Modification, validation and comparison of Naples prognostic score to determine in-hospital mortality in ST-segment elevation myocardial infarction

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

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.