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. 2021 Jun 28;78(8):972–981. doi: 10.1001/jamaneurol.2021.1910

Home Health Management of Parkinson Disease Deep Brain Stimulation

A Randomized Clinical Trial

Gordon Duffley 1,2, Barbara J Lutz 3, Aniko Szabo 4, Adrienne Wright 5, Christopher W Hess 5, Adolfo Ramirez-Zamora 5, Pamela Zeilman 5, Shannon Chiu 5, Kelly D Foote 5, Michael S Okun 5, Christopher R Butson 1,2,5,6,✉
PMCID: PMC8240011  PMID: 34180949

Key Points

Question

Can patients receiving deep brain stimulation for the treatment of Parkinson disease be postoperatively managed from the home by a home health nurse with no prior deep brain stimulation experience?

Findings

In a 1:1 randomized clinical trial at the University of Florida, we found that patients could be managed with significantly fewer trips to the clinic while receiving similar clinical benefit as those receiving the existing standard of care.

Meaning

In this study, a home health–based care deep brain stimulation model was effective, safe, feasible and able to reduce patients' travel burden.

This randomized clinical trial of individuals with Parkinson disease compares the efficacy of deep brain stimulation managed postoperatively by a home health nurse with the existing standard of in-clinic postoperative management.

Abstract

Importance

The travel required to receive deep brain stimulation (DBS) programming causes substantial burden on patients and limits who can access DBS therapy.

Objective

To evaluate the efficacy of home health DBS postoperative management in an effort to reduce travel burden and improve access.

Design, Settings, and Participants

This open-label randomized clinical trial was conducted at University of Florida Health from November 2017 to April 2020. Eligible participants had a diagnosis of Parkinson disease (PD) and were scheduled to receive DBS independently of the study. Consenting participants were randomized 1:1 to receive either standard of care or home health postoperative DBS management for 6 months after surgery. Primary caregivers, usually spouses, were also enrolled to assess caregiver strain.

Interventions

The home health postoperative management was conducted by a home health nurse who chose DBS settings with the aid of the iPad-based Mobile Application for PD DBS system. Prior to the study, the home health nurse had no experience providing DBS care.

Main Outcomes and Measures

The primary outcome was the number of times each patient traveled to the movement disorders clinic during the study period. Secondary outcomes included changes from baseline on the Unified Parkinson’s Disease Rating Scale part III.

Results

Approximately 75 patients per year were scheduled for DBS. Of the patients who met inclusion criteria over the entire study duration, 45 either declined or were excluded for various reasons. Of the 44 patients enrolled, 19 of 21 randomized patients receiving the standard of care (mean [SD] age, 64.1 [10.0] years; 11 men) and 23 of 23 randomized patients receiving home health who underwent a minimum of 1 postoperative management visit (mean [SD] age, 65.0 [10.9] years; 13 men) were included in analysis. The primary outcome revealed that patients randomized to home health had significantly fewer clinic visits than the patients in the standard of care arm (mean [SD], 0.4 [0.8] visits vs 4.8 [0.4] visits; P < .001). We found no significant differences between the groups in the secondary outcomes measuring the efficacy of DBS. No adverse events occurred in association with the study procedure or devices.

Conclusions and Relevance

This study provides evidence supporting the safety and feasibility of postoperative home health DBS management.

Trial Registration

ClinicalTrials.gov Identifier: NCT02474459

Introduction

Although deep brain stimulation (DBS) has been shown to be an effective treatment for refractory tremor and motor fluctuations in select patients with Parkinson disease (PD),1,2,3 the barriers to access to the therapy are substantial. Postoperative management, specifically the selection of the stimulation parameters, plays a large role in the efficacy of DBS.4,5 Changes in DBS settings and medications are the only modifiable variables after lead implantation. Because of the importance and perceived complexity, the parameter selection process, which is commonly referred to as DBS programming, is most commonly performed by highly experienced experts employed by urban-located academic medical centers.6 The programming of DBS typically requires 4 to 5 clinic visits during the first 6 months after surgery and often regular adjustments for the remainder of a patient’s life.7 As a result, patients, especially those who do not live close to a major medical center offering DBS, experience substantial out-of-pocket costs and travel-associated burdens to access DBS programming. Travel demands also create a substantial burden for patients’ caregivers, because PD-associated mobility deficits and other comorbidities often prevent independent travel. This is particularly evident as mobility and balance worsen with disease progression and severity.

Previously, when conducting the randomized National Institutes of Health COMPARE study (NCT00360009) of subthalamic nucleus vs globus pallidus internus as DBS targets, we addressed improvements in motor functioning and the potential worsening of cognitive symptoms.3 As a result of that study, we concluded that by tailoring DBS to the individual patient,8 the therapy can provide a substantial benefit to a wide range of patients with PD. Despite the potential benefit, many patients who are good candidates for DBS still are unable to access the therapy.

To address this gap in DBS access, we designed an intervention to occur in the home, directed by a home health nurse without prior DBS experience. The current study was designed to test a care model that could improve DBS access in 2 ways: first, by reducing the need for the patients to travel to the clinic for DBS postoperative management, and second, by expanding the pool of clinicians who can provide postoperative care to patients. Using home health care to deliver DBS postoperative management is a pragmatic solution, since more than 40% of patients with PD who receive Medicare already use some form of home health care.9 Combined with recent research that demonstrates the efficacy of telemedicine-based preoperative screening,10 effective in-home DBS postoperative management could create a future where the only travel required to receive DBS is for the surgery itself.

To create a home health management model that could be feasibly implemented in a community setting, we designed the experimental arm of the study for home health nurses who lacked the years of experience thought to be needed to manage patients with DBS to treat PD. To address this issue, we developed the Mobile Application for PD DBS (MAP DBS), a mobile decision support system to aid in DBS programming. MAP DBS contains patient-specific computational models that provide interactive visual feedback on the association between DBS settings and activation of anatomical structures near the active electrode(s). The design of this system was based on a retrospective pilot study,11 which found that the platform provided substantial benefit to expert DBS programmers. We followed this with a prospective study (Gordon Duffley, PhD, written communication, October 9, 2020), which demonstrated that MAP DBS improved the efficacy and efficiency of some aspects of DBS programming performed at expert DBS centers. This first prospective study also allowed us to develop workflows in a controlled clinical environment and provided the experience required to deploy the platform in a home health setting. In this study, we hypothesized that the burden of accessing DBS postoperative care could be substantially reduced by enabling home health nurses to perform DBS postoperative management, including programming, within the home setting.

Methods

We conducted a 2-arm clinical trial comparing outcomes between participants who received standard of care (SOC) with those who received home health postoperative DBS management. The study design was a parallel, 1:1 randomized, open-label trial.

Participants

Participants provided written consent to trained study personnel from University of Florida (UF) for a ClinicalTrials.gov–registered (NCT02474459) and UF Institutional Review Board–approved protocol (Supplement 1) at the UF Health Norman Fixel Institute for Neurological Diseases in Gainesville. The inclusion criteria were patients between 30 and 80 years old with idiopathic PD who were planning to receive DBS at UF as a part of standard of care for PD or who already had a DBS device implanted by UF but had not yet received DBS programming. Participants resided within 250 miles of the UF clinic in Gainesville, Florida. Participants had to be speakers of fluent English who had not previously undergone DBS programming conducted by a different institution. Those who had unilateral and bilateral procedures (DBS leads implanted 1 month apart) were eligible to participate, but participants were screened to ensure that they did not expect to receive an additional DBS surgery during the 6-month study period. All were implanted with a Medtronic DBS system (ACTIVA PC or SC). The primary caregiver of each participant, usually a spouse, was offered the opportunity to enroll into the study for the collection of caregiver strain data.

Randomization and Interventions

Participants were randomized to either SOC or home health. Randomization was performed using stratified blocks of size 4 to control for concurrent changes in DBS management and factors potentially contributing to the likelihood of patients traveling to the UF movement disorders clinic for care. These factors were the number of DBS leads implanted (unilateral or bilateral) and whether participants lived greater or less than 100 miles from the UF clinic. The blocked randomization scheme was generated by study staff (G.D.) at the University of Utah, Salt Lake City. No attempt was made to conceal the randomization from the participant or study personnel.

Participants randomized to SOC were given a schedule of 5 clinical DBS postoperative management visits during the first 6 months following surgery. In this arm, DBS programming and medication management for patients were performed by experts at the UF clinic (P.Z. and M.S.O.). Participants could undergo extra, unscheduled programming visits if necessary. The SOC programming consisted of a traditional approach. Participants typically saw the same programmer for all visits, although scheduling conflicts caused occasional exceptions. Programmers did not use patient imaging or any decision support system, including MAP DBS, while programming the devices of participants in the SOC arm.

Participants assigned to the home health arm were given a schedule of 5 DBS postoperative management visits, with the 1-month and 3-month visits occurring in the patients’ homes and the 2-month, 4-month, and 5-month visits occurring via telephone. Telephone visits consisted of the home health nurse (A.W.) calling the participants and recommending DBS setting changes that the participants could make using their patient programming device. The participants assigned to home care were informed that if they were not satisfied with their care, they could receive additional home health visits or appointments with experts at the UF movement disorders clinic. Just as with the SOC arm, expert clinicians performing the in-clinic programming for these participants did not have access to the MAP DBS system. All home health and phone visits were performed by a registered nurse who had no prior experience with PD DBS management. The nurse shadowed expert DBS clinicians at the UF clinic for 4 weeks prior to the start of the study to learn the operation of the programmer tablet and the participant’s remote programmer. Home health postoperative management visits consisted of collecting vital signs, DBS programming with the aid of MAP DBS, medication management, and collection of clinical rating scales. MAP DBS is a mobile decision support system that contains patient-specific lead locations and target nuclei segmentations visualized in both 3 dimensions and overlaid on slices of patient imaging. MAP DBS provides an interface that allows the programming clinician to select a range of DBS stimulation settings and visualize the overlap of the computationally simulated volume of tissue activation with the patient’s target nuclei. The platform enabled the home health nurse to identify stimulation settings that activated tissue within and near the target nuclei, which was used to guide the selection of stimulation parameters. The patient-specific models were downloaded by the home health nurse prior to the patient’s first DBS programming session and used prior to and during each DBS programming session. Additional details of the MAP DBS system are available in other articles,11 as well as the eMethods and eFigure 1 in Supplement 2.

All participants, regardless of study arm assignment, underwent a postoperative, pre–initial DBS programming baseline and a 6-month visit at the UF clinic for outcome collection. No DBS programming or medication management was performed at either of these study visits.

Outcomes

The purpose of the study was to determine whether patients with PD could be safely and effectively managed using home health DBS postoperative care. The primary outcome was the number of times each participant traveled to the UF movement disorders clinic for care between their baseline and 6-month study visits. This metric is subsequently referred to as clinic visits. Secondary outcomes were the change from baseline to 6-month outcomes in PD motor symptoms, as measured by the Unified Parkinson’s Disease Rating Scale part III (UPDRS III) in both the on-medication and off-medication state; overall PD symptom severity, as measured by the total UPDRS (sum of parts I, II, III, and IV) in the off-medication state; PD-associated quality of life, as measured by the 39-question Parkinson’s Disease Questionnaire (PDQ-39); PD medication doses, as measured by levodopa equivalent daily doses (LEDDs; in milligrams); and caregiver strain, as measured by the Multidimensional Caregiver Strain Index (MCSI). The baseline for UPDRS III was the last assessment performed prior to DBS surgery. The baselines for all other rating scales and LEDDs were recorded at the initial postoperative baseline study visit. Baseline rating scales were captured in the off-stimulation state, and all subsequent assessments were done in the on-stimulation state. The MCSI was completed by the primary caregivers who consented to the study. All LEDDs were calculated using the Parkinson Measurement Toolbox (https://www.parkinsonsmeasurement.org). For all rating scales, a negative change from baseline indicates improvement in symptom severity. At the baseline and the 6-month outcomes visits, off-medication UPDRS III assessments were additionally performed via video review by an assessor (S.C.) who was blinded to the study arm of the participant.

We performed 3 unplanned analyses: a comparison of the number of in-person (clinic or home health) and total DBS postoperative management visits (clinic, home health, and telephone) between study arms; a correlation of participant’s initial DBS postoperative management visit order with the secondary outcomes, to see if the home health nurse improved at DBS programming as the study progressed; and a subanalysis of change in rating scales and LEDDs comparing participants who underwent at least 1 clinic DBS postoperative management visit with participants who were exclusively managed in a home health capacity. We performed this subanalysis to test whether participants who were exclusively managed by home health received effective DBS therapy. Adverse events were captured for all participants while they were active study participants. The severity of adverse events, as well as if they were associated with study procedures, was determined by the study’s clinical principal investigator (M.S.O.). The study was overseen by a data and safety monitoring board.

Statistical Methods and Sample Size Calculations

A Mann-Whitney U test was used to evaluate differences in the number of clinic, in-person (home or in clinic), and total DBS postoperative management visits between participants randomized to SOC vs home health. Prior to statistical analysis, participants who discontinued the intervention had the locations of their missed visits linearly extrapolated from their attended visits. Changes in clinical rating scales and LEDD values between the baseline and the 6-month outcomes visits were compared using analysis of covariance, correcting for baseline values as a covariate. Missing rating scale and LEDD data were handled using listwise deletion. To evaluate if the home health nurse’s DBS programming improved over the duration of the study, we computed the Spearman partial rank coefficient, correcting for baseline, between the order of initial DBS postoperative management visits and change in clinical rating scales from baseline for the participants in the home health arm. The Pearson correlation coefficient was used to quantify the agreement between the UPDRS III scores as recorded by the treating clinician and those assessed in a blinded review. Rigidity questions and questions in which the blinded rater (S.C.) felt they could not make an accurate assessment because of video quality were not included in this calculation. Outcomes are reported as mean (SD) values.

We selected a sample size of 21 participants per group (42 total) to have 80% power to detect a difference in number of clinic DBS postoperative management visits at a 2-sided, 5% significance level, if a participant randomly selected from the home health arm had fewer clinic visits than a patient randomly selected from the SOC arm with 75% probability.

Participants were recruited for the study between November 1, 2017, and March 20, 2020, and we finished outcome collection on April 24, 2020. Data analysis was completed with Python version 3.7.2 (Python Software Foundation) and Pingouin version 0.3.1 (Raphael Vallat).

Results

Study Participants

Forty-six patients consented to participate in the study, of whom 44 were randomized. Twenty-one patients were assigned to SOC and 23 patients to home health. All 23 patients in the home health arm underwent at least 1 DBS postoperative management visit (mean [SD] age, 65.0 [10.9] years; 13 men), and 18 completed the 6-month outcomes visit. Nineteen participants in the SOC arm underwent at least 1 DBS postoperative management visit (mean [SD] age, 64.1 [10.0] years; 11 men), and 15 of these participants completed the 6-month outcomes visit (Figure 1). Participants assigned to the home health arm had slightly higher PD symptom severity, reflected by a difference in mean (SD) baseline UPDRS III off-medication scores (SOC: 33.5 [9.4]; home health: 40.5 [12.9]). However, mean (SD) levodopa responsiveness levels at baseline were similar between the 2 study arms (SOC: 31.0% [24.0%]; home health: 37.0% [21.0%]) (Table 1). Because the study site (UF) as a standard frequently uses a staged DBS approach, which is safer, especially in older participants, most study participants in both arms had unilateral lead implants during the study period (SOC: n = 11; home health: n = 12).

Figure 1. CONSORT Patient Flow Diagram.

Figure 1.

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DBS indicates deep brain stimulation.

Table 1. Demographics and Baseline Characteristics.

Characteristic Standard of care Home healtha
Age
Participants, No. 19 23
Mean (SD), y 64.1 (10.0) 65.0 (10.9)
Sex, No. (%)
Male 11 (58) 13 (57)
Female 8 (42) 10 (43)
Time since Parkinson disease diagnosis
Participants, No. 19 23
Mean (SD), y 11.5 (7.2) 12.0 (5.9)
Race, No. (%)
White 19 (100) 21 (91)
African American 0 2 (9)
Ethnicity, No. (%)
Hispanic or Latino 1 (5) 3 (13)
Not Hispanic or Latino 18 (95) 20 (87)
Deep brain stimulation target, No. (%)
Subthalamic nucleus 3 (16) 5 (22)
Globus pallidus internus 16 (84) 18 (78)
No. of deep brain stimulation leads, No. (%)
Unilateral 11 (58) 12 (52)
Bilateral 8 (42) 11 (48)
Total UPDRS off-medication scoreb
Participants, No. 18 23
Mean (SD) 46.8 (14.0) 60.1 (20.3)
Off-levodopa/on-levodopa responsec
Participants, No. 19 23
Mean (SD), % 31.0 (24.0) 37.0 (21.0)
UPDRS III off-medication scorec
Participants, No. 19 23
Mean (SD) 33.5 (9.4) 40.5 (12.9)
UPDRS III on-medication scorec
Participants, No. 19 23
Mean (SD) 23.2 (10.9) 25.3 (11.9)
PDQ-39 score
Participants, No. 18 23
Mean (SD) 21.2 (22.5) 33.6 (21.2)
Levodopa equivalent daily dose, mg
Participants, No. 11 14
Mean (SD) 1104.0 (579.7) 1174.6 (612.7)
Caregivers enrolled, No. (%) 14 (74) 22 (96)
Multidimensional Caregiver Strain Index score
Participants, No. 12 21
Mean (SD) 12.7 (13.8) 16.1 (12.6)
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Abbreviations: PDQ-39, 39-question Parkinson’s Disease Questionnaire; UPDRS, Unified Parkinson’s Disease Rating Scale; UPDRS III, Unified Parkinson’s Disease Rating Scale part III.

a

Patients in the home health arm had slightly more parkinsonism, as indicated by the higher mean total UPDRS and UPDRS III off-medication scores.

b

The total UPDRS is the sum of parts I, II, III, and IV.

c

Preoperative baseline used.

DBS Postoperative Management Visits

We found that participants in the home health arm traveled to the UF movement disorders clinic for care significantly less frequently than participants in the SOC arm (mean [SD]: SOC, 4.8 [0.4] visits; home health, 0.4 [0.8] visits; P < .001). Of the 23 participants randomized to home health, 3 traveled to the UF clinic for a single visit and 2 patients traveled to the UF clinic 2 times each (Figure 2). Because the patients in the home health arm were partially managed using telephone visits, participants randomized to home health also had fewer in-person DBS postoperative management visits (mean [SD]: SOC, 4.8 [0.4] visits; home health, 3.5 [1.0] visits; P < .001). Overall, we found no significant difference in the total number of DBS postoperative management visits (the sum of in-person and telephone visits) for participants in the SOC arm (4.8 [0.4]) compared with those randomized to home health (5.2 [0.7]; P = .06) (eTable 1 in Supplement 2).

Figure 2. Deep Brain Stimulation (DBS) Postoperative Management Visit Types Grouped by Study Arm.

Figure 2.

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Secondary Outcomes and Safety

We found no significant differences between the study arms in the change between baseline and 6-month mean (SD) outcomes in UPDRS III off-medication scores (SOC: −5.3 [14.3]; home health: −13.4 [12.6]; P = .27), UPDRS III on-medication scores (SOC: −2.7 [12.7]; home health, −0.4 [9.6]; P = .90), total UPDRS off-medication scores (SOC: −0.8 [20.2]; home health: −16.9 [15.7]; P = .08), or PDQ-39 scores (SOC: −3.9 [23.4]; home health: −9.0 [15.5]; P = .97). The agreement between the original and blinded off-medication UPDRS III ratings were good (Pearson r values: baseline, 0.73; 6-month outcomes, 0.56). Additionally, we found no significant difference in the changes in caregiver strain as measured by mean (SD) MCSI scores (SOC: −4.2 [11.2]; home health: −1.3 [6.7]; P = .53) or in medications as measured by mean (SD) LEDDs (SOC: −86.8 [397.0] mg; home health: −70.5 [438.2] mg; P = .91) between the 2 study arms (Table 2; eFigure 2 in Supplement 2).

Table 2. Mean Change From Baseline to 6-Month Outcomes for Rating Scales and Levodopa Equivalent Daily Dose Valuesa.

Rating scale Standard of care or some expert care Home health care P value
Participants, No. Mean (SD) score Participants, No. Mean (SD) score
Standard of care vs home health care
UPDRS III off-medication 14 −5.3 (14.3) 16 −13.4 (12.6) .27
UPDRS III on-medication 15 −2.7 (12.7) 16 −0.4 (9.6) .90
Total UPDRS off-medicationb 13 −0.8 (20.2) 16 −16.9 (15.7) .08
PDQ-39 12 −3.9 (23.4) 15 −9.0 (15.5) .97
Multidimensional Caregiver Strain Index score 5 −4.2 (11.2) 12 −1.3 (6.7) .53
Levodopa equivalent daily dose, mg 17 −86.8 (397.0) 17 −70.5 (438.2) .91
Some expert care vs home health care only
UPDRS III off-medication 17 −5.1 (13.1) 13 −15.6 (12.8) .20
UPDRS III on-medication 18 −1.3 (12.2) 13 −1.8 (9.7) .85
Total UPDRS off-medicationb 16 −1.6 (18.7) 13 −19.5 (15.5) .04
PDQ-39 15 −5.0 (21.1) 12 −8.9 (17.2) .90
Multidimensional Caregiver Strain Index score 7 −3.4 (9.3) 10 −1.3 (7.4) .42
Levodopa equivalent daily dose, mg 20 −26.9 (428.6) 14 −152.5 (389.8) .40
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Abbreviations: PDQ-39, 39-question Parkinson’s Disease Questionnaire; UPDRS, Unified Parkinson’s Disease Rating Scale; UPDRS III, Unified Parkinson’s Disease Rating Scale part III.

a

Negative values imply improvement in parkinsonian symptom severity (UPDRS and PDQ-39 scores), lowered caregiver strain (Multidimensional Caregiver Strain Index scores), or reduced levodopa equivalent daily doses. Grouping was done by both study arm and comparing patients who underwent at least 1 clinic deep brain stimulation postoperative management visit compared with those exclusively managed by home health. The UPDRS scores recorded at baseline were in the off-stimulation state, while 6-month outcomes were recorded in the on-stimulation state.

b

The total UPDRS is the sum of parts I, II, III, and IV.

We repeated the analysis to determine if the 24 participants who traveled to the UF clinic for at least 1 DBS postoperative management visit experienced different outcomes than the 18 who were exclusively managed by home health. Participants who were managed exclusively via home health care experienced a significant greater change in total mean (SD) UPDRS off-medication scores compared with those who received some expert care (some expert care: −1.6 [18.7]; home health only: −19.5 [15.5]; P = .04). Similar to the intent-to-treat analysis, there were no significant differences between the 2 subanalysis groups in mean (SD) UPDRS III off-medication values (some expert care: −5.1 [13.1]; home health only: −15.6 [12.8]; P = .20), UPDRS III on-medication values (some expert care: −1.3 [12.2]; home health only: −1.8 [9.7]; P = .85), or PDQ-39 scores (some expert care: −5.0 [21.1]; home health only: −8.9 [17.2]; P = .90). We found no difference in the changes in mean (SD) MCSI scores (some expert care: −3.4 [9.3]; home health only: −1.3 [7.4]; P = .42) or mean (SD) LEDDs (some expert care: −26.9 [428.6] mg; home health only: −152.5 [389.8] mg; P = .40) between the subanalysis groups (Table 2; eFigure 3 in Supplement 2).

Participants randomized to home health experienced 11 adverse events, 4 of which were classified as serious. Participants randomized to the SOC arm experienced 2 adverse events; neither was classified as serious. No adverse events in either arm were found to be associated with study procedures. None resulted in withdrawal of study participants or modifications to study protocols (Table 3).

Table 3. All Recorded Adverse Eventsa.

Serious adverse events Standard of care Home health
Total 0 4
Benign prostatic hypertrophy 0 1
Myocardial infarction 0 1
Wound infection 0 1
Wound dehiscence 0 1
Other adverse events
Total 2 7
Pain 1 1
Headache 1 1
Infection 0 1
Rash 0 1
Hyperthermia 0 1
Vomiting 0 1
Urinary tract infection 0 1
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a

None were determined to be associated with study procedures.

Analysis of Home Health Experience Level

To evaluate the progression in DBS programming ability of the home health nurse over the course of the study, we calculated the correlation between the initial DBS postoperative management visit order of participants in the home health arm and clinical efficacy of their DBS therapy. The analysis did not reveal any significant correlation between the order of initial DBS postoperative management visits, which indicated the progressively increasing level of DBS programming experience for the home health nurse, with changes in UPDRS III off-medication, UPDRS III on-medication, total UPDRS, PDQ-39, or MCSI scores (eTable 2 in Supplement 2).

Discussion

Home Health Management of DBS

The results of this study provide evidence that home health visits and telephone calls can potentially provide an effective and safe alternative to the traditional in-clinic, expert-based, DBS postoperative care model. The overall effectiveness of DBS therapy in the home health arm of the study was no different from the therapy received by the patients in the SOC arm in this study, with UPDRS off-medication values and PDQ-39 scores with patients in the home health arm receiving similar improvement in symptom severity as those in the SOC arm. The lack of difference between the arms occurred despite the patients in the home health arm having undergone fewer in-person DBS postoperative management visits than the participants in the SOC arm. Our data also show that patients who received some expert care received significantly less benefit than participants who received home health–only management. This result was likely associated with the finding that participants in the home health arm who were not responding well to DBS were more likely to ask for expert care than those who were responding well to home health management. We do not interpret this result to mean that home health management is superior to SOC, but rather, we present data to demonstrate that for at least some patients, exclusive home health management is effective. These results suggest that PD DBS postoperative management, including DBS programming using MAP DBS, warrants further investigation in a larger, multicenter trial.

Home health postoperative management is likely suitable for most but not all patients with PD and DBS. As demonstrated by the robust clinical outcome of the patients who were exclusively managed at home, at least a subset of patients can receive effective DBS therapy without ever entering the clinic for postoperative management. However, 5 of the 23 participants (22%) in the home health arm underwent at least 1 clinic visit. The reasons participants in the home health arm underwent clinic visits were a combination of hardware complications, non-PD comorbidities, and lack of response to initial DBS programming. One clinic visit occurred because COVID-19 travel restrictions prevented the home health nurse from traveling to the participant’s home. Although clinic postoperative management may be necessary for some patients, it likely does not need to be as extensive as the current SOC; all participants in the home health arm had most of their DBS postoperative management visits in their homes.

The current DBS postoperative care model needs to be altered, because the time and travel demands associated with multiple clinic visits is burdensome for both patients and caregivers, especially those who live long distances from a DBS center. The primary reason for the repeated visits is DBS programming, which this study demonstrates can be effectively performed by a home health nurse without previous DBS programming experience when provided with our MAP DBS platform. Because of the demands of accessing DBS programming, the therapy is not functionally available to many patients, especially given that the PD population often has difficulty traveling because of severe mobility issues and other comorbidities. Access to DBS is an increasingly important public health problem because of the alarming rate of increase in PD prevalence; it is estimated that approximately 14 million people worldwide will be diagnosed with PD by the year 2040.12 Using home health is a promising direction to address this issue because many patients with PD already receive some form of home health care.9 The current COVID-19 pandemic has also highlighted the need for in-home management options for medical devices such as DBS,13,14,15 which often require in-person contact for management. In recent years, there has been a growing push to create a more home-centric care model for the treatment of chronic neurological conditions such as PD,16,17 and the pandemic has only further highlighted this need.18,19,20,21 Although not directly addressed by this study, the time, burden, and expense of travel contribute to barriers in DBS access, especially in underserved populations.6,22 Patients not only require access to DBS but access specifically during phases of limited disease progression in which DBS has the potential to provide maximal benefit.8,23

This study demonstrates the capacity for patients to be managed in the home. Additionally, the potential for expansion in home health management of DBS remains substantial. The emergence of reliable, kinetic-tracking wearables for monitoring motor symptom profiles could provide remote clinicians with detailed insights into real-time patient status.24 The information from these wearables could be used to inform clinicians when a patient needs care and guide DBS programming.25,26 Wearables are particularly attractive for their ability to monitor fluctuating symptom profiles.27 Emerging technologies will likely facilitate modification of patients’ DBS devices via the internet by the use of remote DBS programming. This approach has been shown to be effective13,15,28; however, safety and security concerns will require further testing.29,30 Remote programming could be paired with a nurse to provide an effective, travel-free solution, which will expand access to even more patients in the future. Combining remote programming with devices designed to measure patient status (ie, wearables) could result in a future where patients with DBS will be managed completely in the home without in-person follow-up visits.

Association With Prior DBS Trials

Higher baseline UPDRS III off-medication scores have been shown to be correlated with larger improvements from DBS.31 To account for this outcome, we tested for differences in changes in rating scales between the study arms, using an analysis of covariance with the baseline as a covariate. Because the home health arm had higher mean baseline scores, it is unsurprising that they trended toward a larger mean improvement than the SOC arm. However, this outcome would not have been possible if the patients did not receive effective DBS programming. After correcting for baseline values, there were no statistically significant differences between the arms in mean improvements in any of the rating scales.

The relatively small sample size likely increased the variability in the SOC group UPDRS III off-medication changes. However, the level of improvement in UPDRS III scores for the patients in the home health arm was consistent with the benefit reported in large randomized clinical trials of DBS conducted at expert centers.2,3,32 The adverse events in our study were similar to those reported in other major DBS trials.2,32 There was a slight trend toward more adverse events for the participants in the home health arm, but none of the reported events were deemed to be associated with the study protocol.

MAP DBS

This study was the second of 2 studies we have done investigating if MAP DBS could disrupt the traditionally expert-based DBS programming care model. Our first study deployed MAP DBS into multiple expert DBS clinics around the US and demonstrated that the platform enabled expert DBS programming clinicians to perform some aspects of programming more efficiently and effectively. Additionally, this first study allowed us to ensure that MAP DBS could be reliably deployed to clinicians performing DBS programming while maintaining the safety net provided by testing the system among experienced DBS programmers. Conducting this study provided us with the experience to safely design and execute the current home health study.

The collective results of the 2 studies demonstrated that the visual feedback on the effects of stimulation provided by MAP DBS positively altered the DBS programming process. Traditionally, DBS programming is performed entirely on visual and verbal feedback from the patient concerning the benefits and adverse effects of each setting tested. By introducing visual models of activation into the DBS programming process, the typically opaque electrophysiological effects of different stimulation settings are made clear and a more complete picture of the therapy emerges.

Adapting to Changes in DBS Technology

At present, DBS systems are evolving in complexity. The additional features available in the current generation of devices recently introduced to the market to improve DBS patient outcomes33 have increased the complexity of DBS programming.34,35,36,37 We argue that further improvements in DBS technology could in some scenarios create greater challenges, because as the complexity of DBS devices increases, more specialized training will be required for programming and management. We see potential for improvement in MAP DBS, in that the current version of the platform makes predictions based only on imaging and does not update based on any information gathered during the programming process. Given the recent advances in wearable technology for tracking PD motor symptoms24 and the possibility to stream recordings of a patient’s physiology (eg, local field potentials),38 updated real-time models should be possible. Finally, image-based connectivity markers39 and stimulation sweet spots40,41 could be integrated into the system to allow for automatic predictions of settings.5,42,43

Limitations

One limitation was that the primary outcome, the number of times each participant traveled to the movement disorders clinic during the study period, was influenced by differences in the schedules of visits provided to the 2 arms of the study. However, we believe that when evaluated within the context of the strong clinical benefit received by the participants in the home health arm, this intervention can be considered a success. A more powerful study would be designed to show noninferiority in clinical outcomes between home health postoperative management and the SOC, but given the high variability of DBS clinical outcomes, a large multicenter trial would be required to adequately power for noninferiority. An additional limitation is the open-label study design, since we could not identify a practical way of blinding the participants or clinicians.

Within the broader goal of comparing a home health care model to the current SOC postoperative care model, the 2 study arms differed in 3 ways: (1) expert vs nonexpert programmers, (2) the use of MAP DBS vs traditional programming, and (3) programming in the home vs the clinic. We have previously compared the effect of MAP DBS on expert programmers in a multicenter randomized clinical trial, which showed that some aspects of programming effectiveness and efficiency are improved when experts use MAP DBS. It has also been shown that patients present symptoms differently in the home than the clinic,44 which likely influences the programming process. Future studies should evaluate these variables independently to establish a care model that maximizes benefit and minimizes cost.

Our study used a single home health nurse caring for patients from a single DBS center. Because we used a single nurse, the experience level of the nurse increased as the study progressed. However, we have no evidence that the home health nurse was a more effective DBS programmer at the end of the study than the beginning. Additionally, our use of a single, centralized home health nurse, who often had to travel long distances, is likely not a cost-effective way to deliver care. This study should be repeated with multiple home health nurses at multiple centers to establish how the care model used in this study generalizes. A challenge for larger-scale implementation of a home health care model will be the training of nurses on the basics of DBS postoperative management. The home health nurse who performed the home health visits for this study shadowed expert DBS clinicians for a 4-week period prior to the initiation of the study. This aspect of the care model is likely not scalable, because multiple logistical hurdles will prevent home health nurses from receiving this level of in-person training. Online simulation-based training may be the most feasible approach for training home health nurses on the basics of DBS postoperative management.45 Training modules could be integrated directly into MAP DBS.

Conducting a larger, multicenter study could also provide insights into whether DBS telemedicine can improve access for populations who are underserved or lack access to necessary technology (eg, broadband internet, computers, videoconferencing). According to a US Census report, households with internet access, including smartphones and tablets, grew from 74% in 2013 to 81.4% in 2016.46 This growth is expected to continue as internet access becomes more ubiquitous, even in nonmetropolitan areas. Furthermore, 84.7% of those aged 45 to 64 years have internet access. As this group continues to age, the percentage of those 65 years and older who have internet access will likely increase. The pandemic “catalyzed rapid adoption of telehealth and transformed healthcare delivery at a breathtaking pace”47(p957) and has led to more older adults becoming familiar with virtual visits, asynchronous communication, and remote monitoring. However, disparities in access will likely continue, especially for those living in the rural South, those with lower education levels and financial resources, those of Hispanic origin, and those who are not English speaking.47 As we move forward with testing innovative mobile health applications, we need to ensure that our research includes groups with less access to these newer technologies. An associated potential change in a future study is to replace telephone visits with video visits. Despite the possible benefit from video interaction between clinician and patient, technology barriers involved with requiring patients have access to video have the potential to further disparities in terms of access to care.48 Further research could be done comparing the efficacy of phone with video programming visits.

The use of 3-dimensional visual models on the MAP DBS platform allows for quick localization of the DBS leads relative to the anatomical target, as well as identification of potentially poor lead placement. Although the MAP DBS system supports and has been evaluated for all DBS leads approved in the US, this study was only evaluated using the Medtronic DBS leads, and the MAP DBS system is currently only available on the Apple iOS operating system.

Conclusions

This study demonstrated that a home health postoperative DBS care model was safe and feasible and significantly reduced the need for traditional clinic management. Further evidence was collected to demonstrate the capability of the MAP DBS technology to simplify the DBS programming process. Disruption of the traditional expert-based care model should lead to the thoughtful development of new care models designed to substantially reduce the burden on patients and caregivers and improve access to DBS therapy.

Supplement 1.

Trial Protocol.

Click here for additional data file. (569.2KB, pdf)
Supplement 2.

eMethods.

eFigure 1. iPad screenshots of the MAP DBS interface.

eTable 1. Comparison of mean number of DBS postoperative management visit types between arms.

eTable 2. Correlation of change from baseline to six-month outcomes for rating scales with order of initial DBS postoperative management visits for patients randomized to home health.

eFigure 2. Clinical rating scales and LEDD values for patients over the study period grouped by study arm.

eFigure 3. Subanalysis of clinical rating scales and LEDD values for patients over the study period grouped by patients who underwent at least one clinic DBS postoperative management session compared to those exclusively managed by home health.

eReferences.

Click here for additional data file. (224.4KB, pdf)
Supplement 3.

Data Sharing Statement.

Click here for additional data file. (70.5KB, pdf)

References

  • 1.Deuschl G, Schade-Brittinger C, Krack P, et al. ; German Parkinson Study Group, Neurostimulation Section . A randomized trial of deep-brain stimulation for Parkinson’s disease. N Engl J Med. 2006;355(9):896-908. doi: 10.1056/NEJMoa060281 [DOI] [PubMed] [Google Scholar]
  • 2.Follett KA, Weaver FM, Stern M, et al. ; CSP 468 Study Group . Pallidal versus subthalamic deep-brain stimulation for Parkinson’s disease. N Engl J Med. 2010;362(22):2077-2091. doi: 10.1056/NEJMoa0907083 [DOI] [PubMed] [Google Scholar]
  • 3.Okun MS, Fernandez HH, Wu SS, et al. Cognition and mood in Parkinson’s disease in subthalamic nucleus versus globus pallidus interna deep brain stimulation: the COMPARE trial. Ann Neurol. 2009;65(5):586-595. doi: 10.1002/ana.21596 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Moro E, Poon Y-YW, Lozano AM, Saint-Cyr JA, Lang AE. Subthalamic nucleus stimulation: improvements in outcome with reprogramming. Arch Neurol. 2006;63(9):1266-1272. doi: 10.1001/archneur.63.9.1266 [DOI] [PubMed] [Google Scholar]
  • 5.Anderson DN, Osting B, Vorwerk J, Dorval AD, Butson CR. Optimized programming algorithm for cylindrical and directional deep brain stimulation electrodes. J Neural Eng. 2018;15(2):026005. doi: 10.1088/1741-2552/aaa14b [DOI] [PubMed] [Google Scholar]
  • 6.Chan AK, McGovern RA, Brown LT, et al. Disparities in access to deep brain stimulation surgery for Parkinson disease: interaction between African American race and Medicaid use. JAMA Neurol. 2014;71(3):291-299. doi: 10.1001/jamaneurol.2013.5798 [DOI] [PubMed] [Google Scholar]
  • 7.Bronstein JM, Tagliati M, Alterman RL, et al. Deep brain stimulation for Parkinson disease: an expert consensus and review of key issues. Arch Neurol. 2011;68(2):165-171. doi: 10.1001/archneurol.2010.260 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Okun MS, Foote KD. Parkinson’s disease DBS: what, when, who and why? the time has come to tailor DBS targets. Expert Rev Neurother. 2010;10(12):1847-1857. doi: 10.1586/ern.10.156 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Pressley JC, Louis ED, Tang MX, et al. The impact of comorbid disease and injuries on resource use and expenditures in parkinsonism. Neurology. 2003;60(1):87-93. doi: 10.1212/WNL.60.1.87 [DOI] [PubMed] [Google Scholar]
  • 10.Witek N, Heath SL, Ouyang B, Tanner CM, Galifianakis NB. Remote telemedicine evaluation of deep brain stimulation candidacy. Neurol Clin Pract. 2020;10(3):199-205. . doi: 10.1212/CPJ.0000000000000723 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Butson CR, Tamm G, Jain S, Fogal T, Krüger J. Evaluation of interactive visualization on mobile computing platforms for selection of deep brain stimulation parameters. IEEE Trans Vis Comput Graph. 2013;19(1):108-117. doi: 10.1109/TVCG.2012.92 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Dorsey ER, Bloem BR. The Parkinson pandemic—a call to action. JAMA Neurol. 2018;75(1):9-10. doi: 10.1001/jamaneurol.2017.3299 [DOI] [PubMed] [Google Scholar]
  • 13.Zhang J, Hu W, Chen H, Meng F, Li L, Okun MS. Implementation of a novel bluetooth technology for remote deep brain stimulation programming: the pre- and post-COVID-19 Beijing experience. Mov Disord. 2020;35(6):909-910. doi: 10.1002/mds.28098 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Miocinovic S, Ostrem JL, Okun MS, et al. Recommendations for deep brain stimulation device management during a pandemic. J Parkinsons Dis. 2020;10(3):903-910. doi: 10.3233/JPD-202072 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Zhang C, Zhu K, Li D, Voon V, Sun B. Deep brain stimulation telemedicine for psychiatric patients during the COVID-19 pandemic. Brain Stimul. 2020;13(5):1263-1264. doi: 10.1016/j.brs.2020.06.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Bloem BR, Henderson EJ, Dorsey ER, et al. Integrated and patient-centred management of Parkinson’s disease: a network model for reshaping chronic neurological care. Lancet Neurol. 2020;19(7):623-634. doi: 10.1016/S1474-4422(20)30064-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Dorsey ER, Vlaanderen FP, Engelen LJLPG, et al. Moving Parkinson care to the home. Mov Disord. 2016;31(9):1258-1262. doi: 10.1002/mds.26744 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Bloem BR, Dorsey ER, Okun MS. The coronavirus disease 2019 crisis as catalyst for telemedicine for chronic neurological disorders. JAMA Neurol. 2020;77(8):927-928. doi: 10.1001/jamaneurol.2020.1452 [DOI] [PubMed] [Google Scholar]
  • 19.Cubo E, Hassan A, Bloem BR, Mari Z; MDS-Telemedicine Study Group . Implementation of telemedicine for urgent and ongoing healthcare for patients with Parkinson’s disease during the COVID-19 pandemic: new expectations for the future. J Parkinsons Dis. 2020;10(3):911-913. doi: 10.3233/JPD-202108 [DOI] [PubMed] [Google Scholar]
  • 20.Helmich RC, Bloem BR. The impact of the COVID-19 pandemic on Parkinson’s disease: hidden sorrows and emerging opportunities. J Parkinsons Dis. 2020;10(2):351-354. doi: 10.3233/JPD-202038 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Dorsey ER, Okun MS, Bloem BR. Care, convenience, comfort, confidentiality, and contagion: the 5 C’s that will shape the future of telemedicine. J Parkinsons Dis. 2020;10(3):893-897. doi: 10.3233/JPD-202109 [DOI] [PubMed] [Google Scholar]
  • 22.Willis AW, Schootman M, Kung N, Wang XY, Perlmutter JS, Racette BA. Disparities in deep brain stimulation surgery among insured elders with Parkinson disease. Neurology. 2014;82(2):163-171. doi: 10.1212/WNL.0000000000000017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Armstrong MJ, Okun MS. Diagnosis and treatment of Parkinson disease: a review. JAMA. 2020;323(6):548-560. doi: 10.1001/jama.2019.22360 [DOI] [PubMed] [Google Scholar]
  • 24.Joshi R, Bronstein JM, Keener A, et al. PKG movement recording system use shows promise in routine clinical care of patients with Parkinson’s disease. Front Neurol. 2019;10(October):1027. doi: 10.3389/fneur.2019.01027 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Mera T, Vitek JL, Alberts JL, Giuffrida JP. Kinematic optimization of deep brain stimulation across multiple motor symptoms in Parkinson’s disease. J Neurosci Methods. 2011;198(2):280-286. doi: 10.1016/j.jneumeth.2011.03.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Pulliam CL, Heldman DA, Orcutt TH, Mera TO, Giuffrida JP, Vitek JL. Motion sensor strategies for automated optimization of deep brain stimulation in Parkinson’s disease. Parkinsonism Relat Disord. 2015;21(4):378-382. doi: 10.1016/j.parkreldis.2015.01.018 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Jankovic J. Motor fluctuations and dyskinesias in Parkinson’s disease: clinical manifestations. Mov Disord. 2005;20(suppl 11):S11-S16. doi: 10.1002/mds.20458 [DOI] [PubMed] [Google Scholar]
  • 28.Li D, Zhang C, Gault J, et al. Remotely programmed deep brain stimulation of the bilateral subthalamic nucleus for the treatment of primary Parkinson disease: a randomized controlled trial investigating the safety and efficacy of a novel deep brain stimulation system. Stereotact Funct Neurosurg. 2017;95(3):174-182. doi: 10.1159/000475765 [DOI] [PubMed] [Google Scholar]
  • 29.Chen Y, Hao H, Chen H, Li L. The study on a telemedicine interaction mode for deep brain stimulation postoperative follow-up. Proc Annu Int Conf IEEE Eng Med Biol Soc EMBS. 2015;186-189. doi: 10.1109/EMBC.2015.7318331 [DOI] [PubMed] [Google Scholar]
  • 30.Zhang C, Zhang Y, Zhan S, et al. Telemedical deep brain stimulation: merits and limitations. Stereotact Funct Neurosurg. 2018;96(4):272-273. doi: 10.1159/000491603 [DOI] [PubMed] [Google Scholar]
  • 31.Deuschl G, Follett KA, Luo P, et al. Comparing two randomized deep brain stimulation trials for Parkinson’s disease. J Neurosurg. 2019;132(5):1376-1384. doi: 10.3171/2018.12.JNS182042 [DOI] [PubMed] [Google Scholar]
  • 32.Vitek JL, Jain R, Chen L, et al. Subthalamic nucleus deep brain stimulation with a multiple independent constant current-controlled device in Parkinson’s disease (INTREPID): a multicentre, double-blind, randomised, sham-controlled study. Lancet Neurol. 2020;19(6):491-501. doi: 10.1016/S1474-4422(20)30108-3 [DOI] [PubMed] [Google Scholar]
  • 33.Dembek TA, Reker P, Visser-Vandewalle V, et al. Directional DBS increases side-effect thresholds—a prospective, double-blind trial. Mov Disord. 2017;32(10):1380-1388. doi: 10.1002/mds.27093 [DOI] [PubMed] [Google Scholar]
  • 34.Schüpbach WMM, Chabardes S, Matthies C, et al. Directional leads for deep brain stimulation: opportunities and challenges. Mov Disord. 2017;32(10):1371-1375. doi: 10.1002/mds.27096 [DOI] [PubMed] [Google Scholar]
  • 35.Anderson DN, Duffley G, Vorwerk J, Dorval AD, Butson CR. Anodic stimulation misunderstood: preferential activation of fiber orientations with anodic waveforms in deep brain stimulation. J Neural Eng. 2019;16(1):016026. doi: 10.1088/1741-2552/aae590 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Anderson DN, Anderson C, Lanka N, et al. The μDBS: multiresolution, directional deep brain stimulation for improved targeting of small diameter fibers. Front Neurosci. 2019;13:1152. doi: 10.3389/fnins.2019.01152 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Anderson CJ, Anderson DN, Pulst SM, Butson CR, Dorval AD. Neural selectivity, efficiency, and dose equivalence in deep brain stimulation through pulse width tuning and segmented electrodes. Brain Stimul. 2020;13(4):1040-1050. doi: 10.1016/j.brs.2020.03.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Swann NC, de Hemptinne C, Miocinovic S, et al. Chronic multisite brain recordings from a totally implantable bidirectional neural interface: experience in 5 patients with Parkinson’s disease. J Neurosurg. 2018;128(2):605-616. doi: 10.3171/2016.11.JNS161162 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Horn A, Reich M, Vorwerk J, et al. Connectivity predicts deep brain stimulation outcome in Parkinson disease. Ann Neurol. 2017;82(1):67-78. doi: 10.1002/ana.24974 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Dembek TA, Roediger J, Horn A, et al. Probabilistic sweet spots predict motor outcome for deep brain stimulation in Parkinson disease. Ann Neurol. 2019;86(4):527-538. doi: 10.1002/ana.25567 [DOI] [PubMed] [Google Scholar]
  • 41.Butson CR, Cooper SE, Henderson JM, Wolgamuth B, McIntyre CC. Probabilistic analysis of activation volumes generated during deep brain stimulation. Neuroimage. 2011;54(3):2096-2104. doi: 10.1016/j.neuroimage.2010.10.059 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Peña E, Zhang S, Patriat R, et al. Multi-objective particle swarm optimization for postoperative deep brain stimulation targeting of subthalamic nucleus pathways. J Neural Eng. 2018;15(6):066020. doi: 10.1088/1741-2552/aae12f [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Vorwerk J, Brock AA, Anderson DN, Rolston JD, Butson CR. A retrospective evaluation of automated optimization of deep brain stimulation parameters. J Neural Eng. 2019;16(6):064002. doi: 10.1088/1741-2552/ab35b1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Heijmans M, Habets JGV, Herff C, et al. Monitoring Parkinson’s disease symptoms during daily life: a feasibility study. NPJ Parkinsons Dis. 2019;5(1):21. doi: 10.1038/s41531-019-0093-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Cook DA, Blachman MJ, Price DW, et al. Educational technologies for physician continuous professional development: a national survey. Acad Med. 2018;93(1):104-112. doi: 10.1097/ACM.0000000000001817 [DOI] [PubMed] [Google Scholar]
  • 46.Ryan C. Computer and internet use in the United States: 2016. Published 2018. Accessed May 19, 2021. https://www.census.gov/content/dam/Census/library/publications/2018/acs/ACS-39.pdf
  • 47.Wosik J, Fudim M, Cameron B, et al. Telehealth transformation: COVID-19 and the rise of virtual care. J Am Med Inform Assoc. 2020;27(6):957-962. doi: 10.1093/jamia/ocaa067 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Roberts ET, Mehrotra A. Assessment of disparities in digital access among Medicare beneficiaries and implications for telemedicine. JAMA Intern Med. 2020;180(10):1386-1389. doi: 10.1001/jamainternmed.2020.2666 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1.

Trial Protocol.

Click here for additional data file. (569.2KB, pdf)
Supplement 2.

eMethods.

eFigure 1. iPad screenshots of the MAP DBS interface.

eTable 1. Comparison of mean number of DBS postoperative management visit types between arms.

eTable 2. Correlation of change from baseline to six-month outcomes for rating scales with order of initial DBS postoperative management visits for patients randomized to home health.

eFigure 2. Clinical rating scales and LEDD values for patients over the study period grouped by study arm.

eFigure 3. Subanalysis of clinical rating scales and LEDD values for patients over the study period grouped by patients who underwent at least one clinic DBS postoperative management session compared to those exclusively managed by home health.

eReferences.

Click here for additional data file. (224.4KB, pdf)
Supplement 3.

Data Sharing Statement.

Click here for additional data file. (70.5KB, pdf)

Articles from JAMA Neurology are provided here courtesy of American Medical Association

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