A Data-Driven Medication Regimen Complexity Score for Critically Ill Patients: MRC-ICU 2.0.

چکیده اصلی

BACKGROUND: The 2019 medication regimen complexity-intensive care unit (MRC-ICU) score is associated with patient outcomes, ICU complications, and critical care pharmacist workload. This score was developed using heuristic component selection and validated in a single-center cohort of 130 ICU patients. We sought to apply data-driven reweighting methodology in a large, multicenter cohort of ICU adults to improve the predictive capabilities of MRC-ICU. METHODS: This was a retrospective, observational cohort study of adults admitted to an ICU between 2015 and 2023 at two academic health systems. Machine learning-based methods, including Principal Component Analysis and Random Forest, were used to create an updated MRC-ICU score optimized to predict three outcomes: hospital mortality, ICU fluid overload (FO) occurrence, and invasive mechanical ventilation (IMV) use. MRC-ICU 2.1 used average mortality, FO, and IMV use; MRC-ICU 2.2 used average mortality and FO and adjusted for prolonged IMV use. Data from one center were used for training and testing, and data from the other for validation. The predictive abilities of MRC-ICU 2.1 and 2.2 for each outcome were compared to MRC-ICU 1.0 and to severity of illness scores (i.e., Acute Physiology and Chronic Health Evaluation [APACHE] II and Sequential Organ Failure Assessment [SOFA]). RESULTS: A total of 19,117 patients across training, testing, and validation datasets were included. MRC-ICU 2.0 scores outperformed MRC-ICU 1.0 for predicting most outcomes, with improvements in Area Under the Receiver Operating Characteristic (AUROC) ranging from +0.03 to +0.08 across datasets. MRC-ICU 2.1 and 2.2 did not consistently outperform APACHE II and SOFA in predicting mortality. The addition of MRC-ICU 2.0 scores to models including APACHE II or SOFA resulted in statistically significant improvements in discrimination in several settings (DeLong p < 0.05), with AUROC increases generally ranging from approximately +0.01 to +0.13 depending on outcome and dataset. CONCLUSIONS: The updated MRC-ICU 2.0 score (MRC-ICU 2.1 and 2.2) demonstrated consistently improved discrimination compared with the original MRC-ICU 1.0 across outcomes and datasets. The performance of MRC-ICU 2.0 (MRC-ICU 2.1 and 2.2) was generally comparable to established severity-of-illness scores (SOFA and APACHE II), although it did not consistently outperform these measures. When incorporated into combined models, MRC-ICU 2.0 provided additional predictive value, indicating that it captures information complementary to traditional severity-of-illness scores. Overall, these findings suggest that MRC-ICU 2.0 represents an improved and clinically interpretable measure of medication regimen complexity that is useful as a complementary predictor.

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