Predicting hospitalization and mortality in patients with heart failure: The BARDICHE-index

Risk prediction of mortality for patients with heart failure in England: observational study in primary care

AIMS

Many risk prediction models have been proposed for heart failure (HF), but few studies have used only information available to general practitioners (GPs) in primary care electronic health records (EHRs). We describe the predictors and performance of models built from GP-based EHRs in two cohorts of patients 10 years apart.

METHODS AND RESULTS

Linked primary and secondary care data for incident HF cases in England were extracted from the Clinical Practice Research Datalink for 2001-02 and 2011-12. Time-to-event models for all-cause mortality were developed using a long list of potential baseline predictors. Discrimination and calibration were calculated. A total of 5966 patients in 156 general practices were diagnosed in 2001-02, and 12 827 patients in 331 practices were diagnosed in 2011-12. The 5-year survival rate was 40.0% in 2001-02 and 40.2% in 2011-12, though the latter population were older, frailer, and more comorbid; for 2001-02, the 10-year survival was 20.8% and 15-year survival 11.1%. Consistent predictors included age, male sex, systolic blood pressure, body mass index, GP domiciliary visits before diagnosis, and some comorbidities. Model performance for both time windows was modest (c = 0.70), but calibration was generally excellent in both time periods.

CONCLUSIONS

Information routinely available to UK GPs at the time of diagnosis of HF gives only modest predictive accuracy of all-cause mortality, making it hard to decide on the type, place, and urgency of follow-up. More consistent recording of data relevant to HF (such as echocardiography and natriuretic peptide results) in GP EHRs is needed to support accurate prediction of healthcare needs in individuals with HF.

A Multivariate Model to Predict Chronic Heart Failure after Acute ST-Segment Elevation Myocardial Infarction: Preliminary Study

A multivariate model for predicting the risk of decompensated chronic heart failure (CHF) within 48 weeks after ST-segment elevation myocardial infarction (STEMI) has been developed and tested.

METHODS

The study included 173 patients with acute STEMI aged 51.4 (95% confidence interval (CI): 42-61) years. Two-dimensional (2D) speckle-tracking echocardiography (STE) has been performed on the 7th-9th days, and at the 12th, 24th, and 48th weeks after the index event with the analysis of volumetric parameters and values for global longitudinal strain (GLS), global circumferential strain (GCS), and global radial strain (GRS). A 24-h ECG monitoring (24 h ECG) of the electrocardiogram (ECG) to assess heart rate turbulence (HRT) has been performed on the 7th-9th days of STEMI. The study involved two stages of implementation. At the first stage, a multivariate model to assess the risk of CHF progression within 48 weeks after STEMI has been built on the basis of examination and follow-up data for 113 patients (group M). At the second stage, the performance of the model has been assessed based on a 48-week follow-up of 60 patients (group T).

RESULTS

A multivariate regression model for CHF progression in STEMI patients has been created based on the results of the first stage. It included the following parameters: HRT, left ventricular (LV) end-systolic dimension (ESD), and GLS. The contribution of each factor for the relative risk (RR) of decompensated CHF has been found: 3.92 (95% CI: 1.66-9.25) (p = 0.0018) for HRT; 1.04 (95% CI: 1.015-1.07) (p = 0.0027) for ESD; 0.9 (95% CI: 0.815-0.98) (p = 0.028) for GLS. The diagnostic efficiency of the proposed model has been evaluated at the second stage. It appeared to have a high specificity of 83.3%, a sensitivity of 95.8%, and a diagnostic accuracy of 93.3%.

CONCLUSION

The developed model for predicting CHF progression within 48 weeks after STEMI has a high diagnostic efficiency and can be used in early stages of myocardial infarction to stratify the risk of patients.

Survival in acute heart failure in intensive cardiac care unit: a prospective study

The aim of this study is to identify the best predictors of mortality among clinical, biochemical and advanced echocardiographic parameters in acute heart failure (AHF) patients admitted to coronary care unit (CCU). AHF is a clinical condition characterized by high mortality and morbidity. Several studies have investigated the potential prognostic factors that could help the risk assessment of cardiovascular events in HF patients, but at the moment it has not been found a complete prognostic score (including clinical, laboratory and echocardiographic parameters), univocally used for AHF patients. Patients (n = 118) admitted to CCU due to AHF de novo or to an exacerbation of chronic heart failure were enrolled. For each patient, clinical and biochemical parameters were reported as well as the echocardiographic data, including speckle tracking echocardiography analysis. These indexes were then related to intra- and extrahospital mortality. At the end of the follow-up period, the study population was divided into two groups, defined as 'survivors' and 'non-survivors'. From statistical analysis, C-reactive protein (CRP) (AUC = 0.75), haemoglobin (AUC = 0.71), creatinine clearance (AUC = 0.74), left atrial strain (AUC = 0.73) and freewall right ventricular strain (AUC = 0.76) showed the strongest association with shortterm mortality and they represented the items of the proposed risk score, whose cut-off of 3 points is able to discriminate patients at higher risk of mortality. AHF represents one of the major challenges in CCU. The use of a combined biochemical and advanced echocardiographic score, assessed at admission, could help to better predict mortality risk, in addition to commonly used indexes.

Evaluating risk prediction models for adults with heart failure: A systematic literature review

Background

The ability to predict risk allows healthcare providers to propose which patients might benefit most from certain therapies, and is relevant to payers’ demands to justify clinical and economic value. To understand the robustness of risk prediction models for heart failure (HF), we conducted a systematic literature review to (1) identify HF risk-prediction models, (2) assess statistical approach and extent of validation, (3) identify common variables, and (4) assess risk of bias (ROB).

Methods

Literature databases were searched from March 2013 to May 2018 to identify risk prediction models conducted in an out-of-hospital setting in adults with HF. Distinct risk prediction variables were ranked according to outcomes assessed and incorporation into the studies. ROB was assessed using Prediction model Risk Of Bias ASsessment Tool (PROBAST).

Results

Of 4720 non-duplicated citations, 40 risk-prediction publications were deemed relevant. Within the 40 publications, 58 models assessed 55 (co)primary outcomes, including all-cause mortality (n = 17), cardiovascular death (n = 9), HF hospitalizations (n = 15), and composite endpoints (n = 14). Few publications reported detail on handling missing data (n = 11; 28%). The discriminatory ability for predicting all-cause mortality, cardiovascular death, and composite endpoints was generally better than for HF hospitalization. 105 distinct predictor variables were identified. Predictors included in >5 publications were: N-terminal prohormone brain-natriuretic peptide, creatinine, blood urea nitrogen, systolic blood pressure, sodium, NYHA class, left ventricular ejection fraction, heart rate, and characteristics including male sex, diabetes, age, and BMI. Only 11/58 (19%) models had overall low ROB, based on our application of PROBAST. In total, 26/58 (45%) models discussed internal validation, and 14/58 (24%) external validation.

Conclusions

The majority of the 58 identified risk-prediction models for HF present particular concerns according to ROB assessment, mainly due to lack of validation and calibration. The potential utility of novel approaches such as machine learning tools is yet to be determined.

Registration number

The SLR was registered in Prospero (ID: CRD42018100709).

Machine learning for prediction of sudden cardiac death in heart failure patients with low left ventricular ejection fraction: study protocol for a retroprospective multicentre registry in China

INTRODUCTION

Left ventricular ejection fraction (LVEF) ≤35%, as current significant implantable cardioverter-defibrillator (ICD) indication for primary prevention of sudden cardiac death (SCD) in heart failure (HF) patients, has been widely recognised to be inefficient. Improvement of patient selection for low LVEF (≤35%) is needed to optimise deployment of ICD. Most of the existing prediction models are not appropriate to identify ICD candidates at high risk of SCD in HF patients with low LVEF. Compared with traditional statistical analysis, machine learning (ML) can employ computer algorithms to identify patterns in large datasets, analyse rules automatically and build both linear and non-linear models in order to make data-driven predictions. This study is aimed to develop and validate new models using ML to improve the prediction of SCD in HF patients with low LVEF.

METHODS AND ANALYSIS

We will conduct a retroprospective, multicentre, observational registry of Chinese HF patients with low LVEF. The HF patients with LVEF ≤35% after optimised medication at least 3 months will be enrolled in this study. The primary endpoints are all-cause death and SCD. The secondary endpoints are malignant arrhythmia, sudden cardiac arrest, cardiopulmonary resuscitation and rehospitalisation due to HF. The baseline demographic, clinical, biological, electrophysiological, social and psychological variables will be collected. Both ML and traditional multivariable Cox proportional hazards regression models will be developed and compared in the prediction of SCD. Moreover, the ML model will be validated in a prospective study.

ETHICS AND DISSEMINATION

The study protocol has been approved by the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (2017-SR-06). All results of this study will be published in international peer-reviewed journals and presented at relevant conferences.

TRIAL REGISTRATION NUMBER

ChiCTR-POC-17011842; Pre-results.

Palliative care needs assessment in chronic heart failure

PURPOSE OF REVIEW

The unmet palliative care needs of patients with chronic heart failure (CHF) are well known. Palliative care needs assessment is paramount for timely provision of palliative care. The present review provides an overview of palliative care needs assessment in patients with CHF: the role of prognostic tools, the role of the surprise question, and the role of palliative care needs assessment tools.

RECENT FINDINGS

Multiple prognostic tools are available, but offer little guidance for individual patients. The surprise question is a simple tool to create awareness about a limited prognosis, but the reliability in CHF seems less than in oncology and further identification and assessment of palliative care needs is required. Several tools are available to identify palliative care needs. Data about the ability of these tools to facilitate timely initiation of palliative care in CHF are lacking.

SUMMARY

Several tools are available aiming to facilitate timely introduction of palliative care. Focus on identification of needs rather than prognosis appears to be more fitting for people with CHF. Future studies are needed to explore whether and to what extent these tools can help in addressing palliative care needs in CHF in a timely manner.

Prognostic value of daytime heart rate, blood pressure, their products and quotients in chronic heart failure

BACKGROUND

Chronic heart failure (CHF) is an important epidemiological and therapeuthic issue with poor prognosis. The aim of the study was to estimate the prognostic value of daytime heart rate (HR), blood pressure (BP), their products and quotients in patients with CHF.

METHODS

The study included 80 stable patients with CHF and reduced left ventricular ejection frac- tion (LVEF ≤ 35%). Physical examination, laboratory blood tests, electrocardiogram, chest X-ray, echocardiography, 6-minute walk test, telemetry monitoring and BP measurements were performed in all participants. We estimated mean daytime: BP, HR, their products and quotients. The follow-up period was 6 months. Major adverse cardiac events (MACE) included: death, cardiovascular death, hospitalization due to CHF exacerbation.

RESULTS

The analysis involved all recruited patients with CHF (91% men) aged 59 ± 12 years, in New York Heart Association class 2.15 ± 0.57 and reduced LVEF (mean LVEF: 23 ± 6%). The 3-month and 6-month mortality rates were 4% and 6%, respectively. There was a significant correlation between diastolic blood pressure (DBP), all-cause mortality (p = 0.048) and CHF decompensation (p = 0.0004) after 3-month observation period. No relationship was found between HR or systolic blood pressure (SBP) and MACE. Both higher SBP × HR and DBP × HR products were related to lower risk of heart failure exacerbations during 6-month follow-up. None of the analyzed products or ratios had an impact on mortality in this study group.

CONCLUSIONS

Diastolic blood pressure, SBP × HR and DBP × HR products may be useful in sub- sequent heart failure exacerbation risk stratification. Moreover, DBP value may predict short-term mortality in patients with CHF.

Prognostic Value of Chronic Kidney Disease Measures in Patients With Cardiac Disease

Chronic kidney disease (CKD) is considered a global public health issue. The latest international clinical guideline emphasizes characterization of CKD with both glomerular filtration rate (GFR) and albuminuria. CKD is closely related to cardiac disease and increases the risk of adverse outcomes among patients with cardiovascular disease (CVD). Indeed, numerous studies have investigated the association of CKD measures with prognosis among patients with CVD, but most of them have focused on kidney function, with limited data on albuminuria. Consequently, although there are several risk prediction tools for patients with CVD incorporating kidney function, to our knowledge, none of them include albuminuria. Moreover, the selection of the kidney function measure (e.g., serum creatinine, creatinine-based estimated GFR, or blood urea nitrogen) in these tools is heterogeneous. In this review, we will summarize these aspects, as well as the burden of CKD in patients with CVD, in the current literature. We will also discuss potential mechanisms linking CKD to secondary events and consider future research directions. Given their clinical and public health importance, for CVD we will focus on 2 representative cardiac diseases: myocardial infarction and heart failure.