Early indication of decompensated heart failure in patients on home-telemonitoring: a comparison of prediction algorithms based on daily weight and noninvasive transthoracic bio-impedance

Background: Heart Failure (HF) is a common reason for hospitalization. Admissions might be prevented by early detection of and intervention for decompensation. Conventionally, changes in weight, a possible measure of fluid accumulation, have been used to detect deterioration. Transthoracic impedance may be a more sensitive and accurate measure of fluid accumulation. Objective: In this study, we review previously proposed predictive algorithms using body weight and noninvasive transthoracic bio-impedance (NITTI) to predict HF decompensations. Methods: We monitored 91 patients with chronic HF for an average of 10 months using a weight scale and a wearable bio-impedance vest. Three algorithms were tested using either simple rule-of-thumb differences (RoT), moving averages (MACD), or cumulative sums (CUSUM). Results: Algorithms using NITTI in the 2 weeks preceding decompensation predicted events (P<.001); however, using weight alone did not. Cross-validation showed that NITTI improved sensitivity of all algorithms tested and that trend algorithms provided the best performance for either measurement (Weight-MACD: 33%, NITTI-CUSUM: 60%) in contrast to the simpler rules-of-thumb (Weight-RoT: 20%, NITTI-RoT: 33%) as proposed in HF guidelines. Conclusions: NITTI measurements decrease before decompensations, and combined with trend algorithms, improve the detection of HF decompensation over current guideline rules; however, many alerts are not associated with clinically overt decompensation

Top ten risk factors for morbidity and mortality in patients with chronic systolic heart failure and elevated heart rate: the SHIFT risk model

Aims We identified easily obtained baseline characteristics associated with outcomes in patients with chronic heart failure (HF) and elevated heart rate (HR) receiving contemporary guideline-recommended therapy in the SHIFT trial, and used them to develop a prognostic model. Methods We selected the 10 best predictors for each of four outcomes (cardiovascular death or HF hospitalisation; all-cause mortality; cardiovascular mortality; and HF hospitalisation). All variables with p &#60; 0.05 for association were entered into a forward stepwise Cox regression model. Our initial analysis excluded baseline therapies, though randomisation to ivabradine or placebo was forced into the model for the composite endpoint and HF hospitalisation. Results Increased resting HR, low ejection fraction, raised creatinine, New York Heart Association class III/IV, longer duration of HF, history of left bundle branch block, low systolic blood pressure and, for three models, age were strong predictors of all outcomes. Additional predictors were low body mass index, male gender, ischaemic HF, low total cholesterol, no history of hyperlipidaemia or dyslipidaemia and presence of atrial fibrillation/flutter. The c-statistics for the four outcomes ranged from 67.6% to 69.5%. There was no evidence for lack of fit of the models with the exception of all-cause mortality (p = 0.017). Similar results were found including baseline therapies. Conclusion The SHIFT Risk Model includes simple, readily obtainable clinical characteristics to produce important prognostic information in patients with chronic HF, systolic dysfunction, and elevated HR. This may help better calibrate management to individual patient risk.</p

Proenkephalin A 119-159 (Penkid) Is an Early Biomarker of Septic Acute Kidney Injury: The Kidney in Sepsis and Septic Shock (Kid-SSS) Study

Introduction: Sepsis is the leading cause of acute kidney injury (AKI) in critically ill patients. The Kidney in Sepsis and Septic Shock (Kid-SSS) study evaluated the value of proenkephalin A 119-159 (penkid)—a sensitive biomarker of glomerular function, drawn within 24 hours upon intensive care unit (ICU) admission and analyzed using a chemiluminescence immunoassay—for kidney events in sepsis and septic shock. Methods: The Kid-SSS study was a substudy of Adrenomedullin and Outcome in Severe Sepsis and Septic Shock (AdrenOSS) (NCT02393781), a prospective, observational, multinational study including 583 patients admitted to the intensive care unit with sepsis or septic shock and a validation cohort of 525 patients from the French and euRopean Outcome reGistry in Intensive Care Units (FROG-ICU) study. The primary endpoint was major adverse kidney events (MAKEs) at day 7, composite of death, renal replacement therapy, and persistent renal dysfunction. The secondary endpoints included AKI, transient AKI, worsening renal function (WRF), and 28-day mortality. Results: Median age was 66 years (interquartile range 55–75), and 28-day mortality was 22% (95% confidence interval [CI] 19%−25%). Of the patients, 293 (50.3%) were in shock upon ICU admission. Penkid was significantly elevated in patients with MAKEs, persistent AKI, and WRF (median = 65 [IQR = 45–106] vs. 179 [114–242]; 53 [39–70] vs. 133 [79–196] pmol/l; and 70 [47–121] vs. 174 [93–242] pmol/l, all P &lt; 0.0001), also after adjustment for confounding factors (adjusted odds ratio = 3.3 [95% CI = 1.8–6.0], 3.9 [95% CI = 2.1–7.2], and 3.4 [95% CI = 1.9–6.2], all P &lt; 0.0001). Penkid increase preceded elevation of serum creatinine with WRF and was low in renal recovery. Conclusion: Admission penkid concentration was associated with MAKEs, AKI, and WRF in a timely manner in septic patients

Chronic Kidney Disease – Where Next? : Predicting Outcomes And Planning Care Pathways

Peer reviewedPublisher PD

What does it take to make integrated care work? A ‘cookbook’ for large-scale deployment of coordinated care and telehealth

The Advancing Care Coordination & Telehealth Deployment (ACT) Programme is the first to explore the organisational and structural processes needed to successfully implement care coordination and telehealth (CC&TH) services on a large scale. A number of insights and conclusions were identified by the ACT programme. These will prove useful and valuable in supporting the large-scale deployment of CC&TH. Targeted at populations of chronic patients and elderly people, these insights and conclusions are a useful benchmark for implementing and exchanging best practices across the EU. Examples are: Perceptions between managers, frontline staff and patients do not always match; Organisational structure does influence the views and experiences of patients: a dedicated contact person is considered both important and helpful; Successful patient adherence happens when staff are engaged; There is a willingness by patients to participate in healthcare programmes; Patients overestimate their level of knowledge and adherence behaviour; The responsibility for adherence must be shared between patients and health care providers; Awareness of the adherence concept is an important factor for adherence promotion; The ability to track the use of resources is a useful feature of a stratification strategy, however, current regional case finding tools are difficult to benchmark and evaluate; Data availability and homogeneity are the biggest challenges when evaluating the performance of the programmes

Heart Rate Variability: A possible machine learning biomarker for mechanical circulatory device complications and heart recovery

Cardiovascular disease continues to be the number one cause of death in the United States, with heart failure patients expected to increase to \u3e8 million by 2030. Mechanical circulatory support (MCS) devices are now better able to manage acute and chronic heart failure refractory to medical therapy, both as bridge to transplant or as bridge to destination. Despite significant advances in MCS device design and surgical implantation technique, it remains difficult to predict response to device therapy. Heart rate variability (HRV), measuring the variation in time interval between adjacent heartbeats, is an objective device diagnostic regularly recorded by various MCS devices that has been shown to have significant prognostic value for both sudden cardiac death as well as all-cause mortality in congestive heart failure (CHF) patients. Limited studies have examined HRV indices as promising risk factors and predictors of complication and recovery from left ventricular assist device therapy in end-stage CHF patients. If paired with new advances in machine learning utilization in medicine, HRV represents a potential dynamic biomarker for monitoring and predicting patient status as more patients enter the mechanotrope era of MCS devices for destination therapy

Practical approach on frail older patients attended for acute heart failure

Acute heart failure (AHF) is a multi-organ dysfunction syndrome. In addition to known cardiac dysfunction, non-cardiac comorbidity, frailty and disability are independent risk factors of mortality, morbidity, cognitive and functional decline, and risk of institutionalization. Frailty, a treatable and potential reversible syndrome very common in older patients with AHF, increases the risk of disability and other adverse health outcomes. This position paper highlights the need to identify frailty in order to improve prognosis, the risk-benefits of invasive diagnostic and therapeutic procedures, and the definition of older-person-centered and integrated care plans

Duration of chronic heart failure affects outcomes with preserved effects of heart rate reduction with ivabradine: findings from SHIFT

Aims: In heart failure (HF) with reduced ejection fraction and sinus rhythm, heart rate reduction with ivabradine reduces the composite incidence of cardiovascular death and HF hospitalization. Methods and results: It is unclear whether the duration of HF prior to therapy independently affects outcomes and whether it modifies the effect of heart rate reduction. In SHIFT, 6505 patients with chronic HF (left ventricular ejection fraction of ≤35%), in sinus rhythm, heart rate of ≥70 b.p.m., treated with guideline-recommended therapies, were randomized to placebo or ivabradine. Outcomes and the treatment effect of ivabradine in patients with different durations of HF were examined. Prior to randomization, 1416 ivabradine and 1459 placebo patients had HF duration of ≥4 weeks and &lt;1.5 years; 836 ivabradine and 806 placebo patients had HF duration of 1.5 years to &lt;4 years, and 989 ivabradine and 999 placebo patients had HF duration of ≥4 years. Patients with longer duration of HF were older (62.5 years vs. 59.0 years; P &lt; 0.0001), had more severe disease (New York Heart Association classes III/IV in 56% vs. 44.9%; P &lt; 0.0001) and greater incidences of co-morbidities [myocardial infarction: 62.9% vs. 49.4% (P &lt; 0.0001); renal dysfunction: 31.5% vs. 21.5% (P &lt; 0.0001); peripheral artery disease: 7.0% vs. 4.8% (P &lt; 0.0001)] compared with patients with a more recent diagnosis. After adjustments, longer HF duration was independently associated with poorer outcome. Effects of ivabradine were independent of HF duration. Conclusions: Duration of HF predicts outcome independently of risk indicators such as higher age, greater severity and more co-morbidities. Heart rate reduction with ivabradine improved outcomes independently of HF duration. Thus, HF treatments should be initiated early and it is important to characterize HF populations according to the chronicity of HF in future trials