A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms

Deep learning has made many advances in data classification using electrocardiogram (ECG) waveforms. Over the past decade, data science research has focused on developing artificial intelligence (AI) based models that can analyze ECG waveforms to identify and classify abnormal cardiac rhythms accurately. However, the primary drawback of the current AI models is that most of these models are heavy, computationally intensive, and inefficient in terms of cost for real-time implementation. In this review, we first discuss the current state-of-the-art AI models utilized for ECG-based cardiac rhythm classification. Next, we present some of the upcoming modeling methodologies which have the potential to perform real-time implementation of AI-based heart rhythm diagnosis. These models hold significant promise in being lightweight and computationally efficient without compromising the accuracy. Contemporary models predominantly utilize 12-lead ECG for cardiac rhythm classification and cardiovascular status prediction, increasing the computational burden and making real-time implementation challenging. We also summarize research studies evaluating the potential of efficient data setups to reduce the number of ECG leads without affecting classification accuracy. Lastly, we present future perspectives on AI's utility in precision medicine by providing opportunities for accurate prediction and diagnostics of cardiovascular status in patients.

Open-source software for respiratory rate estimation using single-lead electrocardiograms

Respiratory rate (RR) is a critical vital sign used to assess pulmonary function. Currently, RR estimating instrumentation is specialized and bulky, therefore unsuitable for remote health monitoring. Previously, RR was estimated using proprietary software that extract surface electrocardiogram (ECG) waveform features obtained at several thoracic locations. However, developing a non-proprietary method that uses minimal ECG leads, generally available from mobile cardiac monitors is highly desirable. Here, we introduce an open-source and well-documented Python-based algorithm that estimates RR requiring only single-stream ECG signals. The algorithm was first developed using ECGs from awake, spontaneously breathing adult human subjects. The algorithm-estimated RRs exhibited close linear correlation to the subjects' true RR values demonstrating an R2 of 0.9092 and root mean square error of 2.2 bpm. The algorithm robustness was then tested using ECGs generated by the ischemic hearts of anesthetized, mechanically ventilated sheep. Although the ECG waveforms during ischemia exhibited severe morphologic changes, the algorithm-determined RRs exhibited high fidelity with a resolution of 1 bpm, an absolute error of 0.07 ± 0.07 bpm, and a relative error of 0.67 ± 0.64%. This optimized Python-based RR estimation technique will likely be widely adapted for remote lung function assessment in patients with cardiopulmonary disease.

National trends in the initial diagnosis and management of carpal tunnel syndrome: results from the ELECTS (ELEctrophysiology in Carpal Tunnel Syndrome) study

INTRODUCTION

The optimal role of nerve conduction studies (NCS) in management of carpal tunnel syndrome (CTS) is unclear, with no standardised guidance. This study aimed to identify variation in practice in the initial diagnosis of patients with suspected CTS, alongside evaluating how NCS findings influence clinical decision making.

METHODS

A national multicentre collaborative survey was conducted in 2021. All centres providing surgery for CTS were invited to participate, primarily via social media. All middle-senior grade orthopaedic/plastic surgeons and advanced care practitioners that regularly manage new referrals for suspected CTS were eligible to respond. Local representatives at each participating site submitted their responses to a central team who collated and analysed the results.

RESULTS

A total of 137 healthcare professionals responded from 18 UK NHS Trusts. Of these 137, 124 (91%) reported not employing any validated clinical questionnaires in their routine practice, preferring to rely on clinical diagnosis and/or NCS if available, whereas 84 (61%) utilised NCS to aid diagnosis, with significant differences among professionals with differing experience (p < 0.01). The most common methods for determining the severity of CTS were history, examination and NCS. In symptomatic CTS with confirmatory NCS, over 50% of clinicians would choose surgical decompression as their first-line intervention. In cases of either negative NCS or atypical presentation, 37% and 51%, respectively, would consider conservative management (e.g. splintage) or steroid injection first line.

CONCLUSIONS

With growing waiting lists for NCS and surgery, national consensus guidelines should be developed to support decision making, while maximising efficient utilisation of increasingly constrained resources.

Radiological Criteria for Acceptable Alignment in Paediatric Mid-Shaft Forearm Fractures: A Systematic Review

Introduction

Forearm fractures are common in children. The remodelling capacity of growing long bones in children makes these potentially forgiving injuries, recovering with good outcomes despite minimal intervention. Clinicians rely on radiological characteristics that vary with age to guide treatment decisions and minimise adverse sequelae. The purpose of this review was to consolidate the evidence base of radiological indications for intervention in paediatric mid-shaft forearm fractures.

Materials and methods

The preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines were followed for this review. Citable research output reporting radiological criteria for mid-shaft forearm fractures in paediatric patients (age ≤16 years) was screened and analysed to ascertain acceptable radiological criteria for non-operative management.

Results

A total of 2,059 papers were initially identified; 14 were selected following screening. Sagittal angulation >15°, coronal angulation >10°, and/or >50% (or >1cm) translation were the most common radiological indications for intervention in children aged 0 to 10 years. For children over 10 years of age, the most common radiological indication for intervention was sagittal angulation >10°, coronal angulation >10°, and/or >50% (or >1cm) translation.

Conclusion

This study revealed a scarcity of high-quality evidence to guide management and significant variation in outcome reporting throughout the published literature. Since Noonan and Price's 1998 recommendations, there has been no significant evolution in the evidence-base guided threshold for intervention in paediatric mid-shaft forearm fractures. There remains a pressing need for a robust multicentre observational study using the patient-reported outcome measurement information system (PROMIS) to address this complex and controversial area of uncertainty in paediatric trauma management.

Mapping of Neuro-Cardiac Electrophysiology: Interlinking Epilepsy and Arrhythmia

The interplay between neurology and cardiology has gained significant attention in recent years, particularly regarding the shared pathophysiological mechanisms and clinical comorbidities observed in epilepsy and arrhythmias. Neuro-cardiac electrophysiology mapping involves the comprehensive assessment of both neural and cardiac electrical activity, aiming to unravel the intricate connections and potential cross-talk between the brain and the heart. The emergence of artificial intelligence (AI) has revolutionized the field by enabling the analysis of large-scale data sets, complex signal processing, and predictive modeling. AI algorithms have been applied to neuroimaging, electroencephalography (EEG), electrocardiography (ECG), and other diagnostic modalities to identify subtle patterns, classify disease subtypes, predict outcomes, and guide personalized treatment strategies. In this review, we highlight the potential clinical implications of neuro-cardiac mapping and AI in the management of epilepsy and arrhythmias. We address the challenges and limitations associated with these approaches, including data quality, interpretability, and ethical considerations. Further research and collaboration between neurologists, cardiologists, and AI experts are needed to fully unlock the potential of this interdisciplinary field.

Prospective of Pancreatic Cancer Diagnosis Using Cardiac Sensing

Pancreatic carcinoma (Ca Pancreas) is the third leading cause of cancer-related deaths in the world. The malignancies of the pancreas can be diagnosed with the help of various imaging modalities. An endoscopic ultrasound with a tissue biopsy is so far considered to be the gold standard in terms of the detection of Ca Pancreas, especially for lesions <2 mm. However, other methods, like computed tomography (CT), ultrasound, and magnetic resonance imaging (MRI), are also conventionally used. Moreover, newer techniques, like proteomics, radiomics, metabolomics, and artificial intelligence (AI), are slowly being introduced for diagnosing pancreatic cancer. Regardless, it is still a challenge to diagnose pancreatic carcinoma non-invasively at an early stage due to its delayed presentation. Similarly, this also makes it difficult to demonstrate an association between Ca Pancreas and other vital organs of the body, such as the heart. A number of studies have proven a correlation between the heart and pancreatic cancer. The tumor of the pancreas affects the heart at the physiological, as well as the molecular, level. An overexpression of the SMAD4 gene; a disruption in biomolecules, such as IGF, MAPK, and ApoE; and increased CA19-9 markers are a few of the many factors that are noted to affect cardiovascular systems with pancreatic malignancies. A comprehensive review of this correlation will aid researchers in conducting studies to help establish a definite relation between the two organs and discover ways to use it for the early detection of Ca Pancreas.

Can Stochastic Pacing Restore Heart Rate Variability in Diseased Hearts? An In-vivo Ovine Case Study

Heart rate variability (HRV) is an important clinical parameter that depicts the autonomic balance. Diminished HRV has been associated with diseased hearts and incorporating stochasticity in pacing has been investigated as a potential mechanism for restoring the altered autonomic balance and preventing cardiac arrhythmias. We studied the change in HRV with the development of chronic myocardial infarction (MI) in adult sheep (n=16). Next, we investigated the utility of stochastic pacing in modulating HRV in-vivo in both sham and MI hearts. The propensity of the heart to the development of cardiac alternans, a known precursor to tachyarrhythmias, was studied under three different pacing techniques, namely periodic pacing, stochastic pacing and constant diastolic interval (DI) pacing in one sham and one MI sheep. Autonomic balance was observed to be altered after 6 weeks of chronic MI. Increased heart rate, QTc interval, standard deviation of the R-R intervals and LF/HF ratio was observed in MI hearts. Stochastic pacing was found to be proarrhythmic and increased T-wave alternans burden was observed with increase in stochasticity. Maintaining a constant DI on every beat demonstrated reduced alternans levels compared to both periodic and stochastic pacing.Clinical Relevance-Our results demonstrate that precise control of the diastolic interval may be more beneficial in inhibiting arrhythmias than stochastic pacing.

Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives

The measurement of physiologic pressure helps diagnose and prevent associated health complications. From typical conventional methods to more complicated modalities, such as the estimation of intracranial pressures, numerous invasive and noninvasive tools that provide us with insight into daily physiology and aid in understanding pathology are within our grasp. Currently, our standards for estimating vital pressures, including continuous BP measurements, pulmonary capillary wedge pressures, and hepatic portal gradients, involve the use of invasive modalities. As an emerging field in medical technology, artificial intelligence (AI) has been incorporated into analyzing and predicting patterns of physiologic pressures. AI has been used to construct models that have clinical applicability both in hospital settings and at-home settings for ease of use for patients. Studies applying AI to each of these compartmental pressures were searched and shortlisted for thorough assessment and review. There are several AI-based innovations in noninvasive blood pressure estimation based on imaging, auscultation, oscillometry and wearable technology employing biosignals. The purpose of this review is to provide an in-depth assessment of the involved physiologies, prevailing methodologies and emerging technologies incorporating AI in clinical practice for each type of compartmental pressure measurement. We also bring to the forefront AI-based noninvasive estimation techniques for physiologic pressure based on microwave systems that have promising potential for clinical practice.

Digital Pulmonology Practice with Phonopulmography Leveraging Artificial Intelligence: Future Perspectives Using Dual Microwave Acoustic Sensing and Imaging

Respiratory disorders, being one of the leading causes of disability worldwide, account for constant evolution in management technologies, resulting in the incorporation of artificial intelligence (AI) in the recording and analysis of lung sounds to aid diagnosis in clinical pulmonology practice. Although lung sound auscultation is a common clinical practice, its use in diagnosis is limited due to its high variability and subjectivity. We review the origin of lung sounds, various auscultation and processing methods over the years and their clinical applications to understand the potential for a lung sound auscultation and analysis device. Respiratory sounds result from the intra-pulmonary collision of molecules contained in the air, leading to turbulent flow and subsequent sound production. These sounds have been recorded via an electronic stethoscope and analyzed using back-propagation neural networks, wavelet transform models, Gaussian mixture models and recently with machine learning and deep learning models with possible use in asthma, COVID-19, asbestosis and interstitial lung disease. The purpose of this review was to summarize lung sound physiology, recording technologies and diagnostics methods using AI for digital pulmonology practice. Future research and development in recording and analyzing respiratory sounds in real time could revolutionize clinical practice for both the patients and the healthcare personnel.

Redifferentiation of Differentiated Thyroid Cancer: Clinical Insights from a Narrative Review of Literature

Background: Patients who have metastatic differentiated thyroid cancer (mDTC) frequently have negative diagnostic and/or post-therapy radioiodine scans. As a result, ¹³¹I therapy is frequently no longer considered a therapeutic option for these patients. However, with the knowledge of genomic alterations of patients with mDTC, the use of selected agents in specific patient groups may be used with the intention to re-establish ¹³¹I uptake (i.e., redifferentiation) and additional ¹³¹I therapy. The objectives of this narrative review are to present definitions of related terminology, a brief overview of the molecular mechanisms of redifferentiating agents, and a narrative review of the literature for redifferentiation in patients who have radioiodine refractory mDTC. Summary: We searched multiple electronic databases and reviewed the relevant English-language literature reported after 2010. Fourteen articles were included in this narrative review. Conclusions: Preliminary data suggest that select agents may offer potential for re-establishing ¹³¹I uptake in selected patients with radioiodine refractory mDTC (e.g., negative diagnostic and/or post-therapy radioiodine scans). These agents may also enhance uptake (e.g., uptake enhancement) in patients who have ¹³¹I uptake in mDTC on a diagnostic and/or post-therapy radioiodine scan. As a result, this may facilitate higher absorbed dose delivered (Gy (rad]) per ¹³¹I activity administered [GBq (mCi)]. This in turn may increase the likelihood of a better therapeutic effect for the planned administered ¹³¹I activity or a reduction in the originally planned administered ¹³¹I activity, while achieving the same intended therapeutic effect with potentially less untoward effects. Further studies are warranted to confirm these preliminary observations and to confirm acceptable subsequent ¹³¹I therapy responses after redifferentiation and/or uptake enhancement.

A comprehensive framework for evaluation of high pacing frequency and arrhythmic optical mapping signals

Introduction: High pacing frequency or irregular activity due to arrhythmia produces complex optical mapping signals and challenges for processing. The objective is to establish an automated activation time-based analytical framework applicable to optical mapping images of complex electrical behavior. Methods: Optical mapping signals with varying complexity from sheep (N = 7) ventricular preparations were examined. Windows of activation centered on each action potential upstroke were derived using Hilbert transform phase. Upstroke morphology was evaluated for potential multiple activation components and peaks of upstroke signal derivatives defined activation time. Spatially and temporally clustered activation time points were grouped in to wave fronts for individual processing. Each activation time point was evaluated for corresponding repolarization times. Each wave front was subsequently classified based on repetitive or non-repetitive events. Wave fronts were evaluated for activation time minima defining sites of wave front origin. A visualization tool was further developed to probe dynamically the ensemble activation sequence. Results: Our framework facilitated activation time mapping during complex dynamic events including transitions to rotor-like reentry and ventricular fibrillation. We showed that using fixed AT windows to extract AT maps can impair interpretation of the activation sequence. However, the phase windowing of action potential upstrokes enabled accurate recapitulation of repetitive behavior, providing spatially coherent activation patterns. We further demonstrate that grouping the spatio-temporal distribution of AT points in to coherent wave fronts, facilitated interpretation of isolated conduction events, such as conduction slowing, and to derive dynamic changes in repolarization properties. Focal origins precisely detected sites of stimulation origin and breakthrough for individual wave fronts. Furthermore, a visualization tool to dynamically probe activation time windows during reentry revealed a critical single static line of conduction slowing associated with the rotation core. Conclusion: This comprehensive analytical framework enables detailed quantitative assessment and visualization of complex electrical behavior.

JD. RespiCo: A novel, flexible, and stand-alone electronic respiratory coaching device

Conscious respiratory pattern and rate control is desired by patients with some forms of pulmonary disease that are undergoing respiratory muscle conditioning and rehabilitation, by practitioners of meditation hoping to improve mindfulness and wellbeing, by athletes striving to obtain breathing control in order to increase competitiveness, and by engineers and scientists that wish to use the data from breathing subjects to test hypotheses and develop physiological monitoring systems. Although prerecorded audio sources and computer applications are available that guide breathing exercises, they often suffer from being inflexible and allow only limited customization of the breathing cues. Here we describe a small, lightweight, battery-powered, microprocessor-based respiratory coaching device (RespiCo), which through wireless or wired connections, can be easily customized to precisely guide subjects to breathe at desired respiratory rates using specific breathing patterns through visual, auditory, or haptic cues. Digital signals can also be captured from the device to document the breathing cues provided by the device for research purposes. It is anticipated that this device will have important utility for those who wish to be guided to breathe in a precise manner or in research and development of physiologic monitoring systems.

Investigating Electrophysiological Markers of Arrhythmogenesis in a Chronic Myocardial Infarction Ovine Model

Cardiac alternans has been associated with an increased propensity to lethal tachyarrhythmias such as ventricular tachycardia and fibrillation (VT/VF). Myocardial infarction (MI), resulting from restricted oxygen supply to the heart, is a known substrate for VT/VF. Here, we investigate the utility of cardiac alternans as a predictor of tachyarrhythmias in a chronic MI ovine model. In-vivo electrophysiological studies were performed to assess the change in microvolt T-wave alternans (TWA) with induction of acute ischemia following coronary artery occlusion. 24-hour telemetry was performed in an ambulatory animal for 6 weeks to monitor the progression of TWA with chronic MI. At 6 weeks, ex-vivo optical mapping experiments were performed to assess the spatiotemporal evolution of alternans in sham (n=5) and chronic MI hearts (n=8). Our results demonstrate that chronic MI leads to significant electrophysiological changes in the cardiac substrate. Significant increase in TWA is observed post occlusion and a steady rise in alternans is seen with progression of chronic MI. Compared to sham, chronic MI hearts show significant presence of localized action potential amplitude alternans, which spatially evolve with an increase in pacing frequency. Clinical Relevance - Our results demonstrate that localized alternans underlie arrhythmogenesis in chronic MI hearts and microvolt TWA can serve as a biomarker of disease progression during chronic MI.

Advances in Cardiac Pacing: Arrhythmia Prediction, Prevention and Control Strategies

Cardiac arrhythmias constitute a tremendous burden on healthcare and are the leading cause of mortality worldwide. An alarming number of people have been reported to manifest sudden cardiac death as the first symptom of cardiac arrhythmias, accounting for about 20% of all deaths annually. Furthermore, patients prone to atrial tachyarrhythmias such as atrial flutter and fibrillation often have associated comorbidities including hypertension, ischemic heart disease, valvular cardiomyopathy and increased risk of stroke. Technological advances in electrical stimulation and sensing modalities have led to the proliferation of medical devices including pacemakers and implantable defibrillators, aiming to restore normal cardiac rhythm. However, given the complex spatiotemporal dynamics and non-linearity of the human heart, predicting the onset of arrhythmias and preventing the transition from steady state to unstable rhythms has been an extremely challenging task. Defibrillatory shocks still remain the primary clinical intervention for lethal ventricular arrhythmias, yet patients with implantable cardioverter defibrillators often suffer from inappropriate shocks due to false positives and reduced quality of life. Here, we aim to present a comprehensive review of the current advances in cardiac arrhythmia prediction, prevention and control strategies. We provide an overview of traditional clinical arrhythmia management methods and describe promising potential pacing techniques for predicting the onset of abnormal rhythms and effectively suppressing cardiac arrhythmias. We also offer a clinical perspective on bridging the gap between basic and clinical science that would aid in the assimilation of promising anti-arrhythmic pacing strategies.

Autonomic Modulation of Cardiac Arrhythmias: Methods to Assess Treatment and Outcomes

The autonomic nervous system plays a central role in the pathogenesis of multiple cardiac arrhythmias, including atrial fibrillation and ventricular tachycardia. As such, autonomic modulation represents an attractive therapeutic approach in these conditions. Notably, autonomic modulation exploits the plasticity of the neural tissue to induce neural remodeling and thus obtain therapeutic benefit. Different forms of autonomic modulation include vagus nerve stimulation, tragus stimulation, renal denervation, baroreceptor activation therapy, and cardiac sympathetic denervation. This review seeks to highlight these autonomic modulation therapeutic modalities, which have shown promise in early preclinical and clinical trials and represent exciting alternatives to standard arrhythmia treatment. We also present an overview of the various methods used to assess autonomic tone, including heart rate variability, skin sympathetic nerve activity, and alternans, which can be used as surrogate markers and predictors of the treatment effect. Although the use of autonomic modulation to treat cardiac arrhythmias is supported by strong preclinical data and preliminary studies in humans, in light of the disappointing results of a number of recent randomized clinical trials of autonomic modulation therapies in heart failure, the need for optimization of the stimulation parameters and rigorous patient selection based on appropriate biomarkers cannot be overemphasized.

Microvolt T-Wave Alternans Is Modulated by Acute Low-Level Tragus Stimulation in Patients With Ischemic Cardiomyopathy and Heart Failure

Aims: Microvolt T-wave alternans (TWA), an oscillation in T-wave morphology of the electrocardiogram (ECG), has been associated with increased susceptibility to ventricular tachy-arrhythmias, while vagus nerve stimulation has shown promising anti-arrhythmic effects in in vivo and ex vivo animal studies. We aimed to examine the effect of non-invasive, acute low-level tragus stimulation (LLTS) on TWA in patients with ischemic cardiomyopathy and heart failure.

Methods: 26 patients with ischemic cardiomyopathy (left ventricular ejection fraction <35%) and chronic stable heart failure, previously implanted with an automatic implantable cardioverter defibrillator (ICD) device with an atrial lead (dual chamber ICD or cardiac resynchronization therapy defibrillator), were enrolled in the study. Each patient sequentially received, (1) Sham LLTS (electrode on tragus, but no stimulation delivered) for 5 min; (2) Active LLTS at two different frequencies (5 and 20 Hz, 15 min each); and (3) Active LLTS, during concomitant atrial pacing at 100 bpm at two different frequencies (5 and 20 Hz, 15 min each). LLTS was delivered through a transcutaneous electrical nerve stimulation device (pulse width 200 μs, frequency 5/20 Hz, amplitude 1 mA lower than the discomfort threshold). TWA burden was assessed using continuous ECG monitoring during sham and active LLTS in sinus rhythm, as well as during atrial pacing.

Results: Right atrial pacing at 100 bpm led to significantly heightened TWA burden compared to sinus rhythm, with or without LLTS. Acute LLTS at both 5 and 20 Hz, during sinus rhythm led to a significant rise in TWA burden in the precordial leads (p < 0.05).

Conclusion: Acute LLTS results in a heart-rate dependent increase in TWA burden.

Ambulatory monitoring promises equitable personalized healthcare delivery in underrepresented patients

The pandemic has brought to everybody's attention the apparent need of remote monitoring, highlighting hitherto unseen challenges in healthcare. Today, mobile monitoring and real-time data collection, processing and decision-making, can drastically improve the cardiorespiratory-haemodynamic health diagnosis and care, not only in the rural communities, but urban ones with limited healthcare access as well. Disparities in socioeconomic status and geographic variances resulting in regional inequity in access to healthcare delivery, and significant differences in mortality rates between rural and urban communities have been a growing concern. Evolution of wireless devices and smartphones has initiated a new era in medicine. Mobile health technologies have a promising role in equitable delivery of personalized medicine and are becoming essential components in the delivery of healthcare to patients with limited access to in-hospital services. Yet, the utility of portable health monitoring devices has been suboptimal due to the lack of user-friendly and computationally efficient physiological data collection and analysis platforms. We present a comprehensive review of the current cardiac, pulmonary, and haemodynamic telemonitoring technologies. We also propose a novel low-cost smartphone-based system capable of providing complete cardiorespiratory assessment using a single platform for arrhythmia prediction along with detection of underlying ischaemia and sleep apnoea; we believe this system holds significant potential in aiding the diagnosis and treatment of cardiorespiratory diseases, particularly in underserved populations.

Comparing surgical thoroughness and recurrence in thyroid cancer patients across race/ethnicity

BACKGROUND

For patients with differentiated thyroid cancer who will receive postoperative radioactive iodine, thyroid remnant uptake can be calculated and may point to the thoroughness of the surgical resection. In the United States, outcome disparities exist among ethnic/racial minorities with differentiated thyroid cancer. Data about surgical thoroughness and recurrence rates across races/ethnicities do not exist. This study compared the amount of thyroid remnant uptake and cancer recurrence rates across race/ethnicity.

METHODS

This was a retrospective analysis of adult patients with differentiated thyroid cancer who had postoperative radioactive iodine in 2017 and 2018 and were followed to 2020. We collected thyroid bed remnant uptake from postoperative radioactive iodine scans and analyzed it as a ratio of percent of uptake to dose of radioactive iodine received to control for varying radioactive iodine doses. Thyroid remnant, uptake to dose of radioactive iodine received, and recurrence were evaluated across race/ethnicity.

RESULTS

Of 218 patients: 61% were White, 21% Black, 11% Asian, and 7% Hispanic; 72% were female. Seventy-one percent of patients had their surgery done by a high-volume surgeon, although volume data were not available for all. In White, Black, Asian, and Hispanic patients, median uptake was 0.68%, 0.44%, 1.5%, and 0.8%, respectively (P = .13). We did not observe differences in median uptake to dose of radioactive iodine received across groups (P = .41). Recurrence rate was 17.0% among White patients, 16.7% among Black patients, 17.6% among Asian patients, and 16.7% among Hispanic patients (P = 1.00).

CONCLUSION

We did not observe differences across race/ethnicity in surgical thoroughness or rate of recurrence. These findings suggest that disparities may be mitigated when ethnic/racial minorities have similar access to quality surgical care.