The value of multiparametric prediction scores in heart failure varies with the type of follow-up after discharge: a comparative analysis.

AIMS: Multiple prediction score models have been validated to predict major adverse events in patients with heart failure. However, these scores do not include variables related to the type of follow-up. This study aimed to evaluate the impact of a protocol-based follow-up programme of patients with heart failure regarding scores accuracy for predicting hospitalizations and mortality occurring during the first year after hospital discharge. METHODS AND RESULTS: Data from two heart failure populations were collected: one composed of patients included in a protocol-based follow-up programme after an index hospitalization for acute heart failure and a second one-the control group-composed of patients not included in a multidisciplinary HF management programme after discharge. For each patient, the risk of hospitalization and/or mortality within a period of 12 months after discharge was calculated using four different scores: BCN Bio-HF Calculator, COACH Risk Engine, MAGGIC Risk Calculator, and Seattle Heart Failure Model. The accuracy of each score was established using the area under the receiver operating characteristic curve (AUC), calibration graphs, and discordance calculation. AUC comparison was established by the DeLong method. The protocol-based follow-up programme group included 56 patients, and the control group, 106 patients, with no significant differences between groups (median age: 67 years vs. 68.4 years; male sex: 58% vs. 55%; median ejection fraction: 28.2% vs. 30.5%; functional class II: 60.7% vs. 56.2%, I: 30.4% vs. 31.9%; P = not significant). Hospitalization and mortality rates were significantly lower in the protocol-based follow-up programme group (21.4% vs. 54.7%; P < 0.001 and 5.4% vs. 17.9%; P < 0.001, respectively). When applied to the control group, COACH Risk Engine and BCN Bio-HF Calculator had, respectively, good (AUC: 0.835) and reasonable (AUC: 0.712) accuracy to predict hospitalization. There was a significant reduction of COACH Risk Engine accuracy (AUC: 0.572; P = 0.011) and a non-significant accuracy reduction of BCN Bio-HF Calculator (AUC: 0.536; P = 0.1) when applied to the protocol-based follow-up programme group. All scores showed good accuracy to predict 1 year mortality (AUC: 0.863, 0.87, 0.818, and 0.82, respectively) when applied to the control group. However, when applied to the protocol-based follow-up programme group, a significant predictive accuracy reduction of COACH Risk Engine, BCN Bio-HF Calculator, and MAGGIC Risk Calculator (AUC: 0.366, 0.642, and 0.277, P < 0.001, 0.002, and <0.001, respectively) was observed. Seattle Heart Failure Model had non-significant reduction in its acuity (AUC: 0.597; P = 0.24). CONCLUSIONS: The accuracy of the aforementioned scores to predict major events in patients with heart failure is significantly reduced when they are applied to patients included in a multidisciplinary heart failure management programme.

Effectiveness and Safety of Sacubitril/Valsartan in Patients with Chronic Kidney Disease-A Real-World Experience.

BACKGROUND: data regarding the effectiveness and safety of sacubitril/valsartan in heart failure and reduced ejection fraction (HFrEF) patients with chronic kidney disease (CKD) are scarse. OBJECTIVE: to evaluate the effectiveness and safety of sacubitril/valsartan in HFrEF and CKD in a real-world population. METHODS: we included consecutive ambulatory HFrEF patients that initiated sacubitril/valsartan between February 2017 and October 2020, stratified by CKD (KDIGO stage 5 excluded). PRIMARY OUTCOMES: the incidence rate per 100 patient-years and the annualized length of stay (LOS) of acute decompensated HF hospitalizations (HFH). SECONDARY OUTCOMES: all-cause mortality, NYHA improvement, and titration of sacubitril/valsartan. RESULTS: We included 179 patients, 77 with CKD, those being older (72 ± 10 vs. 65 ± 12 years, p < 0.001), had higher NT-proBNP (4623 ± 5266 vs. 1901 ± 1835 pg/mL, p < 0.001), and high anaemia incidence (p < 0.001). After 19 ± 11 months, a significant reduction in HFH adjusted incidence rate (57.5% decrease in CKD vs. 74.6%, p = 0.261) was observed, with 5 days there was a reduction in annualized LOS in both groups (p = 0.319). NYHA improved similarly in both groups (p = 0.670). CKD patients presented non-significant higher all-cause mortality (HR = 2.405, 95%CI: [0.841; 6.879], p = 0.102). Both groups had similar sacubitril/valsartan maximum dose achievement and drug withdrawal. CONCLUSION: sacubitril/valsartan was effective on reducing HFH and LOS without affecting all-cause mortality in a CKD real-world population.

Non-invasive telemonitoring improves outcomes in heart failure with reduced ejection fraction: a study in high-risk patients.

AIMS: Non-invasive telemonitoring (TM) in patients with heart failure (HF) and reduced left ventricular ejection fraction (HFrEF) may be useful in the early diagnosis of HF decompensation, allowing therapeutic optimization and avoiding re-hospitalization. We describe a TM programme in this population and evaluate its effectiveness during a 12 month period. METHODS AND RESULTS: We conducted a single-centre study of patients discharged from hospital after decompensated HF, allocated into three groups: prospective TM programme, prospective HF protocol follow-up programme (PFP) with no TM facilities, and retrospective propensity-matched usual care (UC). TM effectiveness was assessed by all-cause hospitalizations and mortality; HF-related hospitalization (HFH), days lost to unplanned hospital admissions/death, functional capacity and quality of life (New York Heart Association, Kansas City Cardiomyopathy Questionnaire, 6 min walk test, and plasma N-terminal pro-brain natriuretic peptide) were also evaluated. A total of 125 patients were included [65.9 ± 11.9 years, 32% female, left ventricular ejection fraction 27% (21-32)]. TM was similar to PFP regarding effectiveness; TM reduced all-cause hospitalization and mortality (HR 0.27; 95% CI 0.11-0.71; P < 0.01) and HFH (HR 0.29; 95% CI 0.10-0.89; P < 0.05) as compared with UC. TM reduced the average number of days lost due to unplanned hospital admissions or all-cause death as compared with PFP (5.6 vs. 12.4 days, P < 0.05) and UC (5.6 vs. 48.8 days, P < 0.01). Impact on quality of life was similar between TM and PFP (P = 0.36). CONCLUSIONS: In patients with HFrEF and recent HF hospitalization, non-invasive TM reduced 12 month all-cause hospitalization/mortality and HFH as compared with usual care. TM also reduced the number of days lost due to unplanned hospital admission/death as compared with either an optimized protocol-based follow-up programme or usual care.

Accuracy and utility of a pacemaker respiratory monitoring algorithm for the detection of obstructive sleep apnea in patients with atrial fibrillation.

INTRODUCTION: The usefulness and diagnostic value of new-generation pacemakers (PM) with enhanced monitoring capabilities are not yet clearly established. The aim of this study was to evaluate the diagnostic utility and accuracy of a PM-incorporated respiratory monitoring algorithm and its interaction with atrial fibrillation (AF). METHODS: A single-center prospective study was performed in consecutive patients who underwent PM implantation featuring a respiratory monitoring algorithm. All patients had polysomnography recording. The respiratory disturbance index of the polysomnography and pacemaker (RDI-PM) were recorded on the same night. Occurrence and burden of AF were also recorded. The diagnostic utility of RDI-PM and its interaction with AF were evaluated. RESULTS: A total of 81 patients were included (age 73 ± 11 years). Obstructive sleep apnea syndrome (OSAS) was diagnosed in 62%. RDI-PM had good diagnostic accuracy for OSAS (area under the curve: 0.767 [95% CI: 0.65-0.88]; p < 0.001), with an ideal diagnostic cut-off of 13.3 (sensitivity 78%; specificity 78%) and 90% sensitivity for the diagnosis of moderate-to-severe OSAS. Time to AF first episode and total AF burden were not significantly different between patients with and without OSAS. However, in those whose OSAS diagnosis was based on RDI-PM, there was a significantly greater AF burden in patients with vs without OSAS (cut-off ≥13, 488 vs 83 min, p = 0.05). In patients with AF, the RDI-PM cut-off of 13.3 decreased specificity (57%) vs the general population, but in patients without AF the specificity was 100% and sensitivity 77%. CONCLUSION: OSAS was prevalent in PM patients. RDI-PM diagnosed OSAS accurately, with high sensitivity for the detection of moderate-to-severe OSAS, making it a suitable screening method. AF, however, significantly decreased the specificity of RDI-PM for OSAS diagnosis.

Protocol-based follow-up program for heart failure patients: Impact on prognosis and quality of life.

INTRODUCTION: Heart failure is associated with high rates of readmission and mortality, and there is a need for measures to improve outcomes. This study aims to assess the impact of the implementation of a protocol-based follow-up program for heart failure patients on readmission and mortality rates and quality of life. METHODS: A quasi-experimental study was performed, with a prospective registry of 50 consecutive patients discharged after hospitalization for acute heart failure. The study group was followed by a cardiologist at days 7-10 and the first, third, sixth and 12th month after discharge, with predefined procedures. The control group consisted of patients hospitalized for heart failure prior to implementation of the program and followed on a routine basis. RESULTS: No significant differences were observed between the two groups regarding mean age (67.1±11.2 vs. 65.8±13.4 years, p=0.5), NYHA functional class (p=0.37), or median left ventricular ejection fraction (27% [19.8-35.3] vs. 29% [23.5-40]; p=0.23) at discharge. Mean follow-up after discharge was similar (11±5.3 vs. 10.9±5.5 months, p=0.81). The protocol-based follow-up program was associated with a significant reduction in all-cause readmission (26% vs. 60%, p=0.003), heart failure readmission (16% vs. 36%, p=0.032), and mortality (4% vs. 20%, p=0.044). In the study group there was a significant improvement in all quality of life measures (p<0.001). CONCLUSION: A protocol-based follow-up program for patients with heart failure led to a significant reduction in readmission and mortality rates, and was associated with better quality of life.