BackgroundThe new category of heart failure (HF), Heart Failure with mid range Ejection Fraction (HFmrEF) has recently been proposed with recent publications reporting that HFmrEF represents a transitional phase. The aim of this study was to determine the prevalence and clinical characteristics of patients with HFmrEF and to establish what proportion of patients transitioned to other types of HF, and how this affected clinical outcomes.Methods and resultsPatients were diagnosed with HF according to the 2016 ESC guidelines. Clinical outcomes and variables were recorded for all consecutive in-patients referred to the heart failure service. In total, 677 patients with new HF were identified; 25.6% with HFpEF, 21% with HFmrEF and 53.5% with HFrEF. While clinical characteristics and prognostic factors of HFmrEF were intermediate between HFrEF and HFpEF, HFmrEF patients had the best outcome, with higher mortality in the HFrEF population (p 0.02) and higher HF rehospitalisation rates in the HFpEF population (p < 0.01).38.7% of the HFmrEF patients transitioned (56.4% to HFpEF and 43.6% to HFrEF) with fewest deaths in the patients that transitioned to HFpEF (p 0.04), and fewest HF readmissions in the patients that remained as HFmrEF (<0.01)ConclusionHFmrEF patients had the best outcomes, compared to high rates of mortality seen in patients with HFrEF and high rates of HF readmissions seen in patients with HFpEF. Only 1/3 of HFmrEF patients transitioned during follow up, with the lowest mortality seen in patients transitioning to HFpEF.

Most patients in a specialist murmur clinic had normal auscultation and point-of-care scans and no additional valve disease was detected by standard echocardiography. This suggests that a murmur clinic is a valid model for reducing demand on hospital echocardiography services.

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Background Tackling health inequalities is a priority in heart failure (HF). We do not fully understand why some patients do not attend their hospital HF clinic appointments. Currently when a patient DNAs (does not attend) they are offered a repeat appointment often months later or are discharged from the service with a request to the primary care physician to re-refer. Non-attendance represents a missed opportunity to improve patients' health. Purpose The purpose of this pilot was to look at the demographics and patient factors that contribute to non-attendance. The aim is to understand and personalise our DNA policy to reduce health inequality, improve outcomes, and reduce inefficiencies in our service. Methods The last consecutive 45 patients who DNAd HF clinic were identified and for each, a patient who did attend the same clinic date (Attender), was chosen at random (random.org). The demographics were obtained (age, ethnicity, contact details) and medical notes reviewed (LVEF%, co-morbidities). The patient address was scored for its Index of Multiple Deprivation (IMD) – a UK government dataset measuring relative deprivation by ranking 32,844 neighbourhoods nationally using 37 indicators across 7 domains of deprivation where neighbourhood 1 is the most deprived nationally. Patients were phoned up to three times to establish the patient's mode, duration and cost of their last journey to clinic and, for those patients who DNAd, to ascertain the reason for non-attendance. Results Demographic and medical history was obtained for all patients. It was not possible to contact 2/45 of the Attenders, and 13/45 of the DNA patients. There was no significant difference in age, gender, number of comorbidities, LVEF%, travel time, or travel cost between DNAs and attenders. The mean one-way journey time was 53.4 mins (range 15–210 mins) and the mean return journey cost was GBP ≤10.95 (range ≤0–≤80). Common reasons for non-attendance were not receiving appointment details, forgetting appointments, being unwell on the day and difficulties with travel. The IMD score for the patients who DNAd was significantly lower confirming these patients lived in more deprived areas (9436±5863 vs. 15414±7801, p<0.001) with 71% of DNA patient's addresses in the bottom third most deprived neighbourhoods nationally. Figure 1 Conclusions There was a significant difference in deprivation score between patients who attended and DNAd their clinics. In addition, we found that all patients were travelling up to an hour each way to attend clinic, and that the cost of travel may be a barrier to attendance, even in a healthcare system that is free at the point of delivery. Despite calling three times, we were unable to speak to 29% of patients who DNAd and 4% of the patients who attended their appointments. Work is ongoing to reduce our DNA rates and personalise our response in this deprived population, with the aim of improving engagement and health inequality.

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