ESC HEART FAILURE ORIGINAL RESEARCH ARTICLE ESC Heart Failure 2021; 8: 1047–1054 Published online 18 December 2020 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/ehf2.13026 Early use of remote dielectric sensing after hospitalization to reduce heart failure readmissions Anuradha Lala1,2, Maya H. Barghash1, Gennaro Giustino1,2, Jesus Alvarez-Garcia1,3 , Swiri Konje4, Aditya Parikh1, Jennifer Ullman1, Brendan Keith1, John Donehey1, Sumeet S. Mitter1, Maria Giovanna Trivieri1, Johanna P. Contreras3, Daniel Burkhoff5, Noah Moss1, Donna M. Mancini1,2 and Sean P. Pinney6* 1 Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, One Gustave Levy Place, PO Box 1030, New York, NY 10129, USA; 2 Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, One Gustave Levy Place, PO Box 1030, New York, NY 10129, USA; 3Cardiology Department, Hospital de la Santa Creu i Sant Pau, IIb-SantPau, CIBERCV, Universitat Autónoma de Barcelona, Barcelona, Spain; 4Mount Sinai, St. Luke’s Hospital, New York, NY, USA; 5Cardiovascular Research Foundation, NY, New York, USA; 6University of Chicago Medicine, Duchossois Center for Advanced Medicine (DCAM), Hyde Park, 5758 S. Maryland Ave., ChicagoIL 60637 Abstract Aims Readmission after hospitalization for acute decompensated heart failure (HF) remains a major public health problem. Use of remote dielectric sensing (ReDS) to measure lung water volume allows for an objective assessment of volume status and may guide medical optimization for HF. We hypothesized that the use of ReDS would lower 30 day readmission in patients referred to rapid follow-up (RFU) clinic after HF discharge. Methods and results We conducted a retrospective analysis of the use of ReDS for patients scheduled for RFU within 10 days post-discharge for HF at Mount Sinai Hospital between 1 July 2017 and 31 July 2018. Diuretics were adjusted using a pre-specified algorithm. The association between use of ReDS and 30 day readmission was evaluated. A total of 220 patients were included. Mean age was 62.9 ± 14.7 years, and 36.4% were female. ReDS was performed in 80 (36.4%) and led to med- ication adjustment in 52 (65%). Use of ReDS was associated with a lower rate of 30 day cardiovascular readmission [2.6% vs. 11.8%, hazard ratio (HR): 0.21; 95% confidence interval (CI): 0.05–0.89; P = 0.04] and a trend towards lower all-cause readmis- sion (6.5% vs. 14.1%, HR: 0.43; 95% CI: 0.16–1.15; P = 0.09) as compared with patients without a ReDS assessment. Conclusions ReDS-guided HF therapy during RFU after HF hospitalization may be associated with lower risk of 30 day readmission. Keywords Heart failure; Readmissions; Remote dielectric sensing; Congestion Received: 18 December 2019; Revised: 26 June 2020; Accepted: 10 September 2020 *Correspondence to: Sean P. Pinney, University of Chicago Medicine, Duchossois Center for Advanced Medicine (DCAM), Hyde Park, 5758 S. Maryland Ave. Chicago, IL 60637. Tel: 1-888-824-0200. Email: sean.pinney@mountsinai.org Anuradha Lala and Maya H. Barghash equally contributed to this work. Introduction failing to achieve a 30 day readmission rate in line with na- tional averages.5 Hospitalization for decompensated heart failure (HF) marks a Despite marked advancements in the treatment of HF, re- turning point in patients’ lives. More than half of hospitalized ducing HF readmissions remains a challenge. Studies of tele- HF patients will be re-hospitalized or die within 6 months of medicine, utilizing remote monitoring of daily weights, discharge. With an increasing prevalence of HF [regardless blood pressure, heart rate, and symptoms, combined with of ejection fraction (EF)], direct costs associated with care nurse health coaching interventions, have yielded inconsis- of hospitalized HF patients are projected to cross $70 billion tent results with respect to demonstrating improvements in by 2030.1–4 Accordingly, the Center for Medicare & Medicaid HF hospitalization and mortality.6–8 Early follow-up appoint- Services have targeted hospitals with financial penalties for ments after hospital discharge, however, have been © 2020 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of the European Society of Cardiology This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any me- dium, provided the original work is properly cited and is not used for commercial purposes. 1048 A. Lala et al. associated with lower rates of readmission for HF.9 More re- The RFU clinic is staffed by a specialized HF nurse practi- cently, remote monitoring of pulmonary arterial pressures tioner with physician oversight. During this visit, HF signs using a fully implantable pressure sensor has been shown to and symptoms were assessed; guideline-directed medical reduce hospitalizations for patients with HF with preserved therapy (GDMT) was reviewed and optimized; adherence, (HFpEF) or reduced EF (HFrEF)10,11; but limited access, reim- lifestyle changes, and education were reinforced. These bursement challenges, and the need for additional staffing changes and interventions were communicated to the pri- have slowed the widespread adoption of this technology. mary care physician or cardiologist. Use of a novel, non-invasive electromagnetic energy-based ReDS testing was considered for all patients as part of rou- technology to directly measure lung water may be an effec- tine care in RFU clinic. ReDS technology has been described tive strategy to lower HF hospitalizations. Remote dielectric previously.11,12 The system employs low-power electromag- sensing (ReDS; Sensible Medical Innovations, Ltd., Netanya, netic signals emitted into the body to measure the dielectric Israel) measures the dielectric properties of tissues, which properties of tissues. The ReDS™ Wearable System consists of are mainly determined by the pulmonary fluid content. Use two sensors in a wearable vest positioned on the front and of this system in a point-of-care (POC) environment can aid back of the patient’s thorax without requiring direct skin con- providers in the assessment of a patient’s volume status after tact, allowing measurements to be performed through light hospital discharge to guide further adjustment of diuretic clothing. These signals are emitted through the right therapy. As such, we hypothesized that among patients re- mid-thorax and received posteriorly after passing through tis- ferred to a HF nurse practitioner-led clinic within 10 days of sue. The characteristics of the received signals are affected by hospital discharge for HF, use of ReDS POC testing would be the fluid content of tissue in the path of the signal. The wear- associated with a lower rate of 30 day cardiovascular (CV) able vest is connected by a cable to a bedside monitor con- and all-cause hospital readmissions as compared with no sole. Measurements are provided after 90 s and recorded in ReDS use. the patient’s chart. Normal intrathoracic fluid content (be- tween 20% and 35%) has been validated using different quan- titative imaging modalities including computed tomography, nuclear magnetic resonance imaging, and positron emission tomography.11,12 Readings over 35% represent increased in- Methods trathoracic fluid content. All changes to GDMT and diuretic therapy during the RFU We conducted a retrospective observational cohort study in- visit were made in accordance with the American College of cluding adult patients (age ≥ 18 years) who presented to a HF Cardiology/American Heart Association/Heart Failure Society rapid follow-up (RFU) clinic within 10 days of discharge after a of America (ACC/AHA/HFSA) HF guideline,13 tempered by vi- hospitalization for HF between 1 July 2017 and 31 July 2018 tal signs, history, and signs and symptoms of HF. For those at the Mount Sinai Hospital in New York, New York. Patients with ReDS readings obtained during clinic visits, GDMT and with HFpEF and HFrEF were included (Figure 1). Baseline diuretic therapy were adjusted based upon a pre-specified al- characteristics, co-morbid conditions, ReDS application, med- gorithm (Table 1). ication changes, 30 day readmission, and cause of readmis- The two primary endpoints were 30 day CV and all-cause sion were collected for all patients. The study was approved hospital readmission after index HF hospitalization. Second- by the Institutional Review Board at the Icahn School of Med- ary endpoints were all-cause death and the combined out- icine at Mount Sinai Hospital. come of all-cause death or re-hospitalization within 30 days. Figure 1 CONSORT diagram. HF, heart failure; ReDS, remote dielectric sensing. ESC Heart Failure 2021; 8: 1047–1054 DOI: 10.1002/ehf2.13026 Remote dielectric sensing reduces heart failure readmission 1049 Table 1 Pre-specified algorithm to adjust guideline-directed medical therapy on the basis of the obtained ReDS values during a follow-up visit ReDS Reading (value, %) Action ≤20% Hold diuretics 21–35% Maintain current diuretic dosing and optimize guideline-directed medical therapy 36–45% Increase diuretics and return to rapid follow-up in 1 week ≥46% Consider outpatient intravenous loop diuretic or hospitalization Follow-up time was based on the last point of contact either and all-cause readmission was evaluated with multivariable via telephone or office appointment. Cox regression modelling and reported with adjusted hazard Baseline characteristics in patients with ReDS vs. without ratios (HRs) and 95% confidence interval (CIs). All covariates ReDS assessment were compared using χ 2 and Fisher’s exact with a level of significance < 0.20 on univariate analysis were tests for categorical variables and Student’s t-tests for contin- sequentially included in the multivariable model, and only uous measures as appropriate. Event rates were estimated those with a level of significance of <0.10 were included in with the Kaplan–Meier method, and univariate Cox regres- the final model. A complete list of covariates included in each sion models were used to evaluate the unadjusted associa- multivariable logistic regression model is reported in the ta- tion between ReDS and 30 day adverse events after the bles. Co-linearity was assessed by the variance inflation fac- index hospital discharge. The instantaneous risk of tor, which is an index that measures how much the re-hospitalization within 30 days was computed using variance of an estimated regression coefficient is increased Epanechnikov kernel smoothed hazard function. The inde- because of collinearity. The proportionality assumption for pendent association between use of ReDS and 30 day CV the Cox models was verified using the Schoenfeld residuals Table 2 Baseline clinical characteristics and medication use on hospital discharge among patients who presented for RFU visit, stratified by ReDS use vs. no ReDS use Overall (n = 220) ReDS (n = 80) No ReDS (n = 140) P-value Clinical characteristics Age, years 62.9 ± 14.7 65.2 ± 13.2 61.5 ± 15.4 0.08 Male sex 139 (63.2%) 53 (66.3%) 86 (61.4%) 0.48 2 Body mass index, kg/m 28.8 ± 7.0 27.3 ± 5.1 29.7 ± 7.7 0.02 White race 46 (20.9%) 17 (21.3%) 29 (20.7%) 0.93 Arterial hypertension 156 (72.2%) 60 (75.0%) 96 (70.6%) 0.49 Diabetes mellitus 90 (41.7%) 33 (41.3%) 57 (41.9%) 0.92 Renal insufficiency 49 (61.3%) 49 (61.3%) 77 (55.0%) 0.37 Chronic obstructive pulmonary disease 77 (35.0%) 28 (35.0%) 49 (35.0%) 1.00 Atrial fibrillation 90 (40.9%) 28 (35.0%) 62 (44.3%) 0.18 Coronary artery disease 112 (52.1%) 44 (55.0%) 68 (50.4%) 0.51 Smoking status 0.38 Never smoker 86 (43.2%) 32 (43.2%) 54 (43.2%) Current smoker 22 (11.1%) 11 (14.9%) 11 (8.8%) Former smoker 91 (45.7%) 31 (41.9%) 60 (48.0%) New York Heart Association class 0.35 I 1 (0.5%) 0 (0.0%) 1 (0.8%) II 53 (26.1%) 19 (25.7%) 34 (26.4%) III 126 (62.1%) 50 (67.6%) 76 (58.9%) IV 23 (11.3%) 5 (6.8%) 18 (14.0%) Left ventricular ejection fraction < 40% 143 (66.2%) 56 (70.9%) 87 (63.5%) 0.27 Number of prior hospitalizations 2.1 ± 1.9 2.0 ± 1.7 2.2 ± 2.1 0.41 Brain natriuretic peptide, pg/mL 917.2 ± 1083.3 979.4 ± 1300 881.0 ± 937.9 0.52 Medications at hospital discharge ARNIs 35 (15.9%) 12 (15.0%) 23 (16.4%) 0.78 ACE inhibitors or ARBs 109 (49.6%) 44 (55.0%) 65 (46.4%) 0.22 Mineralocorticoid receptor antagonists 92 (41.8%) 37 (46.3%) 55 (39.3%) 0.31 Beta-blockers 185 (84.1%) 67 (83.8%) 118 (84.3%) 0.92 Calcium channel blockers 17 (7.7%) 9 (11.3%) 8 (5.7%) 0.14 Loop diuretics 190 (86.4%) 69 (86.3%) 121 (86.4%) 0.97 Thiazide diuretics 12 (5.5%) 3 (3.8%) 9 (6.4%) 0.40 Inotropes 14 (6.4%) 2 (2.5%) 12 (8.6%) 0.08 ACE, angiotensin converting enzyme; ARB, angiotensin receptor blocker; ARNIs, angiotensin receptor-neprilysin inhibitors; ReDS, remote dielectric sensing. Results are reported as n (%) or mean ± standard deviations. ESC Heart Failure 2021; 8: 1047–1054 DOI: 10.1002/ehf2.13026 1050 A. Lala et al. method. Discrimination of the model was quantified with C- was higher in patients who had a ReDS assessment than in statistic. Two-sided P-values < 0.05 were considered to indi- those who did not have a ReDS reading (69.1% vs. 55.7%; uni- cate statistical significance. All statistical analyses were per- variate odds ratio: 1.77; 95% CI: 1.01–3.12; P = 0.047). formed with the use of STATA software, version 14.0 (Stata Corp., College Station, Texas). Outcomes Over a median follow-up time of 211 days (IQR, 123 to Results 354 days), there were 12 deaths and 44 all-cause hospitaliza- tions. When restricted to a 30 day post-discharge period, Baseline characteristics and remote dielectric there were 24 hospitalizations yielding a 30 day all-cause re- sensing assessments hospitalization rate of 11% (Table 3), the majority of which were due to CV causes (8.2% for the whole cohort and A total of 220 patients presented to RFU clinics within 10 days 18/24 hospitalizations). The majority of CV hospitalizations of HF hospitalization. The median time to RFU appointment were due to HF (n = 12/18, 66.7%). The median time to first after discharge was 6 days [inter-quartile range (IQR), 5 to re-hospitalization was 27 days (IQR, 19 to 49 days); the 8 days]. The mean age was 62.9 ± 14.6 years, and 36.4% were highest risk for re-hospitalization was observed between 20 women. Most patients were hospitalized at least two times in and 25 days post-discharge (Figure 2). Among patients who the preceding year, 65.5% had HFrEF, and 68% were New had a ReDS assessment, median time to re-hospitalization York Heart Association (NYHA) Class III and IV (Table 2). A to- was 40 compared with 22 days among patients without a tal of 80 patients (36.4%) had ReDS readings during their visit, ReDS assessment. and the median ReDS value was 33 (IQR, 30 to 38). Reasons Rates of 30 day adverse events in patients with a ReDS vs. ReDS was not performed included body habitus (n = 28; without ReDS assessment are reported in Table 4 and Figure 20%), wearable defibrillator vest (n = 6; 4.3%), presence of 3. A ReDS assessment was associated with significantly lower port for infusion of inotropes or other medication (n = 4; 30 day CV readmissions (2.6% vs. 11.8%; HR: 0.21; 95% CI: 2.9%), and unknown in the remaining 102. Aside from a 0.05–0.89, P = 0.04) (Figure 4A) with a trend towards lower higher body mass index in those who did not have a ReDS rates of 30 day all-cause readmissions (6.5% vs. 14.1%; HR: reading obtained, there were no significant baseline differ- 0.43; 95% CI: 0.16–1.15, P = 0.09) (Figure 4B). A ReDS assess- ences between patients who had a ReDS reading and those ment was also associated with a significantly lower combined who did not (Table 2). The rate of HF medication adjustment endpoint of 30 day death or CV readmissions (HR 0.29 95: CI 0.08–0.99, P = 0.047) (Figure 4C), whereas there was a trend Table 3 Causes of 30 day hospital readmission (n = 24) towards an association with lower combined endpoint of Re-hospitalization for cardiovascular causes 18 (75.0%) 30 day death or all-cause readmissions (HR 0.41; 95% CI Heart failure-related hospitalization 12/18 (66.7%) 0.15–1.09, P = 0.07) (Figure 4D). Non-heart failure-related hospitalization 6/18 (33.3%) Multivariable logistic regression models for all-cause and Re-hospitalization for non-cardiovascular causes 6 (25.0%) CV readmissions at 30 days are reported in Table 5. The Figure 2 Daily hazard of any re-hospitalization within 30 days post-hospital discharge. ESC Heart Failure 2021; 8: 1047–1054 DOI: 10.1002/ehf2.13026 Remote dielectric sensing reduces heart failure readmission 1051 Table 4 Rates of death and re-hospitalization at 30 days after hospital discharge ReDS (n = 80) No ReDS (n = 139) ARD (%) Hazard ratio (95% CI) P-value All-cause death 1.3% (1) 1.5% (2) 0.2 0.86 (0.08–9.50) 0.90 All-cause re-hospitalization 6.5% (5) 14.0% (19) 7.5 0.43 (0.16–1.16) 0.09 Cardiovascular re-hospitalization 2.6% (2) 11.8% (16) 9.2 0.21 (0.05–0.90) 0.04 All-cause death or re-hospitalization 6.5% (5) 14.7% (20) 8.2 0.41 (0.15–1.09) 0.08 All-cause death or cardiovascular re-hospitalization 3.9% (3) 12.5% (17) 8.6 0.29 (0.09–0.99) 0.049 ARD, absolute risk difference; CI, confidence interval; RFU, rapid follow-up; ReDS, remote dielectric sensing. Results are reported as Kaplan–Meier estimates (number of events). Figure 3 Impact of rapid follow-up clinic with ReDS-guided management on 30 day readmission rates. ReDS, remote dielectric sensing; RFU, rapid fol- low-up. covariates associated with 30 day CV readmissions were num- management of HF.13 Accordingly, in our cohort of patients, ber of prior hospitalizations, male sex, atrial fibrillation, and the RFU visit within 10 days of discharge alone was associated use of ReDS. The only factor associated with of 30 day with lower rates of 30 day readmission compared with the all-cause readmissions after adjustment was the number of national average of approximately 19% noted in a previous prior hospitalizations. Get With the Guidelines Registry analysis. Our cohort in- cluded a high-risk HF population with over two-thirds describ- ing NYHA Class III to IV symptoms. Congestion is the main cause for HF decompensation and Discussion hospitalization, manifested as dyspnoea, orthodema, and/or bendopnea.6,14,15 Inadequate decongestion during hospitali- In this retrospective single-centre study, we explored the im- zation for HF and recurrent congestion post-discharge are pact of performing ReDS assessments after HF hospitalization postulated to partially explain the high rates of hospital on 30 day outcomes. The findings of our analysis are as fol- readmission.10,14,16 Yet detecting congestion by physical lows: (i) among patients who presented to an RFU clinic after exam and laboratory markers in the absence of symptoms admission for HF, the rate of all-cause 30 day can be challenging and is subject to limitations.17 Specifically, re-hospitalization was approximately 11%, and (ii) the use of signs, symptoms, and weight changes can be poor surrogates ReDS technology during a RFU clinic visit was associated with for filling pressures and therefore unreliable predictors of HF lower risk of 30 day CV re-hospitalization and a trend towards readmission.18 As such, the use of ReDS wearable vest as a lower risk of 30 day all-cause re-hospitalization as compared simple non-invasive tool to guide optimization of volume sta- with those of patients in whom ReDS was not utilized. tus presents considerable appeal. Reducing readmissions for HF is a major public health fo- This retrospective analysis of over 200 racially diverse pa- cus. Early follow-up post-discharge for HF has been shown tients in a vulnerable post HF hospitalization discharge period to improve outcomes for patients admitted with HF9 and is reveals that the use of ReDS technology is safe and may be recommended by the ACC/AHA/HFSA guidelines for the associated with lower risk of 30 day readmissions. A thoracic ESC Heart Failure 2021; 8: 1047–1054 DOI: 10.1002/ehf2.13026 1052 A. Lala et al. Figure 4 Rates of death and re-hospitalization within 30 days in patients with vs. without ReDS during the rapid follow-up visit. (A) Any re-hospital- ization. (B) Cardiovascular re-hospitalization. (C) Death or any re-hospitalization. (D) Death or cardiovascular re-hospitalization. ReDS, remote dielectric sensing. fluid content reading was provided to the nurse practitioner, Table 5 Predictors of 30 day cardiovascular and all-cause re-hospi- allowing for optimization of volume status using a talizations by multivariable Cox regression modelling pre-specified algorithm to adjust diuretics. The use of this Adjusted HR (95% P- non-invasive POC approach in an outpatient setting was asso- CI) value ciated with lower 30 day readmissions compared with pa- Cardiovascular re-hospitalization tients without ReDS assessments. We hypothesize that this ReDS 0.17 (0.03–0.87) 0.03 benefit may be mediated by the increased number of medica- Male sex 6.87 (1.53–30.91) 0.01 tion changes made in the patients for whom ReDS assess- Atrial fibrillation 2.76 (1.02–7.47) 0.05 Number of prior hospitalizations a 1.31 (1.12–1.53) 0.001 ments were performed. The use of a standardized algorithm Brain natriuretic peptide, per 100 ng/ 1.03 (1.00–1.06) 0.09 to optimize volume status and inhibitors of the renin–angio- mL tensin–aldosterone system may have contributed to the ob- All-cause re-hospitalization ReDS 0.47 (0.17–1.27) 0.14 served statistically significant reduction in CV readmissions. Male sex 2.30 (0.90–5.89) 0.08 Although not formally captured, our anecdotal experience Atrial fibrillation 2.17 (0.96–4.91) 0.06 suggested that the ReDS measurement seemed to create a a Number of prior hospitalizations 1.24 (1.08–1.41) 0.002 ‘teachable moment’ to review medications, compliance, and HR, hazard ratio; ReDS, remote dielectric sensing. dietary restrictions. Further study is needed to reveal C-statistics for the cardiovascular re-hospitalization model: 0.82. a Per number of prior hospitalization increase; C-statistics for the whether use of ReDS in this setting improves adherence all-cause re-hospitalization model: 0.69. and subsequent follow-up. ESC Heart Failure 2021; 8: 1047–1054 DOI: 10.1002/ehf2.13026 Remote dielectric sensing reduces heart failure readmission 1053 There are several limitations that warrant discussion. First, after hospital discharge for HF allowed optimization of medi- owing to the retrospective, single-centre design of this study, cations and appeared to be associated with lower risk of our findings have to be considered hypothesis generating. 30 day CV readmissions in a high-risk cohort. Further experi- Second, the small sample size precluded a ence with POC ReDS testing may provide insights into the fre- non-parsimonious multivariable adjustment of our predictive quency of congestion early after HF discharge, reduction in models; therefore, our effect estimates are potentially sub- hospital readmission, and optimization of diuretic therapy ject to significant confounding bias, and, importantly, we and GDMT. Our preliminary findings warrant evaluation in a could not perform an analysis in sub-groups of clinical inter- randomized controlled trial to evaluate the clinical impact of est such as HFrEF vs. HFpEF. Third, not all patients hospital- using ReDS technology after HF hospitalizations to reduce ized for HF at Mount Sinai Hospital may have been early readmissions. identified for RFU clinic, raising the possibility of selection bias. Further, among those presenting for RFU clinic, ReDS readings were performed in less than 40% of patients, and there was no pre-defined protocol as to which patients underwent ReDS readings. Reasons as to why ReDS readings Funding Information were not performed were often not listed in the chart, which may have also introduced bias. Despite these limitations, the Fundació Privada Daniel Bravo Andreu (ES) Icahn School of fact that patients who had ReDS assessments performed Medicine at Mount Sinai were similar to those who did not have ReDS assessments performed is reassuring. We did not capture readmissions to other hospitals; however, the Mount Sinai Health system is large and far reaching in this geographic location with mul- Conflict of interest tiple hospitals. There are no financial or industry relationships that pose a conflict of interest with the submitted article. Dr. Sean Conclusions Pinney has received consulting fees from Abbott, CareDx, Medtronic, and Procyrion. 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Atar D, Teerlink JR, Califf RM, Massie ESC Heart Failure 2021; 8: 1047–1054 DOI: 10.1002/ehf2.13026