The Prognostic Value of a Naples Score in Determining in-Hospital Mortality in Patients with Acute Ischemic Stroke Undergoing Endovascular Treatment

• Published in: Journal of clinical medicine Vol. 13; no. 21; p. 6434
• Main Authors: Uysal, Onur Kadir, Ozdogru, Derya, Yildirim, Abdullah, Ozturk, Ilker, Tras, Guluzar, Arlier, Zulfikar
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-11-2024; MDPI
• Subjects: Analysis; Blood; Blood pressure; Cardiovascular disease; Care and treatment; Casualties; Coronary vessels; Hemoglobin; Hospital patients; Hospitals; Hypertension; Inflammation; Ischemia; Lymphocytes; Medical examination; Mortality; Patients; Physiology, Pathological; Prognosis; Risk factors; Stroke; Stroke (Disease); Tomography; Variables; Turkey
• ISSN: 2077-0383; 2077-0383
• DOI: 10.3390/jcm13216434

Background/Objectives: The Naples prognostic score (NPS), reflecting inflammation and nutritional status, has prognostic value, especially in cancer. This study evaluated its ability to predict in-hospital mortality in acute ischemic stroke (AIS) patients undergoing endovascular treatment (EVT). Methods: We retrospectively studied 244 patients with AIS who were admitted between April 2020 and December 2023. Patients were included if they presented within 6 h of symptom onset with evidence of intracranial proximal arterial occlusion. The EVT was performed using aspiration catheters, stent retrievers, or both. The NPS was calculated based on the neutrophil–lymphocyte ratio, lymphocyte–monocyte ratio, and albumin and total cholesterol levels. Results: We found a significant association between higher NPS scores and in-hospital mortality. Patients with a high NPS (3 or 4) had a mortality rate of 41.6% compared to 21.0% in the low-NPS group (0, 1, or 2). The full model incorporating NPS showed superior predictive ability for in-hospital mortality compared with the baseline model (areas under the curve 0.881 vs. 0.808). A receiver-operating characteristic analysis at a cutoff of >2.5 for the NPS showed a sensitivity of 86.6% and specificity of 41.9%. This study demonstrated that incorporating the NPS into the predictive model improved the accuracy and calibration for predicting in-hospital mortality. A decision curve analysis showed the net benefit of using the full model incorporating NPS over the baseline model, emphasizing its potential clinical application in prognostication. Conclusions: NPS is a reliable predictor of in-hospital mortality in AIS patients undergoing EVT. Incorporating NPS into clinical practice could help to identify high-risk patients and improve outcomes through tailored interventions.