The implications of the COVID-19 pandemic on eating disorder features and comorbid psychopathology among adolescents with anorexia nervosa and matched controls: a comparative cohort design study

PURPOSE

To examine implications of the COVID-19 pandemic on eating disorder (ED) features and psychopathology in female adolescents with anorexia nervosa (AN).

METHOD

In total 79 females with first-onset AN (aged 12-22 years) were included and were followed up across a period of 1 year. We assessed AN participants recruited pre-pandemic (n = 49) to those recruited peri-pandemic (n = 30). Pre- (n = 37) and peri-pandemic (n = 38) age-, and education-matched typically developing (TD) girls (n = 75) were used as a reference cohort. ED features and psychopathology were assessed at baseline. After 1 year of follow-up the association between pandemic timing and clinical course was assessed. Analyses of covariance were used to examine differences in ED features and psychopathology.

RESULTS

Peri-pandemic AN participants experienced less ED symptoms at baseline compared to pre-pandemic AN participants. In particular, they were less dissatisfied with their body shape, and experienced less interpersonal insecurity. In addition, the peri-pandemic AN group met fewer DSM-IV criteria for comorbid disorders, especially anxiety disorders. In contrast, peri-pandemic AN participants had a smaller BMI increase over time. In TD girls, there were no differences at baseline in ED features and psychopathology between the pre- and peri-pandemic group.

CONCLUSION

Overall, peri-pandemic AN participants were less severely ill, compared to pre-pandemic AN participants, which may be explained by less social pressure and peer contact, and a more protective parenting style during the pandemic. Conversely, peri-pandemic AN participants had a less favorable clinical course, which may be explained by reduced access to health care facilities during the pandemic.

LEVEL OF EVIDENCE

Level III: Evidence obtained from well-designed cohort or case-control analytic studies.

Performance of an artificial intelligence algorithm to detect atrial fibrillation on a 24-hour continuous photoplethysmography recording using a smartwatch: ACURATE study

Background

In the awakening era of mobile health, wearable devices capable of detecting atrial fibrillation (AF) are on the rise. Smartwatches and wristbands are equipped with photoplethysmography (PPG) technology that enables (semi)continuous rhythm monitoring. These devices have been pioneered already in a few screening trials. However, such devices are being spread among consumers at a pace that is not paralleled by the evidence supporting their clinical performance. This imbalance reflects the urgent need for validation studies.

Purpose

To determine the diagnostic performance of an artificial intelligence algorithm to detect AF using photoplethysmography acquired by a smartwatch.

Methods

One hundred patients (≥18 years) without a pacemaker-dependent heart rhythm who were referred to a university hospital or a large tertiary hospital for elective 24-hour ECG Holter monitoring were asked to wear a continuous PPG monitoring smartwatch (i.e. Samsung GWA2 or Empatica E4) simultaneously with the Holter. All activities of daily life were allowed. The ECG trace and PPG waveform were synchronised and fragmented in one-minute fragements. The one-minute ECG fragments were labelled as AF, non-AF, or insufficient quality based on the routine clinical interpretation of the 24-hour Holter (i.e. software + physician overreading). The one-minute PPG fragments were analysed by an artificial intelligence (AI) algorithm (i.e. FibriCheck) and were given the same labels. Diagnostic metrics of the PPG AI algorithm were calculated with respect to the ECG interpretation, for all fragments with sufficient quality for both PPG and ECG.

Results

Four patients had to be excluded due to technical error (3 Holter errors, 1 smartwatch error). The mean age in the remaining study population (n=96) was 59±16 years, 51 (53%) were men and 15 (15.6%) were known with permanent AF. In this population, simultaneous ECG and PPG monitoring was recorded for 115,245 one-minute fragments. Fragments of insufficient quality for ECG (n=1,454; 1.3%), PPG (n=25,704; 22.3%) or both (n=15,362; 13.3%) were excluded. PPG fragments were more frequently of insufficient quality (p<0.001). AF was present in 10,255 (14.1%) of the resulting 72,725 high-quality one-minute fragments. The sensitivity of PPG to detect AF was 93.4% (CI 92.9% - 93.8%). The specificity of PPG to exclude AF was 98.4% (CI 98.3% - 98.5%). As a result, the overall accuracy of the PPG algorithm on one-minute fragment level was 97.7% (CI 97.6%- 97.8%).

Conclusion

Continuous out-of-hospital PPG monitoring using a smartwatch in combination with an AI algorithm can accurately discriminate between AF and non-AF rhythms in a heterogenous patient population. PPG quality is more often affected than ECG quality during daily life activities.

Funding Acknowledgement

Type of funding sources: Foundation. Main funding source(s): Research Foundation-Flanders, Strategic Basic Research Fund

Assessment of heart rate agreement on continuous photoplethysmography monitoring using a smartwatch versus beat-to-beat synchronized ECG monitoring

Background

In the awakening era of mobile health, wearables equipped with photoplethysmography (PPG) technology to monitor the heart rate (HR) and rhythm are on the rise. Smartwatches and wristbands enable HR monitoring for consumers at massive scale. Unfortunately, once consumers become patients, physicians are limited by insufficient evidence to support the clinical use of PPG based wearables. Accurate identification of heartbeats is the first step in the interpretation of PPG traces and should be validated.

Purpose

To assess the agreement between continuous PPG monitoring using a smartwatch and continuous ECG Holter monitoring in the identification of heartbeats and calculation of the HR.

Methods

One hundred patients (≥18 years) without a pacemaker-dependent heart rhythm who were referred to a university hospital and a large tertiary hospital for elective 24-hour ECG Holter monitoring were asked to wear a continuous PPG monitoring smartwatch (i.e. Samsung GWA2 or Empatica E4) simultaneously with the 24-hour Holter monitor. All activities of daily life were allowed. The ECG trace and PPG waveform were synchronised and fragmented in one-minute fragments. The one-minute ECG fragments were labelled as AF, non-AF, or insufficient quality based on the routine clinical interpretation (i.e. software + physician overreading), and the average HR during each fragment was calculated by Holter algorithm. The PPG fragments were analysed by an artificial intelligence (AI) algorithm (i.e. FibriCheck) that labelled fragments as sufficient or insufficient quality, identified the number of heartbeats and calculated the HR. The agreement between the HR on ECG and PPG in sufficient quality tracings was analysed with linear regression, Pearson's product-moment correlation and Bland-Altman analysis. A subanalysis was performed for AF rhythm and non-AF rhythms.

Results

A total of 72,725 simultaneous ECG and PPG one-minute fragments were recorded in 96 patients, after excluding 4 patients (due to 3 Holter and 1 smartwatch technical error) and 42,520 minutes (36.9%) of insufficient quality (ECG 1,454 (1.3%); PPG 25,704 (22.3%), ECG and PPG 15,362 (13.3%)). The correlation (r=0.935) between ECG and PPG HR was statistically significant (CI 0.934–0.936; P<0.001), with a mean difference between ECG and PPG of 0.8bpm. The lower and upper limit boundary (LLB and ULB; defined as ±1.96 SD) were −8.0bpm and 9.7bpm, respectively, i.e. 95% of PPG measurements identified the HR within 8bpm below or 10bpm above the ECG reference. The mean difference between ECG and PPG HR in the AF subgroup (n=10,255 (14.1%)) was 0.9bpm (LLB −8.4bpm; ULB 10.2bpm) and 0.8bpm in the non-AF subgroup (LLB −0.8bpm; ULB 9.6bpm).

Conclusion

The AI algorithm analysing continuous out-of-hospital PPG tracings can annotate heartbeats and assess HR without a clinically significant bias compared to continuous ECG monitoring, both during AF and non-AF rhythms in a heterogenous patient population.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Research Foundation-Flanders, Strategic Basic Research Fund Correlation plot & Bland-Altman plot

Evaluation of the device independent nature of a photoplethysmography-deriving smartphone app

Funding Acknowledgements

Type of funding sources: None.

Background

Smartphone apps using photoplethysmography (PPG) technology enable digital heart rhythm monitoring through their built-in camera, without the need for additional, specific, or costly hardware. This may positively impact the availability and scalability of remote monitoring. However, the diversity of smartphone specifications on the consumer market may raise concerns regarding the robustness of AF detection algorithms between various devices.

Purpose

To study the device independency of AF detection performance by a PPG-based smartphone application.

Methods

Patients from the cardiology department were consecutively enrolled. Patients were handed 7 iOS models and 1 Android model and were asked to consecutively perform one PPG measurement per device. A 12-lead electrocardiogram (ECG) was collected during the same consultation and interpreted by a cardiologist as reference diagnosis. To allow an objective comparison across the devices, patients who failed to perform one successful measurement on each device were excluded. Additional exclusions were atrial flutter rhythms and insufficient quality results. Sensitivity, specificity and accuracy were calculated with respect to the reference diagnosis. McNemar’s analysis was used for the head-to-head comparison of the sensitivity and specificity of the proprietary algorithm on the different smartphone devices.

Results

A total of 150 patients participated in the study with a median CHA2DS2-VASc score of 3 (interquartile range: 1-5). The median age of the study population was 70 (interquartile range: 56-79) years. In total, 54.7% of the population was male and the AF-prevalence was 35.3%. After the exclusion of patients with atrial flutter (n = 14) and patients who did not successfully perform a PPG measurement on each device (n = 5), diagnostic-grade results of 131 patients were used to calculate the performance of the proprietary algorithm. The sensitivity and specificity of the AF detection algorithm ranged from 90.9% (95% CI 75.7-98.1) to 100.0% (95% CI 91.0-100) and 94.5% (95% CI 86.6-98.5) to 100.0% (95% CI 94.6-100), respectively. The overall accuracy across the devices ranged from 94.4% (95% CI 88.3-97.9) to 99.0% (95% CI 94.6-100). Head-to-head comparisons of the results did not reveal significant differences in sensitivity (P = 0.125-1.000) or specificity (P = 0.375-1.000) of the proprietary AF detection algorithm among the different devices.

Conclusion

This study demonstrated the device-independent nature of the PPG-deriving smartphone application with respect to 12-lead ECG diagnosis.

P1457Preliminary results of the FLASH-AF: Validation of the device independent nature of a pulse deriving smartphone application for the detection of atrial fibrillation

Background

Smartphone applications using photoplethysmography (PPG) technology through their camera are becoming an attractive alternative for atrial fibrillation (AF) screening due to their low cost, convenience, and broad accessibility. However, some important questions concerning their diagnostic accuracy, robustness and device independent nature remain to be answered.

Purpose

This study evaluated the diagnostic accuracy of a PPG-based pulse-deriving smartphone application with respect to handheld single-lead ECG and 12-lead ECG. In addition, the device dependent nature and robustness of the performance of the application was assessed.

Methods

300 Patients who are scheduled for a regular consultation or procedure (i.e. ablation or cardioversion) will be recruited from the cardiology ward. Additionally, patients hospitalized for continuous cardiac monitoring will be recruited to enrich the database with AF measurements. After obtaining written informed consent, the patients fill in a questionnaire collecting demographic and medical information.

The pulse-deriving application will be tested on total of 14 different smartphones, 7 iOS devices and 7 Android devices. In total, each device will be measured with 150 times. The patients will additionally perform a single-lead ECG measurement with a handheld device. Subsequently, a 12-lead ECG will be recorded to obtain the reference diagnosis.

Results

A total of 164 patients already participated in the study. The mean age was 64 (±19) years, 58% was male. The AF-prevalence was 37%. On average, patients in AF had a higher CHA2DS2-VASc score; 3.93 (±1.80) compared to 2.02 (±1.63) for non-AF patients.

The amount of insufficient quality measurements recorded with the pulse-deriving smartphone application ranged from 4% (iOS) to 13% (Android). Averaged for all the smartphone devices, the pulse-deriving application scored 81.2% (±5%) sensitivity, 97.1% (±1%) specificity, 88.8% (±2%) NPV, 95.0% (±1%) PPV, and 90.9% (±2%) accuracy. The handheld single-lead ECG device had 78.2% sensitivity, 95.5% specificity, 87.6% NPV, 91.5% PPV, and 88.9% accuracy.

The same calculations were preformed after excluding regular atrial flutter measurements. On average, the pulse-deriving application scored 90.1% (±2%) sensitivity, 97.1% (±1%) specificity, 95.2% (±1%) NPV, 94.0% (±1%) PPV, and 94.8% (±1%) accuracy. The handheld single-lead ECG device had 90.2% sensitivity, 97.7% specificity, 97.7% NPV, 95.1% PPV, and 96.9% accuracy.

Conclusion

The diagnostic accuracy of the pulse-deriving smartphone application and the handheld single-lead ECG device was strongly influenced by the presence of regular atrial flutters, stressing the importance of further thorough validation. For the pulse-deriving smartphone application, there was no significant influence from device type in terms of diagnostic accuracy for the detection of AF. Insufficient quality measurements were more frequently performed on Android devices.

Fasting gastric fluid and fecal polyamine concentrations in premature infants

BACKGROUND

The role of milk polyamines in the development of the gastrointestinal tract of human infants is presently unknown. Polyamine concentrations are higher in human milk than in infant formulas. The aim of the present study was to gather data on luminal polyamines by measuring gastric fluid and fecal polyamine concentrations in premature infants during the postnatal period. We further compared gastric fluid polyamine concentrations with those reported for milk and looked for possible relationships between luminal polyamine concentrations, age, and growth rate.

METHODS

High-performance liquid chromatography was used for the measurement of polyamine concentrations in both fecal and gastric fluid samples.

RESULTS

Ninetieth centiles for gastric polyamines during the first week were 62, 28, 82, and 14 microM for putrescine, spermidine, spermine, and cadaverine, respectively. These values are higher than those reported for human milk and infant formulas. Polyamine concentrations were unrelated to either age or growth rate. Ninetieth centiles for fecal polyamines during the first week were 7668, 5176, 53, and 75 microM for cadaverine, putrescine, spermidine, and spermine, respectively.

CONCLUSIONS

Fasting gastric fluid polyamine concentrations in premature infants are higher than those reported for either human milk or infant formulas. The high fecal cadaverine and putrescine concentrations are probably of bacterial origin.

Fecal polyamine concentration in children with and without nutrient malabsorption

BACKGROUND

Fermentation products of malabsorbed nutrients are thought to be responsible for intestinal adaptation following small bowel resection in rats. It has been suggested that either short-chain fatty acids or polyamines (mainly putrescine and cadaverine) could be the fermentation products involved. There are no data available on fecal polyamine content in humans. The present study compared the fecal polyamine concentrations in children with and without malabsorption.

METHODS

Sixteen (8 girls, 8 boys) malabsorption patients (cystic fibrosis: 13, short bowel syndrome: 2, biliary atresia: 1) with a mean age of 8 years were compared to 17 (9 girls, 8 boys) sick children without malabsorption (mean age 5.7 years). Three-day fecal collections were performed and analyzed for fat and polyamine concentrations. High-performance liquid chromatography (HPLC) was used for the measurement of polyamine concentrations.

RESULTS

Mean and SEM for fecal fat excretion was 13.4 +/- 2.5 g/day and 1.5 +/- 0.3 g/day in the malabsorption and control group respectively. Median fecal cadaverine and putrescine concentrations were 3723 mumol.kg-1 feces and 4737 mumol.kg-1 feces for the malabsorption group and 114 mumol.kg-1 feces and 306 mumol.kg-1 feces for the control group (p < 0.007 and < 0.00001 respectively). No significant differences were found for fecal spermine and spermidine concentrations between the two groups.

CONCLUSIONS

Children with malabsorption show very high fecal putrescine and cadaverine concentrations. Our results support the hypothesis that fecal polyamines could be important.