The Clinical and Hematological Markers of Pulmonary Embolism: Multicenter Retrospective Study in Mongolia

<jats:p>Background/Objectives: Systemic inflammation-derived indices, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII), have been proposed as convenient and cost-effective predictive markers of mortality and complications in patients with pulmonary embolism (PE). We aimed to evaluate the prognostic performance of hematological inflammatory indices for in-hospital adverse outcomes among Mongolian patients diagnosed with PE. Methods: This multicenter retrospective study enrolled patients admitted to three tertiary-level hospitals in Ulaanbaatar, Mongolia, with a confirmed diagnosis of PE between September 2019 and January 2025. Data were collected retrospectively from electronic medical records. We calculated the NLR, PLR, and SII using standard formulas. All patients were stratified according to the sPESI. Moreover, we determined the prognostic performance of hematological indices for in-hospital adverse events. Results: We analyzed information on 232 patients with a confirmed diagnosis of PE. According to sPESI scoring, 69.8% (162) were in the high-risk group, with significant differences in comorbidity and age over 60 years (p &lt; 0.001). The most common symptoms were dyspnea, chest pain, cough, leg pain, and hemoptysis. The median NLR was 2.48 (1.66–5.08) in the low-risk group and 4.21 (2.04–11.35) in the high-risk group (p = 0.001). The median SII level was significantly higher in the high-risk group than in the low-risk group (p = 0.037). However, there was no significant difference in PLR level between the two study groups. ROC analysis of hematological indices for predicting in-hospital adverse outcomes was limited, with poor and non-significant discriminatory performance. Conclusions: In cases of acute pulmonary embolism, elevated inflammatory indices, NLR and SII, were associated with higher sPESI risk stratification. However, their probability to predict in-hospital adverse events was limited by ROC analysis.</jats:p>