A Virtual Training Program for the Tandem t:slim X2 Insulin Pump: Implementation and Outcomes.

Insulin pump training has traditionally been performed in-person. The coronavirus disease 2019 (COVID-19) pandemic necessitated vast increases in the number of virtual pump trainings for Tandem t:slim X2 insulin pump starts. A customized structured pump training curriculum specifically tailored to virtual learning was deployed in early 2020, and included (1) preparation for training with use of the t:simulator app, (2) use of the teach-back method during video training, and (3) automating data uploads for follow-up. Retrospective analysis from >23,000 pump training sessions performed from January 1, 2020 to July 28, 2020 showed sensor time-in-range for up to 6 months after training was 72% (60%-81%) for virtual training versus 67% (54%-78%) for in-person training. Higher user satisfaction (4.78 ± 0.52 vs. 4.64 ± 0.68; P < 0.01) and higher user confidence (4.61 ± 0.75 vs. 4.47 ± 0.0.85; P < 0.01) were reported after the virtual sessions. Virtual pump training was well received and proved safe and effective with the new virtual training curriculum.

Real-World Patient-Reported Outcomes and Glycemic Results with Initiation of Control-IQ Technology.

Background: The t:slim X2™ insulin pump with Control-IQ™ technology, an advanced hybrid closed-loop system, became available in the United States in early 2020. Real-world outcomes with use of this system have not yet been comprehensively reported. Methods: Individuals with type 1 diabetes (T1D) (≥14 years of age) who had ≥21 days of pump usage data were invited via email to participate. Participants completed psychosocial questionnaires (Technology Acceptance Scale [TAS], well-being index [WHO-5], and Diabetes Impact and Devices Satisfaction [DIDS] scale) at timepoint 1 (T1) (at least 3 weeks after starting Control-IQ technology) and the DIDS and WHO-5 at timepoint 2 (T2) (4 weeks from T1). Patient-reported outcomes (PROs) and glycemic outcomes were reviewed at each timepoint. Results: Overall, 9,085 potentially eligible individuals received the study invite. Of these, 3,116 consented and subsequently 1,435 participants completed questionnaires at both T1 and T2 and had corresponding glycemic data available on the t:connect® web application. Time in range was 78.2% (70.2%-85.1%) at T1 and 79.2% (70.3%-86.2%) at T2. PROs reflected high device-related satisfaction and reduced diabetes impact at T2. Factors contributing to high trust in the system included sensor accuracy, improved diabetes control, reduction in extreme blood glucose levels, and improved sleep quality. In addition, participants reported improved quality of life, ease of use, and efficient connectivity to the continuous glucose monitoring system as being valuable features of the system. Conclusions: Continued real-world use of the t:slim X2 pump with Control-IQ technology showed improvements in psychosocial outcomes and persistent achievement of recommended TIR glycemic outcomes in people with T1D.

Descriptive Analysis of Spinal Neuroaxial Injections, Surgical Interventions, and Physical Therapy Utilization for Degenerative Lumbar Spondylolisthesis Within Medicare Beneficiaries from 2000 to 2011.

STUDY DESIGN: A retrospective, observational study. OBJECTIVE: The aim of this study was to determine the utilization of various treatment modalities in the management of degenerative spondylolisthesis within Medicare beneficiaries. SUMMARY OF BACKGROUND DATA: Degenerative lumbar spondylolisthesis is a condition often identified in symptomatic low back pain. A variety of treatment algorithms including physical therapy and interventional techniques can be used to manage clinically significant degenerative spondylolisthesis. METHODS: This study utilized the 5% national sample of Medicare carrier claims from 2000 through 2011. A cohort of beneficiaries with a new International Classification of Diseases 9th edition (ICD-9) diagnosis code for degenerative lumbar spondylolisthesis was identified. Current procedural terminology codes were used to identify the number of procedures performed each year by specialty on this cohort. RESULTS: A total of 95,647 individuals were included in the analysis. Average age at the time of initial diagnosis was 72.8 ±â€Š9.8 years. Within this study cohort, spondylolisthesis was more prevalent in females (69%) than males and in Caucasians (88%) than other racial demographics. Over 50% of beneficiaries underwent at least one injection, approximately one-third (37%) participated in physical therapy, one in five (21%) underwent spinal surgery, and one-third (36%) did not utilize any of these interventions. Greater than half of all procedures (124,280/216,088) occurred within 2 years of diagnosis. The ratio of focal interventions (transforaminal and facet interventions) to less selective (interlaminar) procedures was greater for the specialty of Physical Medicine and Rehabilitation than for the specialties of Anesthesiology, Interventional Radiology, Neurosurgery, and Orthopedic Surgery. The majority of physical therapy was dedicated to passive treatment modalities and range of motion exercises rather than active strengthening modalities within this cohort. CONCLUSION: Interventional techniques and physical therapy are frequently used treatment modalities for symptomatic degenerative spondylolisthesis. Understanding utilization of these techniques is important to determine relative clinical efficacies and to optimize future health care expenditures. LEVEL OF EVIDENCE: N/A.

Very low protein diets supplemented with keto-analogues in ESRD predialysis patients and its effect on vascular stiffness and AVF Maturation.

BACKGROUND: Native arteriovenous fistula (AVF) is the most appropriate type of vascular access for chronic dialysis. Its patency rates depend on vascular wall characteristics. Ketoacid analogues of essential amino acids (KA/EAA) are prescribed in end-stage renal disease (ESRD) pre-dialysis patients to lower toxic metabolic products generation and improve nutritional status. We hypothesized that very-low protein diet (VLPD) supplemented with KA/EAA may influence arterial wall stiffness and affect AVF maturation rates and duration in pre-dialysis ESRD patients. METHODS: In a prospective, cohort, 3 years study we enrolled 67 consecutive non-diabetic early referral ESRD patients that underwent AVF creation in our hospital. Patients were divided in two groups based on their regimen 12 months prior to surgery: a VLPD supplemented with KA/EAA study group versus a low protein diet non-KA/EAA-supplemented control group. For each patient we performed serum analysis for the parameters of bone mineral disease, inflammation and nutritional status, one pulse wave velocity (PWV) measurement and one Doppler ultrasound (US) determination prior the surgery, followed by consequent Doppler US assessments at 4, 6, 8 and 12 weeks after it. Rates and duration of mature AVF achievement were noted. We used logistic regression to analyze the association between AVF maturation and KA/EAA administration, by comparing rates and durations between groups, unadjusted and adjusted for systolic blood pressure, C-reactive protein, PWV, phosphorus values. All parameters in the logistic model were transformed in binary variables. A p-value < α = 0.05 was considered significant; data were processed using SPSS 16 software and Excel. RESULTS: In the study group (n = 28, aged 57 ± 12.35, 13 females) we registered better serum phosphate (p = 0.022) and C-reactive protein control (p = 0.021), lower PWV (p = 0.007) and a higher percent of AVF creation success (33.3 % versus 17.8 %, p < 0.05). AVF maturation duration was lower in study group (5.91 versus 7.15 weeks, p < 0.001). CONCLUSIONS: VLPD supplemented with KA/EAA appear to improve the native AVF primary outcome, decreasing the initial vascular stiffness, possible by preserving vascular wall quality in CKD patients through a better serum phosphate levels control and the limitation of inflammatory response.

Characterization of metabolites in infiltrating gliomas using ex vivo ¹H high-resolution magic angle spinning spectroscopy.

Gliomas are routinely graded according to histopathological criteria established by the World Health Organization. Although this classification can be used to understand some of the variance in the clinical outcome of patients, there is still substantial heterogeneity within and between lesions of the same grade. This study evaluated image-guided tissue samples acquired from a large cohort of patients presenting with either new or recurrent gliomas of grades II-IV using ex vivo proton high-resolution magic angle spinning spectroscopy. The quantification of metabolite levels revealed several discrete profiles associated with primary glioma subtypes, as well as secondary subtypes that had undergone transformation to a higher grade at the time of recurrence. Statistical modeling further demonstrated that these metabolomic profiles could be differentially classified with respect to pathological grading and inter-grade conversions. Importantly, the myo-inositol to total choline index allowed for a separation of recurrent low-grade gliomas on different pathological trajectories, the heightened ratio of phosphocholine to glycerophosphocholine uniformly characterized several forms of glioblastoma multiforme, and the onco-metabolite D-2-hydroxyglutarate was shown to help distinguish secondary from primary grade IV glioma, as well as grade II and III from grade IV glioma. These data provide evidence that metabolite levels are of interest in the assessment of both intra-grade and intra-lesional malignancy. Such information could be used to enhance the diagnostic specificity of in vivo spectroscopy and to aid in the selection of the most appropriate therapy for individual patients.

Identifying malignant transformations in recurrent low grade gliomas using high resolution magic angle spinning spectroscopy.

OBJECTIVE: The objective of this study was to determine whether metabolic parameters derived from ex vivo analysis of tissue samples are predictive of biologic characteristics of recurrent low grade gliomas (LGGs). This was achieved by exploring the use of multivariate pattern recognition methods to generate statistical models of the metabolic characteristics of recurrent LGGs that correlate with aggressive biology and poor clinical outcome. METHODS: Statistical models were constructed to distinguish between patients with recurrent gliomas that had undergone malignant transformation to a higher grade and those that remained grade 2. The pattern recognition methods explored in this paper include three filter-based feature selection methods (chi-square, gain ratio, and two-way conditional probability), a genetic search wrapper-based feature subset selection algorithm, and five classification algorithms (linear discriminant analysis, logistic regression, functional trees, support vector machines, and decision stump logit boost). The accuracy of each pattern recognition framework was evaluated using leave-one-out cross-validation and bootstrapping. MATERIALS: The population studied included fifty-three patients with recurrent grade 2 gliomas. Among these patients, seven had tumors that transformed to grade 4, twenty-four had tumors that transformed to grade 3, and twenty-two had tumors that remained grade 2. Image-guided tissue samples were obtained from these patients using surgical navigation software. Part of each tissue sample was examined by a pathologist for histological features and for consistency with the tumor grade diagnosis. The other part of the tissue sample was analyzed with ex vivo nuclear magnetic resonance (NMR) spectroscopy. RESULTS: Distinguishing between recurrent low grade gliomas that transformed to a higher grade and those that remained grade 2 was achieved with 96% accuracy, using areas of the ex vivo NMR spectrum corresponding to myoinositol, 2-hydroxyglutarate, hypo-taurine, choline, glycerophosphocholine, phosphocholine, glutathione, and lipid. Logistic regression and decision stump boosting models were able to distinguish between recurrent gliomas that transformed to a higher grade and those that did not with 100% training accuracy (95% confidence interval [93-100%]), 96% leave-one-out cross-validation accuracy (95% confidence interval [87-100%]), and 96% bootstrapping accuracy (95% confidence interval [95-97%]). Linear discriminant analysis, functional trees, and support vector machines were able to achieve leave-one-out cross-validation accuracy above 90% and bootstrapping accuracy above 85%. The three feature ranking methods were comparable in performance. CONCLUSIONS: This study demonstrates the feasibility of using quantitative pattern recognition methods for the analysis of metabolic data from brain tissue obtained during the surgical resection of gliomas. All pattern recognition techniques provided good diagnostic accuracies, though logistic regression and decision stump boosting slightly outperform the other classifiers. These methods identified biomarkers that can be used to detect malignant transformations in individual low grade gliomas, and can lead to a timely change in treatment for each patient.