Saddle Pulmonary Embolism: Laboratory and Computed Tomographic Pulmonary Angiographic Findings to Predict Short-term Mortality
Saddle Pulmonary Embolism: Laboratory and Computed Tomographic Pulmonary Angiographic Findings to Predict Short-term Mortality
复制标题
鞍形肺栓塞:实验室和计算机断层扫描肺血管造影结果可预测短期死亡率
DOI:
10.1016/j.hlc.2016.02.019
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发表时间:
2017-02-01
影响因子:
2.6
通讯作者:
Yang, Yuanhua
中科院分区:
文献类型:
--
作者:
Liu, Min;Miao, Ran;Yang, Yuanhua
Background Saddle pulmonary embolism (SPE) is rare type of acute pulmonary embolism and there is debate about its treatment and prognosis. Our aim is to assess laboratory and computed tomographic pulmonary angiographic (CTPA) findings to predict short-term mortality in patients with SPE.Methods This was a five-centre, retrospective study. The clinical information, laboratory and CTPA findings of 88 consecutive patients with SPE were collected. One-month mortality after diagnosis of SPE was the primary end-point. The correlation of laboratory and CTPA findings with one-month mortality was analysed with area under curve (AUC) of receiver operating characteristic (ROC) curves and logistic regression analysis.Results Eighteen patients with SPE died within one month. Receiver operating characteristic curves revealed that the cutoff values for the right and left atrial diameter ratio, the right ventricular area and left ventricular area ratio (RVa/LVa ratio), Mastora score, septal angle, N-terminal pro-brain natriuretic peptide and cardiac troponin I (cTnI) for detecting early mortality were 2.15, 2.13, 69%, 578, 3036 pg/mL and 0.18 ng/mL, respectively. Using logistic regression analysis of laboratory and CTPA findings with regard to one-month mortality of SPE, RVa/LVa ratio and cTnI were shown to be independently associated with early death. A combination of cTnI and RVa/LVa ratio revealed an increase in the AUC value, but the difference did not reach significance compared with RVa/LVa or cTnI, alone (P>0.05).Conclusion In patients with SPE, both the RVa/LVa ratio on CTPA and cTnI appear valuable for the prediction of short-term mortality.