Prognostic models for COVID-19 needed updating to warrant transportability over time and space.

BACKGROUND: Supporting decisions for patients who present to the emergency department (ED) with COVID-19 requires accurate prognostication. We aimed to evaluate prognostic models for predicting outcomes in hospitalized patients with COVID-19, in different locations and across time. METHODS: We included patients who presented to the ED with suspected COVID-19 and were admitted to 12 hospitals in the New York City (NYC) area and 4 large Dutch hospitals. We used second-wave patients who presented between September and December 2020 (2137 and 3252 in NYC and the Netherlands, respectively) to evaluate models that were developed on first-wave patients who presented between March and August 2020 (12,163 and 5831). We evaluated two prognostic models for in-hospital death: The Northwell COVID-19 Survival (NOCOS) model was developed on NYC data and the COVID Outcome Prediction in the Emergency Department (COPE) model was developed on Dutch data. These models were validated on subsequent second-wave data at the same site (temporal validation) and at the other site (geographic validation). We assessed model performance by the Area Under the receiver operating characteristic Curve (AUC), by the E-statistic, and by net benefit. RESULTS: Twenty-eight-day mortality was considerably higher in the NYC first-wave data (21.0%), compared to the second-wave (10.1%) and the Dutch data (first wave 10.8%; second wave 10.0%). COPE discriminated well at temporal validation (AUC 0.82), with excellent calibration (E-statistic 0.8%). At geographic validation, discrimination was satisfactory (AUC 0.78), but with moderate over-prediction of mortality risk, particularly in higher-risk patients (E-statistic 2.9%). While discrimination was adequate when NOCOS was tested on second-wave NYC data (AUC 0.77), NOCOS systematically overestimated the mortality risk (E-statistic 5.1%). Discrimination in the Dutch data was good (AUC 0.81), but with over-prediction of risk, particularly in lower-risk patients (E-statistic 4.0%). Recalibration of COPE and NOCOS led to limited net benefit improvement in Dutch data, but to substantial net benefit improvement in NYC data. CONCLUSIONS: NOCOS performed moderately worse than COPE, probably reflecting unique aspects of the early pandemic in NYC. Frequent updating of prognostic models is likely to be required for transportability over time and space during a dynamic pandemic.

5-Year Activity and Participation Outcomes of the First Successful Pediatric Bilateral Hand Transplantation: A Case Report.

AIMS: Describe the 5-year outcomes of the first successful pediatric bilateral hand transplantation. METHODS: The child underwent quadrimembral amputation at age two and received bilateral hand allografts at age eight. Rehabilitation included biomechanical, neurorehabilitation, and occupational approaches in acute and outpatient settings. Therapist observed outcomes, patient-reported measures, and parent-reported measures were repeated over a 5-year period. RESULTS: Observation assessments revealed functional dexterity skills and modified independence to full independence with self-care activities. The parent reported the child had moderate difficulty with upper extremity functioning 25-, 41-, and 48-months post-transplantation, and mild difficulty at 60-months; the child reported no difficulties in this domain at 41 months. Five years post-transplantation the child reported enjoying many age-appropriate activities, and high-quality peer relations were endorsed by both parent and child. CONCLUSION: The child developed hand movements for daily activities and was completing daily activities with improved efficiency. Health-related quality of life outcomes were favorable.

Identification of cytokine-specific sensory neural signals by decoding murine vagus nerve activity.

The nervous system maintains physiological homeostasis through reflex pathways that modulate organ function. This process begins when changes in the internal milieu (e.g., blood pressure, temperature, or pH) activate visceral sensory neurons that transmit action potentials along the vagus nerve to the brainstem. IL-1ß and TNF, inflammatory cytokines produced by immune cells during infection and injury, and other inflammatory mediators have been implicated in activating sensory action potentials in the vagus nerve. However, it remains unclear whether neural responses encode cytokine-specific information. Here we develop methods to isolate and decode specific neural signals to discriminate between two different cytokines. Nerve impulses recorded from the vagus nerve of mice exposed to IL-1ß and TNF were sorted into groups based on their shape and amplitude, and their respective firing rates were computed. This revealed sensory neural groups responding specifically to TNF and IL-1ß in a dose-dependent manner. These cytokine-mediated responses were subsequently decoded using a Naive Bayes algorithm that discriminated between no exposure and exposures to IL-1ß and TNF (mean successful identification rate 82.9 ± 17.8%, chance level 33%). Recordings obtained in IL-1 receptor-KO mice were devoid of IL-1ß-related signals but retained their responses to TNF. Genetic ablation of TRPV1 neurons attenuated the vagus neural signals mediated by IL-1ß, and distal lidocaine nerve block attenuated all vagus neural signals recorded. The results obtained in this study using the methodological framework suggest that cytokine-specific information is present in sensory neural signals within the vagus nerve.

Measuring Functional Outcomes After Bilateral Vascularized Composite Hand Allotransplantation: A Report of 3 Cases.

PURPOSE: Currently, numerous assessment tools are available to measure functional outcomes after bilateral hand transplantation. The purpose of this article is to present our experience utilizing quantitative assessment tools for functional evaluation and to describe our results after bilateral hand transplantation. METHODS: A single surgeon's experience with bilateral hand transplantation from 2011 to 2016 was retrospectively reviewed. Three bilateral hand transplantations were performed in 2 adults and 1 child. A minimum 2-year follow-up evaluation was available. For the adult patients, postoperative outcome measures included patient-reported pain and disability scores, return of sensation, muscle strength, range of motion, and return to work/activities of daily living, the Hand Transplant Scoring System (HTSS), the Sollerman hand function test, and complications. For our pediatric patient, postoperative outcome measures included the Functional Independence Measure for children (WeeFIM) scale, a functional independence measure for children, the 9-Hole Peg Test, the Box and Block test, and complications. RESULTS: Our 2 adult patients were age 28 and our pediatric patient was age 8 at transplantation. Follow-up ranged from 2 to 7 years. The functional assessments were performed over a period from 11 to 48 months after transplantation. Both adult patients achieved functional independence and the HTSS, Short Form-36 Health Score (SF-36), DASH, and Sollerman tests demonstrated sequential improvement compared with pretransplantation scores assessed with the use of prostheses. Our pediatric patient demonstrated improvement in his Box and Block test score for each hand at sequential visits after transplantation. His 9-Hole Peg Test demonstrated improvement, and his WeeFIM assessment at 20 months indicated a greater level of independence. CONCLUSIONS: Patient-reported outcomes and the Sollerman test when used in addition to the HTSS appear to reflect functional improvement in adult patients after bilateral hand transplantation. Although children pose a unique challenge with functional assessment, we found the WeeFIM assessment, 9-Hole Peg Test, and the Box and Block test helpful in evaluating functional outcomes in our pediatric patient. TYPE OF STUDY/LEVEL OF EVIDENCE: Therapeutic V.

Longitudinal dynamic clinical phenotypes of in-hospital COVID-19 patients across three dominant virus variants in New York.

BACKGROUND: COVID-19 is a challenging disease to characterize given its wide-ranging heterogeneous symptomatology. Several studies have attempted to extract clinical phenotypes but often relied on data from small patient cohorts, usually limited to only one viral variant and utilizing a static snapshot of patient data. OBJECTIVE: This study aimed to identify clinical phenotypes of hospitalized COVID-19 patients and investigate their longitudinal dynamics throughout the pandemic, with the goal to relate these phenotypes to clinical outcomes and treatment strategies. METHODS: We utilized routinely collected demographic and clinical data throughout the hospitalization of 38,077 patients admitted between 3/2020 to 5/2022, in 12 New York hospitals. Uniform Manifold Approximation and Projection and agglomerative hierarchical clustering were used to derive the clusters, followed by exploratory data analysis to compare the prevalence of comorbidities and treatments per cluster. RESULTS: 4 distinct clinical phenotypes remained robust in multi-site validation and were associated with different mortality rates. The temporal progression of these phenotypes throughout the COVID-19 pandemic demonstrated increased variability across the waves of the three dominant viral variants (alpha, delta, omicron). Longitudinal analysis evaluating changes in clinical phenotypes of each patient throughout the course of a 4-week hospital stay exemplified the dynamic nature of the disease progression. Factors such as sex, race/ethnicity and specific treatment modalities revealed significant and clinically relevant differences between the observed phenotypes. CONCLUSIONS: Our proposed methodology has the potential of enabling clinicians and policy makers to draw evidence-based conclusions for guiding treatment modalities in a dynamic fashion.

Factors associated with immune responses to SARS-CoV-2 vaccination in individuals with autoimmune diseases.

Patients with autoimmune diseases are at higher risk for severe infection due to their underlying disease and immunosuppressive treatments. In this real-world observational study of 463 patients with autoimmune diseases, we examined risk factors for poor B and T cell responses to SARS-CoV-2 vaccination. We show a high frequency of inadequate anti-spike IgG responses to vaccination and boosting in the autoimmune population but minimal suppression of T cell responses. Low IgG responses in B cell-depleted patients with multiple sclerosis (MS) were associated with higher CD8 T cell responses. By contrast, patients taking mycophenolate mofetil (MMF) exhibited concordant suppression of B and T cell responses. Treatments with highest risk for low anti-spike IgG response included B cell depletion within the last year, fingolimod, and combination treatment with MMF and belimumab. Our data show that the mRNA-1273 (Moderna) vaccine is the most effective vaccine in the autoimmune population. There was minimal induction of either disease flares or autoantibodies by vaccination and no significant effect of preexisting anti-type I IFN antibodies on either vaccine response or breakthrough infections. The low frequency of breakthrough infections and lack of SARS-CoV-2-related deaths suggest that T cell immunity contributes to protection in autoimmune disease.

The association of clinically relevant variables with chest radiograph lung disease burden quantified in real-time by radiologists upon initial presentation in individuals hospitalized with COVID-19.

OBJECTIVES: We aimed to correlate lung disease burden on presentation chest radiographs (CXR), quantified at the time of study interpretation, with clinical presentation in patients hospitalized with coronavirus disease 2019 (COVID-19). MATERIAL AND METHODS: This retrospective cross-sectional study included 5833 consecutive adult patients, aged 18 and older, hospitalized with a diagnosis of COVID-19 with a CXR quantified in real-time while hospitalized in 1 of 12 acute care hospitals across a multihospital integrated healthcare network between March 24, 2020, and May 22, 2020. Lung disease burden was quantified in real-time by 118 radiologists on 5833 CXR at the time of exam interpretation with each lung annotated by the degree of lung opacity as clear (0%), mild (1-33%), moderate (34-66%), or severe (67-100%). CXR findings were classified as (1) clear versus disease, (2) unilateral versus bilateral, (3) symmetric versus asymmetric, or (4) not severe versus severe. Lung disease burden was characterized on initial presentation by patient demographics, co-morbidities, vital signs, and lab results with chi-square used for univariate analysis and logistic regression for multivariable analysis. RESULTS: Patients with severe lung disease were more likely to have oxygen impairment, an elevated respiratory rate, low albumin, high lactate dehydrogenase, and high ferritin compared to non-severe lung disease. A lack of opacities in COVID-19 was associated with a low estimated glomerular filtration rate, hypernatremia, and hypoglycemia. CONCLUSIONS: COVID-19 lung disease burden quantified in real-time on presentation CXR was characterized by demographics, comorbidities, emergency severity index, Charlson Comorbidity Index, vital signs, and lab results on 5833 patients. This novel approach to real-time quantified chest radiograph lung disease burden by radiologists needs further research to understand how this information can be incorporated to improve clinical care for pulmonary-related diseases.. An absence of opacities in COVID-19 may be associated with poor oral intake and a prerenal state as evidenced by the association of clear CXRs with a low eGFR, hypernatremia, and hypoglycemia.

Organ- and function-specific anatomical organization of vagal fibers supports fascicular vagus nerve stimulation.

Vagal fibers travel inside fascicles and form branches to innervate organs and regulate organ functions. Existing vagus nerve stimulation (VNS) therapies activate vagal fibers non-selectively, often resulting in reduced efficacy and side effects from non-targeted organs. The transverse and longitudinal arrangement of fibers inside the vagal trunk with respect to the functions they mediate and organs they innervate is unknown, however it is crucial for selective VNS. Using micro-computed tomography imaging, we tracked fascicular trajectories and found that, in swine, sensory and motor fascicles are spatially separated cephalad, close to the nodose ganglion, and merge caudad, towards the lower cervical and upper thoracic region; larynx-, heart- and lung-specific fascicles are separated caudad and progressively merge cephalad. Using quantified immunohistochemistry at single fiber level, we identified and characterized all vagal fibers and found that fibers of different morphological types are differentially distributed in fascicles: myelinated afferents and efferents occupy separate fascicles, myelinated and unmyelinated efferents also occupy separate fascicles, and small unmyelinated afferents are widely distributed within most fascicles. We developed a multi-contact cuff electrode to accommodate the fascicular structure of the vagal trunk and used it to deliver fascicle-selective cervical VNS in anesthetized and awake swine. Compound action potentials from distinct fiber types, and physiological responses from different organs, including laryngeal muscle, cough, breathing, and heart rate responses are elicited in a radially asymmetric manner, with consistent angular separations that agree with the documented fascicular organization. These results indicate that fibers in the trunk of the vagus nerve are anatomically organized according to functions they mediate and organs they innervate and can be asymmetrically activated by fascicular cervical VNS.

Neurological and Functional Outcomes after Pediatric Stroke.

Pediatric stroke results in life-long morbidity for many patients, but the outcomes can vary depending on factors such as age of injury, or mechanism, size, and location of stroke. In this review, we summarize the current understanding of outcomes in different neurological domains (eg, motor, cognitive, language) for children with stroke of different mechanisms (ie, arterial ischemic stroke, cerebral sinus venous thrombosis, and hemorrhagic stroke), but with a focus on World Health Organization International Classification for Functioning, Disability, and Health (ICF-CY) framework for measuring health and disability for children and youth. We describe outcomes for the population as a whole and certain factors that may further refine prognostication.

Development and validation of self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients.

Clinical prognostic models can assist patient care decisions. However, their performance can drift over time and location, necessitating model monitoring and updating. Despite rapid and significant changes during the pandemic, prognostic models for COVID-19 patients do not currently account for these drifts. We develop a framework for continuously monitoring and updating prognostic models and apply it to predict 28-day survival in COVID-19 patients. We use demographic, laboratory, and clinical data from electronic health records of 34912 hospitalized COVID-19 patients from March 2020 until May 2022 and compare three modeling methods. Model calibration performance drift is immediately detected with minor fluctuations in discrimination. The overall calibration on the prospective validation cohort is significantly improved when comparing the dynamically updated models against their static counterparts. Our findings suggest that, using this framework, models remain accurate and well-calibrated across various waves, variants, race and sex and yield positive net-benefits.

High Frequency of COVID-19 Vaccine Hesitancy among Canadians Immunized for Influenza: A Cross-Sectional Survey.

We assessed the frequency and correlates of COVID-19 vaccine hesitancy before Canada's vaccine rollout. A cross-sectional vaccine hesitancy survey was completed by consecutive patients/family members/staff who received the influenza vaccine at McGill University affiliated hospitals. Based on the self-reported likelihood of receiving a future vaccine (scale 0-10), the following three groups were defined: non-hesitant (score 10), mildly hesitant (7.1-9.9), and significantly hesitant (0-7). Factors associated with vaccine hesitancy were assessed with multivariate logistic regression analyses and binomial logistic regression machine learning modelling. The survey was completed by 1793 people. Thirty-seven percent of participants (n = 669) were hesitant (mildly: 315 (17.6%); significantly: 354 (19.7%)). Lower education levels, opposition and uncertainty about vaccines being mandatory, feelings of not receiving enough information about COVID-19 prevention, perceived social pressure to get a future vaccine, vaccine safety concerns, uncertainty regarding the vaccine risk-benefit ratio, and distrust towards pharmaceutical companies were factors associated with vaccine hesitancy. Vaccine safety concerns and opposition to mandatory vaccinations were the strongest correlates of vaccine hesitancy in both the logistic regressions and the machine learning model. In conclusion, in this study, over a third of people immunized for influenza before the COVID-19 vaccine rollout expressed some degree of vaccine hesitancy. Effectively addressing COVID-19 vaccine safety concerns may enhance vaccine uptake.

Pediatric Hand Transplantation: A Decision Analysis.

Background: The first successful bilateral pediatric hand transplant was performed in 2015. Previous hand transplant decision analysis models have focused on the adult population. This model principally aimed to determine whether adverse outcomes associated with immunosuppression outweigh the benefits of performing bilateral hand transplant surgery in a pediatric candidate. The model also conceptualized the valuation of losing years of life and sought to determine the impact of that valuation on the surgical decision. Methods: A decision model compared undergoing bilateral hand transplant surgery with using prosthetics for an 8-year-old patient. The outcome measure used was quality adjusted life years (QALYs), and sensitivity analysis was performed on the immunosuppressive risks associated with the surgical decision, as well as the perceived valuation of aversion to life years lost. Results: The decision to perform surgery was marginally optimal compared to the prosthetic decision (50.11 QALY vs. 47.95 QALY). A Monte Carlo simulation revealed that this difference may be too marginal to detect an optimal decision (50.14 ± 8.28 QALY vs. 47.95 ± 2.12 QALY). Sensitivity analysis identified decision thresholds related to immunosuppression risks (P = 29% vs. P = 33% modeled), and a trend of increasing risk as a patient is more averse to losing life years. Conclusions: The marginally optimal treatment strategy currently is bilateral hand transplant, compared to prosthetics for pediatric patients. Key determinants of the future optimal strategy will be whether immunosuppressive regimens become safer, with a reduced risk of losing life years due to immunosuppressive complications, and whether prosthetics become more acceptable and enable higher functioning.

A method to quantify autonomic nervous system function in healthy, able-bodied individuals.

BACKGROUND: The autonomic nervous system (ANS) maintains physiological homeostasis in various organ systems via parasympathetic and sympathetic branches. ANS function is altered in common diffuse and focal conditions and heralds the beginning of environmental and disease stresses. Reliable, sensitive, and quantitative biomarkers, first defined in healthy participants, could discriminate among clinically useful changes in ANS function. This framework combines controlled autonomic testing with feature extraction during physiological responses. METHODS: Twenty-one individuals were assessed in two morning and two afternoon sessions over two weeks. Each session included five standard clinical tests probing autonomic function: squat test, cold pressor test, diving reflex test, deep breathing, and Valsalva maneuver. Noninvasive sensors captured continuous electrocardiography, blood pressure, breathing, electrodermal activity, and pupil diameter. Heart rate, heart rate variability, mean arterial pressure, electrodermal activity, and pupil diameter responses to the perturbations were extracted, and averages across participants were computed. A template matching algorithm calculated scaling and stretching features that optimally fit the average to an individual response. These features were grouped based on test and modality to derive sympathetic and parasympathetic indices for this healthy population. RESULTS: A significant positive correlation (p = 0.000377) was found between sympathetic amplitude response and body mass index. Additionally, longer duration and larger amplitude sympathetic and longer duration parasympathetic responses occurred in afternoon testing sessions; larger amplitude parasympathetic responses occurred in morning sessions. CONCLUSIONS: These results demonstrate the robustness and sensitivity of an algorithmic approach to extract multimodal responses from standard tests. This novel method of quantifying ANS function can be used for early diagnosis, measurement of disease progression, or treatment evaluation. TRIAL REGISTRATION: This study registered with Clinicaltrials.gov , identifier NCT04100486 . Registered September 24, 2019, https://www.clinicaltrials.gov/ct2/show/NCT04100486 .

Machine learning to assist clinical decision-making during the COVID-19 pandemic.

BACKGROUND: The number of cases from the coronavirus disease 2019 (COVID-19) global pandemic has overwhelmed existing medical facilities and forced clinicians, patients, and families to make pivotal decisions with limited time and information. MAIN BODY: While machine learning (ML) methods have been previously used to augment clinical decisions, there is now a demand for "Emergency ML." Throughout the patient care pathway, there are opportunities for ML-supported decisions based on collected vitals, laboratory results, medication orders, and comorbidities. With rapidly growing datasets, there also remain important considerations when developing and validating ML models. CONCLUSION: This perspective highlights the utility of evidence-based prediction tools in a number of clinical settings, and how similar models can be deployed during the COVID-19 pandemic to guide hospital frontlines and healthcare administrators to make informed decisions about patient care and managing hospital volume.

Development and Validation of a Survival Calculator for Hospitalized Patients with COVID-19.

BACKGROUND: Chinese studies reported predictors of severe disease and mortality associated with coronavirus disease 2019 (COVID-19). A generalizable and simple survival calculator based on data from US patients hospitalized with COVID-19 has not yet been introduced. OBJECTIVE: Develop and validate a clinical tool to predict 7-day survival in patients hospitalized with COVID-19. DESIGN: Retrospective and prospective cohort study. SETTING: Thirteen acute care hospitals in the New York City area. PARTICIPANTS: Adult patients hospitalized with a confirmed diagnosis of COVID-19. The development and internal validation cohort included patients hospitalized between March 1 and May 6, 2020. The external validation cohort included patients hospitalized between March 1 and May 5, 2020. MEASUREMENTS: Demographic, laboratory, clinical, and outcome data were extracted from the electronic health record. Optimal predictors and performance were identified using least absolute shrinkage and selection operator (LASSO) regression with receiver operating characteristic curves and measurements of area under the curve (AUC). RESULTS: The development and internal validation cohort included 11 095 patients with a median age of 65 years [interquartile range (IQR) 54-77]. Overall 7-day survival was 89%. Serum blood urea nitrogen, age, absolute neutrophil count, red cell distribution width, oxygen saturation, and serum sodium were identified as the 6 optimal of 42 possible predictors of survival. These factors constitute the NOCOS (Northwell COVID-19 Survival) Calculator. Performance in the internal validation, prospective validation, and external validation were marked by AUCs of 0.86, 0.82, and 0.82, respectively. LIMITATIONS: All participants were hospitalized within the New York City area. CONCLUSIONS: The NOCOS Calculator uses 6 factors routinely available at hospital admission to predict 7-day survival for patients hospitalized with COVID-19. The calculator is publicly available at https://feinstein.northwell.edu/NOCOS.

An impedance matching algorithm for common-mode interference removal in vagus nerve recordings.

BACKGROUND: The peripheral nervous system is involved in a multitude of physiological functions. Recording neural signals provides information that can be used by diagnostic bioelectronic medicine devices, closed-loop neuromodulation therapies and other neuroprosthetic applications. The ability to accurately record these signals is challenging, due to the presence of various biological and instrument-related interference sources. NEW METHOD: We developed a common-mode interference rejection algorithm based on an impedance matching approach for bipolar cuff electrodes. Two unipolar channels were recorded from the two electrode contacts of a bipolar cuff. The impedance mismatch was estimated and used to correct one of the two channels. RESULTS: When applied to electrocardiographic (ECG) artifacts collected from three mice using CorTec electrodes, the algorithm reduced the interference to noise ratio (INR) over simple subtraction by 12 dB on average. The algorithm also reduced the INR of stimulation artifacts in recordings from three rats collected using flexible electrodes by an additional 2.4 dB. In the same experiments evoked electromyographic (EMG) interference was suppressed by 1.3 dB. COMPARISON WITH EXISTING METHODS: Simple subtraction is the common approach for reducing common-mode interference in bipolar recordings, however impedance mismatches that exist or emerge compromise its efficiency. CONCLUSIONS: The algorithm significantly reduced the common-mode interference from ECG artifacts, stimulation artifacts, and evoked EMG interference, while retaining neural signals, in two animal models and two recording setups. This approach can be used in a variety of different neurophysiological setups to remove common-mode interference from a variety of sources.

Identification of hypoglycemia-specific neural signals by decoding murine vagus nerve activity.

BACKGROUND: Glucose is a crucial energy source. In humans, it is the primary sugar for high energy demanding cells in brain, muscle and peripheral neurons. Deviations of blood glucose levels from normal levels for an extended period of time is dangerous or even fatal, so regulation of blood glucose levels is a biological imperative. The vagus nerve, comprised of sensory and motor fibres, provides a major anatomical substrate for regulating metabolism. While prior studies have implicated the vagus nerve in the neurometabolic interface, its specific role in either the afferent or efferent arc of this reflex remains elusive. METHODS: Here we use recently developed methods to isolate and decode specific neural signals acquired from the surface of the vagus nerve in BALB/c wild type mice to identify those that respond robustly to hypoglycemia. We also attempted to decode neural signals related to hyperglycemia. In addition to wild type mice, we analyzed the responses to acute hypo- and hyperglycemia in transient receptor potential cation channel subfamily V member 1 (TRPV1) cell depleted mice. The decoding algorithm uses neural signals as input and reconstructs blood glucose levels. RESULTS: Our algorithm was able to reconstruct the blood glucose levels with high accuracy (median error 18.6 mg/dl). Hyperglycemia did not induce robust vagus nerve responses, and deletion of TRPV1 nociceptors attenuated the hypoglycemia-dependent vagus nerve signals. CONCLUSION: These results provide insight to the sensory vagal signaling that encodes hypoglycemic states and suggest a method to measure blood glucose levels by decoding nerve signals. TRIAL REGISTRATION: Not applicable.