Dexmedetomidine (Precedex, Abbott Laboratories, Abbott Park, IL) is an alpha 2 adrenergic agonist that possesses a high ratio of specificity for the alpha 2 versus the alpha 1 receptor. It is currently approved for the provision of sedation during mechanical ventilation in adults. Given previous experience with clonidine for the treatment of substance withdrawal and the preliminary anecdotal experience with dexmedetomidine, it appears that dexmedetomidine may be a useful agent for treatment of substance withdrawal in the intensive care setting. The authors present their experience with the use of dexmedetomidine to control withdrawal behavior in 3 patients following cardiothoracic surgery. Previous reports regarding the use of dexmedetomidine to treat withdrawal and its potential application in this clinical arena are reviewed.
These preliminary data suggest that dexmedetomidine may be an effective agent for sedation in spontaneously breathing patients, in the treatment of drug withdrawal, and in the treatment of two common postanesthesia problems.
The authors retrospectively review the clinical course and outcome of 6 pediatric patients, ranging in age from 2 to 13 years, who were treated with TPA for complex empyema. Efficacy was assessed by evaluating pleural fluid drainage for 6 hours prior to and subsequent to each dose of TPA, as well as by resolution of fever and length of hospital stay. The average volume drained for 6 hours before infusion of TPA was 22.5 mL +/- 18.4 mL, and the average volume 6 hours after TPA therapy was 141.7 mL +/- 28.3 mL, P <.0001. After initiation of TPA therapy, 5 out of 6 patients became afebrile within 48 hours. The median length of stay after initiation of TPA therapy was 6 days, with a range from 4 days to 12 days. A discussion of other current therapies for empyema, along with a comparison of these therapies to TPA regarding the costs of therapies and risk-benefit ratios, is also included.
Williams syndrome, initially described by Williams, Barratt-Boyes, and Lowe in 1961, consists of characteristic dysmorphic features, congenital heart disease, and distinctive behavioral and emotional traits. In addition to acquired and congenital heart disease, manifestations in the renal, endocrine, musculoskeletal, and central nervous system may have implications during the perioperative period. Congenital and acquired heart disease can be a significant issue as sudden death, related to abnormalities of the coronary arteries, has been reported perioperatively in these patients. The authors present a 7-month-old infant, previously diagnosed with Williams syndrome, who required anesthetic care for repair of subaortic and supravalvular aortic stenosis. The potential perioperative implications of Williams syndrome are discussed.
Background
Rare diseases (RD) are a diverse collection of more than 7–10,000 different disorders, most of which affect a small number of people per disease. Because of their rarity and fragmentation of patients across thousands of different disorders, the medical needs of RD patients are not well recognized or quantified in healthcare systems (HCS).
Methodology
We performed a pilot IDeaS study, where we attempted to quantify the number of RD patients and the direct medical costs of 14 representative RD within 4 different HCS databases and performed a preliminary analysis of the diagnostic journey for selected RD patients.
Results
The overall findings were notable for: (1) RD patients are difficult to quantify in HCS using ICD coding search criteria, which likely results in under-counting and under-estimation of their true impact to HCS; (2) per patient direct medical costs of RD are high, estimated to be around three–fivefold higher than age-matched controls; and (3) preliminary evidence shows that diagnostic journeys are likely prolonged in many patients, and may result in progressive, irreversible, and costly complications of their disease
Conclusions
The results of this small pilot suggest that RD have high medical burdens to patients and HCS, and collectively represent a major impact to the public health. Machine-learning strategies applied to HCS databases and medical records using sentinel disease and patient characteristics may hold promise for faster and more accurate diagnosis for many RD patients and should be explored to help address the high unmet medical needs of RD patients.
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