 4) evaluated greater than or equal to 1 of the following outcome(s): short-term mortality, delirium occurrence, mechanical ventilation (MV) duration, or ICU length of stay (LOS). DATA EXTRACTION: Two authors independently reviewed articles for eligibility, number of PAD/PADIS domains, quality according to National Heart, Lung, and Blood Institute assessment tools, implementation strategy use (including Assess, prevent, and manage pain; Both SAT and SBT; Choice of analgesia and sedation; Delirium: assess, prevent, and manage; Early mobility and exercise; Family engagement and empowerment [ABCDEF] bundle) by Cochrane Effective Practice and Organization of Care (EPOC) category, and clinical outcomes. Certainty of evidence was assessed using Grading of Recommendations Assessment, Development, and Evaluation. DATA SYNTHESIS: Among the 25 of 243 (10.3%) full-text articles included ( n = 23,215 patients), risk of bias was high in 13 (52%). Most studies were cohort ( n = 22, 88%). A median of 5 (interquartile range [IQR] 4-7) EPOC strategies were used to implement recommendations from two (IQR 2-3) PAD/PADIS domains. Cohort and randomized studies were pooled separately. In the cohort studies, use of EPOC strategies was not associated with a change in mortality (risk ratio [RR] 1.01; 95% CI, 0.9-1.12), or delirium (RR 0.92; 95% CI, 0.82-1.03), but was associated with a reduction in MV duration (weighted mean difference [WMD] -0.84 d; 95% CI, -1.25 to -0.43) and ICU LOS (WMD -0.77 d; 95% CI, -1.51 to 0.04). For randomized studies, EPOC strategy use was associated with reduced mortality and MV duration but not delirium or ICU LOS. CONCLUSIONS: Using multiple implementation strategies to adopt PAD/PADIS guideline recommendations may reduce mortality, duration of MV, and ICU LOS. Further prospective, controlled studies are needed to identify the most effective strategies to implement PAD/PADIS recommendations.

Relationship Between Pain and Delirium in Critically Ill Adults.

OBJECTIVES: Although opioids are frequently used to treat pain, and are an important risk for ICU delirium, the association between ICU pain itself and delirium remains unclear. We sought to evaluate the relationship between ICU pain and delirium. DESIGN: Prospective cohort study. SETTING: A 32-bed academic medical-surgical ICU. PATIENTS: Critically ill adults (n = 4,064) admitted greater than or equal to 24 hours without a condition hampering delirium assessment. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Daily mental status was classified as arousable without delirium, delirium, or unarousable. Pain was assessed six times daily in arousable patients using a 0-10 Numeric Rating Scale (NRS) or the Critical Care Pain Observation Tool (CPOT); daily peak pain score was categorized as no (NRS = 0/CPOT = 0), mild (NRS = 1-3/CPOT = 1-2), moderate (NRS = 4-6/CPOT = 3-4), or severe (NRS = 7-10/CPOT = 5-8) pain. To address missingness, a Multiple Imputation by Chained Equations approach that used available daily pain severity and 19 pain predictors was used to generate 25 complete datasets. Using a first-order Markov model with a multinomial logistic regression analysis, that controlled for 11 baseline/daily delirium risk factors and considered the competing risks of unarousability and ICU discharge/death, the association between peak daily pain and next-day delirium in each complete dataset was evaluated. RESULTS: Among 14,013 ICU days (contributed by 4,064 adults), delirium occurred on 2,749 (19.6%). After pain severity imputation on 1,818 ICU days, mild, moderate, and severe pain were detected on 2,712 (34.1%), 1,682 (21.1%), and 894 (11.2%) of the no-delirium days, respectively, and 992 (36.1%), 513 (18.6%), and 27 (10.1%) of delirium days (p = 0.01). The presence of any pain (mild, moderate, or severe) was not associated with a transition from awake without delirium to delirium (aOR 0.96; 95% CI, 0.76-1.21). This association was similar when days with only mild, moderate, or severe pain were considered. All results were stable after controlling for daily opioid dose. CONCLUSIONS: After controlling for multiple delirium risk factors, including daily opioid use, pain may not be a risk factor for delirium in the ICU. Future prospective research is required.

Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU.

Fluid overload, while common in the ICU and associated with serious sequelae, is hard to predict and may be influenced by ICU medication use. Machine learning (ML) approaches may offer advantages over traditional regression techniques to predict it. We compared the ability of traditional regression techniques and different ML-based modeling approaches to identify clinically meaningful fluid overload predictors. This was a retrospective, observational cohort study of adult patients admitted to an ICU ≥ 72 h between 10/1/2015 and 10/31/2020 with available fluid balance data. Models to predict fluid overload (a positive fluid balance ≥ 10% of the admission body weight) in the 48-72 h after ICU admission were created. Potential patient and medication fluid overload predictor variables (n = 28) were collected at either baseline or 24 h after ICU admission. The optimal traditional logistic regression model was created using backward selection. Supervised, classification-based ML models were trained and optimized, including a meta-modeling approach. Area under the receiver operating characteristic (AUROC), positive predictive value (PPV), and negative predictive value (NPV) were compared between the traditional and ML fluid prediction models. A total of 49 of the 391 (12.5%) patients developed fluid overload. Among the ML models, the XGBoost model had the highest performance (AUROC 0.78, PPV 0.27, NPV 0.94) for fluid overload prediction. The XGBoost model performed similarly to the final traditional logistic regression model (AUROC 0.70; PPV 0.20, NPV 0.94). Feature importance analysis revealed severity of illness scores and medication-related data were the most important predictors of fluid overload. In the context of our study, ML and traditional models appear to perform similarly to predict fluid overload in the ICU. Baseline severity of illness and ICU medication regimen complexity are important predictors of fluid overload.

Data Missingness Reporting and Use of Methods to Address It in Critical Care Cohort Studies.

IMPORTANCE: Failure to recognize and address data missingness in cohort studies may lead to biased results. Although Strengthening the Reporting of Observational Studies in Epidemiology reporting guidelines advocate data missingness reporting, the degree to which missingness is reported and addressed in the critical care literature remains unclear. OBJECTIVES: To review published ICU cohort studies to characterize data missingness reporting and the use of methods to address it. DESIGN SETTING AND PARTICIPANTS: We searched the 2022 table of contents of 29 critical care/critical care subspecialty journals having a 2021 impact factor greater than or equal to 3 to identify published prospective clinical or retrospective database cohort studies enrolling greater than or equal to 100 patients. MAIN OUTCOMES AND MEASURES: In duplicate, two trained researchers conducted a manuscript/supplemental material PDF word search for "missing*" and extracted study type, patient age, ICU type, sample size, missingness reporting, and the use of methods to address it. RESULTS: A total of 656 studies were reviewed. Of the 334 of 656 (50.9%) studies mentioning missingness, missingness was reported for greater than or equal to 1 variable in 234 (70.1%) and it exceeded 5% for at least one variable in 160 (47.9%). Among the 334 studies mentioning missingness, 88 (26.3%) used exclusion criteria, 36 (10.8%) used complete-case analysis, and 164 (49.1%) used a formal method to avoid missingness. In these 164 studies, imputation only was used in 100 (61.0%), an analytic strategy only in 24 (14.6%), and both in 40 (24.4%). Only missingness greater than 5% (in ≥ 1 variable) was independently associated with greater use of a missingness method (adjusted odds ratio 2.91; 95% CI, 1.85-4.60). Among 140 studies using imputation, multiple imputation was used in 87 studies (62.1%) and simple imputation in 49 studies (35.0%). For the 64 studies using an analytic method, 12 studies (18.8%) assigned missingness as an unknown category, whereas sensitivity analysis was used in 47 studies (73.4%). CONCLUSIONS AND RELEVANCE: Among published critical care cohort studies, only half mentioned result missingness, one-third reported actual missingness and only one-quarter used a method to manage missingness. Educational strategies to promote missingness reporting and resolution methods are required.

Frequency of screening and SBT Technique Trial-North American Weaning Collaboration (FAST-NAWC): an update to the protocol and statistical analysis plan.

BACKGROUND: This update summarizes key changes made to the protocol for the Frequency of Screening and Spontaneous Breathing Trial (SBT) Technique Trial-North American Weaning Collaborative (FAST-NAWC) trial since the publication of the original protocol. This multicenter, factorial design randomized controlled trial with concealed allocation, will compare the effect of both screening frequency (once vs. at least twice daily) to identify candidates to undergo a SBT and SBT technique [pressure support + positive end-expiratory pressure vs. T-piece] on the time to successful extubation (primary outcome) in 760 critically ill adults who are invasively ventilated for at least 24 h in 20 North American intensive care units. METHODS/DESIGN: Protocols for the pilot, factorial design trial and the full trial were previously published in J Clin Trials ( https://doi.org/10.4172/2167-0870.1000284 ) and Trials (https://doi: 10.1186/s13063-019-3641-8). As planned, participants enrolled in the FAST pilot trial will be included in the report of the full FAST-NAWC trial. In response to the onset of the coronavirus disease of 2019 (COVID-19) pandemic when approximately two thirds of enrollment was complete, we revised the protocol and consent form to include critically ill invasively ventilated patients with COVID-19. We also refined the statistical analysis plan (SAP) to reflect inclusion and reporting of participants with and without COVID-19. This update summarizes the changes made and their rationale and provides a refined SAP for the FAST-NAWC trial. These changes have been finalized before completion of trial follow-up and the commencement of data analysis. TRIAL REGISTRATION: Clinical Trials.gov NCT02399267.

Efficacy of haloperidol to decrease the burden of delirium in adult critically ill patients: the EuRIDICE randomized clinical trial.

BACKGROUND: The role of haloperidol as treatment for ICU delirium and related symptoms remains controversial despite two recent large controlled trials evaluating its efficacy and safety. We sought to determine whether haloperidol when compared to placebo in critically ill adults with delirium reduces days with delirium and coma and improves delirium-related sequelae. METHODS: This multi-center double-blind, placebo-controlled randomized trial at eight mixed medical-surgical Dutch ICUs included critically ill adults with delirium (Intensive Care Delirium Screening Checklist ≥ 4 or a positive Confusion Assessment Method for the ICU) admitted between February 2018 and January 2020. Patients were randomized to intravenous haloperidol 2.5 mg or placebo every 8 h, titrated up to 5 mg every 8 h if delirium persisted until ICU discharge or up to 14 days. The primary outcome was ICU delirium- and coma-free days (DCFDs) within 14 days after randomization. Predefined secondary outcomes included the protocolized use of sedatives for agitation and related behaviors, patient-initiated extubation and invasive device removal, adverse drug associated events, mechanical ventilation, ICU length of stay, 28-day mortality, and long-term outcomes up to 1-year after randomization. RESULTS: The trial was terminated prematurely for primary endpoint futility on DSMB advice after enrolment of 132 (65 haloperidol; 67 placebo) patients [mean age 64 (15) years, APACHE IV score 73.1 (33.9), male 68%]. Haloperidol did not increase DCFDs (adjusted RR 0.98 [95% CI 0.73-1.31], p = 0.87). Patients treated with haloperidol (vs. placebo) were less likely to receive benzodiazepines (adjusted OR 0.41 [95% CI 0.18-0.89], p = 0.02). Effect measures of other secondary outcomes related to agitation (use of open label haloperidol [OR 0.43 (95% CI 0.12-1.56)] and other antipsychotics [OR 0.63 (95% CI 0.29-1.32)], self-extubation or invasive device removal [OR 0.70 (95% CI 0.22-2.18)]) appeared consistently more favorable with haloperidol, but the confidence interval also included harm. Adverse drug events were not different. Long-term secondary outcomes (e.g., ICU recall and quality of life) warrant further study. CONCLUSIONS: Haloperidol does not reduce delirium in critically ill delirious adults. However, it may reduce rescue medication requirements and agitation-related events in delirious ICU patients warranting further evaluation. TRIAL REGISTRATION: ClinicalTrials.gov (#NCT03628391), October 9, 2017.

Evaluation of medication regimen complexity as a predictor for mortality.

While medication regimen complexity, as measured by a novel medication regimen complexity-intensive care unit (MRC-ICU) score, correlates with baseline severity of illness and mortality, whether the MRC-ICU improves hospital mortality prediction is not known. After characterizing the association between MRC-ICU, severity of illness and hospital mortality we sought to evaluate the incremental benefit of adding MRC-ICU to illness severity-based hospital mortality prediction models. This was a single-center, observational cohort study of adult intensive care units (ICUs). A random sample of 991 adults admitted ≥ 24 h to the ICU from 10/2015 to 10/2020 were included. The logistic regression models for the primary outcome of mortality were assessed via area under the receiver operating characteristic (AUROC). Medication regimen complexity was evaluated daily using the MRC-ICU. This previously validated index is a weighted summation of medications prescribed in the first 24 h of ICU stay [e.g., a patient prescribed insulin (1 point) and vancomycin (3 points) has a MRC-ICU = 4 points]. Baseline demographic features (e.g., age, sex, ICU type) were collected and severity of illness (based on worst values within the first 24 h of ICU admission) was characterized using both the Acute Physiology and Chronic Health Evaluation (APACHE II) and the Sequential Organ Failure Assessment (SOFA) score. Univariate analysis of 991 patients revealed every one-point increase in the average 24-h MRC-ICU score was associated with a 5% increase in hospital mortality [Odds Ratio (OR) 1.05, 95% confidence interval 1.02-1.08, p = 0.002]. The model including MRC-ICU, APACHE II and SOFA had a AUROC for mortality of 0.81 whereas the model including only APACHE-II and SOFA had a AUROC for mortality of 0.76. Medication regimen complexity is associated with increased hospital mortality. A prediction model including medication regimen complexity only modestly improves hospital mortality prediction.

Pharmacophenotype identification of intensive care unit medications using unsupervised cluster analysis of the ICURx common data model.

BACKGROUND: Identifying patterns within ICU medication regimens may help artificial intelligence algorithms to better predict patient outcomes; however, machine learning methods incorporating medications require further development, including standardized terminology. The Common Data Model for Intensive Care Unit (ICU) Medications (CDM-ICURx) may provide important infrastructure to clinicians and researchers to support artificial intelligence analysis of medication-related outcomes and healthcare costs. Using an unsupervised cluster analysis approach in combination with this common data model, the objective of this evaluation was to identify novel patterns of medication clusters (termed 'pharmacophenotypes') correlated with ICU adverse events (e.g., fluid overload) and patient-centered outcomes (e.g., mortality). METHODS: This was a retrospective, observational cohort study of 991 critically ill adults. To identify pharmacophenotypes, unsupervised machine learning analysis with automated feature learning using restricted Boltzmann machine and hierarchical clustering was performed on the medication administration records of each patient during the first 24 h of their ICU stay. Hierarchical agglomerative clustering was applied to identify unique patient clusters. Distributions of medications across pharmacophenotypes were described, and differences among patient clusters were compared using signed rank tests and Fisher's exact tests, as appropriate. RESULTS: A total of 30,550 medication orders for the 991 patients were analyzed; five unique patient clusters and six unique pharmacophenotypes were identified. For patient outcomes, compared to patients in Clusters 1 and 3, patients in Cluster 5 had a significantly shorter duration of mechanical ventilation and ICU length of stay (p < 0.05); for medications, Cluster 5 had a higher distribution of Pharmacophenotype 1 and a smaller distribution of Pharmacophenotype 2, compared to Clusters 1 and 3. For outcomes, patients in Cluster 2, despite having the highest severity of illness and greatest medication regimen complexity, had the lowest overall mortality; for medications, Cluster 2 also had a comparably higher distribution of Pharmacophenotype 6. CONCLUSION: The results of this evaluation suggest that patterns among patient clusters and medication regimens may be observed using empiric methods of unsupervised machine learning in combination with a common data model. These results have potential because while phenotyping approaches have been used to classify heterogenous syndromes in critical illness to better define treatment response, the entire medication administration record has not been incorporated in those analyses. Applying knowledge of these patterns at the bedside requires further algorithm development and clinical application but may have the future potential to be leveraged in guiding medication-related decision making to improve treatment outcomes.

Measures for the Core Outcome Set for Research Evaluating Interventions to Prevent and/or Treat Delirium in Critically Ill Adults: An International Consensus Study (Del-COrS).

To gain consensus on measurement methods for outcomes (delirium occurrence, severity, time to resolution, mortality, health-related quality of life [HrQoL], emotional distress including anxiety, depression, acute stress, and post-traumatic stress disorder, and cognition) of our Core Outcome Set (COS) for trials of interventions to prevent and/or treat delirium in critically ill adults. DESIGN: International consensus process. SETTING: Three virtual meetings (April 2021). PATIENTS/SUBJECTS: Critical illness survivors/family, clinicians, and researchers from six Countries. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Measures (selected based on instrument validity, existing recommendations, and feasibility) and measurement time horizons were discussed. Participants voted on instruments and measurement timing (a priori consensus threshold ≥ 70%). Eighteen stakeholders (28% ICU survivors/family members) participated. We achieved consensus on the Confusion Assessment Method-ICU or Intensive Care Delirium Screening Checklist to measure delirium occurrence and delirium resolution (100%), Hospital Anxiety and Depression Scale for emotional distress (71%), and Montreal Cognitive Assessment-Blind for cognition (83%). We did not achieve consensus on EQ-5D five-level for HrQoL (69%) or its measurement at 6 months. We also did not achieve consensus on the Impact of Event Scale (IES)-Revised or IES-6 for post-traumatic stress (65%) or on measurement instruments for delirium severity incorporating delirium-related emotional distress. We were unable to gain consensus on when to commence and when to discontinue assessing for delirium occurrence and time to resolution, when to determine mortality. We gained consensus that emotional distress and cognition should be measured up to 12 months from hospital discharge. CONCLUSIONS: Consensus was reached on measurement instruments for four of seven outcomes in the COS for delirium prevention or treatment trials for critically ill adults. Further work is required to validate instruments for delirium severity that include delirium-related emotional distress.

Causes, Consequences, and Treatments of Sleep and Circadian Disruption in the ICU: An Official American Thoracic Society Research Statement.

Background: Sleep and circadian disruption (SCD) is common and severe in the ICU. On the basis of rigorous evidence in non-ICU populations and emerging evidence in ICU populations, SCD is likely to have a profound negative impact on patient outcomes. Thus, it is urgent that we establish research priorities to advance understanding of ICU SCD. Methods: We convened a multidisciplinary group with relevant expertise to participate in an American Thoracic Society Workshop. Workshop objectives included identifying ICU SCD subtopics of interest, key knowledge gaps, and research priorities. Members attended remote sessions from March to November 2021. Recorded presentations were prepared and viewed by members before Workshop sessions. Workshop discussion focused on key gaps and related research priorities. The priorities listed herein were selected on the basis of rank as established by a series of anonymous surveys. Results: We identified the following research priorities: establish an ICU SCD definition, further develop rigorous and feasible ICU SCD measures, test associations between ICU SCD domains and outcomes, promote the inclusion of mechanistic and patient-centered outcomes within large clinical studies, leverage implementation science strategies to maximize intervention fidelity and sustainability, and collaborate among investigators to harmonize methods and promote multisite investigation. Conclusions: ICU SCD is a complex and compelling potential target for improving ICU outcomes. Given the influence on all other research priorities, further development of rigorous, feasible ICU SCD measurement is a key next step in advancing the field.

Critical Care Pharmacist Attitudes and Perceptions of Neuromuscular Blocker Infusions in ARDS.

BACKGROUND: Current critical care pharmacist (CCP) practices and perceptions related to neuromuscular infusion (NMBI) use for acute respiratory distress syndrome (ARDS) maybe different with the COVID-19 pandemic and the publication of 2020 NMBI practice guidelines. OBJECTIVE: To evaluate CCP practices and perceptions regarding NMBI use for patients with moderate-severe ARDS. METHODS: We developed, tested, and electronically administered a questionnaire (7 parent-, 42 sub-questions) to 409 American College of Clinical Pharmacy (ACCP) Critical Care Practice and Research Network members in 12 geographically diverse states. The questionnaire focused on adults with moderate-severe ARDS (PaO2:FiO2<150) whose causes of dyssynchrony were addressed. Two reminders were sent at 10-day intervals. RESULTS: Respondents [131/409 (32%)] primarily worked in a medical intensive care unit (ICU) 102 (78%). Compared to COVID-negative(-) ARDS patients, COVID positive(+) ARDS patients were twice as likely to receive a NMBI (34 ± 18 vs.16 ± 17%; P < 0.01). Respondents somewhat/strongly agreed a NMBI should be reserved until after trials of deep sedation (112, 86%) or proning (92, 81%) and that NMBI reduced barotrauma (88, 67%), dyssynchrony (87, 66%), and plateau pressure (79, 60%). Few respondents somewhat/strongly agreed that a NMBI should be initiated at ARDS onset (23, 18%) or that NMBI reduced 90-day mortality (12, 10%). Only 2/14 potential NMBI risks [paralysis awareness (101, 82%) and prolonged muscle weakness (84, 68%)] were frequently reported to be of high/very high concern. Multiple NMBI titration targets were assessed as very/extremely important including arterial pH (109, 88%), dyssynchrony (107, 86%), and PaO2: FiO2 ratio (82, 66%). Train-of-four (55, 44%) and BIS monitoring (36, 29%) were deemed less important. Preferred NMBI discontinuation criteria included absence of dysschrony (84, 69%) and use ≥48 hour (72, 59%). CONCLUSIONS AND RELEVANCE: Current critical care pharmacists believe NMBI for ARDS patients are best reserved until after trials of deep sedation or proning; unique considerations exist in COVID+ patients. Our results should be considered when ICU NMBI protocols are being developed and bedside decisions regarding NMBI use in ARDS are being formulated.