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.

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.

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.

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.

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.

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.

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.

Pharmacist Metrics in the Pediatric Intensive Care Unit: an Exploration of the Medication Regimen Complexity-Intensive Care Unit (MRC-ICU) Score.

INTRODUCTION: The medication regimen complexity-intensive care unit (MRC-ICU) score has been developed and validated as an objective predictive metric for patient outcomes and pharmacist workload in the adult critically ill population. The purpose of this study was to explore the MRC-ICU and other workload metrics in the pediatric ICU (PICU). METHODS: This study was a retrospective cohort of pediatric ICU patients admitted to a single institution -between February 2, 2022 - August 2, 2022. Two scores were calculated, including the MRC-ICU and the pediatric Daily Monitoring System (pDMS). Data were extracted from the electronic health record. The primary outcome was the correlation of the MRC-ICU to mortality, as measured by Pearson -correlation -coefficient. Additionally, the correlation of MRC-ICU to number of orders was evaluated. Secondary -analyses explored the correlation of the MRC-ICU with pDMS and with hospital and ICU length of stay. RESULTS: A total of 2,232 patients were included comprising 2,405 encounters. The average age was 6.9 years (standard deviation [SD] 6.3 years). The average MRC-ICU score was 3.0 (SD 3.8). For the primary outcome, MRC-ICU was significantly positively correlated to mortality (0.22 95% confidence interval [CI 0.18 - 0.26]), p<0.05. Additionally, MRC-ICU was significantly positively correlated to ICU length of stay (0.38 [CI 0.34 - 0.41]), p<0.05. The correlation between the MRC-ICU and pDMS was (0.72 [CI 0.70 - 0.73]), p<0.05. CONCLUSION: In this pilot study, MRC-ICU demonstrated an association with existing prioritization metrics and with mortality and length of ICU stay in PICU population. Further, larger scale studies are required.

The use of electronic health record embedded MRC-ICU as a metric for critical care pharmacist workload.

Objectives: A lack of pharmacist-specific risk-stratification scores in the electronic health record (EHR) may limit resource optimization. The medication regimen complexity-intensive care unit (MRC-ICU) score was implemented into our center's EHR for use by clinical pharmacists. The purpose of this evaluation was to evaluate MRC-ICU as a predictor of pharmacist workload and to assess its potential as an additional dimension to traditional workload measures. Materials and methods: Data were abstracted from the EHR on adult ICU patients, including MRC-ICU scores and 2 traditional measures of pharmacist workload: numbers of medication orders verified and interventions logged. This was a single-center study of an EHR-integrated MRC-ICU tool. The primary outcome was the association of MRC-ICU with institutional metrics of pharmacist workload. Associations were assessed using the initial 24-h maximum MRC-ICU score's Pearson's correlation with overall admission workload and the day-to-day association using generalized linear mixed-effects modeling. Results: A total of 1205 patients over 5083 patient-days were evaluated. Baseline MRC-ICU was correlated with both cumulative order volume (Spearman's rho 0.41, P < .001) and cumulative interventions placed (Spearman's rho 0.27, P < .001). A 1-point increase in maximum daily MRC-ICU was associated with a 31% increase in order volume (95% CI, 24%-38%) and 4% increase in interventions (95% CI, 2%-5%). Discussion and conclusion: The MRC-ICU is a validated score that has been previously correlated with important patient-centered outcomes. Here, MRC-ICU was modestly associated with 2 traditional objective measures of pharmacist workload, including orders verified and interventions placed, which is an important step for its use as a tool for resource utilization needs.

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.

Machine learning based prediction of prolonged duration of mechanical ventilation incorporating medication data.

Rationale: Duration of mechanical ventilation is associated with adverse outcomes in critically ill patients and increased use of resources. The increasing complexity of medication regimens has been associated with increased mortality, length of stay, and fluid overload but has never been studied specifically in the setting of mechanical ventilation. Objective: The purpose of this analysis was to develop prediction models for mechanical ventilation duration to test the hypothesis that incorporating medication data may improve model performance. Methods: This was a retrospective cohort study of adults admitted to the ICU and undergoing mechanical ventilation for longer than 24 hours from October 2015 to October 2020. Patients were excluded if it was not their index ICU admission or if the patient was placed on comfort care in the first 24 hours of admission. Relevant patient characteristics including age, sex, body mass index, admission diagnosis, morbidities, vital signs measurements, severity of illness, medication regimen complexity as measured by the MRC-ICU, and medical treatments before intubation were collected. The primary outcome was area under the receiver operating characteristic (AUROC) of prediction models for prolonged mechanical ventilation (defined as greater than 5 days). Both logistic regression and supervised learning techniques including XGBoost, Random Forest, and Support Vector Machine were used to develop prediction models. Results: The 318 patients [age 59.9 (SD 16.9), female 39.3%, medical 28.6%] had mean 24-hour MRC-ICU score of 21.3 (10.5), mean APACHE II score of 21.0 (5.4), mean SOFA score of 9.9 (3.3), and ICU mortality rate of 22.6% (n=72). The strongest performing logistic model was the base model with MRC-ICU added, with AUROC of 0.72, positive predictive value (PPV) of 0.83, and negative prediction value (NPV) of 0.92. The strongest overall model was Random Forest with an AUROC of 0.78, a PPV of 0.53, and NPV of 0.90. Feature importance analysis using support vector machine and Random Forest revealed severity of illness scores and medication related data were the most important predictors. Conclusions: Medication regimen complexity is significantly associated with prolonged duration of mechanical ventilation in critically ill patients, and prediction models incorporating medication information showed modest improvement in this prediction.

Cluster analysis driven by unsupervised latent feature learning of medications to identify novel pharmacophenotypes of critically ill patients.

Unsupervised clustering of intensive care unit (ICU) medications may identify unique medication clusters (i.e., pharmacophenotypes) in critically ill adults. We performed an unsupervised analysis with Restricted Boltzmann Machine of 991 medications profiles of patients managed in the ICU to explore pharmacophenotypes that correlated with ICU complications (e.g., mechanical ventilation) and patient-centered outcomes (e.g., length of stay, mortality). Six unique pharmacophenotypes were observed, with unique medication profiles and clinically relevant differences in ICU complications and patient-centered outcomes. While pharmacophenotypes 2 and 4 had no statistically significant difference in ICU length of stay, duration of mechanical ventilation, or duration of vasopressor use, their mortality differed significantly (9.0% vs. 21.9%, p < 0.0001). Pharmacophenotype 4 had a mortality rate of 21.9%, compared with the rest of the pharmacophenotypes ranging from 2.5 to 9%. Phenotyping approaches have shown promise in classifying the heterogenous syndromes of critical illness to predict treatment response and guide clinical decision support systems but have never included comprehensive medication information. This first-ever machine learning approach revealed differences among empirically-derived subgroups of ICU patients that are not typically revealed by traditional classifiers. Identification of pharmacophenotypes may enable enhanced decision making to optimize treatment decisions.

Optimizing Pharmacist Team-Integration for ICU Patient Management: Rationale, Study Design, and Methods for a Multicentered Exploration of Pharmacist-to-Patient Ratio.

BACKGROUND: The workload of healthcare professionals including physicians and nurses in the ICU has an established relationship to patient outcomes, including mortality, length of stay, and other quality indicators; however, the relationship of critical care pharmacist workload to outcomes has not been rigorously evaluated and determined. The objective of our study is to characterize the relationship of critical care pharmacist workload in the ICU as it relates to patient-centered outcomes of critically ill patients. METHODS: Optimizing Pharmacist Team-Integration for ICU patient Management is a multicenter, observational cohort study with a target enrollment of 20,000 critically ill patients. Participating critical care pharmacists will enroll patients managed in the ICU. Data collection will consist of two observational phases: prospective and retrospective. During the prospective phase, critical care pharmacists will record daily workload data (e.g., census, number of rounding teams). During the retrospective phase, patient demographics, severity of illness, medication regimen complexity, and outcomes will be recorded. The primary outcome is mortality. Multiple methods will be used to explore the primary outcome including multilevel multiple logistic regression with stepwise variable selection to exclude nonsignificant covariates from the final model, supervised and unsupervised machine learning techniques, and Bayesian analysis. RESULTS: Our protocol describes the processes and methods for an observational study in the ICU. CONCLUSIONS: This study seeks to determine the relationship between pharmacist workload, as measured by pharmacist-to-patient ratio and the pharmacist clinical burden index, and patient-centered outcomes, including mortality and length of stay.

Improving irregular temporal modeling by integrating synthetic data to the electronic medical record using conditional GANs: a case study of fluid overload prediction in the intensive care unit.

Objective: The challenge of irregular 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 hours. Four machine learning algorithms to predict fluid overload after 48-72 hours 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 (CT-GAN)) 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 metamodel 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.

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.

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.