Duplex-scanning of the deep venous drainage in the evaluation of blood flow velocity of the cerebral vascular system in infants
- PMID: 2646588
- DOI: 10.1007/BF02387891
Duplex-scanning of the deep venous drainage in the evaluation of blood flow velocity of the cerebral vascular system in infants
Abstract
Doppler investigations of the anterior cerebral (pericallosal) or internal carotid arteries have two major limitations: (1) flow velocity in one cerebral artery does not necessarily represent flow velocity in other cerebral arteries; (2) flow velocity spectra are influenced significantly by transducer-transferred pressure. Furthermore, there are substantial pitfalls in the interpretation of arterial flow velocity data. We therefore examined Doppler-shift waveforms of the deep venous drainage in addition to arterial flow velocity recordings in 186 infants, including a reference group of 49 infants without significant disease. In this group, mean values of peak velocities were 13.2 cm/s, 5.6 cm/s, 5.9 cm/s and 5.6 cm/s for the straight sinus, the vein of Galen, the right and the left basilar veins respectively. Venous flow velocities were increasing significantly with age, but not or only marginally with the weight at the time of examination. Three main flow velocity patterns were observed: (1) bandlike (straight sinus 25%, vein of Galen 59% and basilar veins 63%); (2) sinusoid and synchronous with arterial pulse (46%, 27% and 29% respectively; (3) intermittent (3%, 0% and 0% respectively). In contrast to the reference group, the intermittent pattern was common in severely ill premature infants and was associated with increased intrathoracic pressure and adverse outcome, indicating reduced venous volume flow irrespective of the arterial flow velocity pattern. Although difficult to perform, transcranial and transfontanellar Duplex-scanning of the deep cerebral veins might help to avoid pitfalls of arterial velocity interpretation, reveal further pathophysiological aspects of intensive care and be an important factor for the prediction of outcome.
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