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To familiarize clinicians with the emerging concepts in critical care research of Bayesian thinking and personalized medicine through phenotyping and explain their clinical relevance by highlighting how they address the issues of frequent negative trials and heterogeneity of treatment effect.
The past decades have seen many negative (effect-neutral) critical care trials of promising interventions, culminating in calls to improve the field's research through adopting Bayesian thinking and increasing personalization of critical care medicine through phenotyping. Bayesian analyses add interpretive power for clinicians as they summarize treatment effects based on probabilities of benefit or harm, contrasting with conventional frequentist statistics that either affirm or reject a null hypothesis. Critical care trials are beginning to include prospective Bayesian analyses, and many trials have undergone reanalysis with Bayesian methods. Phenotyping seeks to identify treatable traits to target interventions to patients expected to derive benefit. Phenotyping and subphenotyping have gained prominence in the most syndromic and heterogenous critical care disease states, acute respiratory distress syndrome and sepsis. Grouping of patients has been informative across a spectrum of clinically observable physiological parameters, biomarkers, and genomic data. Bayesian thinking and phenotyping are emerging as elements of adaptive clinical trials and predictive enrichment, paving the way for a new era of high-quality evidence. These concepts share a common goal, sifting through the noise of heterogeneity in critical care to increase the value of existing and future research.
The future of critical care medicine will inevitably involve modification of statistical methods through Bayesian analyses and targeted therapeutics via phenotyping. Clinicians must be familiar with these systems that support recommendations to improve decision-making in the gray areas of critical care practice.
Short courses of antibiotics (7-10 days) are effective for uncomplicated gram-negative bloodstream infections (GN-BSI). However, prior studies have been limited to small cohorts of critically ill ...patients.
The objective of this study was to evaluate the safety and efficacy of short courses of therapy compared with longer courses in patients admitted to the intensive care unit (ICU) with GN-BSI.
Propensity-matched, retrospective cohort study of critically ill patients with GN-BSI. The primary outcome was a composite of 30-day mortality or 60-day relapse. Secondary endpoints were components of the composite, 30-day relapse, cure with or without adverse drug events (ADE), and ADEs. Regression analysis was performed to identify factors predictive of the composite outcome.
225 patients were included in the propensity analysis, 145 in the long cohort and 80 in the short cohort. The primary outcome occurred in 3.8% of patients in the short group and 9.0% of patients in the long group (
= 0.24). There was no difference in 30-day mortality (3.8% vs 5.5%,
= 0.79), 60-day relapse (0% vs 3.4%,
= 0.23), or 30-day readmission (20% vs 22.8%,
= 0.76). ADEs were more common in the long group (47.2% vs 34.1%, OR 1.7, 95% CI 1.04-2.9), primarily attributable to diarrhea.
In critically ill patients with GN-BSI, there were no efficacy outcome differences in patients treated with a short course of antibiotics compared with longer. However, patients in the short group were less likely to experience ADE. These findings suggest that short courses of antibiotics are effective for GN-BSI in critically ill patients.
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.
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.
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.
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.
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.
Background:
Critically ill patients are at increased risk for fluid overload, but objective prediction tools to guide clinical decision-making are lacking. The MRC-ICU scoring tool is an objective ...tool for measuring medication regimen complexity.
Objective:
To evaluate the relationship between MRC-ICU score and fluid overload in critically ill patients.
Methods:
In this multi-center, retrospective, observational study, the relationship between MRC-ICU and the risk of fluid overload was examined. Patient demographics, fluid balance at day 3 of ICU admission, MRC-ICU score at 24 hours, and clinical outcomes were collected from the medical record. The primary outcome was relationship between MRC-ICU and fluid overload. To analyze this, MRC-ICU scores were divided into tertiles (low, moderate, high), and binary logistic regression was performed. Linear regression was performed to determine variables associated with positive fluid balance.
Results:
A total of 125 patients were included. The median MRC-ICU score at 24 hours of ICU admission for low, moderate, and high tertiles were 9, 15, and 21, respectively. For each point increase in MRC-ICU, a 13% increase in the likelihood of fluid overload was observed (OR 1.128, 95% CI 1.028-1.238, p = 0.011). The MRC-ICU score was positively associated with fluid balance at day 3 (β-coefficient 218.455, 95% CI 94.693-342.217, p = 0.001) when controlling for age, gender, and SOFA score.
Conclusions:
Medication regimen complexity demonstrated a weakly positive correlation with fluid overload in critically ill patients. Future studies are necessary to establish the MRC-ICU as a predictor to identify patients at risk of fluid overload.
OBJECTIVES/GOALS: To determine if incorporating specific laboratory values and plasma biomarkers (club cell secretory protein (CC16), matrix metalloproteinase 3 (MMP3), interleukin 8 (IL-8), protein ...C) to the Lung Injury Prediction (LIP) Score improves the predictive value for development of acute respiratory distress syndrome (ARDS) in ICU patients. METHODS/STUDY POPULATION: Adult patients admitted to the ICU on supplemental oxygen over baseline requirement with a LIP Score ≥6 will be included. Patients admitted to the ICU >24 hours, end-stage renal disease, decompensated heart failure, or <100 µL plasma available will be excluded. Whole blood will be collected from the core lab, centrifuged, and plasma will be stored at -80°C. Protein biomarkers will be measured using enzyme-linked immunosorbent assay. Baseline characteristics, laboratory values, ventilator parameters, and clinical outcomes will be collected from the medical record. ARDS will be defined by the Berlin criteria. Machine learning methods will be used to identify the model with the highest predictive accuracy. Area under the receiver operating characteristic curve of each model will be compared to the LIP Score. RESULTS/ANTICIPATED RESULTS: Research is in progress. Plasma samples and clinical data have been collected for 148 of the 160 samples required to achieve power. Biomarker analysis will take place after sample collection is complete. We anticipate a machine learning model incorporating laboratory values and one or more plasma biomarkers into the LIP Score will outperform the baseline LIP Score for prediction of ARDS development. DISCUSSION/SIGNIFICANCE: Delayed diagnosis and intervention contribute to poor ARDS outcomes. Current predictive models for ARDS have low accuracy and enriching these models with plasma biomarkers may increase their predictive value. Development of accurate models may facilitate earlier ARDS diagnosis and intervention as well as enrichment strategies for ARDS trials.
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.
The practice of modern craniomaxillofacial surgery has been defined by emergent technologies allowing for the acquisition, storage, utilization, and transfer of massive amounts of sensitive and ...identifiable patient data. This alone has thrust providers into an unlikely and unprecedented role as the stewards of vast databases of digital information. This data powers the potent surgical tool of virtual surgical planning, a method by which craniomaxillofacial surgeons plan and simulate procedural outcomes in a digital environment. Further complicating this new terrain is the involvement of third‐party contractors—a necessary presence in bringing raw data to bear in the office, virtual space, and operating room. The individual privileges and responsibilities of patients, providers, and vendors towards data are situated within the most recent U.S. court rulings and regulations. This paper offers guidance for overseeing the safe and responsible transfer to third‐party contractors, and provides suggestions for negotiating the trinary relationship between physicians, their patients, and the vendors offering this transformative technology.
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