Objective To explore the risk factors of hospital death of acute pulmonary embolism, and construct the prediction model.

Methods A total of 197 patients with acute pulmonary embolism were selected from the Emergency Department. 37 patients who died in hospital and other 160 patients were divided into the death group and survival group, respectively. The general data and related clinical indicators of two groups were collected, and analysed using the univariate analysis and logistic multivariate regression analysis methods to construct the prediction model.

Results The results of the single factor analysis showed that the didfferences of the systolic blood pressure, chest tightness, syncope, electrolyte disturbance, hypertension, diabetes, pH, partial pressure of oxygen, partial pressure of carbon dioxide, N-terminal B-type natriuretic peptide (NT-proBNP), lactic acid, hypersensitive troponin, right atrial diameter, left ventricular ejection fraction, anticoagulation and simplified pulmonary embolism score (sPESI) risk classification ≥1 were statistically significant between two groups (P ＜ 0.05 ~ P ＜ 0.01). The results of binary logistic regression analysis showed that the electrolyte disturbance, hypertension history, hypoxia partial pressure, high level of NT-proBNP, high level of hypersensitive troponin, low left ventricular ejection fraction and sPESI risk layer ≥ 1 were the independent risk factors of hospital death of acute pulmonary embolism (P ＜ 0.05 ~ P ＜ 0.01). The predictive models showed that the Prob = 1/ (e^–Y), Y = 29.945 – 1.507* (electrolyte disturbance) – 0.089* (hypertension) + 3.324* (oxygen partial pressure) – 1.530* (NT-proBNP) – 0.086* (hypersensitive troponin) + 0.594* (left ventricular ejection fraction) – 0.862* (sP ESI risk stratification ≥ 1).

Conclusions The electrolyte disturbance, hypertensive history, oxygen partial pressure, NT-proBNP, hypersensitive troponin, left ventricular ejection fraction and sPESI risk stratification were the independent influencing factors of in-hospital death of acute pulmonary embolism. The predictive model based on this method has good efficacy, which can help to predict the risk of hospital death of acute pulmonary embolism patients, and help medical staff to focus on screening and clinical intervention for high-risk patients.