Paravertebral versus Pectoralis-II (Interpectoral and Pectoserratus) Nerve Blocks for Postoperative Analgesia after Non-Mastectomy Breast Surgery: A Randomized, Controlled, Observer-Masked Noninferiority Trial.

BACKGROUND: Pectoralis-II and paravertebral nerve blocks are both used to treat pain following breast surgery. Most previous studies involving mastectomy identified little difference of significance between the two approaches. Whether this is also accurate for non-mastectomy procedures remains unknown. METHODS: Participants undergoing uni- or bilateral non-mastectomy breast surgery anticipated to have at least moderate postoperative pain were randomized to a pectoralis-II or paravertebral block (90 mg ropivacaine/side for both). Surgeons and recovery room staff were masked to treatment group assignment, and participants were not informed of their treatment group. Injectate for pectoralis-II blocks was ropivacaine 0.3% (30 mL) per side. Injectate for paravertebral blocks was ropivacaine 0.5% (9 mL in each of 2 levels) per side. We hypothesized that pectoralis-II blocks would have noninferior (1) analgesia [Numeric Rating Scale] and (2) cumulative opioid consumption within the operating and recovery rooms combined (dual primary outcomes). The study was adequately powered with n=100, but the target enrollment was raised to n=150 to account for higher-than-anticipated variability. RESULTS: The trial was ended prematurely with 119 (79%) of the original target of 150 participants enrolled due to (masked) surgeon preference. Within the recovery room, pain scores were higher in participants with pectoralis-II (n=60) than paravertebral blocks (n=59): median [IQR] 3.3 [2.3, 4.8] vs 1.3 [0, 3.6]; 95% CI: 0.5 to 2.6; P < 0.001. Similarly, intravenous morphine equivalents were higher in the pectoralis-II group: 17.5 [12.5, 21.9] vs 10.0 mg [10, 20]; 95% CI: 0.1 to 7.5; P = 0.004. No block-related adverse events were identified in either group. CONCLUSIONS: Following non-mastectomy breast surgery, 2-level paravertebral blocks provided superior analgesia and opioid sparing compared with pectoralis-II blocks. This is a contrary finding to the majority of studies in patients having mastectomy in which little significant difference was identified between the two types of blocks.

Rapid Acute Coronary Syndrome Evaluation Over One Hour With High-Sensitivity Cardiac Troponin I: A United States-Based Stepped-Wedge, Randomized Trial.

STUDY OBJECTIVE: The real-world effectiveness and safety of a 0/1-hour accelerated protocol using high-sensitivity cardiac troponin (hs-cTn) to exclude myocardial infarction (MI) compared to routine care in the United States is uncertain. The objective was to compare a 0/1-hour accelerated protocol for evaluation of MI to a 0/3-hour standard care protocol. METHODS: The RACE-IT trial was a stepped-wedge, randomized trial across 9 emergency departments (EDs) that enrolled 32,609 patients evaluated for possible MI from July 2020 through April 2021. Patients undergoing high-sensitivity cardiac troponin I testing with concentrations less than or equal to 99th percentile were included. Patients who had MI excluded by the 0/1-hour protocol could be discharged from the ED. Patients in the standard care protocol had 0- and 3-hour troponin testing and application of a modified HEART score to be eligible for discharge. The primary endpoint was the proportion of patients discharged from the ED without 30-day death or MI. RESULTS: There were 13,505 and 19,104 patients evaluated in the standard care and accelerated protocol groups, respectively, of whom 19,152 (58.7%) were discharged directly from the ED. There was no significant difference in safe discharges between standard care and the accelerated protocol (59.5% vs 57.8%; adjusted odds ratio (aOR)=1.05, 95% confidence interval [CI] 0.95 to 1.16). At 30 days, there were 90 deaths or MIs with 38 (0.4%) in the standard care group and 52 (0.4%) in the accelerated protocol group (aOR=0.84, 95% CI 0.43 to 1.68). CONCLUSION: A 0/1-hour accelerated protocol using high-sensitivity cardiac troponin I did not lead to more safe ED discharges compared with standard care.

Neuromechanical wave resonance in jellyfish swimming.

For organisms to have robust locomotion, their neuromuscular organization must adapt to constantly changing environments. In jellyfish, swimming robustness emerges when marginal pacemakers fire action potentials throughout the bell's motor nerve net, which signals the musculature to contract. The speed of the muscle activation wave is dictated by the passage times of the action potentials. However, passive elastic material properties also influence the emergent kinematics, with time scales independent of neuromuscular organization. In this multimodal study, we examine the interplay between these two time scales during turning. A three-dimensional computational fluid-structure interaction model of a jellyfish was developed to determine the resulting emergent kinematics, using bidirectional muscular activation waves to actuate the bell rim. Activation wave speeds near the material wave speed yielded successful turns, with a 76-fold difference in turning rate between the best and worst performers. Hyperextension of the margin occurred only at activation wave speeds near the material wave speed, suggesting resonance. This hyperextension resulted in a 34-fold asymmetry in the circulation of the vortex ring between the inside and outside of the turn. Experimental recording of the activation speed confirmed that jellyfish actuate within this range, and flow visualization using particle image velocimetry validated the corresponding fluid dynamics of the numerical model. This suggests that neuromechanical wave resonance plays an important role in the robustness of an organism's locomotory system and presents an undiscovered constraint on the evolution of flexible organisms. Understanding these dynamics is essential for developing actuators in soft body robotics and bioengineered pumps.

A scoping review of artificial intelligence in medical education: BEME Guide No. 84.

BACKGROUND: Artificial Intelligence (AI) is rapidly transforming healthcare, and there is a critical need for a nuanced understanding of how AI is reshaping teaching, learning, and educational practice in medical education. This review aimed to map the literature regarding AI applications in medical education, core areas of findings, potential candidates for formal systematic review and gaps for future research. METHODS: This rapid scoping review, conducted over 16 weeks, employed Arksey and O'Malley's framework and adhered to STORIES and BEME guidelines. A systematic and comprehensive search across PubMed/MEDLINE, EMBASE, and MedEdPublish was conducted without date or language restrictions. Publications included in the review spanned undergraduate, graduate, and continuing medical education, encompassing both original studies and perspective pieces. Data were charted by multiple author pairs and synthesized into various thematic maps and charts, ensuring a broad and detailed representation of the current landscape. RESULTS: The review synthesized 278 publications, with a majority (68%) from North American and European regions. The studies covered diverse AI applications in medical education, such as AI for admissions, teaching, assessment, and clinical reasoning. The review highlighted AI's varied roles, from augmenting traditional educational methods to introducing innovative practices, and underscores the urgent need for ethical guidelines in AI's application in medical education. CONCLUSION: The current literature has been charted. The findings underscore the need for ongoing research to explore uncharted areas and address potential risks associated with AI use in medical education. This work serves as a foundational resource for educators, policymakers, and researchers in navigating AI's evolving role in medical education. A framework to support future high utility reporting is proposed, the FACETS framework.

Float like a butterfly, swim like a biohybrid neuromuscular robot.

A butterfly-like robot swims using an electronic device to stimulate human-derived motor neurons and cardiac muscle cells.

Experimental Studies of Bioinspired Shark Denticles for Drag Reduction.

Shark skin is composed of denticles, or complex scale-like features, which have been shown to reduce drag in turbulent and laminar flow. The denticle crown features undulating structures called riblets that interact with the turbulent boundary layer to reduce drag. Two mechanisms thought to contribute to the drag-reducing properties of riblets include the lifting of streamwise vortices and the hampering of spanwise vortex interactions to reduce crossflow, which could translate to similar flow mechanisms for denticles. Because of the varied morphologies of dermal denticles on different shark species, which also depend on body location, the impact of these denticle geometries on flow is of interest to the biology community, including related fields such as fluid mechanics and oceanography. This review highlights the past 15 years of manufacturing techniques and experimental measurements of drag over denticle-inspired surface structures, including real shark skin samples and engineered denticles and riblets. State-of-the-art additive manufacturing and other techniques are primarily limited to mm-length denticle scales, which have demonstrated drag reduction in lower flow speeds, under 1 m s-1. New manufacturing approaches can create sub-mm length denticles and nanotextured surface structures, which have achieved reported drag reductions of up to 31%. We synthesize results from the literature to illustrate the drag reduction properties of bioinspired denticles and riblets according to their geometry and flow conditions. Using these trends, we suggest design features and focus areas for future research, such as increasing studies of different denticle morphologies, hydrophobicity, antifouling properties, and acoustic noise reduction. Continued work on bioinspired denticles for drag reduction has wider implications in comparative biology and applications to design more energy-efficient, persistent vehicles for environmental monitoring.

The Treatment of Insomnia Secondary to Generalized Anxiety Disorder: Evaluating Escitalopram Use With Concomitant High-Resolution, Relational, Resonance-Based, Electroencephalic Mirroring (HIRREM).

Patient autonomy is important. However, what if a patient seeks out poorly studied treatment options? In this case report, we describe a patient with insomnia secondary to generalized anxiety disorder (GAD) who was prescribed escitalopram but additionally used High-Resolution, Relational, Resonance-Based, Electroencephalic Mirroring (HIRREM) therapy. HIRREM is an electroencephalogram (EEG)-based therapy that has been evaluated for use in the treatment of various conditions including insomnia. However, there has only been one randomized clinical trial supporting the use of HIRREM for insomnia, and the Food and Drug Administration (FDA) has not approved HIRREM for insomnia. A few months after the patient initiated HIRREM therapy and escitalopram cessation, the patient's insomnia did not recur. We propose a case for how we approached educating a patient who was seeking out an alternative poorly tested therapy by helping him perform a cost-benefit analysis composed of treatment efficacy, cost, and side effects.

Valproate-Induced Encephalopathy Presenting at Therapeutic Blood Concentrations: A Case Report and Literature Review.

Patients presenting with hyperammonemic encephalopathy are likely to have hepatic encephalopathy. However, valproate (an anticonvulsant and mood stabilizer) can also cause hyperammonemic encephalopathy and belongs on the differential for patients taking it, especially if there are recent contributory medication changes. We present a case report of a 61-year-old woman with valproate-induced hyperammonemic encephalopathy but with an initial valproate level within the therapeutic range (50-100 mcg/dL). After withholding valproate and before additional treatment could be initiated, she became fully alert and oriented. We present a literature review exploring valproate toxicity and treatment. Our case shows that clinical suspicion for valproate-induced hyperammonemic encephalopathy is warranted even if the valproate level is within the therapeutic range.

The Need for Data Standards and Implementation Policies to Integrate CGM Data into the Electronic Health Record.

The current lack of continuous glucose monitor (CGM) data integration into the electronic health record (EHR) is holding back the use of this wearable technology for patient-generated health data (PGHD). This failure to integrate with other healthcare data inside the EHR disrupts workflows, removes the data from critical patient context, and overall makes the CGM data less useful than it might otherwise be. Many healthcare organizations (HCOs) are either struggling with or delaying designing and implementing CGM data integrations. In this article, the current status of CGM integration is reviewed, goals for integration are proposed, and a consensus plan to engage key stakeholders to facilitate integration is presented.

Retained Diabetes Devices-A Literature Review.

BACKGROUND: Diabetes management and treatment requires the use of many devices that frequently must puncture the skin, creating a risk of unintentional retention in the body as a retained diabetes device. In this article, we reviewed case studies about retained diabetes devices and presented analyses of the success rate of current imaging techniques in identifying retained devices and the success rate of device removal. METHODS: PubMed and Google Scholar were searched for articles about retained diabetes devices. Relevant articles that included sufficient details about discovery and removal of the device were included. The success rate of identification and the success rate of removal of retained devices were both calculated as percentages. RESULTS: Sixteen case studies of retained diabetes devices were identified. These devices included parts of continuous glucose monitors and infusion sets, a lancet, and various types of needles for insulin injection. Each case is presented with details about the year of publication, the retained diabetes device, the company that produced the device, the age and gender of the patient, the type of diabetes that the patient had, the location of the device, the reason for initial discovery of the retained device, the process of locating the device, the success rate for removal of the device, and the removal procedure of the device. Analysis revealed a 100% success rate for the use of imaging technology including X-rays and computed tomography to identify a retained diabetes device. The patients with retained diabetes devices had a 62.5% success rate for eventual removal of the device. CONCLUSIONS: With the increasing use of injected, inserted, and implanted diabetes wearables for digital health, it is likely that some of the devices will detach, break apart, or otherwise become retained in the body. It is important to be aware of available technologies to identify retained diabetes devices so that it will be possible in most cases to surgically remove these devices if they detach or become retained.

Use of Continuous Glucose Monitors by People Without Diabetes: An Idea Whose Time Has Come?

BACKGROUND: Continuous glucose monitor (CGM) systems were originally intended only for people with diabetes. Recently, there has been interest in monitoring glucose concentrations in a variety of other situations. As data accumulate to support the use of CGM systems in additional states unrelated to diabetes, the use of CGM systems is likely to increase accordingly. METHODS: PubMed and Google Scholar were searched for articles about the use of CGM in individuals without diabetes. Relevant articles that included sufficient details were queried to identify what cohorts of individuals were adopting CGM use and to define trends of use. RESULTS: Four clinical user cases were identified: (1) metabolic diseases related to diabetes with a primary dysregulation of the insulin-glucose axis, (2) metabolic diseases without a primary pathophysiologic derangement of the insulin-glucose axis, (3) health and wellness, and (4) elite athletics. Seven trends in the use of CGM systems in people without diabetes were idenfitied which pertained to both FDA-cleared medical grade products as well as anticipated future products, which may be regulated differently based on intended populations and indications for use. CONCLUSIONS: Wearing a CGM has been used not only for diabetes, but with a goal of improving glucose patterns to avoid diabetes, improving mental or physical performance, and promoting motivate healthy behavioral changes. We expect that clinicians will become increasingly aware of (1) glycemic patterns from CGM tracings that predict an increased risk of diabetes, (2) specific metabolic glucotypes from CGM tracings that predict an increased risk of diabetes, and (3) new genetic and genomic biomarkers in the future.

Six Digital Health Technologies That Will Transform Diabetes.

The digital health revolution is transforming the landscape of medicine through innovations in sensor data, software, and wireless communication tools. As one of the most prevalent chronic diseases in the United States, diabetes is particularly impactful as a model disease for which to apply innovation. As with any other newly developed technologies, there are three key questions to consider: 1) How can the technology benefit people with diabetes?, 2) What barriers must be overcome to further advance the technology?, and 3) How will the technology be applied in the future?. In this article, we highlight six areas of innovation that have the potential to reduce the burden of diabetes for individuals living with the condition and their families as well as provide measurable benefits for all stakeholders involved in diabetes care. The six technologies which have the potential to transform diabetes care are (i) telehealth, (ii) incorporation of diabetes digital data into the electronic health record, (iii) qualitative hypoglycemia alarms, (iv) artificial intelligence, (v) cybersecurity of diabetes devices, and (vi) diabetes registries. To be successful, a new digital health technology must be accessible and affordable. Furthermore, the people and communities that would most likely benefit from the technology must be willing to use the innovation in their management of diabetes.

Continuous Subcutaneous Infusion Versus Multiple Daily Injections of Insulin for Pregestational Diabetes in Pregnancy: A Systematic Review and Meta-Analysis.

BACKGROUND: The use of continuous subcutaneous insulin infusion (CSII) therapy in pregnancies affected by pregestational diabetes mellitus (DM) has generated mixed outcome data worthy of further investigation. This systematic review and meta-analysis aims to evaluate clinical outcomes associated with CSII versus multiple daily injections (MDIs) in pregnant persons with pregestational DM. METHODS: A predefined, systematic, librarian-assisted search of MEDLINE (PubMed), Embase, Cochrane Library, Scopus, ClinicalTrials.gov, and World Health Organization International Clinical Trial Registry Platform (published from 2010 to 2022) yielded 3003 studies describing pregnancy outcomes associated with CSII and/or MDI for pregestational DM. The primary exposure was mode of insulin administration, with cesarean delivery and neonatal hypoglycemia as the primary maternal and neonatal outcomes, respectively. Secondary outcomes included hypertensive disorders of pregnancy, first and third-trimester glycemic control, large-for-gestational age (LGA) neonate, preterm birth, neonatal intensive care unit admission, need for respiratory support, hyperbilirubinemia, 5-minute Apgar <7, shoulder dystocia, and perinatal mortality. We calculated pooled odds ratios (OR) with 95% confidence intervals (CI) using random-effects models. RESULTS: Among 39 eligible studies, 39% of the 5518 pregnancies included were exposed to CSII. Odds of cesarean delivery were higher with CSII (20 studies: 63% vs 56%, odds ratio [OR] 1.3 [95% confidence interval (CI) 1.2-1.5]), but we did not identify a difference in the odds of neonatal hypoglycemia (23 studies: 31% vs 34%, OR 1.1 [95% CI 0.9-1.5]). Among secondary outcomes, only the odds of LGA (20 studies: 47% vs 38%, OR 1.4 [95% CI 1.2-1.6]) were higher in individuals using CSII versus MDI. CONCLUSIONS: Use of CSII (vs MDI) for pregestational DM in pregnancy is associated with higher odds of cesarean delivery and delivery of an LGA neonate. Further evaluation of how CSII use may influence neonatal size and delivery route is warranted.

Diabetes Research and Resource Sharing During the COVID-19 Pandemic: A Systematic Review and Experience from an Academic/Non-Profit Resource Website.

BACKGROUND: The magnitude of the response of the diabetes professional community to the COVID-19 pandemic is not known. We aimed to examine diabetes technology research trends and resources offered by professional organizations during this period. METHODS: We explored patterns of the response from the professional diabetes community to the pandemic by (1) systematically searching for articles related to diabetes, COVID-19, and diabetes technologies; (2) examining publication trends of research protocols (clinicaltrials.gov) and preprints (medRxiv); and (3) reviewing online resources from professional organizations including our website (COVIDinDiabetes.org; an Emory University-Diabetes Technology Society collaboration). RESULTS: We identified 492 articles published between December 2019 and December 2022 meeting our inclusion criteria. Telemedicine and continuous glucose monitoring were the most common reported technologies from most parts of the world. The largest number of preprint articles was published in 2020, with a decline in 2021 and 2022. The number of research protocols related to COVID-19 was the highest in 2020 and declined in 2021 and 2022. Resources from organizations included protocols adapted to treat patients with diabetes and COVID-19, training programs, emergency preparedness, and literature on diabetes and COVID-19. On our website (COVIDinDiabetes.org), there were 12 236 visits and 18 149 pageviews, with 1.6 actions per visits, with most visits coming from North America (N = 7233, 54.2%), South America (N = 2663, 21.8%), and Europe (N = 1219). CONCLUSIONS: We conclude that the COVID-19 pandemic promoted unprecedented global research productivity related to diabetes and COVID-19 and that the transition to the use of technology resources has been evident during this period.

A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings.

BACKGROUND: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. METHODS: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. RESULTS: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. CONCLUSION: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

Flapping Soft Fin Deformation Modeling using Planar Laser-Induced Fluorescence Imaging.

Propulsive mechanisms inspired by the fins of various fish species have been increasingly researched, given their potential for improved maneuvering and stealth capabilities in unmanned vehicle systems. Soft materials used in the membranes of these fin mechanisms have proven effective at increasing thrust and efficiency compared with more rigid structures, but it is essential to measure and model the deformations in these soft membranes accurately. This study presents a workflow for characterizing the time-dependent shape deformation of flexible underwater flapping fins using planar laser-induced fluorescence (PLIF). Pigmented polydimethylsiloxane fin membranes with varying stiffnesses (0.38 MPa and 0.82 MPa) are fabricated and mounted to an assembly for actuation in two degrees of freedom: pitch and roll. PLIF images are acquired across a range of spanwise planes, processed to obtain fin deformation profiles, and combined to reconstruct time-varying 3D deformed fin shapes. The data are then used to provide high-fidelity validation for fluid-structure interaction simulations and improve the understanding of the performance of these complex propulsion systems.

SNPAAMapper-Python: A highly efficient genome-wide SNP variant analysis pipeline for Next-Generation Sequencing data.

Currently, there are many publicly available Next Generation Sequencing tools developed for variant annotation and classification. However, as modern sequencing technology produces more and more sequencing data, a more efficient analysis program is desired, especially for variant analysis. In this study, we updated SNPAAMapper, a variant annotation pipeline by converting perl codes to python for generating annotation output with an improved computational efficiency and updated information for broader applicability. The new pipeline written in Python can classify variants by region (Coding Sequence, Untranslated Regions, upstream, downstream, intron), predict amino acid change type (missense, nonsense, etc.), and prioritize mutation effects (e.g., synonymous > non-synonymous) while being faster and more efficient. Our new pipeline works in five steps. First, exon annotation files are generated. Next, the exon annotation files are processed, and gene mapping and feature information files are produced. Afterward, the python scrips classify the variants based on genomic regions and predict the amino acid change category. Lastly, another python script prioritizes and ranks the mutation effects of variants to output the result file. The Python version of SNPAAMapper accomplished the overall speed by running most annotation steps in a substantially shorter time. The Python script can classify variants by region in 53 s compared to 166 s for the Perl script in a test sample run on a Latitude 7480 Desktop computer with 8GB RAM and an Intel Core i5-6300 CPU @ 2.4Ghz. Steps of predicting amino acid change type and prioritizing mutation effects of variants were executed within 1 s for both pipelines. SNPAAMapper-Python was developed and tested on the ClinVar database, a NCBI database of information on genomic variation and its relationship to human health. We believe our developed Python version of SNPAAMapper variant annotation pipeline will benefit the community by elucidating the variant consequence and speed up the discovery of causative genetic variants through whole genome/exome sequencing. Source codes, test data files, instructions, and further explanations are available on the web at https://github.com/BaiLab/SNPAAMapper-Python.

Biohybrid robots: recent progress, challenges, and perspectives.

The past ten years have seen the rapid expansion of the field of biohybrid robotics. By combining engineered, synthetic components with living biological materials, new robotics solutions have been developed that harness the adaptability of living muscles, the sensitivity of living sensory cells, and even the computational abilities of living neurons. Biohybrid robotics has taken the popular and scientific media by storm with advances in the field, moving biohybrid robotics out of science fiction and into real science and engineering. So how did we get here, and where should the field of biohybrid robotics go next? In this perspective, we first provide the historical context of crucial subareas of biohybrid robotics by reviewing the past 10+ years of advances in microorganism-bots and sperm-bots, cyborgs, and tissue-based robots. We then present critical challenges facing the field and provide our perspectives on the vital future steps toward creating autonomous living machines.

Spinal Cord Stimulation for Painful Diabetic Neuropathy.

Spinal cord stimulation (SCS) technology has been recently approved by the US Food and Drug Administration (FDA) for painful diabetic neuropathy (PDN). The treatment involves surgical implantation of electrodes and a power source that delivers electrical current to the spinal cord. This treatment decreases the perception of pain in many chronic pain conditions, such as PDN. The number of patients with PDN treated with SCS and the amount of data describing their outcomes is expected to increase given four factors: (1) the large number of patients with this diagnosis, (2) the poor results that have been obtained for pain relief with pharmacotherapy and noninvasive non-pharmacotherapy, (3) the results to date with investigational SCS technology, and (4) the recent FDA approval of systems that deliver this treatment. Whereas traditional SCS replaces pain with paresthesias, a new form of SCS, called high-frequency 10-kHz SCS, first used for pain in 2015, can relieve PDN pain without causing paresthesias, although not all patients experience pain relief by SCS. This article describes (1) an overview of SCS technology, (2) the use of SCS for diseases other than diabetes, (3) the use of SCS for PDN, (4) a comparison of high-frequency 10-kHz and traditional SCS for PDN, (5) other SCS technology for PDN, (6) deployment of SCS systems, (7) barriers to the use of SCS for PDN, (8) risks of SCS technology, (9) current recommendations for using SCS for PDN, and (10) future developments in SCS.

Painful Diabetic Neuropathy: The Need for New Approaches.

Painful diabetic neuropathy is a common vexing problem for people with diabetes and a costly problem for society. The pathophysiology is not well understood, and no safe and effective mechanistically-based treatment has been identified. Poor glycemic control is a risk factor for painful diabetic neuropathy. Excessive intraneuronal glucose in people with diabetes can be shunted away from physiological glycolysis into multiple pathological pathways associated with neuropathy and pain. The first three treatments that are traditionally offered consist of risk factor reduction, lifestyle modifications, and pharmacological therapy, which includes only three drugs that are approved for this indication by the United States Food and Drug Administration. All of these traditional treatments are often inadequate for relieving neuropathic pain, and thus, new approaches are needed. Modern devices based on neuromodulation technology, which act directly on the nervous system, have been recently cleared by the United States Food and Drug Administration for painful diabetic neuropathy and offer promise as next-in-line therapy when traditional therapies fail.