Evaluation of Critical Care Pharmacist Evening Services at an Academic Medical Center.

Purpose: Critical care pharmacists are considered essential members of the healthcare team; however, justification and recruitment of new positions, especially in the evening or weekend shifts, remains a significant challenge. The purpose of this study was to investigate the number of interventions, type of interventions, and associated cost savings with the addition of 1 board certified critical care clinical pharmacist to evening shift. Methods: This was a prospective collection and characterization of 1 evening shift critical care pharmacist's clinical interventions over a 12-week period. Interventions were collected and categorized daily from 13:00 to 22:00 Monday through Friday. After collection was complete, cost savings estimates were calculated using pharmacy wholesaler acquisition cost. Results: Interventions were collected on 52 of 60 weekdays. A total of 510 interventions were collected with an average of 9.8 interventions accepted per day. The most common interventions included transitions of care, medication dose adjustment, and antibiotic de-escalation and the highest proportion of interventions occurred in the medical intensive care unit. An estimated associated cost avoidance of $66 537.80 was calculated for an average of $1279.57 saved per day. Additionally, 22 (4.1%) of interventions were considered high yield interventions upon independent review by 2 pharmacists. Conclusion: The addition of 1 board-certified critical care pharmacist to evening shift resulted in multiple interventions across several categories and a significant cost avoidance when calculated using conservative measures.

The Reviewer Academy of the Society of Critical Care Medicine: Key Principles and Strategic Plan.

The Society of Critical Care Medicine (SCCM) Reviewer Academy seeks to train and establish a community of trusted, reliable, and skilled peer reviewers with diverse backgrounds and interests to promote high-quality reviews for each of the SCCM journals. Goals of the Academy include building accessible resources to highlight qualities of excellent manuscript reviews; educating and mentoring a diverse group of healthcare professionals; and establishing and upholding standards for insightful and informative reviews. This manuscript will map the mission of the Reviewer Academy with a succinct summary of the importance of peer review, process of reviewing a manuscript, and the expected ethical standards of reviewers. We will equip readers to target concise, thoughtful feedback as peer reviewers, advance their understanding of the editorial process and inspire readers to integrate medical journalism into diverse professional careers.

Plasma matrix metalloproteinase-3 predicts mortality in acute respiratory distress syndrome: a biomarker analysis of a randomized controlled trial.

BACKGROUND: Matrix metalloproteinase-3 (MMP-3) is a proteolytic enzyme involved in acute respiratory distress syndrome (ARDS) pathophysiology that may serve as a lung-specific biomarker in ARDS. METHODS: This study was a secondary biomarker analysis of a subset of Albuterol for the Treatment of Acute Lung Injury (ALTA) trial patients to determine the prognostic value of MMP-3. Plasma sample MMP-3 was measured by enzyme-linked immunosorbent assay. The primary outcome was the area under the receiver operating characteristic (AUROC) of MMP-3 at day 3 for the prediction of 90-day mortality. RESULTS: A total of 100 unique patient samples were evaluated and the AUROC analysis of day three MMP-3 showed an AUROC of 0.77 for the prediction of 90-day mortality (95% confidence interval: 0.67-0.87), corresponding to a sensitivity of 92% and specificity of 63% and an optimal cutoff value of 18.4 ng/mL. Patients in the high MMP-3 group (≥ 18.4 ng/mL) showed higher mortality compared to the non-elevated MMP-3 group (< 18.4 ng/mL) (47% vs. 4%, p < 0.001). A positive difference in day zero and day three MMP-3 concentration was predictive of mortality with an AUROC of 0.74 correlating to 73% sensitivity, 81% specificity, and an optimal cutoff value of + 9.5 ng/mL. CONCLUSIONS: Day three MMP-3 concentration and difference in day zero and three MMP-3 concentrations demonstrated acceptable AUROCs for predicting 90-day mortality with a cut-point of 18.4 ng/mL and + 9.5 ng/mL, respectively. These results suggest a prognostic role of MMP-3 in ARDS.

The Potential Synergistic Risk of Albuterol and Vasoactives in Acute Lung Injury Trials.

BACKGROUND: Critically ill patients are often prescribed both inhaled beta-agonists and intravenous vasoactive; however, the interaction of the additive beta-agonist effects of these 2 agents remains largely uncharacterized. OBJECTIVE: The purpose of this study was to evaluate how concomitant use of albuterol and vasoactive or inotropes affected ventilator-free days (VFDs) by re-analyzing the data from the Albuterol to Treat Acute Lung Injury (ALTA) trial. METHODS: In this study, subjects were grouped to albuterol-vasoactive (n = 84) versus (vs) placebo-vasoactive (n = 62). Ventilator-free days, intensive care unit (ICU)-free days, organ failure-free days, cardiovascular adverse events, and 90-day mortality were compared. The primary outcome was VFDs. RESULTS: Patients in the albuterol-vasoactive group had significantly fewer VFDs than patients in the placebo-vasoactive group (11 vs 19, P = 0.05). Patients in the albuterol-vasoactive group also had significantly fewer ICU-free days (9.5 vs 18.5, P = .006). The 90-day mortality was similar between groups (36.9% vs 27.4%, P = .20). Similarly, no significant difference in cardiac adverse events between the groups (14.3% vs 11.3%, P = 0.59). CONCLUSION AND RELEVANCE: This study has shown fewer VFDs for patients who received both vasoactive and albuterol. There were also fewer ICU-free days when compared to those on vasoactive only. Given the common use of both agents, a prospective evaluation of the additive adverse effects of beta-agonism is warranted.

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.

Clinician-Designed Dashboards.

Critical care pharmacists play a crucial role in direct and indirect patient-care and professional service. Despite this, there is still an ongoing discussion on how to justify their role in the ICU and encourage the opening of more positions. A clinician-designed dashboard is an example of how to present relevant metrics to stakeholders. An example dashboard could include metrics such as pharmacist-to-patient ratio, number of interventions, and stewardship efforts. A dashboard could also convey contributions a critical care pharmacist makes outside of the ICU. This includes institutional services such as education and research. The measurement of such outcomes would justify new positions and protect current critical care pharmacists from unsustainable workloads by recognizing domains of value brought on by a pharmacist. The development of such a dashboard would be a step towards improving outcomes via interprofessional culture and patient-centered care.

Medication regimen complexity (MRC-ICU) for in-hospital mortality prediction in COVID-19 patients.

Purpose: The medication regimen complexity-intensive care unit (MRC-ICU) score was developed prior to the existence of COVID-19. The purpose of this study was to assess if MRC-ICU could predict in-hospital mortality in patients with COVID-19. Methods: A single-center, observational study was conducted from August 2020 to January 2021. The primary outcome of this study was the area under the receiver operating characteristic (AUROC) for in-hospital mortality for the 48-hour MRC-ICU. Age, sequential organ failure assessment (SOFA), and World Health Organization (WHO) COVID-19 Severity Classification were assessed. Logistic regression was performed to predict in-hospital mortality as well as WHO Severity Classification at 7 days. Results: A total of 149 patients were included. The median SOFA score was 8 (IQR 5-11) and median MRC-ICU score at 48 hours was 15 (IQR 7-21). The in-hospital mortality rate was 36% (n = 54). The AUROC for MRC-ICU was 0.71 (95% Confidence Interval (CI), 0.62-0.78) compared to 0.66 for age, 0.81 SOFA, and 0.72 for the WHO Severity Classification. In univariate analysis, age, SOFA, MRC-ICU, and WHO Severity Classification all demonstrated significant association with in-hospital mortality, while SOFA, MRC-ICU, and WHO Severity Classification demonstrated significant association with WHO Severity Classification at 7 days. In univariate analysis, all 4 characteristics showed significant association with mortality; however, only age and SOFA remained significant following multivariate analysis. Conclusion: In the first analysis of medication-related variables as a predictor of severity and in-hospital mortality in COVID-19, MRC-ICU demonstrated acceptable predictive ability as represented by AUROC; however, SOFA was the strongest predictor in both AUROC and regression analysis.

An Evaluation of the Relationship Between Medication Regimen Complexity as Measured by the MRC-ICU to Medication Errors in Critically Ill Patients.

Purpose: The purpose of this study was to determine the relationship between medication regimen complexity-intensive care unit (MRC-ICU) score at 24 hours and medication errors identified throughout the ICU. Methods: A single-center, observational study was conducted from August to October 2021. The primary outcome was the association between MRC-ICU at 24 hours and total medication errors identified. During the prospective component, ICU pharmacists recorded medication errors identified over an 8-week period. During the retrospective component, the electronic medical record was reviewed to collect patient demographics, outcomes, and MRC-ICU score at 24 hours. The primary outcome of the relationship of MRC-ICU at 24 hours to medication errors was assessed using Pearson correlation. Results: A total of 150 patients were included. There were 2 pharmacists who recorded 634 errors during the 8-week study period. No significant relationship between MRC-ICU and medication errors was observed (r2 = .13, P = .11). Exploratory analyses of MRC-ICU relationship to major interventions and harm scores showed that MRC-ICU scores >10 had more major interventions (27 vs 14, P = .27) and higher harm scores (15 vs 7, P = .33), although these values were not statistically significant. Conclusion: Medication errors appear to occur independently of medication regimen complexity. Critical care pharmacists were responsible for mitigating a large number of medication errors.

Impact of Pharmacists to Improve Patient Care in the Critically Ill: A Large Multicenter Analysis Using Meaningful Metrics With the Medication Regimen Complexity-ICU (MRC-ICU) Score.

OBJECTIVES: Despite the established role of the critical care pharmacist on the ICU multiprofessional team, critical care pharmacist workloads are likely not optimized in the ICU. Medication regimen complexity (as measured by the Medication Regimen Complexity-ICU [MRC-ICU] scoring tool) has been proposed as a potential metric to optimize critical care pharmacist workload but has lacked robust external validation. The purpose of this study was to test the hypothesis that MRC-ICU is related to both patient outcomes and pharmacist interventions in a diverse ICU population. DESIGN: This was a multicenter, observational cohort study. SETTING: Twenty-eight ICUs in the United States. PATIENTS: Adult ICU patients. INTERVENTIONS: Critical care pharmacist interventions (quantity and type) on the medication regimens of critically ill patients over a 4-week period were prospectively captured. MRC-ICU and patient outcomes (i.e., mortality and length of stay [LOS]) were recorded retrospectively. MEASUREMENTS AND MAIN RESULTS: A total of 3,908 patients at 28 centers were included. Following analysis of variance, MRC-ICU was significantly associated with mortality (odds ratio, 1.09; 95% CI, 1.08-1.11; p < 0.01), ICU LOS (ß coefficient, 0.41; 95% CI, 00.37-0.45; p < 0.01), total pharmacist interventions (ß coefficient, 0.07; 95% CI, 0.04-0.09; p < 0.01), and a composite intensity score of pharmacist interventions (ß coefficient, 0.19; 95% CI, 0.11-0.28; p < 0.01). In multivariable regression analysis, increased patient: pharmacist ratio (indicating more patients per clinician) was significantly associated with increased ICU LOS (ß coefficient, 0.02; 0.00-0.04; p = 0.02) and reduced quantity (ß coefficient, -0.03; 95% CI, -0.04 to -0.02; p < 0.01) and intensity of interventions (ß coefficient, -0.05; 95% CI, -0.09 to -0.01). CONCLUSIONS: Increased medication regimen complexity, defined by the MRC-ICU, is associated with increased mortality, LOS, intervention quantity, and intervention intensity. Further, these results suggest that increased pharmacist workload is associated with decreased care provided and worsened patient outcomes, which warrants further exploration into staffing models and patient outcomes.

Mapping the next steps for critical care pharmacists.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.

Improving mixed-integer temporal modeling by generating synthetic data using conditional generative adversarial networks: A case study of fluid overload prediction in the intensive care unit.

OBJECTIVE: The challenge of mixed-integer temporal data, which is particularly prominent for medication use in the critically ill, limits the performance of predictive models. The purpose of this evaluation was to pilot test integrating synthetic data within an existing dataset of complex medication data to improve machine learning model prediction of fluid overload. MATERIALS AND METHODS: This retrospective cohort study evaluated patients admitted to an ICU ≥ 72 h. Four machine learning algorithms to predict fluid overload after 48-72 h of ICU admission were developed using the original dataset. Then, two distinct synthetic data generation methodologies (synthetic minority over-sampling technique (SMOTE) and conditional tabular generative adversarial network (CTGAN)) were used to create synthetic data. Finally, a stacking ensemble technique designed to train a meta-learner was established. Models underwent training in three scenarios of varying qualities and quantities of datasets. RESULTS: Training machine learning algorithms on the combined synthetic and original dataset overall increased the performance of the predictive models compared to training on the original dataset. The highest performing model was the meta-model trained on the combined dataset with 0.83 AUROC while it managed to significantly enhance the sensitivity across different training scenarios. DISCUSSION: The integration of synthetically generated data is the first time such methods have been applied to ICU medication data and offers a promising solution to enhance the performance of machine learning models for fluid overload, which may be translated to other ICU outcomes. A meta-learner was able to make a trade-off between different performance metrics and improve the ability to identify the minority class.

Long-Term Retention of Advanced Cardiovascular Life Support Knowledge and Confidence in Doctor of Pharmacy Students.

OBJECTIVE: This study aimed to evaluate the impact of American Heart Association (AHA) advanced cardiovascular life support (ACLS) education and training on long-term retention of ACLS knowledge and confidence in Doctor of Pharmacy (PharmD) students. METHODS: This multicenter study included PharmD students who received ACLS training through different means: 1-hour didactic lecture (didactic), 1-hour didactic lecture with 2-hour skills practice (didactic + skills), and comprehensive AHA ACLS certification through an elective course (elective-certification). Students completed a survey before training, immediately after training, and at least 6-12 months after training to assess demographics and ACLS confidence and knowledge. The primary outcome was a passing score, defined as ≥ 84% on the long-term knowledge assessment. Secondary outcomes included overall knowledge score and perceived confidence, assessed using the Dreyfus model. RESULTS: The long-term assessment was completed by 160 students in the didactic group, 66 in the didactic + skills group, and 62 in the elective-certification group. Six (4%), 8 (12%), and 14 (23%) received a passing score on the long-term knowledge assessment in the didactic, didactic + skills, and elective-certification groups, respectively. The median (IQR) scores on the long-term knowledge assessment were 50% (40-60), 60% (50-70), and 65% (40-80) in the 3 groups. On the long-term assessment, confidence was higher in the elective-certification group, demonstrated by more self-ratings of competent, proficient, and expert, and fewer self-ratings of novice and advanced beginner. CONCLUSION: Long-term retention of ACLS knowledge was low in all groups, but was higher in students who received AHA ACLS certification through an ACLS elective course.

Secondary Analysis of Fluids and Catheters Treatment Trial (FACTT) data reveal poor clinical outcomes in acute respiratory distress syndrome patients with diabetes.

OBJECTIVES: Conflicting reports exist about the link between diabetes mellitus (DM) and acute respiratory distress syndrome (ARDS). Our study examines the impact of pre-existing DM on ARDS patients within the Fluid and Catheter Treatment Trial (FACTT). DESIGN: Conducting a secondary analysis of FACTT data, we incorporated 967 participants with identified DM status (173 with DM, 794 without DM) and examined outcomes like 90-day mortality, hospital and ICU stays, and ventilator days until unassisted breathing. The primary outcome of hospital mortality at day 90 was evaluated through logistic regression using IBM SPSS software. Additionally, we assessed plasma cytokines and chemokines utilizing a human magnetic bead-based multiplex assay. RESULTS: Patients with pre-existing DM exhibited a lower survival rate compared to non-DM patients (61.3 vs. 72.3 %, p = 0.006). Subjects with DM experienced significantly longer hospital lengths of stay (24.5 vs. 19.7 days; p = 0.008) and prolonged ICU stays (14.8 vs. 12.4 days; p = 0.029). No significant difference was found in ventilator days until unassisted breathing between the two groups (11.7 vs. 10; p = 0.1). Cytokine/chemokine analyses indicated a non-significant trend toward heightened levels of cytokines (TNF-α, IL-10, and IL-6) and chemokines (CRP, MCP-1) in DM patients compared to non-DM on both days 0 and 1. Notably, lipopolysaccharide-binding protein (LBP) exhibited significantly higher levels in DM compared to non-DM individuals. CONCLUSIONS: ARDS patients with DM suffered worse clinical outcomes compared to non-DM patients, indicating that DM may negatively affect the respiratory functions in these subjects. Further comprehensive clinical and pre-clinical studies will strengthen this relationship.

Translating Clinical Decision-Making Skills From a Critical Care Pharmacy Elective to the Required Curriculum.

OBJECTIVE: To examine the impact of a critical care pharmacy elective (CCPE) on student performance in other courses in the Doctor of Pharmacy curriculum that emphasize clinical reasoning and decision making. METHODS: This is a retrospective, cohort study including all students from the 2019-2021 graduating classes enrolled in required courses, Pharmacotherapy and Integrated Patient Cases (IPCs). Students were divided for comparison based on completion of the CCPE. The primary outcome was outstanding performance, defined by a final course grade ≥90%, in Pharmacotherapy and IPC. Baseline characteristics and outcomes were analyzed using descriptive statistics and the χ2 test or two-sided t test for categorical and continuous variables, respectively. Binary logistic regression models were constructed to identify variables associated with the primary outcome. RESULTS: Of 377 students included, 129 (34%) completed the CCPE. Baseline characteristics were similar between both groups, except more females completed the CCPE. Students that completed the CCPE were not more likely to demonstrate outstanding performance in Pharmacotherapy III (20% vs 30%) or Pharmacotherapy IV (27% vs 24%), but were more likely in IPC (34% vs 23%). In the adjusted analysis, CCPE students were almost twice as likely to exhibit outstanding performance in IPC. CONCLUSION: Students that completed the CCPE were more likely to demonstrate outstanding performance in IPC, but not in either of the Pharmacotherapy courses. Students may benefit from practicing clinical reasoning earlier in the curriculum to build-up to effective and efficient clinical decision-making. Implications of course structure on student performance should be further explored.

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.

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.

Critical care pharmacist perspectives on optimal practice models and prioritization of professional activities: A cross-sectional survey.

DISCLAIMER: In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. PURPOSE: Critical care pharmacists (CCPs) are essential members of the multidisciplinary critical care team. Professional activities of the CCP are outlined in a 2020 position paper on critical care pharmacy services. This study looks to characterize CCP perspectives for priorities in optimizing pharmacy practice models and professional activities. METHODS: This was a cross-sectional survey conducted from July 24 to September 20, 2023. A 41-question survey instrument was developed to assess 7 domains: demographics, CCP resource utilization, patient care, quality improvement, research and scholarship, training and education, and professional development. This voluntary survey was sent to members of the American College of Clinical Pharmacy's Critical Care Practice and Research Network. The survey was open for a total of 6 weeks. RESULTS: There was a response rate of 20.7% (332 of 1,605 invitees), with 66.6% of respondents (n = 221) completing at least 90% of the survey questions. Most respondents were clinical specialists (58.2%) and/or practiced at an academic medical center (58.5%). Direct patient care, quality improvement and medication safety, and teaching and precepting were identified as the CCP activities of highest importance to CCPs. The CCP-to-patient ratios considered ideal were 1:11-15 (selected by 49.8% of respondents) and 1:16-20 (33.9% of respondents). The ideal percentage of time dedicated to direct patient care activities, as identified by survey respondents, was 50% (interquartile range, 40-50). CONCLUSION: These findings highlight the professional activities viewed as having the highest priority by CCPs. Future research is needed to define optimal CCP practice models for the delivery of patient care in real-world settings.

Critical Care Pharmacists: A Focus on Horizons.

Critical care pharmacy has evolved rapidly over the last 50 years to keep pace with the rapid technological and knowledge advances that have characterized critical care medicine. The modern-day critical care pharmacist is a highly trained individual well suited for the interprofessional team-based care that critical illness necessitates. Critical care pharmacists improve patient-centered outcomes and reduce health care costs through three domains: direct patient care, indirect patient care, and professional service. Optimizing workload of critical care pharmacists, similar to the professions of medicine and nursing, is a key next step for using evidence-based medicine to improve patient-centered outcomes.

MMP3 in Severe COVID-19: A Biomarker or Therapeutic Target?

Identifying novel therapies is a critical need in the treatment of coronavirus disease-19 (COVID-19) and acute respiratory distress syndrome (ARDS). Stromelysin-1, also known as matrixmetalloproteinase 3 (MMP3), has been investigated as a diagnostic biomarker and a potential pharmacological target. Here, we discuss the recent findings of Gelzo et al. in the context of additional MMP3 investigations to delineate its exact role in diagnosis, prognostication, and phenotyping, in addition to its promising role as a therapeutic target in COVID-19-associated respiratory failure.

Validation of Prognostic Club Cell Secretory Protein (CC16) Cut-point in an Independent ALTA Cohort.

Background: Club cell secretory protein (CC16) has demonstrated utility as a lung-specific biomarker in predicting mortality in acute respiratory distress syndrome (ARDS). These findings have been observed in pre-clinical trials and a re-analysis of a large, randomized controlled trial of ARDS (Fluid and Catheter Treatment Trial (FACTT)). Objectives: The purpose of this study was to validate previous findings by evaluating CC16 level as a mortality predictor in patients from the albuterol to treat acute lung injury (ALTA) trial. Design and Method: In this secondary biomarker analysis, plasma CC16 level was measured from 100 ALTA subjects using enzyme-linked immunosorbent assay (ELISA). The rate of mortality was assessed in patients with high (⩾45 ng/mL) versus low CC16 (<45 ng/mL) levels. This cut-off level was applied based on our previous analysis from FACTT trial. Significance was assessed using Kaplan-Meier curves and a log-rank test. Results: Subjects were an average of 50 years old and 46% of them were females. Patients with high CC16 levels had higher 90-day mortality compared to those with low CC16 levels, (37.73% vs 8.95%, P < .001). Other clinical outcomes including ICU-free days, ventilator-free days, and organ failure free days were significantly different between the groups (All P < .05). Conclusion: In this validation study, we demonstrated that ARDS patients with high plasma CC16 concentration had a higher mortality rate than those with low CC16 levels, confirming previous findings that CC16 levels are associated with ARDS mortality.