Exploring the role of in-patient magnetic resonance imaging use among admitted ischemic stroke patients in improving patient outcomes and reducing healthcare resource utilization
Bibliographic record
Abstract
Purpose: Despite the diagnostic and etiological significance of in-patient MRI in ischemic stroke (IS), its utilization is considered resource-intensive, expensive, and thus limiting feasibility and relevance. This study investigated the utilization of in-patient MRI for IS patients and its impact on patient and healthcare resource utilization outcomes. Methods: This retrospective registry-based study analyzed 1,956 IS patients admitted to Halifax's QEII Health Centre between 2015 and 2019. Firstly, temporal trends of MRI and other neuroimaging utilization were evaluated. Secondly, we categorized the cohort into two groups (MRI vs. No MRI; in addition to a non-contrast CT) and investigated adjusted differences in patient outcomes at admission, discharge, and post-discharge using logistic regression. Additionally, we analyzed healthcare resource utilization using Poisson log-linear regression. Furthermore, patient outcomes significantly associated with MRI use underwent subgroup analysis for stroke severity (mild stroke including transient ischemic attack vs. moderate and severe stroke) and any acute stage treatment (thrombolytic or thrombectomy or both vs. no treatment) subgroups, while using an age and sex-adjusted logistic regression model. Results: MRI was used in 40.5% patients; non-contrast CT in 99.3%, CT angiogram in 61.8%, and CT perfusion in 50.3%. Higher MRI utilization was associated with male sex, younger age, mild stroke, wake-up stroke, and no thrombolytic or thrombectomy treatment. MRI use was independently associated with lower in-hospital mortality (adjusted OR, 0.23; 95% CI, 0.15-0.36), lower symptomatic neurological status changes (0.64; 0.43-0.94), higher home discharge (1.32; 1.07-1.63), good functional outcomes at discharge (mRS score 0-2) (1.38; 1.11-1.72), lower 30-day stroke re-admission rates (0.48; 0.26-0.89), shorter hospital stays (regression coefficient, 0.92; 95% CI, 0.90-0.94), and reduced direct costs of hospitalization (0.90; 0.89-0.91). Subgroup analysis revealed significantly positive association of MRI use with most patient outcomes in moderate and severe strokes subgroup and non-acutely treated subgroup. Conversely, outcomes in mild strokes (including TIAs) subgroup and acute treatment subgroup were comparable regardless of MRI use. Conclusion: A substantial proportion of admitted IS patients underwent MRI, and MRI use was associated with improved patient outcomes and reduced healthcare resource utilization. Considering the multifactorial nature of IS patient outcomes, further randomized controlled trials are suggested to investigate the role of increased MRI utilization in optimizing in-patient IS management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".