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Sikora A, Keats K, Murphy DJ, Devlin JW, Smith SE, Murray B, Buckley MS, Rowe S, Coppiano L, Kamaleswaran R. A common data model for the standardization of intensive care unit medication features. JAMIA Open 2024; 7:ooae033. [PMID: 38699649 PMCID: PMC11064096 DOI: 10.1093/jamiaopen/ooae033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 02/12/2024] [Accepted: 04/09/2024] [Indexed: 05/05/2024] Open
Abstract
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.
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Affiliation(s)
- Andrea Sikora
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Augusta, GA 30912, United States
| | - Kelli Keats
- Department of Pharmacy, Augusta University Medical Center, Augusta, GA 30912, United States
| | - David J Murphy
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University, Atlanta, GA 30322, United States
| | - John W Devlin
- Northeastern University School of Pharmacy, Boston, MA 02115, United States
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA 02115, United States
| | - Susan E Smith
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Athens, GA 30601, United States
| | - Brian Murray
- Department of Pharmacy, University of North Carolina Medical Center, Chapel Hill, NC 27514, United States
| | - Mitchell S Buckley
- Department of Pharmacy, Banner University Medical Center Phoenix, Phoenix, AZ 85032, United States
| | - Sandra Rowe
- Department of Pharmacy, Oregon Health and Science University, Portland, OR 97239, United States
| | - Lindsey Coppiano
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA 30322, United States
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30322, United States
| | - Rishikesan Kamaleswaran
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA 30322, United States
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30322, United States
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Keats K, Deng S, Chen X, Zhang T, Devlin JW, Murphy DJ, Smith SE, Murray B, Kamaleswaran R, Sikora A. Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.21.24304663. [PMID: 38562806 PMCID: PMC10984037 DOI: 10.1101/2024.03.21.24304663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
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.
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Affiliation(s)
- Kelli Keats
- Augusta University Medical Center, Department of Pharmacy, Augusta, GA
| | - Shiyuan Deng
- University of Georgia Franklin College of Arts and Sciences, Department of Statistics, Athens, GA, USA
| | - Xianyan Chen
- University of Georgia Franklin College of Arts and Sciences, Department of Statistics, Athens, GA, USA
| | - Tianyi Zhang
- University of Georgia Franklin College of Arts and Sciences, Department of Statistics, Athens, GA, USA
| | - John W Devlin
- Northeastern University School of Pharmacy, Boston, MA
- Brigham and Women's Hospital, Division of Pulmonary and Critical Care Medicine, Boston, MA
| | - David J Murphy
- Emory University, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Atlanta, GA, USA
| | - Susan E Smith
- University of Georgia College of Pharmacy, Department of Clinical and Administrative Pharmacy, Athens, GA, USA
| | - Brian Murray
- University of Colorado Skaggs School of Pharmacy, Aurora, CO, USA
| | - Rishikesan Kamaleswaran
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Andrea Sikora
- 1120 15th Street, HM-118 Augusta, GA 30912
- University of Georgia College of Pharmacy, Department of Clinical and Administrative Pharmacy, Augusta, GA, USA
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Patel N, To L, Griebe K, Efta J, Knoth N, Johnson J, Fitzmaurice MG, Bajwa M, Stuart M, Procopio V, Stine J, MacDonald NC, Peters M, Ratusznik M, Kalus J. Scoring big: Aligning inpatient clinical pharmacy services through implementation of an electronic scoring system. Am J Health Syst Pharm 2024; 81:226-234. [PMID: 38070494 DOI: 10.1093/ajhp/zxad313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024] Open
Abstract
PURPOSE Data are limited on utilizing a comprehensive scoring system in the electronic health record to help prioritize, align, and standardize clinical pharmacy services across multiple hospitals and practice models within a health system. The purpose of this article is to describe the development and implementation of an electronic scoring system to help inpatient pharmacists prioritize patient care activities and standardize clinical services across a diverse health system. SUMMARY Inpatient pharmacists from all specialty areas across the health system partnered with health information technology pharmacists to develop a scoring system directly integrated into the electronic health record that would help triage patient care, identify opportunities for pharmacist intervention, and prioritize clinical pharmacy services. Individual variables were built based on documented patient parameters such as use of high-risk medications, pharmacy consults, laboratory values, disease states, and patient acuity. Total overall scores were assigned to patients based on the sum of the scores for the individual variables, which update automatically in real time. The total scores were designed to help inpatient pharmacists prioritize patients with higher scores, thus reducing the need for manual chart review to identify high-risk patients. CONCLUSION An electronic scoring system with a tiered point system developed for inpatient pharmacists creates a method to prioritize and align clinical pharmacy services across a health system with diverse pharmacy practice models.
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Affiliation(s)
- Nisha Patel
- Department of Pharmacy Services, Henry Ford Hospital, Detroit, MI, USA
| | - Long To
- Department of Pharmacy Services, Henry Ford Hospital, Detroit, MI, USA
| | - Kristin Griebe
- Department of Pharmacy Services, Henry Ford Hospital, Detroit, MI, USA
| | - Jessica Efta
- Department of Pharmacy Services, Henry Ford Hospital, Detroit, MI, USA
| | - Nicole Knoth
- Department of Pharmacy Services, Henry Ford Jackson Hospital, Jackson, MI, USA
| | - Joey Johnson
- Department of Pharmacy Services, US Department of Veterans Affairs Medical Center, Ann Arbor, MI, USA
| | | | - Manisha Bajwa
- Department of Pharmacy Services, John D. Dingell Veterans Affairs Medical Center, Detroit, MI, USA
| | - Misa Stuart
- Department of Pharmacy Services, Henry Ford Hospital, Detroit, MI, USA
| | - Vince Procopio
- Department of Pharmacy Services, Henry Ford Macomb Hospital, Clinton Township, MI, USA
| | - John Stine
- Department of Pharmacy Services, Henry Ford Hospital, Detroit, MI, USA
| | - Nancy C MacDonald
- Department of Pharmacy Services, Henry Ford Hospital, Detroit, MI, USA
| | - Mike Peters
- Department of Pharmacy Services, Henry Ford Hospital, Detroit, MI, USA
| | - Martin Ratusznik
- Department of Pharmacy Services, Henry Ford Hospital, Detroit, MI, USA
| | - Jamie Kalus
- Department of Pharmacy Services, Henry Ford Hospital, Detroit, MI, USA
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Kandaswamy S, Dawson TE, Moore WH, Howell K, Beus J, Adu O, Sikora A. Pharmacist Metrics in the Pediatric Intensive Care Unit: an Exploration of the Medication Regimen Complexity-Intensive Care Unit (MRC-ICU) Score. J Pediatr Pharmacol Ther 2023; 28:728-734. [PMID: 38094672 PMCID: PMC10715388 DOI: 10.5863/1551-6776-28.8.728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 04/18/2023] [Indexed: 02/01/2024]
Abstract
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.
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Affiliation(s)
| | - Thomas E Dawson
- Division of IS&T (TED, KH, JB), Children’s Healthcare of Atlanta, Atlanta, GA
| | - Whitney H. Moore
- Wolfson Children’s Hospital/Baptist Health (WHM), Jacksonville, FL
| | - Katherine Howell
- Division of IS&T (TED, KH, JB), Children’s Healthcare of Atlanta, Atlanta, GA
| | - Jonathan Beus
- Division of IS&T (TED, KH, JB), Children’s Healthcare of Atlanta, Atlanta, GA
- Division of Hospitalist Medicine (JB), Children’s Healthcare of Atlanta, Atlanta, GA
| | - Olutola Adu
- Division of Pharmacy (OA) Children’s Healthcare of Atlanta, Atlanta, GA
| | - Andrea Sikora
- College of Pharmacy (AS), University of Georgia, Athens, GA
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Webb AJ, Carver B, Rowe S, Sikora A. The use of electronic health record embedded MRC-ICU as a metric for critical care pharmacist workload. JAMIA Open 2023; 6:ooad101. [PMID: 38058680 PMCID: PMC10697785 DOI: 10.1093/jamiaopen/ooad101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/27/2023] [Accepted: 11/20/2023] [Indexed: 12/08/2023] Open
Abstract
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.
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Affiliation(s)
- Andrew J Webb
- Department of Pharmacy, Oregon Health and Science University, Portland, OR 97239, United States
- Department of Pharmacy, Massachusetts General Hospital, Boston, MA 02114, United States
| | - Bayleigh Carver
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Augusta, GA 30912, United States
| | - Sandra Rowe
- Department of Pharmacy, Massachusetts General Hospital, Boston, MA 02114, United States
| | - Andrea Sikora
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Augusta, GA 30912, United States
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Chase AM, Azimi HA, Forehand CC, Keats K, Taylor A, Wu S, Blotske K, Sikora A. An Evaluation of the Relationship Between Medication Regimen Complexity as Measured by the MRC-ICU to Medication Errors in Critically Ill Patients. Hosp Pharm 2023; 58:569-574. [PMID: 38560536 PMCID: PMC10977060 DOI: 10.1177/00185787231170386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
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.
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Affiliation(s)
| | | | | | - Kelli Keats
- Augusta University Medical Center, Augusta, GA, USA
| | | | - Stephen Wu
- University of Georgia College of Pharmacy, Augusta, GA, USA
| | | | - Andrea Sikora
- Augusta University Medical Center, Augusta, GA, USA
- University of Georgia College of Pharmacy, Augusta, GA, USA
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7
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Sikora A, Zhang T, Murphy DJ, Smith SE, Murray B, Kamaleswaran R, Chen X, Buckley MS, Rowe S, Devlin JW. Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU. Sci Rep 2023; 13:19654. [PMID: 37949982 PMCID: PMC10638304 DOI: 10.1038/s41598-023-46735-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 11/04/2023] [Indexed: 11/12/2023] Open
Abstract
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.
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Affiliation(s)
- Andrea Sikora
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, 1120 15th Street, HM-118, Augusta, GA, 30912, USA
| | - Tianyi Zhang
- Department of Statistics, University of Georgia Franklin College of Arts and Sciences, Athens, GA, USA
| | - David J Murphy
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University, Atlanta, GA, USA
| | - Susan E Smith
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, 1120 15th Street, HM-118, Augusta, GA, 30912, USA
| | - Brian Murray
- Department of Pharmacy, University of North Carolina Medical Center, Chapel Hill, NC, USA
| | - Rishikesan Kamaleswaran
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Xianyan Chen
- Department of Statistics, University of Georgia Franklin College of Arts and Sciences, Athens, GA, USA
| | | | - Sandra Rowe
- Department of Pharmacy, Oregon Health and Science University, Portland, OR, USA
| | - John W Devlin
- Northeastern University School of Pharmacy, Boston, MA, USA.
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA.
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Sikora A, Jeong H, Yu M, Chen X, Murray B, Kamaleswaran R. Cluster analysis driven by unsupervised latent feature learning of medications to identify novel pharmacophenotypes of critically ill patients. Sci Rep 2023; 13:15562. [PMID: 37730817 PMCID: PMC10511715 DOI: 10.1038/s41598-023-42657-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 09/13/2023] [Indexed: 09/22/2023] Open
Abstract
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.
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Affiliation(s)
- Andrea Sikora
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Augusta, GA, USA.
| | | | - Mengyun Yu
- Department of Statistics, University of Georgia Franklin College of Arts and Sciences, Athens, USA
| | - Xianyan Chen
- Department of Statistics, University of Georgia Franklin College of Arts and Sciences, Athens, USA
| | - Brian Murray
- Department of Pharmacy, University of North Carolina Medical Center, Chapel Hill, NC, USA
| | - Rishikesan Kamaleswaran
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
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Sikora A. Critical Care Pharmacists: A Focus on Horizons. Crit Care Clin 2023; 39:503-527. [PMID: 37230553 DOI: 10.1016/j.ccc.2023.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
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.
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Affiliation(s)
- Andrea Sikora
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, 120 15th Street, HM-118, Augusta, GA 30912, USA; Department of Pharmacy, Augusta University Medical Center, Augusta, GA, USA.
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10
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Sikora A, Rafiei A, Rad MG, Keats K, Smith SE, Devlin JW, Murphy DJ, Murray B, Kamaleswaran R. Pharmacophenotype identification of intensive care unit medications using unsupervised cluster analysis of the ICURx common data model. Crit Care 2023; 27:167. [PMID: 37131200 PMCID: PMC10155304 DOI: 10.1186/s13054-023-04437-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 04/10/2023] [Indexed: 05/04/2023] Open
Abstract
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.
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Affiliation(s)
- Andrea Sikora
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Augusta, GA USA
| | - Alireza Rafiei
- Department of Computer Science and Informatics, Emory University, Atlanta, GA USA
| | - Milad Ghiasi Rad
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA USA
| | - Kelli Keats
- Department of Pharmacy, Augusta University Medical Center, Augusta, GA USA
| | - Susan E. Smith
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Augusta, GA USA
| | - John W. Devlin
- Northeastern University School of Pharmacy, Boston, MA USA
- Brigham and Women’s Hospital, Division of Pulmonary and Critical Care Medicine, Boston, MA USA
| | - David J. Murphy
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University, Atlanta, GA USA
| | - Brian Murray
- Department of Pharmacy, University of North Carolina Medical Center, Chapel Hill, NC USA
| | - Rishikesan Kamaleswaran
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA USA
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA USA
| | - MRC-ICU Investigator Team
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Augusta, GA USA
- Department of Computer Science and Informatics, Emory University, Atlanta, GA USA
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA USA
- Department of Pharmacy, Augusta University Medical Center, Augusta, GA USA
- Northeastern University School of Pharmacy, Boston, MA USA
- Brigham and Women’s Hospital, Division of Pulmonary and Critical Care Medicine, Boston, MA USA
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University, Atlanta, GA USA
- Department of Pharmacy, University of North Carolina Medical Center, Chapel Hill, NC USA
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA USA
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA USA
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11
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Sikora A, Ayyala D, Rech MA, Blackwell SB, Campbell J, Caylor MM, Condeni MS, DePriest A, Dzierba AL, Flannery AH, Hamilton LA, Heavner MS, Horng M, Lam J, Liang E, Montero J, Murphy D, Plewa-Rusiecki AM, Sacco AJ, Sacha GL, Shah P, Smith MP, Smith Z, Radosevich JJ, Vilella AL. 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. Crit Care Med 2022; 50:1318-1328. [PMID: 35678204 PMCID: PMC9612633 DOI: 10.1097/ccm.0000000000005585] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
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 (β coefficient, 0.41; 95% CI, 00.37-0.45; p < 0.01), total pharmacist interventions (β coefficient, 0.07; 95% CI, 0.04-0.09; p < 0.01), and a composite intensity score of pharmacist interventions (β 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 (β coefficient, 0.02; 0.00-0.04; p = 0.02) and reduced quantity (β coefficient, -0.03; 95% CI, -0.04 to -0.02; p < 0.01) and intensity of interventions (β 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.
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Affiliation(s)
- Andrea Sikora
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Augusta, GA
| | - Deepak Ayyala
- Department of Population Health Science: Biostats & Data Science, Medical College of Georgia, Augusta, GA
| | - Megan A Rech
- Department of Pharmacy, Loyola University Medical Center, Maywood, IL
| | - Sarah B Blackwell
- Department of Pharmacy Services, Princeton Baptist Medical Center, Birmingham, AL
| | - Joshua Campbell
- Department of Pharmacy, Guthrie Robert Packer Hospital, Sayre, PA
| | - Meghan M Caylor
- Department of Pharmacy, Hospital of the University of Pennsylvania, Philadelphia, PA
| | | | - Ashley DePriest
- Department of Pharmacy, Wellstar Kennestone Regional Medical Center, Marietta, GA
| | - Amy L Dzierba
- Department of Pharmacy, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, New York, NY
| | - Alexander H Flannery
- Department of Pharmacy, University of Kentucky College of Pharmacy, Lexington, KY
| | - Leslie A Hamilton
- Department of Pharmacy, The University of Tennessee Health Science Center College of Pharmacy, Knoxville, TN
| | - Mojdeh S Heavner
- Department of Pharmacy, University of Maryland School of Pharmacy, Baltimore, MD
| | - Michelle Horng
- Department of Pharmacy, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Joseph Lam
- Department of Pharmacy, Highland Hospital, Alameda Health System, Oakland, CA
| | - Edith Liang
- Department of Pharmacy, Critical Care/Emergency Medicine Clinical Pharmacy Specialist, AMITA Health Saints Mary and Elizabeth Medical Center, Chicago, IL
| | | | - David Murphy
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University School of Medicine, Atlanta, GA
| | | | - Alicia J Sacco
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Phoenix, AZ
| | | | - Poorvi Shah
- Department of Pharmacy, Advocate Christ Medical Center, Oak Lawn, IL
| | | | - Zachary Smith
- Department of Pharmacy, Henry Ford Hospital, Detroit, MI
| | - John J Radosevich
- Department of Pharmacy, St. Joseph's Hospital and Medical Center, Phoenix, AZ
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12
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Evaluating the Medication Regimen Complexity Score as a Predictor of Clinical Outcomes in the Critically Ill. J Clin Med 2022; 11:jcm11164705. [PMID: 36012944 PMCID: PMC9410153 DOI: 10.3390/jcm11164705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/31/2022] [Accepted: 08/08/2022] [Indexed: 12/03/2022] Open
Abstract
Background: Medication Regimen Complexity (MRC) refers to the combination of medication classes, dosages, and frequencies. The objective of this study was to examine the relationship between the scores of different MRC tools and the clinical outcomes. Methods: We conducted a retrospective cohort study at Roger William Medical Center, Providence, Rhode Island, which included 317 adult patients admitted to the intensive care unit (ICU) between 1 February 2020 and 30 August 2020. MRC was assessed using the MRC Index (MRCI) and MRC for the Intensive Care Unit (MRC-ICU). A multivariable logistic regression model was used to identify associations among MRC scores, clinical outcomes, and a logistic classifier to predict clinical outcomes. Results: Higher MRC scores were associated with increased mortality, a longer ICU length of stay (LOS), and the need for mechanical ventilation (MV). MRC-ICU scores at 24 h were significantly (p < 0.001) associated with increased ICU mortality, LOS, and MV, with ORs of 1.12 (95% CI: 1.06−1.19), 1.17 (1.1−1.24), and 1.21 (1.14−1.29), respectively. Mortality prediction was similar using both scoring tools (AUC: 0.88 [0.75−0.97] vs. 0.88 [0.76−0.97]. The model with 15 medication classes outperformed others in predicting the ICU LOS and the need for MV with AUCs of 0.82 (0.71−0.93) and 0.87 (0.77−0.96), respectively. Conclusion: Our results demonstrated that both MRC scores were associated with poorer clinical outcomes. The incorporation of MRC scores in real-time therapeutic decision making can aid clinicians to prescribe safer alternatives.
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13
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Smith SE, Shelley R, Sikora A. Medication regimen complexity vs patient acuity for predicting critical care pharmacist interventions. Am J Health Syst Pharm 2022; 79:651-655. [PMID: 34864850 PMCID: PMC8975577 DOI: 10.1093/ajhp/zxab460] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
PURPOSE Quantifying and predicting critical care pharmacist (CCP) workload has significant ramifications for expanding CCP services that improve patient outcomes. Medication regimen complexity has been proposed as an objective, pharmacist-oriented metric that demonstrates relationships to patient outcomes and pharmacist interventions. The purpose of this evaluation was to compare the relationship of medication regimen complexity versus a traditional patient acuity metric for evaluating pharmacist interventions. SUMMARY This was a post hoc analysis of a previously completed prospective, observational study. Pharmacist interventions were prospectively collected and tabulated at 24 hours, 48 hours, and intensive care unit (ICU) discharge, and the electronic medical record was reviewed to collect patient demographics, medication data, and outcomes. The primary outcome was the relationship between medication regimen complexity-intensive care unit (MRC-ICU) score, Acute Physiology and Chronic Health Evaluation (APACHE) II score, and pharmacist interventions at 24 hours, 48 hours, and ICU discharge. These relationships were determined by Spearman rank-order correlation (rS) and confirmed by calculating the beta coefficient (β) via multiple linear regression adjusting for patient age, gender, and admission type. Data on 100 patients admitted to a mixed medical/surgical ICU were retrospectively evaluated. Both MRC-ICU and APACHE II scores were correlated with ICU interventions at all 3 time points (at 24 hours, rS = 0.370 [P < 0.001] for MRC-ICU score and rS = 0.283 [P = 0.004] for APACHE II score); however, this relationship was not sustained for APACHE II in the adjusted analysis (at 24 hours, β = 0.099 [P = 0.001] for MRC-ICU and β = 0.031 [P = 0.085] for APACHE II score). CONCLUSION A pharmacist-oriented score had a stronger relationship with pharmacist interventions as compared to patient acuity. As pharmacists have demonstrated value across the continuum of patient care, these findings support that pharmacist-oriented workload predictions require tailored metrics, beyond that of patient acuity.
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Affiliation(s)
- Susan E Smith
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Athens, GA, USA
| | - Rachel Shelley
- University of Georgia College of Pharmacy, Augusta, GA, USA
| | - Andrea Sikora
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Augusta, GA
- Department of Pharmacy, Augusta University Medical Center, Augusta, GA, USA
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14
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Abstract
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.
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Affiliation(s)
- Susan E Smith
- Department of Pharmacy, University of North Carolina Medical Center, Chapel Hill, NC, USA
| | - Brian Murray
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Athens, GA, USA
| | - Andrea Sikora
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Athens, GA, USA
- Department of Pharmacy, Augusta University Medical Center, Augusta, GA, USA
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15
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Haas CE, Dick TB. Productivity, workload, and clinical pharmacists: Definitions matter. Am J Health Syst Pharm 2022; 79:728-729. [PMID: 35015815 DOI: 10.1093/ajhp/zxac003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
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.
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Affiliation(s)
- Curtis E Haas
- Department of Pharmacy, University of Rochester Medical Center, Rochester, NY, USA
| | - Travis B Dick
- Department of Pharmacy, University of Rochester Medical Center, Rochester, NY, USA
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16
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Newsome AS, Murray B, Smith SE, Brothers T, Al-Mamun MA, Chase AM, Rowe S, Buckley MS, Murphy D, Devlin JW. Optimization of critical care pharmacy clinical services: A gap analysis approach. Am J Health Syst Pharm 2021; 78:2077-2085. [PMID: 34061960 PMCID: PMC8195049 DOI: 10.1093/ajhp/zxab237] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Affiliation(s)
- Andrea Sikora Newsome
- Department of Pharmacy, Augusta University Medical Center, Augusta, GA, USA.,Department of Pharmacy, University of North Carolina Medical Center, Chapel Hill, NC, USA
| | - Brian Murray
- Department of Pharmacy, University of North Carolina Medical Center, Chapel Hill, NC, USA
| | - Susan E Smith
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Athens, GA, USA
| | - Todd Brothers
- Department of Pharmacy Practice, University of Rhode Island College of Pharmacy, Kingston, RI, and Department of Pharmacy, Roger Williams Medical Center, Providence, RI, USA
| | - Mohammad A Al-Mamun
- Department of Pharmacy Practice, University of Rhode Island College of Pharmacy, Kingston, RI, USA
| | - Aaron M Chase
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Augusta, GA, and Department of Pharmacy, Augusta University Medical Center, Augusta, GA, USA
| | - Sandra Rowe
- Department of Pharmacy, Oregon Health and Science University, Portland, OR, USA
| | - Mitchell S Buckley
- Department of Pharmacy, Banner University Medical Center Phoenix, Phoenix, AZ, USA
| | - David Murphy
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Emory University, Atlanta, GA, USA
| | - John W Devlin
- Northeastern University School of Pharmacy, Boston, MA, and Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
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