Relationship Between Nutritional Indexes and Clinical Outcomes in Stroke Patients Undergoing Mechanical Thrombectomy
Bibliographic record
Abstract
Background/objectives: Nutritional status is increasingly acknowledged as a pivotal determinant of clinical course and recovery in patients with acute ischemic stroke (AIS). Malnutrition can compromise immune competence, delay neurological recovery, and exacerbate adverse outcomes, particularly in those undergoing intensive interventions such as mechanical thrombectomy (MT). To objectively assess nutritional status, indices such as the Prognostic Nutritional Index (PNI) and the Controlling Nutritional Status (CONUT) score have been utilized in various clinical populations. These indices incorporate routinely available laboratory parameters, reflecting both nutritional and inflammatory states. This study explores whether PNI and CONUT scores are associated with 90-day clinical outcomes in AIS patients treated with MT, aiming to evaluate their potential utility as prognostic biomarkers in acute stroke care. Methods: A total of 404 patients with AIS who underwent MT between 2023 and 2024 were retrospectively evaluated. Demographic, clinical, and laboratory data were collected, and nutritional status was assessed using PNI and CONUT scores. Clinical outcomes were stratified as favorable (modified Rankin Scale [mRS] 0–2) or unfavorable (mRS 3–6) at 90 days post-stroke. Results: Among the 404 patients included in the study, 50.5% had favorable and 49.5% had unfavorable clinical outcomes. Patients with favorable outcomes were younger (71 vs. 78 years, p = 0.001), had lower National Institutes of Health Stroke Scale (NIHSS) scores, and higher Alberta Stroke Program Early CT Scores (ASPECTS) (p = 0.001). The puncture-to-recanalization time was significantly longer in the unfavorable outcome group (47.5 min vs. 30.0 min, p = 0.003). Laboratory findings revealed higher glucose levels (p = 0.029), and lower serum albumin (p = 0.003) and lymphocyte levels (p = 0.001) in the unfavorable outcome group. Among nutritional indices, the CONUT score was significantly higher in the unfavorable outcome group (p = 0.001), whereas the PNI score was higher in the favorable outcome group (p = 0.001). ROC analysis showed that the CONUT score had significant prognostic performance (AUC = 0.721, p < 0.001), while the PNI had poor discriminatory power (AUC = 0.274, p < 0.001). Multivariate logistic regression identified age, baseline NIHSS, ASPECT score, and CONUT score as independent predictors of clinical outcome (p < 0.05). Conclusions: Among the two nutritional indices evaluated, the CONUT score demonstrated significant prognostic value in predicting 90-day clinical outcomes after MT. In contrast, the PNI showed limited discriminatory power, highlighting the superiority of CONUT as a reliable biomarker in acute stroke care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".