Acute Pulmonary Embolism (APE) is characterized by a notable incidence of misdiagnosis and heightened short-term mortality, rendering it the third most lethal cardiovascular ailment following acute myocardial infarction and stroke [1]. Patients with APE typically manifest a spectrum of symptoms, ranging from asymptomatic presentations to manifestations of shock or abrupt fatality. Notably, individuals initially presenting with mild symptoms may undergo rapid progression to severe disease, thereby contributing to a heightened mortality rate among APE patients. A large-scale observational study reported 30-day and 1-year mortality rates of 9.4% and 24.1%, respectively, among APE patients [2]. Therefore, timely and precise risk stratification assumes paramount importance in facilitating prognostic insights and guiding judicious treatment decision-making in the context of APE.

Currently, the foremost clinical instrument for predicting 30-day mortality in patients with APE is the pulmonary embolism severity index (PESI), a validated tool substantiated by multiple studies. PESI incorporates various parameters, including age, gender, co-morbidities (chronic lung disease, chronic heart failure, tumors), vital signs (pulse rate, respiratory rate, body temperature, mental status), etc. However, its intricate scoring system and the complexity of variable weights hinder its acceptance among emergency medical personnel [3]. Consequently, simplified versions such as the simplified PESI (sPESI) have been developed to predict the mortality risk [4]. However, these simplified versions do not fully integrate post-hospitalization testing and treatment-related data, which could potentially enhance accuracy in evaluating disease severity and forecasting prognosis.

The primary aim of the present study is to identify independent risk factors for 30-day mortality in patients with APE; subsequently, a nomogram prediction model will be developed and rigorously validated based on the elucidated risk factors.