Characterization of medication regimen complexity, pharmacist interventions, and patient outcomes in critically ill patients Open Access

Newsome, Andrea Sikora (Spring 2021)

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Despite the established role of the critical care pharmacist (CCP) on the intensive care unit (ICU) interprofessional team and their proven benefit to critically ill adults, CCP workloads are not optimized in the ICU. Lack of optimization has important ramifications for patients, CCPs, and institutions. Challenges to optimizing CCP staffing models include lack of validated predictive metrics to define CCP resource needs across different ICU and hospital types. Medication regimen complexity (as measured by the MRC-ICU Scoring Tool) has been proposed as a potential metric to optimize CCP workload. This algorithmic tool is a 39-line item medication weighted scoring system based on the patient’s current medications. The purpose of this multi-center, observational cohort study was to test the hypothesis that medication regimen complexity is related to both patient outcomes and pharmacist activity. CCP interventions on the medication regimens of critically ill patients over a four week period were captured. MRC-ICU, patient outcomes (i.e., mortality and length of stay (LOS)), and CCP interventions (quantity and type) and were recorded retrospectively from review of electronic medical records. The co-primary outcomes included the relationship of MRC-ICU to mortality and number of CCP interventions. Multivariable analysis was performed to identify factors associated with patient outcomes and CCP interventions. A total of 1,216 patients at 28 centers were included. The most common practice setting was the medical ICU (48.8%) followed by neurosciences (18.5%) and mixed ICU (10.7%). The mean MRC-ICU score was 10.4 (± 6.3). Following analysis of variance (ANOVA), MRC-ICU was significantly associated with mortality (p < 0.01), ICU LOS (p < 0.01), and total pharmacist interventions (p < 0.01). When comparing the first vs. fourth MRC-ICU quartiles, the incidence of mortality doubled (14.1% vs. 30.5%, p < 0.01), ICU LOS tripled (8.0 v. 24.1 days, p < 0.001), and number of interventions increased (7.6 v. 9.8 interventions, p < 0.01). Multiple linear regression demonstrated that for every one point increase in MRC-ICU score, pharmacy intervention quantity increased by 0.11 interventions (95% CI 0.06 – 0.15, p < 0.01) and a composite score of intervention quality increased by 0.23 (95% CI 0.05 – 0.41, p = 0.03). Further, a multiple linear regression model demonstrated that ICU LOS independently increased by 0.75 days (95% CI 0.32 – 1.19, p<0.01) for each point increase in the MRC-ICU score independent of potential confounding patient and organizational factors. In multivariate regression analysis, pharmacist-to-patient ratio was significantly associated with total number of interventions (ß coefficient -0.02, 95% CI -0.04 – -0.01, p = 0.03) showing that increased patient load was associated with reduced overall interventions. In summary, quantification of medication regimen complexity in critically ill adults has shown promise by its relationship to important process and outcome measures, and future research should evaluate use of objective metrics like medication regimen complexity to inform CCP staffing models and how they affect patient outcomes.

Table of Contents



pp. 8-10

1.    Barriers to defining the optimal pharmacist-to-patient ratio

pp. 9-10



pp. 11-14

1.    Existing tools to describe pharmacist workload

pp. 11

2.    MRC-ICU Scoring Tool

pp. 11-14



pp. 15-17

1.    Specific Aims


pp. 15

2.    Study Design


pp. 15

3.    Study Population


pp. 15

4.    Variables


pp. 16

5.    Statistical Analysis


pp. 16-17



pp. 18-19

1.    Aim 1


pp. 18

2.    Aim 2


pp. 19



pp. 20-24

1.    Future direction for metrics to optimize CCP services

pp. 21-22

2.    Strengths and limitations

pp. 22-24


pp. 25



pp. 26-34

1.    Table 1. Knowledge gaps to CCP staffing model optimization and strategies to resolve

2.    Table 2. Clinical pharmacy resource prediction tools

3.    Table 3. Low, medium, and high quality intervention categories

4.    Table 4. Demographic characteristics

5.    Table 5. Demographic features and outcomes by MRC-ICU quartile

6.    Table 6. Univariate and multivariate regression of factors related to mortality

7.    Table 7. Univariate and multivariate regression of factors related to ICU LOS

8.    Table 8. Univariate and multivariate regression of factors related to intervention quantity

9.    Table 9. Univariate and multivariate regression of factors related to intervention quality



pp. 35-36

1.    Figure 1. MRC-ICU research pathway


pp. 35

2.    Figure 2. Construct for machine learning methodology


pp. 36



pp. 37-38

1.    Appendix 1. MRC-ICU Scoring Tool

2.    Appendix 2. Pharmacist to patient ratio vs. outcomes of interest


pp. 39-44

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