Literature DB >> 6722431

Parathyroid venous sampling and ultrasonography in primary hyperparathyroidism due to multigland disease.

A R Manhire, P N Anderson, E Milroy.   

Abstract

The results of pre-operative venous sampling and ultrasonography in 20 patients with primary hyperparathyroidism, who were shown histologically to have multigland disease, were reviewed. Ultrasonography (15 patients) demonstrated abnormal parathyroid tissue in 46% (seven patients), but correctly predicted only 33% of the involvement or non-involvement of individual glands. The main deficiency was the high number of false negatives. Parathyroid venous sampling (19 patients) showed elevated levels of parathyroid hormone in 18 (94.7%), but overall the sites of normal and abnormal glands were predicted in 56 of 76 sites (73.6%). The investigation was misleading in one patient (5.2%), in that no abnormal glands were predicted. The sampling maps of the above 19 patients were randomly mixed with those of 100 consecutive patients, whose disease was due to a single adenoma. All but one of the multigland disease cases were identified correctly but six of the single adenoma patients were predicted as having two glands involved, and were therefore thought to be multigland. The usefulness of, and difficulties in, diagnosing multigland disease in primary hyperparathyroidism are discussed.

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Year:  1984        PMID: 6722431     DOI: 10.1259/0007-1285-57-677-375

Source DB:  PubMed          Journal:  Br J Radiol        ISSN: 0007-1285            Impact factor:   3.039


  3 in total

1.  Prospective comparison of radionuclide, ultrasound, and computed tomography in the preoperative localization of parathyroid glands.

Authors:  H L Carmalt; D J Gillett; J Chu; R A Evans; S Kos
Journal:  World J Surg       Date:  1988-12       Impact factor: 3.352

2.  Management of primary hyperparathyroidism caused by multiple gland disease.

Authors:  P E Goretzki; C Dotzenrath; H D Roeher
Journal:  World J Surg       Date:  1991 Nov-Dec       Impact factor: 3.352

3.  Early prediction of circulatory failure in the intensive care unit using machine learning.

Authors:  Stephanie L Hyland; Martin Faltys; Matthias Hüser; Xinrui Lyu; Thomas Gumbsch; Cristóbal Esteban; Christian Bock; Max Horn; Michael Moor; Bastian Rieck; Marc Zimmermann; Dean Bodenham; Karsten Borgwardt; Gunnar Rätsch; Tobias M Merz
Journal:  Nat Med       Date:  2020-03-09       Impact factor: 53.440

  3 in total

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