Research

JPPT | Single Center Retrospective Study

Pharmacist Metrics in the Pediatric Intensive Care Unit:

an Exploration of the Medication Regimen Complexity-
Intensive Care Unit (MRC-ICU) Score
Swaminathan Kandaswamy, PhD; Thomas E Dawson, PharmD; Whitney H. Moore, PharmD; Katherine Howell, PharmD;
Jonathan Beus, MD, MSCR; Olutola Adu, PharmD; and Andrea Sikora, PharmD, MSCR

INTRODUCTION The medication regimen complexity-intensive care unit (MRC-ICU) score has been devel-
oped 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 c­ orrelation
­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.
ABBREVIATIONS AHFS, american hospital formulary service; ATC, anatomical therapeutic chemical; APACHE,
acute physiology and chronic health evaluation; CI, confidence interval; EHR, electronic health record;
ECMO, extracorporeal membrane oxygenation; LVAD, left ventricular assist devices; MRC-ICU, medication
regimen complexity-intensive care unit; ICU, intensive care unit; PICU, pediatric intensive care unit; pDMS,
pediatric daily monitoring system; REMS, risk evaluation mitigation strategies; SD, standard deviation; SQL,
structured query language; TPN, total parenteral nutrition.
KEYWORDS critical care; pharmacy; pediatrics; medication regimen complexity; pharmacists; informatics
J Pediatr Pharmacol Ther 2023;28(8):728–734

DOI: 10.5863/1551-6776-28.8.728

Introduction the adult population.1–10 In adults, this metric has shown

Metrics that quantify and predict pharmacist work- promise with its relationship to both patient outcomes
load have the potential to improve patient care delivery and pharmacist workload. The MRC-ICU correlated
by optimization of pharmacist workload, thus ensuring with severity of illness (as measured by the Acute
that each critically ill patient receives the care of a Physiology and Chronic Health Evaluation (APACHE)
critical care pharmacist.1,2 Moreover, metrics can help III), patient-centered outcomes (e.g., mortality and
to prioritize daily workflow for clinical pharmacists such length of stay), ICU-related complications (e.g., fluid
that they review the patients with the highest need for overload and drug-drug interactions), and pharmacist
their cognitive services first.3 workload, as measured by documented pharmacist
The medication regimen complexity-intensive care interventions.2–10 However, it is well known that adult
unit (MRC-ICU) Scoring Tool is the first metric designed and pediatric patient populations differ significantly with
specifically for critical care pharmacy practice with the representative guidelines for those differences in care,
goal of describing critical care pharmacist workload in including in the domain of critical care.11,12

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Kandaswamy, S et al Pharmacist Metrics in the Pediatric Intensive Care Unit

The purpose of this study was to validate the MRC- ranging from 1-3.4–11 These values are then summed
ICU in a pediatric population by assessing its relation- to provide the total score. For example, a patient re-
ship to patient-centered outcomes (i.e., mortality, length ceiving meropenem (2 points), tobramycin (3 points),
of stay) and its convergent and divergent association norepinephrine (1 point), and vasopressin (1 point) on
to existing measures of pharmacy workload. ICU Day 2, would have a day 2 MRC-ICU score of 7.
The goal was to replicate all the elements included in
Methods the original MRC-ICU study as closely as possible for
Study Design. This study was conducted at a large validation in the pediatric population at this institu-
pediatric health system in the greater Atlanta area tion. There were certain elements in the scoring tool
with more than 2,600 pediatric providers and 638 that were not applicable to the workflow for pediatric
licensed beds. Data from all patients’ encounters in- care at our health system. Hence, we used a modi-
cluding demographics, medication administrations, fied version of the MRC-ICU specifically adapted for
flags for dialysis, ECMO, Mechanical Ventilation as the pediatric population. Changes to the original
well as their outcomes, length of stay, mortality, were calculations included (1) exclusion of chlorhexidine
retrospectively extracted for a 6-month period be- because this is not used for ventilator prophylaxis;
tween February 2, 2022, and August 2, 2022, using (2) Clinical dieticians order TPNs for patients, the staff/
query of the electronic health record. TPN pharmacist verifies the TPN order, and they will
Pediatric Daily Monitoring System Development. usually reach out to the dietician if there is an issue
A local scoring tool was developed based on group with the TPN order. The pharmacy specialist is typi-
consensus of pediatric clinical pharmacists in con- cally not involved in the ordering or review process.
junction with the informatics system pharmacists. Pharmacists do review labs for electrolyte imbalanc-
The goal of this score was to support clinical pharma- es (especially if patients are on diuretics), the staff
cist prioritization during their comprehensive thera- pharmacist covering the TPN shift is responsible for
peutic review of patients. The tool was designed to verifying and compounding the TPN. Since clinical
support clinical pharmacists in managing their daily pharmacists are involved in reviewing electrolyte im-
workflow by helping quickly identify those patients balances but not in the ordering or verification of the
that require increased or more thorough monitoring. TPN, 2 points were assigned for TPN management;
The tool is comprised of eight categories including: (3) to reflect pediatric practices for opioids and seda-
1. Medications requiring pharmacokinetics; 2. Seda- tives, 2 points were assigned for each continuous
tion; 3. Anticoagulation; 4. Nephrotoxicity; 5. Immu- infusion and 1 point for patients receiving scheduled
nosuppressants; 6. Anticonvulsants; 7. Medications intermittent doses; (4) enoxaparin doses >1.75 mg/kg/
requiring Risk Evaluation and Mitigation Strategies day were considered therapeutic heparins; (5) exclu-
(REMS) programs; 8. Intravenous (IV) to oral (PO) tran- sion of left ventricular assist devices (LVADs) as pe-
sition. Within each category, there are individual line diatric patients with LVADs at our institution are not
items that are weighted. For example, under medi- cared for in the PICU. Each element in the MRC-ICU
cations requiring pharmacokinetics, the medication score was extracted from the Epic Clarity® database
‘vancomycin’ is awarded one point. The sum of each using Structured Query Language (SQL) queries. The
category is also applied a weight, and this total value validity of data capture for devices was verified on
is then assigned one of four color-coded categories: 5 random patients. To capture each medication, a
1-5 (green), 6-12 (yellow), 13-18 (red), and >18 (critical, Pharmacy Informaticist (KH) developed a set of iden-
dark red). The score is described in Table S1. The color- tifiers that captured the medications in the formulary.
ing scheme served as a visual indicator of a patient’s These included a set of
“total acuity”. Patients with high “total acuity” receive 1. American Hospital Formulary Service (AHFS)
a crimson or red icon, patients with moderate acuity codes,13
received yellow and green represented patients with 2. Anatomical Therapeutic Chemical (ATC) Codes,14
modest acuity. These patients represent patients 3. Epic® and First DataBank®-specific identifiers (in-
whose overall clinical picture presents the greatest cluding unique medication records and assigned
opportunity for clinical pharmacy intervention. As pharmaceutical classes).
such, the tool is a quick visual cue for the pharmacist This list was then checked for accuracy and was
in addition to a more granular breakdown of medica- updated on review by another Pharmacy Informaticist
tion regimen components. The tool was built within (TD). The queries built based on the identifiers were
the EHR and is available as a score within patient verified using two approaches
flowsheet (Figure). (1) the relative total number of medications identified
MRC-ICU Scoring Tool Adaptation. The MRC-ICU by the query was checked to see if it matches
is a 37-line-item score calculated at a given time point anecdotal evidence of usage of these meds
where each medication included in the score that is (2) five patient charts were reviewed to validate ac-
prescribed to a patient is assigned a weighted value curacy of the medications pulled by the query

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Pharmacist Metrics in the Pediatric Intensive Care Unit Kandaswamy, S et al

Figure. Screenshot of the Pediatric Daily Monitoring System

The color schema for Toal Acuity Score is described in the methods section. See table S1 below for description of scores, including total acuity
score. For Kinetics, Anti-coag, Anticonvulsants, and Add’l kinetics columns, presence of an icon in column indicates a score for that parameter.
Additional columns of scoring system (not shown above) follow similar schema. Icons in RX consults and I-vents columns indicate the presence
of specific procedure orders and clinical notations relevant to pharmacy workflow, respectively.

against chart documentation. The components Pearson correlation was calculated between MRC-
used for the MRC-ICU and their weightage for the ICU and pDMS scores with total number of medica-
scoring tool are described in Table S2. tions and total number of orders was used to examine
Primary and Secondary Outcomes. The primary convergent validity. Additionally, divergent validity was
outcome of this study was the correlation of the MRC- assessed by correlation of the two scores with age.
ICU measured at 24 hours to mortality. Secondary out- Logistic regression was applied to identify log odds
comes included the correlation of the MRC-ICU with of MRC-ICU and pediatric Daily Monitoring System as-
sociation with mortality. Similarly, linear regression was
ICU and hospital length of stay. Finally, in line with the
applied to identify the relationship between continu-
historical validation of MRC-ICU, convergent valid-
ous outcomes (number of patient medication orders,
ity was assessed with number of medication orders
hospital length of stay, ICU length of stay) and the two
for a patient at 24 hours, and divergent validity was scoring tools.
assessed with correlation to patient age. All of these
analyses were repeated for the institutionally em- Results
ployed pediatric Daily Monitoring Score (pDMS). Ad-
A total of 2,239 patients comprising 2,405 encounters
ditionally, the correlation of the MRC-ICU and pDMS
were included. The average age was 6.9 years (SD 6.3),
was assessed. and the population was 54.4% male. Overall, the mortality
Statistical Analysis. All statistical analyses were rate was 1.95%, and the average ICU length of stay was
performed using R version 3.6.3. Descriptive statis- 94.7 hours (SD 142.4). The mean MRC-ICU score was 3.0
tics were applied to this dataset, and all data are de- (SD 3.8), and the mean pDMS was 1.9 (SD 3.0). Complete
scribed as mean (standard deviation) and n (percent) results can be found summarized in Table 1.
unless otherwise stated. Statistical significance was For the primary outcome, MRC-ICU demonstrated
assessed at 0.05. significant correlation to mortality as measured by

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Kandaswamy, S et al Pharmacist Metrics in the Pediatric Intensive Care Unit

Pearson correlation (0.22 [CI 0.18 – 0.26]), p<0.05. Addi- pDMS also demonstrated significant correlation to
tionally, MRC-ICU showed significant correlation with mortality as measured by Pearson correlation (0.17
hospital length of stay (0.36 [CI 0.32 – 0.39]), p<0.05 [0.13 – 0.21]), p < 0.05. Additionally, it showed signifi-
as well as ICU length of stay (0.38 [CI 0.34 – 0.41]), cant correlation with hospital length of stay (0.33 [CI
p<0.05. Moreover, it showed appropriate convergent 0.29 – 0.36]), p<0.05 and ICU length of stay (0.27
validity with number of orders (0.51 [CI 0.48 – 0.54]), [CI 0.24 – 0.31]), p<0.05. pDMS showed appropri-
p<0.05 and number of medications (0.65 [CI 0.63 – ate convergent validity with number of orders (0.47
0.68]), p<0.05. Further, it showed divergent validity [CI 0.44 – 0.50]), p<0.05, and number of medications
with patient sex (0.02 [CI –0.02 – 0.06]), p = 0.40. (0.55 [CI 0.52 – 0.58]), p<0.05. Further, it showed
But divergent validity was not found with patient age divergent validity with patient sex (–0.01 [CI 0.05 –
(0.12 [CI 0.08 – 0.16]), p<0.05 as well as with weight 0.03]), p = 0.8. Divergent validity was not found with
(0.08 [CI 0.04 – 0.12]), p<0.05. Complete results are patient age (0.16 [CI 0.12 – 0.20]), p<0.05 as well as
summarized in Table 2. with weight (0.11 [CI 0.07 – 0.15]), p<0.05. Finally, the

Table 1. Summary of demographic and outcome Table 3. Relationship of pharmacy metrics using
characteristics regression analysis
Characteristic 2,405 Encounters Outcome Score Coefficient Std p-value
Estimate Error
Demographics
Age, Years 6.9 (6.3) Mortality MRC-ICU 0.23 0.03 <0.005
Sex (male) 1308 (54.4%) pDMS 0.19 0.03 <0.005

Outcomes Hospital MRC-ICU 38.44 2.12 <0.005

Mortality 47 (1.95%) Length of
Length of stay (hrs.) 215.1 (411.2) stay
ICU length of stay (hrs.) 94.7 (142.4) pDMS 44.47 2.68 <0.005
Number of medication 68.5 (115.8) ICU MRC-ICU 14.40 0.72 <0.005
orders length of
stay
Number of medications 28.7 (25.5)
pDMS 13.25 0.93 <0.005
Metrics For binary outcome (Mortality) coefficient estimate corresponds to
MRC-ICU at 24 hours 3.0 (3.8) logistic regression
pDMS at 24 hours 1.9 (3.0) For continuous outcomes coefficient estimate corresponds to linear
regression
Data are presented as mean (standard deviation) except Sex and
Mortality which are expressed as n (percent)
ICU = intensive care unit; MRC-ICU – medication regimen complexity
intensive care unit; PDMS = pediatric daily monitoring system

Table 2. Validation of pharmacy metrics using ­correlation analysis

MRC-ICU p-value pDMS p-value

Mortality 0.22 (0.18 – 0.26) <0.05 0.17 (0.13 – 0.21) <0.05

ICU Length of stay 0.38 (0.34 – 0.41) <0.05 0.27 (0.24 – 0.31) <0.05

Hospital length of stay 0.36 (0.32 – 0.39) <0.05 0.33 (0.29 – 0.36) <0.05

Number of medication orders 0.51 (0.48 – 0.54) <0.05 0.47 (0.44 – 0.50) <0.05

Number of medications 0.65 (0.63 – 0.68) <0.05 0.55 (0.52 – 0.58) <0.05

Age 0.12 (0.08 – 0.16) <0.05 0.16 (0.12 – 0.20) <0.05

Weight 0.08 (0.04 – 0.12) <0.05 0.11 (0.07 – 0.15) <0.05

Sex 0.02 (0.02 – 0.06) 0.40 –0.01 (–0.05 – 0.03) 0.80

MRC-ICU — — 0.72 (0.70 – 0.73) <0.05

pDMS 0.72 (0.70 – 0.73) <0.05 — —

All data are described as r (Pearson correlation coefficient), 95% CI

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Pharmacist Metrics in the Pediatric Intensive Care Unit Kandaswamy, S et al

Table 4. Comparison chart of pediatric scoring tools

Previous National children’s hospital CAMEO II scoring tool17 PRISM III scoring tool18
Scoring tool pediatric specific pharmacy
scoring tool16

Description Provides pediatric patient Nurse based scoring tool Evaluates the pediatric risk of
prioritization for pharmacist to quantify the acuity of mortality based on lab values
review reducing the need for pediatric ICU nursing utilized from the first and second
chart searching for staffing models 12 hours of ICU stay

Comments Does not specifically target Not used to prioritize Not used to prioritize
ICU patients, does not pharmacist intervention pharmacist intervention
include patient outcomes, based on medication based on medication
focused on quantifying time complexity complexity
spent on each patient by
clinical pharmacist

two measures pDMS and MRC-ICU demonstrated advanced clinical pharmacist expertise and time for
high correlation (0.72 [CI 0.70 – 0.73]), p<0.05. appropriate assessment. For example, the pDMS
Following logistic regression controlling for age, sex captures how monitoring a patient on vancomycin or
and weight every 1-point increase in MRC-ICU was an aminoglycoside has multiple clinical aspects that
associated with 1.26 increased in odds of mortality the pharmacist must consider, including dose, renal
(1.26 [CI 1.19 – 1.32]), p<0.05. Similarly, based on linear function, other nephrotoxins, etc. Similar to the MRC-
regression every 1-point increase in MRC-ICU was as- ICU, the pDMS accounts for these considerations with
sociated with 38.44 hours of additional hospital length a weighted point system. For example, one point is
of stay (38.44 [CI 34.28 – 42.60]), p<0.05 as well as awarded for any patient on vancomycin, one point
14.40 hours of additional ICU length of stay (14.40 [CI for any patient with a Creatinine Clearance (CrCl)
13.00 – 15.80]), p <0.05. < 30 mL/min/m2 and on vancomycin, and two points
Following logistic regression controlling for age, sex for any patient receiving vancomycin pulse dosing.
and weight every 1-point increase in pDMS was as- If the patient meets the criteria, points are awarded and
sociated with 1.22 increased in odds of mortality (1.22 added together for a total acuity score. The higher the
[CI 1.15 – 1.29]), p<0.05. Every 1-point increase in pDMS score, the more monitoring and time the pharmacist will
was associated with 44.47 hours of additional hospital need to dedicate to that patient during their review. The
length of stay (44.47 [CI 39.21 – 49.73]), p<0.05 as well higher score also informs the pharmacist on the order
as 14.40 hours of additional ICU length of stay (13.25 in which they should review patients getting to the
[CI 11.43 – 15.08]), p<0.05. Complete results can be most critical first. The patient with CrCl <30 mL/min/m2
found summarized in Table 3. and on vancomycin (2 points) has the potential to
require additional monitoring and is more complex
Discussion compared to a patient on vancomycin with normal renal
In the first evaluation of the MRC-ICU Scoring Tool function (1 point).
in the pediatric ICU population, this measurement of Unlike other previous scoring tools, the modified
medication regimen complexity demonstrated appro- MRC-ICU and pDMS tool focuses on the ICU patient
priate clinical validity. Similar to the original validation population. Providing validation for the use of this tool
studies of the MRC-ICU in the adult population, MRC- in pediatric population may allow for more tailored
ICU demonstrated convergent validity, as evidenced use of the pediatric clinical pharmacist within the ICU.
by significant correlation with number of medications Table 4 describes popular pediatric scoring tools. The
and number of medication orders, while showing current pediatric ICU tools are used for staffing or
divergent validity by not relating to patient sex. From risk stratification, but no tool focuses on pharmacist
a clinical perspective, MRC-ICU showed significant intervention equating to outcomes in the pediatric ICU
correlation to patient-centered outcomes, including setting. Pharmacist prioritization tools are available for
mortality and length of stay. Most interestingly, it also the general pediatric ward but are not ICU specific and
showed a relationship with an existing institutional are only based on the amount of time a pharmacist
metric intended to capture patient priority from the spends with a patient.
perspective of pharmacist workload. This study is limited by its single-center, retro-
Both the pDMS and MRC-ICU had similar goals in spective design which precludes robust evaluation
mind, with the concept of quantifying, in a reproduc- of external validity as well as causative inferences.
ible manner, the types of medications that require The present study did not include any adjustments

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Kandaswamy, S et al Pharmacist Metrics in the Pediatric Intensive Care Unit

for severity of illness scores, which may confound Copyright. Pediatric Pharmacy Association. All rights reserved.
interpretations of medication regimen complexity. For permissions, email: membership@pediatricpharmacy.org
Unlike the MRC scoring tool, there was no divergent
Supplemental Material. DOI: 10.5863/1551-6776-28.8.728.ST1
validity for the patients age and weight. Developmental
DOI: 10.5863/1551-6776-28.8.728.ST2
changes seen with aging in pediatric patients’ effects DOI: 10.5863/1551-6776-28.8.728.ST3
both the pharmacokinetics and pharmacodynamics of
drugs, therefore age, not just weight must be consid- References
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