Background: Disorders of gastric function are highly prevalent, but diagnosis often remains symptom-based and inconclusive. Body surface gastric mapping is an emerging diagnostic solution, but current approaches lack scalability and are cumbersome and clinically impractical. We present a novel scalable system for non-invasively mapping gastric electrophysiology in high-resolution (HR) at the body surface. Methods:The system comprises a custom-designed stretchable high-resolution "peel-and-stick" sensor array (8 × 8 pre-gelled Ag/AgCl electrodes at 2 cm spacing; area 225 cm 2 ), wearable data logger with custom electronics incorporating bioamplifier chips, accelerometer and Bluetooth synchronized in real-time to an App with cloud connectivity. Automated algorithms filter and extract HR biomarkers including propagation (phase) mapping. The system was tested in a cohort of 24 healthy subjects to define reliability and characterize features of normal gastric activity (30 m fasting, standardized meal, and 4 h postprandial).Key Results: Gastric mapping was successfully achieved non-invasively in all cases (16 male; 8 female; aged 20-73 years; BMI 24.2 ± 3.5). In all subjects, gastric electrophysiology and meal responses were successfully captured and quantified non-invasively (mean frequency 2.9 ± 0.3 cycles per minute; peak amplitude at mean 60 m postprandially with return to baseline in <4 h). Spatiotemporal mapping showed regular and consistent wave activity of mean direction 182.7° ± 73 (74.7% antegrade, 7.8% retrograde, 17.5% indeterminate). Conclusions and Inferences:BSGM is a new diagnostic tool for assessing gastric function that is scalable and ready for clinical applications, offering several biomarkers that are improved or new to gastroenterology practice.

Chronic nausea and vomiting syndromes (NVSs) are prevalent and debilitating disorders. Putative mechanisms include gastric neuromuscular disease and dysregulation of brain-gut interaction, but clinical tests for objectively defining gastric motor function are lacking. A medical device enabling noninvasive body surface gastric mapping (BSGM) was developed and applied to evaluate NVS pathophysiology. BSGM was performed in 43 patients with NVS and 43 matched controls using Gastric Alimetry (Alimetry), a conformable high-resolution array (8 × 8 electrodes; 20-mm interelectrode spacing), wearable reader, and validated symptom-logging app. Continuous measurement encompassed a fasting baseline (30 minutes), 482-kilocalorie meal, and 4-hour postprandial recording, followed by spectral and spatial biomarker analyses. Meal responses were impaired in NVS, with reduced amplitudes compared to controls (median, 23.3 microvolts versus 38.0 microvolts, P < 0.001), impaired fed-fasting power ratios (1.1 versus 1.6, P = 0.02), and disorganized slow waves (spatial frequency stability, 13.6 versus 49.5; P < 0.001). Two distinct NVS subgroups were evident with indistinguishable symptoms (all P > 0.05). Most patients (62%) had normal BSGM studies with increased psychological comorbidities (43.5% versus 7.7%; P = 0.03) and anxiety scores (median, 16.5 versus 13.0; P = 0.035). A smaller subgroup (31%) had markedly abnormal BSGM, with biomarkers correlating with symptoms (nausea, pain, excessive fullness, early satiety, and bloating; all r > 0.35, P < 0.05). Patients with NVS share overlapping symptoms but comprise distinct underlying phenotypes as revealed by a BSGM device. These phenotypes correlate with symptoms, which should inform clinical management and therapeutic trial design.

Importance Chronic nausea and vomiting syndromes (NVS) are prevalent and debilitating disorders. Putative mechanisms include gastric neuromuscular disease and dysregulation of brain-gut interaction, but clinical tests for objectively defining gastric motor function are lacking. Objective A novel medical device enabling non-invasive body surface gastric mapping (BSGM) was developed and applied to evaluate NVS pathophysiology. Design A case-control study where BSGM was performed in NVS patients and matched controls using Gastric Alimetry (Alimetry, New Zealand), comprising a conformable high-resolution array (8x8 electrodes; 20 mm inter-electrode spacing), wearable Reader, and validated symptom logging App. Continuous measurement encompassed a fasting baseline (30 min), 482 kCal meal (10 min), and 4-hr post-prandial recording. Setting Multicenter study in Auckland, New Zealand and Calgary, Canada. Participants 43 NVS patients (gastroparesis and Rome IV chronic NVS) and 43 matched controls. Main outcomes and measures Symptom severity and quality of life were measured using Patient Assessment of Upper Gastrointestinal Disorders-Symptom Severity Index (PAGI-SYM), Gastroparesis Cardinal Symptom Index (GCSI), and Patient Assessment of Upper Gastrointestinal Disorders-Quality of Life (PAGI-QOL) instruments. Health psychology metrics included the State Trait Anxiety Inventory Short Form (STAI-SF) and Patient Health Questionnaire-2 (PHQ-2) questionnaires. Spectral analyses including frequency, amplitude, and fed-fasting power ratio. Spatial biomarker analyses included spatial frequency stability and average spatial covariance. Results Meal responses were impaired in NVS, with reduced amplitudes compared to controls (median 23.3 vs 38.0 microV, p<0.001), impaired fed-fasting power-ratios (1.1 vs 1.6, p=0.02), and disorganized slow-waves (spatial frequency stability 13.6 vs 49.5; p<0.001). However, two distinct NVS subgroups were evident with indistinguishable symptoms (all p>0.05). A majority (62%) had normal BSGM studies (all biomarkers non-significant vs controls) with increased psychological comorbidities (43.5% vs 7.7%; p=0.03) and anxiety scores (median 16.5 vs 13.0; p=0.035). A smaller subgroup (31%) had markedly abnormal BSGM, with test biomarkers correlating with symptoms (nausea, pain, excessive fullness, early satiety, bloating; all r>0.35, p<0.05). Conclusions and Relevance NVS patients share overlapping symptoms, but comprise distinct underlying phenotypes as revealed by a novel BSGM device. These phenotypes correlate with symptoms, which should inform clinical management and allocations into therapeutic trials.

INTRODUCTION:Body surface gastric mapping (BSGM) is a new noninvasive test of gastric function. BSGM offers several novel and improved biomarkers of gastric function capable of differentiating patients with overlapping symptom profiles. The aim of this study was to define normative reference intervals for BSGM spectral metrics in a population of healthy controls.METHODS:BSGM was performed in healthy controls using Gastric Alimetry (Alimetry, New Zealand) comprising a stretchable high-resolution array (8 × 8 electrodes; 196 cm2), wearable Reader, and validated symptom-logging App. The evaluation encompassed a fasting baseline (30 minutes), 482 kCal meal, and 4-hour postprandial recording. Normative reference intervals were calculated for BSGM metrics including the Principal Gastric Frequency, Gastric Alimetry Rhythm Index (a measure of the concentration of power in the gastric frequency band over time), body mass index (BMI)–adjusted amplitude (μV), and fed:fasted amplitude ratio. Data were reported as median and reference interval (5th and/or 95th percentiles).RESULTS:A total of 110 subjects (55% female, median age 32 years [interquartile range 24–50], median BMI 23.8 kg/m2 [interquartile range 21.4–26.9]) were included. The median Principal Gastric Frequency was 3.04 cycles per minute; reference interval: 2.65–3.35 cycles per minute. The median Gastric Alimetry Rhythm Index was 0.50; reference interval: ≥0.25. The median BMI-adjusted amplitude was 37.6 μV; reference interval: 20–70 μV. The median fed:fasted amplitude ratio was 1.85; reference interval ≥1.08. A higher BMI was associated with a shorter meal-response duration (P = 0.014).DISCUSSION:This study provides normative reference intervals for BSGM spectral data to inform diagnostic interpretations of abnormal gastric function.

Disorders of gastric function are highly prevalent, but diagnosis often remains symptom-based and inconclusive. Body surface gastric mapping is an emerging diagnostic solution, but current approaches lack scalability and are cumbersome and clinically impractical. We present a novel scalable system for non-invasively mapping gastric electrophysiology in high-resolution (HR) at the body-surface. The system comprises a custom-designed flexible HR sensor array and portable data-logger synchronized to an App, with automated analysis and visualization algorithms. The novel system underwent performance testing then validation in 24 healthy subjects. In all subjects, gastric electrophysiology and meal responses were successfully captured and mapped non-invasively (mean frequency 2.9 ± 0.3 cycles per minute; peak amplitude at mean 60 m postprandially with return to baseline in <4 h). Spatiotemporal mapping showed regular and consistent wave activity of mean direction 182.7°±73 (74.7% antegrade, 7.8% retrograde, 17.5% indeterminate). The presented system is a new diagnostic tool for assessing gastric function that is scalable, validated, and ready for clinical applications, offering several biomarkers that are new to gastroenterology practice.

Background Functional gastroduodenal disorders include functional dyspepsia, chronic nausea and vomiting syndromes, and gastroparesis. These disorders are common, but their overlapping symptomatology poses challenges to diagnosis, research, and therapy. This study aimed to introduce and validate a standardized patient symptom‐logging system and App to aid in the accurate reporting of gastroduodenal symptoms for clinical and research applications. Methods The system was implemented in an iOS App including pictographic symptom illustrations, and two validation studies were conducted. To assess convergent and concurrent validity, a diverse cohort with chronic gastroduodenal symptoms undertook App‐based symptom logging for 4 h after a test meal. Individual and total post‐prandial symptom scores were averaged and correlated against two previously validated instruments: PAGI‐SYM (for convergent validity) and PAGI‐QOL (for concurrent validity). To assess face and content validity, semi‐structured qualitative interviews were conducted with patients. Key Results App‐based symptom reporting demonstrated robust convergent validity with PAGI‐SYM measures of nausea (rS =0.68), early satiation (rS =0.55), bloating (rS =0.48), heartburn (rS =0.47), upper gut pain (rS =0.40), and excessive fullness (rS =0.40); all p < 0.001 (n = 79). The total App‐reported Gastric Symptom Burden Score correlated positively with PAGI‐SYM (rS =0.56; convergent validity; p < 0.001), and negatively with PAGI‐QOL (rS = −0.34; concurrent validity; p = 0.002). Interviews demonstrated that the pictograms had adequate face and content validity. Conclusions and Inferences The continuous patient symptom‐logging App demonstrated robust convergent, concurrent, face, and content validity when used within a 4‐h post‐prandial test protocol. The App will enable standardized symptom reporting and is anticipated to provide utility in both research and clinical practice.

Background Electrogastrography (EGG) non‐invasively evaluates gastric function but has not achieved common clinical adoption due to several technical limitations. Body Surface Gastric Mapping (BSGM) has been introduced to overcome these limitations, but pitfalls in traditional metrics used to analyze spectral data remain unaddressed. This study critically evaluates five traditional EGG metrics and introduces improved BSGM spectral metrics, with validation in a large cohort. Methods Pitfalls in five EGG metrics were assessed (dominant frequency, percentage time normogastria, amplitude, power ratio, and instability coefficient), leading to four revised BSGM spectral metrics. Traditional and revised metrics were compared to validate performance using a standardized 100‐subject database of BSGM tests (30 min baseline; 4‐h postprandial) recorded using Gastric Alimetry® (Alimetry). Key Results BMI and amplitude were highly correlated (r = −0.57, p < 0.001). We applied a conservative BMI correction to obtain a BMI‐adjusted amplitude metric (r = −0.21, p = 0.037). Instability coefficient was highly correlated with both dominant frequency (r = −0.44, p < 0.001), and percent bradygastria (r = 0.85, p < 0.001), in part due to misclassification of low frequency transients as gastric activity. This was corrected by introducing distinct gastric frequency and stability metrics (Principal Gastric Frequency and Gastric Alimetry Rhythm Index (GA‐RI)TM) that were uncorrelated (r = 0.14, p = 0.314). Only 28% of subjects showed a maximal averaged amplitude within the first postprandial hour. Calculating Fed:Fasted Amplitude Ratio over a 4‐h postprandial window yielded a median increase of 0.31 (IQR 0–0.64) above the traditional ratio. Conclusions & Inferences The revised metrics resolve critical pitfalls impairing the performance of traditional EGG, and should be applied in future BSGM spectral analyses.

Background Body surface gastric mapping (BSGM) is a new clinical tool for gastric motility diagnostics, providing high‐resolution data on gastric myoelectrical activity. Artifact contamination was a key challenge to reliable test interpretation in traditional electrogastrography. This study aimed to introduce and validate an automated artifact detection and rejection system for clinical BSGM applications. Methods Ten patients with chronic gastric symptoms generated a variety of artifacts according to a standardized protocol (176 recordings) using a commercial BSGM system (Alimetry, New Zealand). An automated artifact detection and rejection algorithm was developed, and its performance was compared with a reference standard comprising consensus labeling by 3 analysis experts, followed by comparison with 6 clinicians (3 untrained and 3 trained in artifact detection). Inter‐rater reliability was calculated using Fleiss' kappa. Key Results Inter‐rater reliability was 0.84 (95% CI:0.77–0.90) among experts, 0.76 (95% CI:0.68–0.83) among untrained clinicians, and 0.71 (95% CI:0.62–0.79) among trained clinicians. The sensitivity and specificity of the algorithm against experts was 96% (95% CI:91%–100%) and 95% (95% CI:90%–99%), respectively, vs 77% (95% CI:68%–85%) and 99% (95% CI:96%–100%) against untrained clinicians, and 97% (95% CI:92%–100%) and 88% (95% CI:82%–94%) against trained clinicians. Conclusions & Inferences An automated artifact detection and rejection algorithm was developed showing >95% sensitivity and specificity vs expert markers. This algorithm overcomes an important challenge in the clinical translation of BSGM and is now being routinely implemented in patient test interpretations.