Predictive Value of Ambulatory Objective Movement Measurement for Outcomes of Levodopa/Carbidopa Intestinal Gel Infusion

Patients with Parkinson’s disease that may benefit from device-assisted therapy can be identified with guidelines like Navigate PD. The decision to offer advanced treatment and the choice of treatment modality are, however, not straightforward, and some patients respond less favorably to a chosen therapy. Measurements with the Parkinson Kinetigraph (PKG) can detect motor fluctuations and could therefore predict patients that respond better or worse to intestinal levodopa/carbidopa gel infusion (LCIG). In a retrospective analysis of 45 patients that had been selected to start LCIG between 2014 and 2020, the effects of baseline PKG and clinical characteristic on the outcome were determined with ordinal regression. Although all patients had been found to have handicapping medication-related symptom fluctuations, patients without clear objective off fluctuations in the baseline PKG had low odds ratio for success. Lower odds for success were also found with increasing age, whereas gender, medication intensity and baseline PKG summary scores (median bradykinesia and dyskinesia scores, fluctuation dyskinesia score and percent time with tremor) had no significant effect. Absence of easily identified off-periods in the PKG has a negative prognostic value for the effect of LCIG and could prompt noninvasive infusion evaluation before surgery.

Efficient and Comprehensive Integrity Diagnostics for Dual Completion String Wells, Using Spectral Noise Analyzer Tool

Well operators often face various technical challenges when intervening and repairing older, mature field wells. The most common problem associated with aging wells are tubing and casing integrity. Uncertain sources of downhole leaks and data ambiguity often lead to incorrect diagnostics that can hinder repair work or even contribute to additional or worsened integrity issues. Operators continuously challenge service companies and technology providers to drive innovation. One such challenge is in finding efficient and comprehensive integrity diagnostics for dual-string wells. A basic and general diagnostic method to verify well integrity in dual-string wells involves setting plugs in the long and short strings and pressure testing the tubings. These operations are generally time consuming, and the test data does not usually pinpoint the location of the leak, if any. Since 2016 a new diagnostic solution for this challenge has been implemented using a slickline-deployed passive acoustic logging technique. Carefully designed intervention planning, combined with efficient data acquisition, led to significant time saving and improved data quality. A more complete assessment of the integrity of both strings is now more frequent and often necessary, while challenging the conventional thinking of having to assess the lower string only while assuming the upper string is in good condition. However, investigating dual-string integrity with uncertainty on the source of leak, restrictions on facilities and limitations on surveillance time will often waste more time and money if not approached carefully. This paper discusses two case studies, including a dual-string oil producer in the South China Sea that had sustained pressure in production casing annulus. The well operator initially considered that the long string had an integrity issue, while the short string did not, based on their surface-based annulus pressure diagnostics. Consequently, the operator decided to diagnose only the long string. The passive acoustic memory tool. combined with a fast-response temperature and spinner used for the diagnosis, identified a possible short string leak while logging through the long string. This result clearly demonstrated that surface analyses can be misleading, and a comprehensive downhole diagnostic should be the recommended method to identify leaks, especially in dual-string completions. This well operator has completed more than 100 integrity diagnostic runs in the last five years. The passive acoustic diagnostic interventions have resulted in an average 50-percent time saving compared to legacy methods, and data analysis results have led to significant improvements in well productivity.

Incorporating Prior Information in Adaptive Model Predictive Control for Multivariable Artificial Pancreas Systems

Background: Adaptive model predictive control (MPC) algorithms that recursively update the glucose prediction model are shown to be promising in the development of fully automated multivariable artificial pancreas systems. However, the recursively updated glycemic prediction models do not explicitly consider prior knowledge in the identification of the model parameters. Prior information of the glycemic effects of meals and physical activity can improve model accuracy and yield better glycemic control algorithms. Methods: A glucose prediction model based on regularized partial least squares (rPLS) method where the prior information is encoded as the regularization term is developed to provide accurate predictions of the future glucose concentrations. An adaptive MPC is developed that incorporates dynamic trajectories for the glucose setpoint and insulin dosing constraints based on the estimated plasma insulin concentration (PIC). The proposed adaptive MPC algorithm is robust to disturbances caused by unannounced meals and physical activities even in cases with missing glucose measurements. The effectiveness of the proposed adaptive MPC based on rPLS is investigated with in silico subjects of the multivariable glucose-insulin-physiological variables simulator (mGIPsim). Results: The efficacy of the proposed adaptive MPC strategy in regulating the blood glucose concentration (BGC) of people with T1DM is assessed using the average percent time in range (TIR) for glucose, defined as 70 to 180 mg/dL inclusive, and the average percent time in hypoglycemia (<70 and >54 mg/dL) and level 2 hypoglycemia (≤54 mg/dL). The TIR for a cohort of 20 virtual subjects of mGIPsim is 81.9% ± 7.4% (with no hypoglycemia or severe hypoglycemia) for the proposed MPC compared with 73.9% ± 7.6% (0.2% ± 0.1% in hypoglycemia and 0.1% ± 0.1% in level 2 hypoglycemia) for an MPC based on a recursive autoregressive exogenous (ARX) model. Conclusions: The adaptive MPC algorithm that incorporates prior knowledge in the recursive updating of the glucose prediction model can contribute to the development of fully automated artificial pancreas systems that can mitigate meal and physical activity disturbances.

Physical and Cognitive Correlates of GPS-Derived Life-Space Characteristics in Older Adults

Life-space mobility, movement within one’s living environment, is important for functional independence in later life. It is unclear which life-space characteristics (i.e., space, duration, shape) are most affected by physical and cognitive limitations. GPS-derived measures mitigate recall bias and offer novel ways to characterize life-space. We examined associations between physical and cognitive performance and GPS-derived life-space characteristics. Participants were 164 community-dwelling adults (Age: M=77.3±6.5) from baseline data of a clinical trial to improve walking in older adults. Participants carried a portable GPS for 7 days, which passively collected real-time location. Standard deviational ellipses (SDEs) and minimum convex polygons (MCPs) were derived for each day. Area and compactness of these measures quantified activity space and shape, respectively. For each measure, 7-day medians and median absolute deviations (MAD) were computed to capture both central tendency and variability of weekly activity. Activity duration was quantified as percentage of time outside home. Adjusting for age and sex, percent time outside home was associated with lower mobility performance (i.e., 6-minute walk (6MWT), figure 8 walk, ρ’s=.17-.18, p’s&lt;.05) and executive functioning (i.e., Trail Making Test, Part A: ρ=.16, p=.04, Part B: ρ=.19, p=.01). Median MCP and SDE areas, but not compactness, were associated with 6MWT performance (ρ’s=.18-.20, p’s&lt;.05). MCP area MAD was associated with greater global cognition (3MSE, ρ=.15, p=.05). Life-space characteristics were differentially associated with performance measures, suggesting physical and cognitive limitations may constrain life-space mobility via different mechanisms. Variation in these associations by neighborhood walkability and active versus passive travel will also be examined.

Blood pressure goals: Is cerebral saturation the new mean arterial pressure?

Objective: To correlate hypotension and cerebral saturation from near-infrared spectroscopy (cNIRS) in neonates on dopamine. Study Design: Retrospective review of neonates receiving dopamine between August 2018-2019 was performed. Hypotension thresholds included mean arterial pressure (MAP) of postmenstrual age (PMA) ± 5mmHg, 30mmHg, and gestational age (GA) ± 5mmHg. Time below threshold MAP was compared to time with cerebral hypoxia (cNIRS <55%). Results: Hypotension occurred 6-33% of time on dopamine in 59 cases. Hypotension did not correlate with abnormal cNIRS overall, within PMA subgroups, or by outcomes. Hypotensive periods with MAP<GA had fewer corresponding percent time with abnormal cNIRS events (3.7±1.3%) compared to MAP <PMA (11.9±4.9%, p<0.003) or 30 mm Hg thresholds (12.2±4.7%, p<0.0001). In the most premature infants, mean cNIRS values during hypotension were still within normal range (57±6%). Conclusion: cNIRS may be a more clinically relevant measure than MAP for assessment of neonatal hypotension.

Assessing inertial measurement unit locations for freezing of gait detection and patient preference

ABSTRACTBackgroundFreezing of gait, a common symptom of Parkinson’s disease, presents as sporadic episodes in which an individual’s feet suddenly feel stuck to the ground. Inertial measurement units (IMUs) promise to enable at-home monitoring and personalization of therapy, but there is a lack of consensus on the number and location of IMUs for detecting freezing of gait. The purpose of this study was to assess IMU sets in the context of both freezing of gait detection performance and patient preference.MethodsSixteen people with Parkinson’s disease were surveyed about sensor preferences. Raw IMU data from seven people with Parkinson’s disease, wearing up to eleven sensors, were used to train convolutional neural networks to detect freezing of gait. Models trained with data from different sensor sets were assessed for technical performance; a best technical set and minimal IMU set were identified. Clinical utility was assessed by comparing model- and human-rater-determined percent time freezing and number of freezing events.ResultsThe best technical set consisted of three IMUs (lumbar and both ankles, AUROC = 0.83), all of which were rated highly wearable. The minimal IMU set consisted of a single ankle IMU (AUROC = 0.80). Correlations between these models and human raters were good to excellent for percent time freezing (ICC = 0.93, 0.89) and number of freezing events (ICC = 0.95, 0.86), for the best technical set and minimal IMU set, respectively.ConclusionsSeveral IMU sets consisting of three IMUs or fewer were highly rated for both technical performance and wearability, and more IMUs did not necessarily perform better in FOG detection. We openly share our data and software to further the adoption of a general, open-source model that uses raw signals and a standard sensor set for at-home monitoring of freezing of gait.

Advanced Closed-Loop Control System Improves Postprandial Glycemic Control Compared With a Hybrid Closed-Loop System Following Unannounced Meal

<b>Objective:</b> Meals are a major hurdle to glycemic control in type 1 diabetes (T1D). Our objective was to test a fully-automated closed-loop control (CLC) system in the absence of announcement of carbohydrate ingestion among adolescents with T1D, who are known to commonly omit meal announcement. <p><b>Research Design and Methods: </b>Eighteen adolescents with T1D (age 15.6±1.7 years; HbA1c 7.4%±1.5; 9F/9M) participated in a randomized crossover clinical trial comparing our legacy hybrid CLC system (USS-Virginia) with a novel fully-automated CLC system (RocketAP), during two 46h supervised admissions (each with one announced and one unannounced dinner), following 2 weeks of data collection. Primary outcome was the percent time-in-range 70-180mg/dL (TIR) following the unannounced meal, with secondary outcomes related to additional CGM-based metrics.</p> <p><b>Results: </b> Both TIR and time-in-tight-range 70-140mg/dL (TTR) were significantly higher using RocketAP than using USS-Virginia during the 6h following the unannounced meal (83% [64-93] vs. 53% [40-71]; p=0.004 and 49% [41-59] vs. 27% [22-36]; p=0.002, respectively), primarily driven by reduced time-above-range (TAR >180mg/dL 17% [1.3-34] vs. 47% [28-60]), with no increase in time-below-range (TBR <70mg/dL 0% median for both). RocketAP also improved control following the announced meal (mean difference TBR: -0.7%, TIR: +7%, TTR: +6%), overall (TIR: +5%, TAR: -5%, TTR: +8%), and overnight (TIR: +7%, TTR: +19%, TAR: -5%). RocketAP delivered less insulin overall (78±23U vs. 85±20U, p=0.01)</p> <p><b>Conclusions:</b> A new fully-automated CLC system with automatic prandial dosing was proven to be safe and feasible and outperformed our legacy USS-Virginia in an adolescent population with and without meal announcement.</p>

Advanced Closed-Loop Control System Improves Postprandial Glycemic Control Compared With a Hybrid Closed-Loop System Following Unannounced Meal

<b>Objective:</b> Meals are a major hurdle to glycemic control in type 1 diabetes (T1D). Our objective was to test a fully-automated closed-loop control (CLC) system in the absence of announcement of carbohydrate ingestion among adolescents with T1D, who are known to commonly omit meal announcement. <p><b>Research Design and Methods: </b>Eighteen adolescents with T1D (age 15.6±1.7 years; HbA1c 7.4%±1.5; 9F/9M) participated in a randomized crossover clinical trial comparing our legacy hybrid CLC system (USS-Virginia) with a novel fully-automated CLC system (RocketAP), during two 46h supervised admissions (each with one announced and one unannounced dinner), following 2 weeks of data collection. Primary outcome was the percent time-in-range 70-180mg/dL (TIR) following the unannounced meal, with secondary outcomes related to additional CGM-based metrics.</p> <p><b>Results: </b> Both TIR and time-in-tight-range 70-140mg/dL (TTR) were significantly higher using RocketAP than using USS-Virginia during the 6h following the unannounced meal (83% [64-93] vs. 53% [40-71]; p=0.004 and 49% [41-59] vs. 27% [22-36]; p=0.002, respectively), primarily driven by reduced time-above-range (TAR >180mg/dL 17% [1.3-34] vs. 47% [28-60]), with no increase in time-below-range (TBR <70mg/dL 0% median for both). RocketAP also improved control following the announced meal (mean difference TBR: -0.7%, TIR: +7%, TTR: +6%), overall (TIR: +5%, TAR: -5%, TTR: +8%), and overnight (TIR: +7%, TTR: +19%, TAR: -5%). RocketAP delivered less insulin overall (78±23U vs. 85±20U, p=0.01)</p> <p><b>Conclusions:</b> A new fully-automated CLC system with automatic prandial dosing was proven to be safe and feasible and outperformed our legacy USS-Virginia in an adolescent population with and without meal announcement.</p>

Advanced Closed Loop Control System Improves Postprandial Glycemic Control Compared to a Hybrid Closed-Loop System Following Unannounced Meal

<b>Objective:</b> Meals are a major hurdle to glycemic control in type 1 diabetes (T1D). Our objective was to test a fully-automated closed-loop control (CLC) system in the absence of announcement of carbohydrate ingestion among adolescents with T1D, who are known to commonly omit meal announcement. <p><b>Research Design and Methods: </b>Eighteen adolescents with T1D (age 15.6±1.7 years; HbA1c 7.4%±1.5; 9F/9M) participated in a randomized crossover clinical trial comparing our legacy hybrid CLC system (USS-Virginia) with a novel fully-automated CLC system (RocketAP), during two 46h supervised admissions (each with one announced and one unannounced dinner), following 2 weeks of data collection. Primary outcome was the percent time-in-range 70-180mg/dL (TIR) following the unannounced meal, with secondary outcomes related to additional CGM-based metrics.</p> <p><b>Results: </b> Both TIR and time-in-tight-range 70-140mg/dL (TTR) were significantly higher using RocketAP than using USS-Virginia during the 6h following the unannounced meal (83% [64-93] vs. 53% [40-71]; p=0.004 and 49% [41-59] vs. 27% [22-36]; p=0.002, respectively), primarily driven by reduced time-above-range (TAR >180mg/dL 17% [1.3-34] vs. 47% [28-60]), with no increase in time-below-range (TBR <70mg/dL 0% median for both). RocketAP also improved control following the announced meal (mean difference TBR: -0.7%, TIR: +7%, TTR: +6%), overall (TIR: +5%, TAR: -5%, TTR: +8%), and overnight (TIR: +7%, TTR: +19%, TAR: -5%). RocketAP delivered less insulin overall (78±23U vs. 85±20U, p=0.01)</p> <p><b>Conclusions:</b> A new fully-automated CLC system with automatic prandial dosing was proven to be safe and feasible and outperformed our legacy USS-Virginia in an adolescent population with and without meal announcement.</p>

Comparison of Conventional Methods for Bowel Length Measurement in Laparoscopic Surgery to a Novel Computer-Assisted 3D Measurement System

Purpose Accurate laparoscopic bowel length measurement (LBLM), which is used primarily in metabolic surgery, remains a challenge. This study aims to three conventional methods for LBLM, namely using visual judgment (VJ), instrument markings (IM), or premeasured tape (PT) to a novel computer-assisted 3D measurement system (BMS). Materials and Methods LBLM methods were compared using a 3D laparoscope on bowel phantoms regarding accuracy (relative error in percent, %), time in seconds (s), and number of bowel grasps. Seventy centimeters were measured seven times. As a control, the first, third, fifth, and seventh measurements were performed with VJ. The interventions IM, PT, and BMS were performed following a randomized order as the second, fourth, and sixth measurements. Results In total, 63 people participated. BMS showed better accuracy (2.1±3.7%) compared to VJ (8.7±13.7%, p=0.001), PT (4.3±6.8%, p=0.002), and IM (11±15.3%, p<0.001). Participants performed LBLM in a similar amount of time with BMS (175.7±59.7s) and PT (166.5±63.6s, p=0.35), but VJ (64.0±24.0s, p<0.001) and IM (144.9±55.4s, p=0.002) were faster. Number of bowel grasps as a measure for the risk of bowel lesions was similar for BMS (15.8±3.0) and PT (15.9±4.6, p=0.861), whereas VJ required less (14.1±3.4, p=0.004) and IM required more than BMS (22.2±6.9, p<0.001). Conclusions PT had higher accuracy than VJ and IM, and lower number of bowel grasps than IM. BMS shows great potential for more reliable LBLM. Until BMS is available in clinical routine, PT should be preferred for LBLM. Graphical abstract