Physiological Metrics Collected from Wearable Devices Identify Inflammatory and Clinical Inflammatory Bowel Disease Flares

BACKGROUND: Inflammatory bowel disease (IBD) flares are common and unpredictable. Disease monitoring relies on symptom reporting or single timepoint assessments of stool, blood, imaging, or endoscopy-these are inconvenient and invasive and do not always reflect the patient perspective. Advances in wearable technology allow for passive, continuous and non-invasive assessment of physiological metrics including heart rate variability (HRV), the measure of small time differences between each heartbeat, a marker of autonomic nervous system function. Our group has previously demonstrated that changes in autonomic function precedes an IBD flare, can predict psychological state transitions and even identify inflammatory events including SARS-CoV-2 infection. To develop algorithms that can predict IBD flares using wearable device signatures, we launched a national wearable device study called The IBD Forecast study. To assess data quality and feasibility, the first 125 Apple Watch users to enroll were evaluated. METHOD(S): The IBD Forecast study is a prospective cohort study enrolling anyone >=18 years of age in the United States (US) with IBD who is willing to (1) use a commercially available wearable device, (2) download our custom eHive app and (3) answer daily survey questions. HRV metrics (mean of the standard deviations of all the NN intervals [SDNN]) were analyzed using a mixed-effect cosigner model that incorporated body mass index, age, and sex. SDNN is a time domain HRV index that reflects both sympathetic and parasympathetic nervous system activity and is calculated from the variance of intervals between adjacent QRS complexes (the normal-to-normal [NN] intervals). Clinical flare was assessed with daily Patient Reported Outcome (PRO)-2 surveys (flare;PRO-2 Crohn's disease >7, PRO-2 ulcerative colitis >2). Inflammatory flare was assessed via patient reported C-reactive protein (CRP), with inflammatory flare defined as >5 mg/L. RESULT(S): The first 125 study participants were enrolled across 29 states in the US (Table 1). Circadian features of changes of HRV were modelled (Figure 1). The mesor, or midline of the circadian pattern of the SDNN was higher in those with clinical flare (mean 44.43;95% CI 41.25-47.75) compared to those in clinical remission (mean 43.03;95% CI 39.94-46.22) (p<0.004). The mesor of the circadian pattern of the SDNN was lower in those with an inflammatory flare (mean 38.16;95% CI 30.86-45.72) compared to those with normal inflammatory markers (mean 49.51;95% CI 43.12-56.26) (p<0.001). CONCLUSION(S): Longitudinally collected HRV metrics from a commonly worn commercial wearable device can identify symptomatic and inflammatory flares. This preliminary analysis of a small proportion of the IBD Forecast Study cohort demonstrates the feasibility of using wearable devices to identify, and may potentially predict, IBD flares. [Formula presented] [Formula presented]Copyright © 2023

Physiological Metrics Collected from Wearable Devices Identify Inflammatory and Clinical Inflammatory Bowel Disease Flares

BACKGROUND: Inflammatory bowel disease (IBD) flares are common and unpredictable. Disease monitoring relies on symptom reporting or single timepoint assessments of stool, blood, imaging, or endoscopy-these are inconvenient and invasive and do not always reflect the patient perspective. Advances in wearable technology allow for passive, continuous and non-invasive assessment of physiological metrics including heart rate variability (HRV), the measure of small time differences between each heartbeat, a marker of autonomic nervous system function. Our group has previously demonstrated that changes in autonomic function precedes an IBD flare, can predict psychological state transitions and even identify inflammatory events including SARS-CoV-2 infection. To develop algorithms that can predict IBD flares using wearable device signatures, we launched a national wearable device study called The IBD Forecast study. To assess data quality and feasibility, the first 125 Apple Watch users to enroll were evaluated. METHOD(S): The IBD Forecast study is a prospective cohort study enrolling anyone >=18 years of age in the United States (US) with IBD who is willing to (1) use a commercially available wearable device, (2) download our custom eHive app and (3) answer daily survey questions. HRV metrics (mean of the standard deviations of all the NN intervals [SDNN]) were analyzed using a mixed-effect cosigner model that incorporated body mass index, age, and sex. SDNN is a time domain HRV index that reflects both sympathetic and parasympathetic nervous system activity and is calculated from the variance of intervals between adjacent QRS complexes (the normal-to-normal [NN] intervals). Clinical flare was assessed with daily Patient Reported Outcome (PRO)-2 surveys (flare;PRO-2 Crohn's disease >7, PRO-2 ulcerative colitis >2). Inflammatory flare was assessed via patient reported C-reactive protein (CRP), with inflammatory flare defined as >5 mg/L. RESULT(S): The first 125 study participants were enrolled across 29 states in the US (Table 1). Circadian features of changes of HRV were modelled (Figure 1). The mesor, or midline of the circadian pattern of the SDNN was higher in those with clinical flare (mean 44.43;95% CI 41.25-47.75) compared to those in clinical remission (mean 43.03;95% CI 39.94-46.22) (p<0.004). The mesor of the circadian pattern of the SDNN was lower in those with an inflammatory flare (mean 38.16;95% CI 30.86-45.72) compared to those with normal inflammatory markers (mean 49.51;95% CI 43.12-56.26) (p<0.001). CONCLUSION(S): Longitudinally collected HRV metrics from a commonly worn commercial wearable device can identify symptomatic and inflammatory flares. This preliminary analysis of a small proportion of the IBD Forecast Study cohort demonstrates the feasibility of using wearable devices to identify, and may potentially predict, IBD flares. (Table Presented).

Association of SARS-CoV-2 viral load at admission with in-hospital acute kidney injury: a retrospective cohort study

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the associated Coronavirus Disease 2019 (COVID-19) is a public health emergency. Acute kidney injury (AKI) is a common complication in hospitalized patients with COVID-19 although mechanisms underlying AKI are yet unclear. There may be a direct effect of SARS-CoV-2 virus on the kidney;however, there is currently no data linking SARS-CoV-2 viral load (VL) to AKI. We explored the association of SARS-CoV-2 VL at admission to AKI in a large diverse cohort of hospitalized patients with COVID-19. Methods and findings: We included patients hospitalized between March 13th and May 19th, 2020 with SARS-CoV-2 in a large academic healthcare system in New York City (N = 1,049) with available VL at admission quantified by real-time RT-PCR. We extracted clinical and outcome data from our institutional electronic health records (EHRs). AKI was defined by KDIGO guidelines. We fit a Fine-Gray competing risks model (with death as a competing risk) using demographics, comorbidities, admission severity scores, and log10 transformed VL as covariates and generated adjusted hazard ratios (aHR) and 95% Confidence Intervals (CIs). VL was associated with an increased risk of AKI (aHR = 1.04, 95% CI: 1.01-1.08, p = 0.02) with a 4% increased hazard for each log10 VL change. Patients with a viral load in the top 50th percentile had an increased adjusted hazard of 1.27 (95% CI: 1.02-1.58, p = 0.03) for AKI as compared to those in the bottom 50th percentile. Conclusions: VL is weakly but significantly associated with in-hospital AKI after adjusting for confounders. This may indicate the role of VL in COVID-19 associated AKI. This data may inform future studies to discover the mechanistic basis of COVID-19 associated AKI.

Combining Initial Radiographs and Clinical Variables Improves Deep Learning Prognostication in Patients with COVID-19 from the Emergency Department

Purpose: To train a deep learning classification algorithm to predict chest radiograph severity scores and clinical outcomes in patients with coronavirus disease 2019 (COVID-19). Materials and Methods: In this retrospective cohort study, patients aged 21-50 years who presented to the emergency department (ED) of a multicenter urban health system from March 10 to 26, 2020, with COVID-19 confirmation at real-time reverse-transcription polymerase chain reaction screening were identified. The initial chest radiographs, clinical variables, and outcomes, including admission, intubation, and survival, were collected within 30 days (n = 338;median age, 39 years;210 men). Two fellowship-trained cardiothoracic radiologists examined chest radiographs for opacities and assigned a clinically validated severity score. A deep learning algorithm was trained to predict outcomes on a holdout test set composed of patients with confirmed COVID-19 who presented between March 27 and 29, 2020 (n = 161;median age, 60 years;98 men) for both younger (age range, 21-50 years;n = 51) and older (age >50 years, n = 110) populations. Bootstrapping was used to compute CIs. Results: The model trained on the chest radiograph severity score produced the following areas under the receiver operating characteristic curves (AUCs): 0.80 (95% CI: 0.73, 0.88) for the chest radiograph severity score, 0.76 (95% CI: 0.68, 0.84) for admission, 0.66 (95% CI: 0.56, 0.75) for intubation, and 0.59 (95% CI: 0.49, 0.69) for death. The model trained on clinical variables produced an AUC of 0.64 (95% CI: 0.55, 0.73) for intubation and an AUC of 0.59 (95% CI: 0.50, 0.68) for death. Combining chest radiography and clinical variables increased the AUC of intubation and death to 0.88 (95% CI: 0.79, 0.96) and 0.82 (95% CI: 0.72, 0.91), respectively. Conclusion: The combination of imaging and clinical information improves outcome predictions. Supplemental material is available for this article.© RSNA, 2020.