Rationale and Objectives
This prospective study aimed to evaluate the diagnostic performance of dual-input computed tomography perfusion technique (DI-CTP) in identifying the bronchial-pulmonary artery fistula in patients tuberculosis with massive hemoptysis.
Material and Methods
Twenty patients with tuberculosis with massive hemoptysis were enrolled from January 2015 to December 2015. The association between DI-CTP parameters and the diagnostic outcomes of digital subtraction angiography was assessed. Diagnostic efficacy of DI-CTP was evaluated by receiver operating curve (ROC) analyses using the diagnostic outcomes of digital subtraction angiography, which is the gold standard for identifying bronchial-pulmonary artery fistula.
Results
Compared to lung segments with normal blood flow ( n = 304), those with bronchial-pulmonary artery fistula ( n = 164) had a reduced pulmonary flow value, perfusion index (PI) value, and an elevated bronchial artery (BF) value in the DI-CTP scan, which was further confirmed by multivariate logistic regression. ROC analysis showed that PI and bronchial artery has an excellent diagnostic performance (both area under the ROC curve > 0.9, P < .001) and high sensitivity and specificity (from 0.79 to 0.95 at the optimal cutoff). PI has the best diagnostic performance, with an overall diagnostic accuracy of 0.91.
Conclusions
DI-CTP scan possesses the diagnostic value for detecting bronchial-pulmonary artery fistula in patients with tuberculosis with massive hemoptysis, providing an alternative diagnostic method.
Introduction
Massive hemoptysis is a life-threatening condition which is most frequently defined as the expectoration of an amount of blood >300 mL within a period of 24 hours . Tuberculosis is one of the most common causes of hemoptysis , and bronchial-pulmonary artery fistula is an important pathologic mechanism for massive hemoptysis in patients with tuberculosis .
Conventional diagnostic methods for hemoptysis include chest radiography, bronchoscopy, multidetector computed tomography (CT) angiography (MDCTA), and digital subtraction angiography (DSA) . Chest radiography and bronchoscopy are considered to be the primary methods for diagnosis of hemoptysis and for identifying the source of bleeding, but both the sensitivities are relatively low . MDCTA can provide a comprehensive assessment of pulmonary vascular morphology and the potential causes of hemoptysis before bronchial arterial embolization (BAE) . However, the sensitivity of MDCTA for identifying bronchial-pulmonary artery fistula is relatively low because it is incapable of dynamically showing the abnormal blood flow in the bronchial and pulmonary artery and vein fistula, as well as simultaneously displaying the fine vascular structures in the pulmonary artery, pulmonary vein, and bronchial artery . Currently, DSA is the gold standard for identifying bronchial-pulmonary artery fistula . Nevertheless, DSA is limited by its invasiveness and high cost. Therefore, it is required to develop an alternative or auxiliary diagnostic method for locating the bronchial-pulmonary artery fistula in patients with massive hemoptysis.
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Materials and Methods
Participants
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DI-CTP Imaging Technique
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Digital Subtraction Angiography (DSA)
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Statistical Analysis
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Results
Patients’ Demographic and Clinical Characteristics
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TABLE 1
Patients’ Demographic and Clinical Characteristics
No Gender Age (y) Pathological Type Length of Disease Course (mo) Daily Hemoptysis Amount (mL) 1 Male 39 Chronic fibrous cavitary pulmonary tuberculosis 60 30 2 Male 55 Chronic fibrous cavitary pulmonary tuberculosis 12 100 3 Male 49 Infiltrative pulmonary tuberculosis 120 600 4 Male 66 Infiltrative pulmonary tuberculosis 3 100 5 Male 29 Infiltrative pulmonary tuberculosis 48 500 6 Male 38 Chronic fibrous cavitary pulmonary tuberculosis 120 450 7 Male 53 Infiltrative pulmonary tuberculosis 120 200 8 Male 45 Tuberculous pleurisy 24 300 9 Male 49 Infiltrative pulmonary tuberculosis 12 500 10 Male 57 Infiltrative pulmonary tuberculosis 108 600 11 Male 46 Infiltrative pulmonary tuberculosis 48 400 12 Male 57 Infiltrative pulmonary tuberculosis 24 400 13 Male 26 Infiltrative pulmonary tuberculosis 7 400 14 Female 52 Chronic fibrous cavitary pulmonary tuberculosis 240 600 15 Female 17 Acute miliary tuberculosis 4 100 16 Female 53 Infiltrative pulmonary tuberculosis 24 150 17 Male 43 Infiltrative pulmonary tuberculosis 24 400 18 Male 61 Infiltrative pulmonary tuberculosis 144 150 19 Male 39 Chronic fibrous cavitary pulmonary tuberculosis 60 30 20 Male 33 Infiltrative pulmonary tuberculosis 24 50
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Association Between DI-CTP Parameters and DSA Diagnosis
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TABLE 2
Comparisons of the DI-CTP Parameters Between Normal and Bronchial-Pulmonary Artery Fistula Groups
DI-CTP Parameters Normal Group \* ( n = 229) Bronchial-Pulmonary Artery Fistula Group \* ( n = 131) Total_P_ PF 153.82 ± 63.39 129.29 ± 57.55 144.90 ± 62.38 <.001 BF 5.74 ± 12.34 30.69 ± 32.44 14.82 ± 24.94 <.001 PI 97.18 ± 4.68 84.52 ± 8.35 92.57 ± 8.74 <.001
BF, bronchial flow; DI-CTP, dual-input computed tomography perfusion; PF, pulmonary flow; PI, perfusion index.
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TABLE 3
The Results of Univariate and Multivariate Logistic Regression
DI-CTP Parameters OR (95% CI)P Univariate PF 0.99 (0.99–1.00) <.001 BF 1.11 (1.08–1.14) <.001 PI 0.69 (0.64–0.74) <.001 Multivariate \* PF 0.99 (0.99–1.00) <.001 BF 1.14 (1.10–1.17) <.001 PI 0.66 (0.61–0.72) <.001
BF, bronchial flow; CI, confidence interval; DI-CTP, dual-input computed tomography perfusion; OR, odds ratio; PF, pulmonary flow; PI, perfusion index.
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ROC Analyses for Diagnostic Efficacy of DI-CTP Parameters
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TABLE 4
The Results of ROC Analyses and Likelihoods
DI-CTP Parameters AUC (95% CI)P Optimal Cutoff \* Sensitivity Specificity Youden Index PPV NPV PLR NLR Accuracy PF 0.616 (0.556–0.676) <.001 87.5 0.27 0.87 0.14 0.54 0.67 2.04 0.84 0.65 BF 0.901 (0.870–0.932) <.001 9.1 0.82 0.83 0.65 0.73 0.89 4.84 0.21 0.83 PI 0.955 (0.934–0.976) <.001 92.75 0.88 0.93 0.81 0.88 0.93 12.56 0.13 0.91
AUC, area under the ROC curve; BF, bronchial flow; DI-CTP, dual-input computed tomography perfusion; NLR, negative likelihood ratio; NPV, negative-predicted value; PF, pulmonary flow; PI, perfusion index; PLR, positive likelihood ratio; PPV, positive-predicted value.
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Discussion
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Conclusion
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