MRC-ICU

The Medication Regimen Complexity - Intensive Care Unit (MRC-ICU) Scoring Tool is designed to bring optimal pharmacotherapeutic care to every critically ill patient. Its purpose is to objectively and reproducibly quantitate the complexity of an ICU patient’s medication regimen. As a metric, it has been shown to correlate to pharmacist workload and may improve patient-centered outcome prediction modeling. Read more about its applications below.

Vision: data-driven, optimal pharmacotherapeutic care for every critically ill patient

Mission: to serve as an electronic medical health record metric that connects patient-level data to individualized pharmacist resource predictions to improve patient outcomes and reduce healthcare costs through optimization of the pharmacist-to-patient ratio

MRC-ICU NCBI: https://www.ncbi.nlm.nih.gov/myncbi/andrea.sikora%20newsome.1/bibliography/public/

AHRQ Profile: https://digital.ahrq.gov/ahrq-funded-projects/artificial-intelligence-based-health-it-tools-optimize-critical-care

AHRQ Profile: https://digital.ahrq.gov/ahrq-funded-projects/machine-learning-validation-medication-regimen-complexity-critical-care

AHRQ Research Story: https://digital.ahrq.gov/program-overview/research-stories/use-artificial-intelligence-and-machine-learning-improve-care

The MRC-ICU Scoring Tool is a 39-line item tool consisting of medications & devices that quantitatively describes medication regimen complexity by scoring and weighting each individual line item.

An Excel file with the scoring tool is available for free download.

Each individual medication or medication class listed is associated with a weighted score (i.e., 1, 2, or 3 points). If a multiplier is list, this indicates that for every medication that meets those criteria, the score is multiplied (e.g., if fentanyl and midazolam are prescribed, this would be 2 points x each medication for a total of 4 points). The MRC-ICU is the sum of all the points assigned to the medications.

MRC-ICU Peer-Reviewed Publications:

  1. Gwynn ME. Poisson MO. Waller JL. Sikora Newsome A. Development and validation of a medication regimen complexity scoring tool for critically ill patients. Am J Health Syst Pharm. 2019 May 17;76(Supplement_2):S34-S40. doi: 10.1093/ajhp/zxy054. PMID: 31067298.

  2. Sikora Newsome A. Anderson D. Gwynn ME. Waller JL. Characterization of changes in medication complexity using a modified scoring tool. Am J Health Syst Pharm. 2019 Nov 13;76(Supplement_4):S92-S95. doi: 10.1093/ajhp/zxz213. PMID: 31586396.

  3. Sikora Newsome A. Smith SE. Olney WJ. Jones TW. Multicenter validation of a novel medication-regimen complexity scoring tool. Am J Health Syst Pharm. 2020 Mar 5;77(6):474-478. doi: 10.1093/ajhp/zxz330. PMID: 34086844.

  4. Al-Mamun MA. Brothers T. Sikora Newsome A. Development of Machine Learning Models to Validate a Medication Regimen Complexity Scoring Tool for Critically Ill Patients. Ann Pharmacother. 2021 Apr;55(4):421-429. doi: 10.1177/1060028020959042.Epub 2020 Sep 15. PMID: 32929977.

  5. Olney WJ. Chase AM. Hannah SA. Smith SE. Sikora Newsome A. Medication Regimen Complexity Score as an Indicator of Fluid Balance in Critically Ill Patients. J Pharm Pract. 2021 Mar 9;897190021999792. doi: 10.1177/0897190021999792.Online ahead of print. PMID: 33685269.

  6. Sikora Newsome A. Smith SE. Olney WJ. Jones TW. Forehand CC. Jun AH. Coppiano L. Medication regimen complexity is associated with pharmacist interventions and drug-drug interactions: A use of the novel MRC-ICU scoring tool. Journal of the American College of Clinical Pharmacy. 2019 Jun 12; 3(1): 47-56. https://doi.org/10.1002/jac5.1146.

  7. Sikora A, Ayyala D, Rech M, Blackwell B, Campbell J, Caylor M, Condeni M, Depriest A, Dzierba A, Flannery A, Hamilton L, Heavner M, Horng M, Lam J, Liang E, Montero J, Murphy D, Plewa-Rusiecki A, Sacco A, Sacha G, Shah P, Smith M, Smith Z, Radosevich J, Vilella A, The MRC-ICU Investigator Team. Impact of pharmacists to improve patient care in the critically ill: A large multicenter analysis using meaningful metrics with the MRC-ICU. Crit Care Med 2022 Jun 10. doi: 10.1097/CCM.0000000000005585. PMID: 35678204.

  8. Webb A. Rowe S. Sikora Newsome A. A descriptive report of the rapid implementation of automated MRC-ICU calculations in the EMR of an academic medical center. Am J Health Syst Pharm. 2022 Feb 21;zxac059. DOI: 10.1093/ajhp/zxac059. PMID. 35187576.

  9. Smith SE. Shelley R. Sikora Newsome A. Medication regimen complexity vs. patient acuity for predicting critical care pharmacist interventions. Am J Health Syst Pharm. 2021 Dec 3;zxab460. DOI: 10.1093/ajhp/zxab460. PMID: 34864850.

  10. Azimi H, Johnson L, Loudermilk C, Chase A, Forehand CC, Sikora A. Medication regimen complexity (MRC-ICU) for in-hospital mortality prediction in COVID-19 patients. Hosp Pharm. 2023 May 3:00185787231169460. doi: 10.1177/00185787231169460. PMCID: PMC10158809.

  11. 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. Hospital Pharmacy. 2023;0(0). doi:10.1177/00185787231170386.

  12. Sikora A, Rafiei A, Rad MG, Keats K, Smith SE, Devlin JW, Murphy DJ, Murray B, Kamaleswaran R; MRC-ICU Investigator Team. Pharmacophenotype identification of intensive care unit medications using unsupervised cluster analysis of the ICURx common data model. Crit Care. 2023 May 2;27(1):167. doi: 10.1186/s13054-023-04437-2. PMID: 37131200; PMCID: PMC10155304.

  13. Kandaswamy S, Dawson TE, Moore WH, Howell K, Beus K, 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(8): 728–734. PMID: 38094672.

  14. Sikora A, Devlin JW, Yu M†, Zhang T†, Chen X, Smith SE, Murray B, Buckley MS, Rowe S, Murphy DJ. Evaluation of medication regimen complexity as a predictor for mortality. Sci Rep. 2023 Jul 4;13(1):10784. doi: 10.1038/s41598-023-37908-1. PMID: 37402869; PMCID: PMC10319715.

  15. 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 Sep 20;13(1):15562. doi: 10.1038/s41598-023-42657-2. PMID: 37730817; PMCID: PMC10511715.

  16. Keats K, Sikora A, Heavner MS, Chen X, Smith SE. Optimizing Pharmacist Team-Integration for ICU Patient Management: Rationale, Study Design, and Methods for a Multicentered Exploration of Pharmacist-to-Patient Ratio. Crit Care Explor. 2023 Aug 25;5(9):e0956. doi: 10.1097/CCE.0000000000000956. PMID: 37644971; PMCID: PMC10461940.

  17. Sikora A, Zhang T, Murphy DJ, et al. Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU. Sci Rep. 2023 Nov 10;13(1):19654. doi: 10.1038/s41598-023-467353. PMID: 37949982.

  18. Webb A, 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. https://doi.org/10.1093/jamiaopen/ooad101.

  19. Rafiei A, Rad MG, Sikora A, Kamaleswaran R. 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. Computers in Biology and Medicine. Accepted. 2023. https://doi.org/10.1016/j.compbiomed.2023.107749.

    Pre-prints:

    1.     Liu Z†, Hu M†, Zhao B†, Zhao L†, Zhang T†, Dai H, Chen X, Shen Y, Li S, Murray B, Liu T, Sikora A. PharmacyGPT: The AI Pharmacist. https://arxiv.org/abs/2307.10432 2023. (under revisions).

    2.     Sikora A, Zhao B†, Kong Y†, Murray B, Shen Y. Machine learning based prediction of prolonged duration of mechanical ventilation incorporating medication data. medRxiv. 2023:2023.09.18.23295724. (under review).

    3.     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 (ICU) medication features in artificial intelligence (AI) applications. https://www.medrxiv.org/content/10.1101/2023.09.18.23295727v1. (JAMIA Open, accepted pending revisions 2023).

Summary Table of Studies:

https://www.a-sikora.com/s/2024-Summary-Table-of-MRC-ICU-Studies.pdf