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Deep Brain Stimulation (DBS) surgeries are not new, although they were only granted approval in the U.S. by the U.S. Food and Drug Administration (FDA) in 2002 for advanced Parkinson’s Disease (PD). In 2016, DBS surgery was approved for earlier stages of PD. This does not mean that DBS surgery, generally considered minimally invasive, does not come without commensurate risks. The Mayo Clinic identifies DBS as a serious and potential risky procedure, whereby those eligible must carefully weigh pros and cons. The aim of this paper is to provide a general overview of deep brain stimulation surgery and to present the findings of available informational resources on 14 hospital and medical center web sites that were reviewed, pertaining to surgical procedures and policies: pre-operative to post-operative. The article focuses on critiquing available educational DBS materials and their adequacy in addressing potential risks of DBS surgery. The findings indicate that hospital informational resources on the DBS surgical technique reaffirm each other’s educational materials and that they positively inform patient decision-making. These factors can be linked to better post-operative recovery. However, the materials provided by the hospitals overemphasize the positive aspects of DBS with relatively little detail about potential side effects. This article also outlines the potential short-term and long-term side effects of DBS surgery as identified by the DBS educational literature found on the hospital web sites reviewed.
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Modeling recovery curves with application to prostatectomy
Summary In many clinical settings, a patient outcome takes the form of a scalar time series with a recovery curve shape, which is characterized by a sharp drop due to a disruptive event (e.g., surgery) and subsequent monotonic smooth rise towards an asymptotic level not exceeding the pre-event value. We propose a Bayesian model that predicts recovery curves based on information available before the disruptive event. A recovery curve of interest is the quantified sexual function of prostate cancer patients after prostatectomy surgery. We illustrate the utility of our model as a pre-treatment medical decision aid, producing personalized predictions that are both interpretable and accurate. We uncover covariate relationships that agree with and supplement that in existing medical literature.
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- Award ID(s):
- 1737673
- PAR ID:
- 10213969
- Date Published:
- Journal Name:
- Biostatistics
- Volume:
- 20
- Issue:
- 4
- ISSN:
- 1465-4644
- Page Range / eLocation ID:
- 549 to 564
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/biostatistics/kxy002
