Background Hypocalcemia has been shown to be involved in the adverse outcomes of acute pulmonary embolism (APE). We aimed to determine the incremental value of adding hypocalcemia, defined as serum calcium level ≤ 2.12 mmol/L, on top of the European Society of Cardiology (ESC) prognostic algorithm, for the prediction of in-hospital mortality in APE patients, which in turn could lead to the optimization of APE management. Methods This study was conducted at West China Hospital of Sichuan University from January 2016 to December 2019. Patients with APE were retrospectively analyzed and divided into 2 groups based on serum calcium levels. Associations between hypocalcemia and adverse outcomes were assessed by Cox analysis. The accuracy of risk stratification for in-hospital mortality was assessed with the addition of serum calcium to the current ESC prognostic algorithm. Results Among 803 patients diagnosed with APE, 338 (42.1%) patients had serum calcium levels ≤ 2.12 mmol/L. Hypocalcemia was significantly associated with higher in-hospital and 2-year all-cause mortality compared to the control group. The addition of serum calcium to ESC risk stratification enhanced net reclassification improvement. Low-risk group with serum calcium level > 2.12 mmol/L had a 0% mortality rate, improving the negative predictive value up to 100%, while high-risk group with serum calcium level ≤ 2.12 mmol/L indicated a higher mortality of 25%. Conclusion Our study identified serum calcium as a novel predictor of mortality in patients with APE. In the future, serum calcium may be added to the commonly used ESC prognostic algorithm for better risk stratification of patients suffering from APE.

Background Hypocalcemia has been shown to be involved in the adverse outcomes of acute pulmonary embolism (APE). We aimed to determine the incremental value of adding hypocalcemia, defined as serum calcium level ≤ 2.12 mmol/L, on top of the European Society of Cardiology (ESC) prognostic algorithm, for the prediction of in-hospital mortality in APE patients, which in turn could lead to the optimization of APE management. Methods This study was conducted at West China Hospital of Sichuan University from January 2016 to December 2019. Patients with APE were retrospectively analyzed and divided into 2 groups based on serum calcium levels. Associations between hypocalcemia and adverse outcomes were assessed by Cox analysis. The accuracy of risk stratification for in-hospital mortality was assessed with the addition of serum calcium to the current ESC prognostic algorithm. Results Among 803 patients diagnosed with APE, 338 (42.1%) patients had serum calcium levels ≤ 2.12 mmol/L. Hypocalcemia was significantly associated with higher in-hospital and 2-year all-cause mortality compared to the control group. A serum calcium level ≤ 2.12 mmol/L in patients with ESC-defined low risk identified a group with a mortality of 2.5%, improving the negative predictive value up to 100%, while in high-risk patients, it indicated a group of high early mortality of 25%. Conclusion Our study identified serum calcium as a novel predictor of mortality in patients with APE. In the future, serum calcium may be added to the commonly used ESC prognostic algorithm for better risk stratification of patients suffering from APE.

Introduction We aim to explore the risk factors for in-hospital mortality and to derive a prognostic model for patients with APE in China. Materials and methods Inpatients with APE were enrolled from West China Hospital between January 2016 and December 2019. Logistic regression analyses were used to explore risk factors for in-hospital mortality and develop a prognostic model. Results A total of 813 subjects with APE were included in this study, of whom 542 were in the training set and 271 were in the test set. Multivariable regression analyses indicated that age, male, heart rate, systolic blood pressure, elevated NT-proBNP or troponin T, malignancy, chronic renal insufficiency, and respiratory failure were independent risk factors for in-hospital mortality. For the training set, the area under the curve (AUC) of the ROC curve was 0.899, with a sensitivity and specificity of 89.7% and 77.7%, respectively. The model had higher prediction accuracy than the PESI and sPESI. Conclusions The prediction model has proven excellent discrimination and calibration, which may be a useful tool to help physicians make decisions regarding the best treatment strategy.