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Development and external validation of a nomogram for predicting short-term prognosis in patients with acute pulmonary embolism

医学 列线图 肺栓塞 心脏病学 内科学 期限(时间) 重症监护医学 物理 量子力学
作者
Chaowei Ding,Chao Liu,Ziping Zhang,Chunyan Cheng,Guangsheng Pei,Zhi‐Cheng Jing,Jiayong Qiu
出处
期刊:International Journal of Cardiology [Elsevier]
卷期号:407: 132065-132065
标识
DOI:10.1016/j.ijcard.2024.132065
摘要

Background Accurate assessment and timely intervention play a crucial role in ameliorating poor short-term prognosis of acute pulmonary embolism (APE) patients. The currently employed scoring models exhibit a degree of complexity, and some models may not comprehensively incorporate relevant indicators, thereby imposing limitations on the evaluative efficacy. Our study aimed to construct and externally validate a nomogram that predicts 30-day all-cause mortality risk in APE patients. Methods Clinical data from APE patients in Intensive Care-IV database was included as a training cohort. Additionally, we utilized our hospital's APE database as an external validation cohort. The nomogram was developed, and its predictive ability was evaluated using receiver operating characteristic (ROC) curves, calibration plots and decision curve analysis. Results A collective of 1332 patients and 336 patients were respectively enrolled as the training cohort and the validation cohort in this study. Five variables including age, malignancy, oxygen saturation, blood glucose, and the use of vasopressor, were identified based on the results of the multivariate Cox regression model. The ROC value for the nomogram in the training cohort yielded 0.765, whereas in the validation group, it reached 0.907. Notably, these values surpassed the corresponding ROC values for the Pulmonary Embolism Severity Index, which were 0.713 in the training cohort and 0.754 in the validation cohort. Conclusions The nomogram including five indicators had a good performance in predicting short-term prognosis in patients with APE, which was easier to apply and provided better recommendations for clinical decision-making.
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