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Background: 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., Methods: 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'., Results: A total of 103 elderly HF patients were included. The MDQ classified 50 patients as 'decompensated', with 17 of them being correctly classified (34%). Additionally, 53 patients were categorised as 'not decompensated', with 48 of them being correctly classified (90%). The calculated area under the curve was 0.69 (95% confidence interval: 0.57-0.81; p < 0.05). Cronbach's alpha reliability coefficient for the MDQ was 0.85., Conclusions: The MDQ helps identify decompensated HF patients through clinical signs and symptoms. Further trials with larger samples are needed to confirm its validity, reliability and applicability. Tailoring the MDQ to individual patient profiles may improve its accuracy., Competing Interests: Conflict of interest: H.-P. Brunner–La Rocca received grants from Vifor, Roche Diagnostics and IHI (iCARE4CVD) and is a member of the Committee Medication of Dutch Society of Cardiology; he received consulting fees from Novartis, Roche Diagnostics, Boehringer-Ingelheim, AstraZeneca, Novo Nordisk, and Vifor; he received payments for expert testimony from Novartis. A.J. Gingele, F. Beckers and J.J. Boyne declare that they have no competing interests., (© 2024. The Author(s).)

Introduction: 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., Methods: 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., Results: 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., Conclusion: The MIS-HF questionnaire may improve referral policies, as it helps to identify HF patients that can safely be referred to primary care., (© 2022. The Author(s).)

Background: 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., Methods and Results: 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., Conclusions: 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.