Construction and verification of a nomogram model for postoperative pulmonary embolism in patients with spontaneous cerebral hemorrhage

Abstract

Objective To investigate the risk factors for postoperative pulmonary embolism in patients with spontaneous cerebral hemorrhage, and construct and verify the nomogram model. Methods A retrospective cohort study was conducted on 558 patients admitted in the First Affiliated Hospital of Chongqing Medical University and the Three Gorges Hospital Affiliated to Chongqing University. And 393 of them who hospitalized from January 2015 to January 2021 were assigned into a modeling group, and the other 165 patients from February 2021 to January 2023 into a validation group. Univariate and multivariate stepwise logistic regression analyses were used to screen out the risk factors associated with pulmonary embolism after spontaneous cerebral hemorrhage surgery. Then a nomogram model was build based on these factors and verified. Results Based on age, blood loss, Glasgow coma scale (GCS) score, surgical treatments, levels of fibrin degradation products, D-dimer and hemoglobin, plasma osmolality, and deep vein thrombosis, a risk model of pulmonary embolism was built. Receiver operating characteristic (ROC) curve analysis showed the model had good discriminability for the presence of pulmonary embolism, and the area under the curve (AUC) value was 0.908. Hosmer-Lemeshow goodness-fit test indicated that the model had a good fit to the verification set (Chi-square=14.805, df=8, P=0.063), the correction curve was close to the ideal curve, and the prediction probability of the model was close to the actual occurrence probability, suggesting the model having good accuracy. Decision curve analysis revealed that the established nomogram model can get benefits under a large range of threshold probabilities. Conclusion We develop a prediction model for postoperative pulmonary embolism in patients with spontaneous cerebral hemorrhage after surgical treatment, which shows good prediction performance in both the training and validation groups, and can be used for accurate, prompt and quick identification for the occurrence of pulmonary embolism in clinical practice.

Keywords

• spontaneous cerebral hemorrhage
• pulmonary embolism
• prediction model
• risk factors
• nomograph