Aims Heart failure is an escalating burden on global healthcare systems. Modernizing heart failure care is inevitable, with eHealth products poised to play an important role. However, eHealth devices that can initiate and adjust heart failure medication are currently lacking. Consequently, this study aimed to develop an artificial intelligence-based decision engine to provide guideline-based recommendations for disease-modifying medication in heart failure patients.Methods and results We developed the decision engine by converting the European Society of Cardiology (ESC) heart failure guidelines into Business Process Model and Notation, a visual modelling language suitable for developing complex decision engines. A safety evaluation, based on clinical parameters, was conducted to ascertain the system's applicability to specific cases. The decision engine renders specific decisions concerning disease- modifying therapy for heart failure patients. We defined 72 virtual heart failure patient scenarios, encompassing a broad spectrum of baseline characteristics and background medication. All recommendations offered by the engine were evaluated by an independent heart failure specialist. All but three recommendations (94%) were identical to the treatment decisions by the heart failure specialist, and all (100%) were in line with the 2021 ESC heart failure guidelines.Conclusion The decision engine offers guideline-based recommendations for disease-modifying therapy, positioning it as a tool to enhance self-care among heart failure patients. To validate our results, the decision engine is being prospectively tested in real-world patients in a multicentre clinical trial (NCT04699253).

eHealth products have the potential to enhance heart failure (HF) care by identifying at-risk patients. However, existing risk models perform modestly and require extensive data, limiting their practical application in clinical settings. This study aims to address this gap by validating a more suitable risk model for eHealth integration. We developed the Maastricht Decompensation Questionnaire (MDQ) based on expert opinion to assess HF patients’ fluid status using common signs and symptoms. Subsequently, the MDQ was administered to a cohort of HF outpatients at Maastricht University Medical Centre. Patients with ≥ 10 MDQ points were categorised as ‘decompensated’, patients with < 10 MDQ points as ‘not decompensated’. HF nurses, blinded to MDQ scores, served as the gold standard for fluid status assessment. Patients were classified as ‘correctly’ if MDQ and nurse assessments aligned; otherwise, they were classified as ‘incorrectly’. A total of 103 elderly HF patients were included. The MDQ classified 50 patients as ‘decompensated’, with 17 of them being correctly classified (34

Hohe Serumkonzentrationen (>1800pg/ml) des Growth Differentiation Factor 15 (GDF-15) haben bei Patienten mit chronischer Herzinsuffizienz (HF) konsistent in verschiedenen Studien einen prognostischen Vorhersagewert für kardiovaskulären Tod und/oder Krankenhauseinweisungen aufgrund von HF gezeigt. Patienten mit HF und Diabetes mellitus oder reduzierter reduzierte Nierenfunktion weisen in diesen Studien einen erhöhten GDF-15-Spiegel auf.auf. Die Bedeutung für Patienten mit Prädiabetes ist unklar, obwohl bereits in dem Stadium Inzidenz kardiovaskulärer Ereignisse häufiger sind. Wir haben in einer Beobachtungsstudie mit 141 HF-Patienten (Mittleres Alter 65±12,8 Jahre, 22,7% weiblich) untersucht, ob bei HF-Patienten mit Prädiabetes (HbA1c 5,7-6,4%), Diabetes oder steigenden GFR-Kategorien (G1-G4) nach der Kidney Disease Improving Global Outcomes (KDIGO) erhöhte GDF-15-Spiegeln bestehen und wie sich im Vergleich die NT-proBNP-Konzentrationen sind.

INTRODUCTION:Heart failure is a serious burden on health care systems due to frequent hospital admissions. Early recognition of outpatients at risk for clinical deterioration could prevent hospitalization. Still, the role of signs and symptoms in monitoring heart failure patients is not clear. The heart failure coach is a web-based telemonitoring application consisting of a 9-item questionnaire assessment of heart failure signs and symptoms and developed to identify outpatients at risk for clinical deterioration. If deterioration was suspected, patients were contacted by a heart failure nurse for further evaluation.METHODS:Heart failure coach questionnaires completed between 2015 and 2018 were collected from 287 patients, completing 18,176 questionnaires. Adverse events were defined as all-cause mortality, heart failure- or cardiac-related hospital admission or emergency cardiac care visits within 30 days after completion of each questionnaire. Multilevel logistic regression analyses were performed to assess the association between the heart failure coach questionnaire items and the odds of an adverse event.RESULTS:No association between dyspnea and adverse events was observed (odds ratio 1.02, 95% confidence interval 0.79-1.30). Peripheral edema (odds ratio 2.21, 95% confidence interval 1.58-3.11), persistent chest pain (odds 2.06, 95% confidence interval 1.19-3.58), anxiety about heart failure (odds ratio 2.12, 95% confidence interval 1.44-3.13), and extensive struggle to perform daily activities (odds ratio 2.23, 95% confidence interval 1.38-3.62) were significantly associated with adverse outcome.DISCUSSION:Regular assessment of more than the classical signs and symptoms may be helpful to identify heart failure patients at risk for clinical deterioration and should be an integrated part of heart failure telemonitoring programs.

Background In Europe, more than 15 million people live with heart failure (HF). It imposes an enormous social, organizational and economic burden. As a reaction to impending impact on healthcare provision, different country-specific structures for HF-care have been established. The aim of this report is to provide an overview and compare the HF-care approaches of Germany, Ireland, the Netherlands and the UK, and to open the possibility of learning from each other's experience. Methods A mixed methods approach was implemented that included a literature analysis, interviews and questionnaires with HF-patients and caregivers, and expert interviews with representatives from healthcare, health service research and medical informatics. Results The models of HF-care in all countries analyzed are based on the European Society of Cardiology guidelines for diagnosis and treatment of HF. Even though the HF-models differed in design and implementation in practice, key challenges were similar: (i) unequal distribution of care between urban and rural areas, (ii) long waiting times, (iii) unequal access to and provision of healthcare services, (iv) information and communication gaps and (v) inadequate implementation and financing of digital applications. Conclusion Although promising approaches exist to structure and improve HF-care, across the four countries, implementation was reluctant to embrace novel methods. A lack of financial resources and insufficient digitalization making it difficult to adopt new concepts. Integration of HF-nurses seems to be an effective way of improving current models of HF-care. Digital solutions offer further opportunities to overcome communication and coordination gaps and to strengthen self-management skills.

Heart failure is a severe condition and telemedicine can improve the care of heart failure. Many patients are unable to use telemedicine applications due to visual impairment and limited health-related literacy. Avatar technology might help to overcome these limitations. A telemedicine application was combined with a nurse avatar and offered to heart failure outpatients for 3 months. System usability and patient satisfaction were evaluated monthly by the system usability score (maximum score=100) and the patient satisfaction scale (maximum score=50). In total, 37 heart failure patients were enrolled. The mean system usability score after 1 month was 73 (standard deviation=24) and 72 (standard deviation=10) after 3 months of follow-up, which was not significantly different (p = 0.40). The mean patient satisfaction scale after 1 month was 42 (standard deviation=5) and 39 (standard deviation=8) after 3 months, which was not significantly different (p = 0.10). A nurse look-a-like avatar integrated into a telemedicine application was positively assessed by heart failure patients. Future studies are warranted to clarify the role of avatar technology in telemedicine.

Heart failure (HF) poses a burden on specialist care, making referral of clinically stable HF patients to primary care a desirable goal. However, a structured approach to guide patient referral is lacking. The Maastricht Instability Score—Heart Failure (MIS-HF) questionnaire was developed to objectively stratify the clinical status of HF patients: patients with a low MIS-HF (0–2 points, indicating a stable clinical condition) were considered for treatment in primary care, whereas high scores (> 2 points) indicated the need for specialised care. The MIS-HF was evaluated in 637 consecutive HF patients presenting between 2015 and 2018 at Maastricht University Medical Centre. Of the 637 patients, 329 (52%) had a low score and 205 of these 329 (62%) patients were referred to primary care. The remaining 124 (38%) patients remained in secondary care. Of the 308 (48%) patients with a high score (> 2 points), 265 (86%) remained in secondary care and 41 (14%) were referred to primary care. The primary composite endpoint (mortality, cardiac hospital admissions) occurred more frequently in patients with a high compared to those with a low MIS-HF after 1 year of follow-up (29.2% vs 10.9%; odds ratio (OR) 3.36, 95% confidence interval (CI) 2.20–5.14). No significant difference in the composite endpoint (9.8% vs 12.9%; OR 0.73, 95% CI 0.36–1.47) was found between patients with a low MIS-HF treated in primary versus secondary care. The MIS-HF questionnaire may improve referral policies, as it helps to identify HF patients that can safely be referred to primary care.

Functional status and health-related quality of life (HRQoL) are important in patients with heart failure (HF). Little is known about the effect of telemonitoring on functional status and HRQoL in that population. A total of 382 patients with HF (New York Heart Association class 2–4) were included in a randomised controlled trial to investigate the effect of tailored telemonitoring on improving HRQoL and functional status in HF patients. Randomisation was computer-generated with stratification per centre. At baseline and after 12 months, patients’ functional status was determined by metabolic equivalent scores (METS). HRQoL was measured with the EuroQol five dimensions questionnaire (EQ-5D), visual analogue scale (VAS) and Borg rating of perceived exertion scale (Borg). Additional outcome data included number of HF-related outpatient clinic visits and mortality. Telemonitoring was statistically significantly related to an increase in METS after 1 year (regression coefficient 0.318; p = 0.01). Telemonitoring did not improve Borg, EQ-5D or VAS scores after 1 year. EQ-5D [hazard ratio (HR) 0.20, 95% confidence interval (CI) 0.07–0.54], VAS (HR 0.98, 95% CI 0.96–0.99), Borg (HR 1.21, 95% CI 1.11–1.31) and METS (HR 0.73, 95% CI 0.58–0.93) at baseline were significantly associated with survival after 12 months. Tailored telemonitoring stabilised the functional status of HF patients but did not improve HRQoL. Therefore, telemonitoring may help to prevent deterioration of exercise capacity in patients with HF. However, because our study is a reanalysis of a randomised controlled trial (RCT), this is considered hypothesis-generating and should be confirmed by adequately powered RCTs.

Introduction Evidence suggests that telemonitoring decreases mortality and heart failure (HF)-related hospital admission in patients with HF. However, most studies follow their patients for only several months. Little is known about the long-term effects of telemonitoring after a period of application. Methods In 2007, the TEHAF study was initiated to compare tailored telemonitoring with usual care with respect to time until first HF-related hospital admission. In total, 301 patients completed the study after a follow-up period of one year. No differences could be found in time to first HF-related admission between intervention and control groups. Here, we performed a retrospective analysis in order to investigate potential long-term effects of telemonitoring. The primary endpoint was time to first HF-related hospital admission. Secondary endpoints were, amongst others, all-cause mortality, hospital admission due to HF and days alive and out of hospital (DAOOH). Electronic files of all included patients were reviewed between October 2007 and September 2015. Result Mean follow-up duration was 1652 days (standard deviation: 1055 days). No significant difference in time to first HF-related hospital admission (log-rank test, p = 0.15), all-cause mortality (log-rank test, p = 0.43), or DAOOH (two-sample t-test, p = 0.87) could be found. However, patients that underwent telemonitoring had significantly fewer HF-related hospital admissions (incident rate ratio 0.54, 95% confidence interval 0.31–0.88). Discussion Telemonitoring did not significantly influence the long-term outcome in our study. Therefore, extending the follow-up period of telemonitoring studies in HF patients is probably not beneficial.

A 76-year-old female was admitted to our hospital because of severe diarrhea and lethargy. Diarrhea persisted since several weeks. Past medical history revealed cholecystectomy, appendectomy, coronary artery bypass surgery, and hypertension. Palpation of the right hemi abdomen was slightly painful. No pathologic findings could be detected with abdominal ultrasound, abdominal X-ray, and computed tomography of the cerebrum. Laboratory tests revealed several electrolyte abnormalities. The clinical chemist, however, suspected that a laboratory error had occurred and suggested to repeat the test. In order to start electrolyte supplementation as soon as possible, we tried to verify the electrolyte abnormality without waiting for the second test. Hence, a blood pressure cuff was placed around her right arm and inflated to a pressure above systolic blood pressure (Video 1). As shown in the video, flection of the wrist, extension of the interphalangeal joints and adduction of the fingers occurred. This phenomenon is known as Trousseau sign, which is typical for hypocalcaemia [[1]Cooper M.S. Gittoes N.J. Diagnosis and management of hypocalcaemia.BMJ. 2008; 336: 1298-1302Crossref PubMed Scopus (244) Google Scholar]. The described muscle contraction is the result of increased neural excitability due to hypocalcaemia, enforced by ischemia due to the inflated blood pressure cuff and can be found in 94% of all patients with hypocalcaemia [[2]Urbano F.L. Signs of hypocalcemia: Chvostek's and Trousseau's signs. Turner White Communications Inc., Wayne, PA2000Google Scholar]. In this case, laboratory tests indeed confirmed a very low total calcium level of 1.36 mmol/l (reference value 2.10–2.55 mmol/l). Serum albumin (37.7 g/l; reference value 32–47 g/l), and parathyroid hormone level (3.3 pmol/l; reference value1.3–6.8 pmol/l) were normal. Presumably, hypocalcaemia was the result of relative hypoparathyroidism caused by severe hypomagnesaemia (0.17 mmol/l; reference value 0.7–1.0 mmol/l) due to fecal loss. Severe hypomagnesaemia interferes with the parathyroid calcium-sensing receptor [[3]Shoback D.M. Bilezikian J.P. Costa A.G. Dempster D. Dralle H. Khan A.A. et al.Presentation of hypoparathyroidism: etiologies and clinical features.J Clin Endocrinol Metab. 2016; 101: 2300-2312Crossref PubMed Scopus (190) Google Scholar], resulting in relative hypoparathyroidism, i.e. parathyroid hormone secretion is insufficient to maintain normal serum calcium levels. Because of the presence of Trousseau sign, calcium and magnesium supplementation was started immediately. As a result calcium and magnesium levels normalized and parathyroid hormone levels increased to 17.0 pmol/l. After treatment with metronidazole, diarrhea ceased. This case shows how Trousseau sign could be used to verify deviant calcium levels in clinical practice. The following is the supplementary data related to this article.eyJraWQiOiI4ZjUxYWNhY2IzYjhiNjNlNzFlYmIzYWFmYTU5NmZmYyIsImFsZyI6IlJTMjU2In0.eyJzdWIiOiJjMDYzMjA0NDdkNjY0NjBhNjMyMDJjMDZjZmRmOTRkZCIsImtpZCI6IjhmNTFhY2FjYjNiOGI2M2U3MWViYjNhYWZhNTk2ZmZjIiwiZXhwIjoxNjc4MTc0Njk4fQ.QQqTzJgDOPa3DTjF_6qlJM6r4HWFgWLd32oJDJxThN96SFe_0Qp_0SDtVWnjz2DOO3XJbBWyzqsNl2keP8Sz_fpytP_C1Q5cCRoFjgOm92Ryh0_M9yBG9LnsadDcL7OJ_16FhUXwn6zmFk3zGKNKPPHE6gIRNdH3EieqzpZBlpL11uEvF2hyPas0zCXrqEjbsvBSp8l5PzbtDytC8jo8JAWw6KTAUlfqj869QdDG0exz70SjFRAKAenq0R-aTyHf8U3yFV64yMG_RGiVQ3knyc13gIiR1QSKJ2JUGCBT5xyKyv4Xlf5TCD5ll7H6O4NhCVdt6KkNHo9Ne-0Qq7ghbA Download .mp4 (35.24 MB) Help with .mp4 files Video 1A blood pressure cuff was placed around her right arm and inflated to a pressure aboven systolic blood pressure. None.