Assessing Risk and Preventing 30-Day Readmissions in Decompensated Heart Failure: Opportunity to Intervene?

Predicting emergency department visits and hospitalizations for patients with heart failure in home healthcare using a time series risk model

OBJECTIVES

Little is known about proactive risk assessment concerning emergency department (ED) visits and hospitalizations in patients with heart failure (HF) who receive home healthcare (HHC) services. This study developed a time series risk model for predicting ED visits and hospitalizations in patients with HF using longitudinal electronic health record data. We also explored which data sources yield the best-performing models over various time windows.

MATERIALS AND METHODS

We used data collected from 9362 patients from a large HHC agency. We iteratively developed risk models using both structured (eg, standard assessment tools, vital signs, visit characteristics) and unstructured data (eg, clinical notes). Seven specific sets of variables included: (1) the Outcome and Assessment Information Set, (2) vital signs, (3) visit characteristics, (4) rule-based natural language processing-derived variables, (5) term frequency-inverse document frequency variables, (6) Bio-Clinical Bidirectional Encoder Representations from Transformers variables, and (7) topic modeling. Risk models were developed for 18 time windows (1-15, 30, 45, and 60 days) before an ED visit or hospitalization. Risk prediction performances were compared using recall, precision, accuracy, F1, and area under the receiver operating curve (AUC).

RESULTS

The best-performing model was built using a combination of all 7 sets of variables and the time window of 4 days before an ED visit or hospitalization (AUC = 0.89 and F1 = 0.69).

DISCUSSION AND CONCLUSION

This prediction model suggests that HHC clinicians can identify patients with HF at risk for visiting the ED or hospitalization within 4 days before the event, allowing for earlier targeted interventions.

Trial of an Intervention to Improve Acute Heart Failure Outcomes

BACKGROUND

Patients with acute heart failure are frequently or systematically hospitalized, often because the risk of adverse events is uncertain and the options for rapid follow-up are inadequate. Whether the use of a strategy to support clinicians in making decisions about discharging or admitting patients, coupled with rapid follow-up in an outpatient clinic, would affect outcomes remains uncertain.

METHODS

In a stepped-wedge, cluster-randomized trial conducted in Ontario, Canada, we randomly assigned 10 hospitals to staggered start dates for one-way crossover from the control phase (usual care) to the intervention phase, which involved the use of a point-of-care algorithm to stratify patients with acute heart failure according to the risk of death. During the intervention phase, low-risk patients were discharged early (in ≤3 days) and received standardized outpatient care, and high-risk patients were admitted to the hospital. The coprimary outcomes were a composite of death from any cause or hospitalization for cardiovascular causes within 30 days after presentation and the composite outcome within 20 months.

RESULTS

A total of 5452 patients were enrolled in the trial (2972 during the control phase and 2480 during the intervention phase). Within 30 days, death from any cause or hospitalization for cardiovascular causes occurred in 301 patients (12.1%) who were enrolled during the intervention phase and in 430 patients (14.5%) who were enrolled during the control phase (adjusted hazard ratio, 0.88; 95% confidence interval [CI], 0.78 to 0.99; P = 0.04). Within 20 months, the cumulative incidence of primary-outcome events was 54.4% (95% CI, 48.6 to 59.9) among patients who were enrolled during the intervention phase and 56.2% (95% CI, 54.2 to 58.1) among patients who were enrolled during the control phase (adjusted hazard ratio, 0.95; 95% CI, 0.92 to 0.99). Fewer than six deaths or hospitalizations for any cause occurred in low- or intermediate-risk patients before the first outpatient visit within 30 days after discharge.

CONCLUSIONS

Among patients with acute heart failure who were seeking emergency care, the use of a hospital-based strategy to support clinical decision making and rapid follow-up led to a lower risk of the composite of death from any cause or hospitalization for cardiovascular causes within 30 days than usual care. (Funded by the Ontario SPOR Support Unit and others; COACH ClinicalTrials.gov number, NCT02674438.).

A comparison between hospital follow-up and collaborative follow-up in patients with acute heart failure

AIMS

There are no previous studies focusing on collaborative follow-ups between hospitals and clinics for patients discharged after acute heart failure (AHF) in Japan. The purpose of this study was to determine the status of collaboration between hospitals and clinics for patients with AHF in Japan and to compare patient characteristics and clinical outcomes using a large Japanese observational database.

METHODS AND RESULTS

Of 4056 consecutive patients hospitalized for AHF in the Kyoto Congestive Heart Failure registry, we analysed 2862 patients discharged to go home, who were divided into 1674 patients (58.5%) followed up at hospitals with index hospitalization (hospital follow-up group) and 1188 (41.5%) followed up in a collaborative fashion with clinics or other general hospitals (collaborative follow-up group). The primary outcome was a composite of all-cause death or heart failure (HF) hospitalization within 1 year after discharge. Previous hospitalization for HF and length of hospital stay longer than 15 days were associated with hospital follow-up. Conversely, ≥80 years of age, hypertension, and cognitive dysfunction were associated with collaborative follow-up. The cumulative 1-year incidence of the primary outcome, all cause death, and cardiovascular death were similar between the hospital and collaborative follow-up groups (31.6% vs. 29.6%, P = 0.51, 13.1% vs, 13.9%, P = 0.35, 8.4% vs. 8.2%, P = 0.96). Even after adjusting for confounders, the difference in risk for patients in the hospital follow-up group relative to those in the collaborative follow-up group remained insignificant for the primary outcome, all-cause death, and cardiovascular death (HR: 1.11, 95% CI: 0.97-1.27, P = 0.14, HR: 1.10, 95% CI: 0.91-1.33, P = 0.33, HR: 0.96, 95% CI: 0.87-1.05, P = 0.33). The cumulative 1-year incidence of HF hospitalization was higher in the hospital follow-up group than in the collaborative follow-up group (25.5% vs. 21.3%, P = 0.02). The risk of HF hospitalization was higher in the hospital follow-up group than in the collaborative follow-up group (HR: 1.19, 95% CI: 1.01-1.39, P = 0.04).

CONCLUSIONS

In patients hospitalized for AHF, 41.5% received collaborative follow-up after discharge. The risk of HF hospitalization was higher in the hospital follow-up group than in the collaborative follow-up, although risk of the primary outcome, all-cause death, and cardiovascular death were similar between groups.

The State of Heart Failure Care in Canada: Minimal Improvement in Readmissions Over Time Despite an Increased Number of Evidence-Based Therapies

Background

Methods

Results

Conclusions

Comparison of machine learning and the regression-based EHMRG model for predicting early mortality in acute heart failure

BACKGROUND

Although risk stratification of patients with acute decompensated heart failure (HF) is important, it is unknown whether machine learning (ML) or conventional statistical models are optimal. We developed ML algorithms to predict 7-day and 30-day mortality in patients with acute HF and compared these with an existing logistic regression model at the same timepoints.

METHODS

Patients presenting to one of 86 hospitals, who were either admitted to hospital or discharged home directly from the emergency department, were randomly selected using stratified random sampling. ML approaches, including neural networks, random forest, XGBoost, and the Lasso, were compared with a validated logistic regression model for discrimination and calibration.

RESULTS

Among 12,608 patients in our analysis, lasso regression (c-statistic 0.774; 95% CI, 0.743, 0.806) performed better than other ML models for 7-day mortality but did not outperform the baseline logistic regression model (0.794; 95% CI, 0.789, 0.800). For 30-day mortality, XGBoost performed better than other ML models (c-statistic 0.759; 95% CI; 0.740, 0.779), but was not significantly better than logistic regression (c-statistic 0.755; 95% CI, 0.750, 0.762). Logistic regression demonstrated better calibration at 7 days (calibration-in-the-large 0.017; 95% CI, -0.657, 0.692, and calibration slope 0.954; 95% CI,0.769, 1.139) and at 30 days (-0.026; 95% CI, -0.374, 0.322 and 0.964; 95% CI, 0.831, 1.098), and best Brier scores, compared to ML approaches.

CONCLUSIONS

Logistic regression was comparable to ML in discrimination, but was superior to ML algorithms in calibration overall. ML algorithms for prognosis should routinely report calibration metrics in addition to discrimination.

A review of literature on risk prediction tools for hospital readmissions in older adults

PURPOSE

The purpose of this paper is to identify and analyse the readmission risk prediction tools reported in the literature and their benefits when it comes to healthcare organisations and management.

DESIGN/METHODOLOGY/APPROACH

Readmission risk prediction is a growing topic of interest with the aim of identifying patients in particular those suffering from chronic diseases such as congestive heart failure, chronic obstructive pulmonary disease and diabetes, who are at risk of readmission. Several models have been developed with different levels of predictive ability. A structured and extensive literature search of several databases was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analysis strategy, and this yielded a total of 48,984 records.

FINDINGS

Forty-three articles were selected for full-text and extensive review after following the screening process and according to the eligibility criteria. About 34 unique readmission risk prediction models were identified, in which their predictive ability ranged from poor to good (c statistic 0.5-0.86). Readmission rates ranged between 3.1 and 74.1% depending on the risk category. This review shows that readmission risk prediction is a complex process and is still relatively new as a concept and poorly understood. It confirms that readmission prediction models hold significant accuracy at identifying patients at higher risk for such an event within specific context.

RESEARCH LIMITATIONS/IMPLICATIONS

Since most prediction models were developed for specific populations, conditions or hospital settings, the generalisability and transferability of the predictions across wider or other contexts may be difficult to achieve. Therefore, the value of prediction models remains limited to hospital management. Future research is indicated in this regard.

ORIGINALITY/VALUE

This review is the first to cover readmission risk prediction tools that have been published in the literature since 2011, thereby providing an assessment of the relevance of this crucial KPI to health organisations and managers.

Predicting mortality and readmission based on chief complaint in emergency department patients: a cohort study

Background

Emergency department (ED) patients present with complaints and not diagnoses. Characterization and risk stratification based on chief complaint can therefore help clinicians improve ED workflow and clinical outcome. In this study we investigated the 30-day mortality and readmission among ED patients based on chief complaint.

Methods

In this cohort study we retrieved routinely collected data from electronic medical records and the Danish Civil Registration System of all ED contacts from July 1, 2016 through June 30, 2017. All patients triaged with one chief complaint using the Danish Emergency Process Triage system were included. Patients with minor injuries were excluded. The chief complaint assigned by the triaging nurse was used as exposure, and 30-day mortality and 30-day readmission were the primary outcomes. Logistic regression was used to determine crude and adjusted ORs with reference to the remaining study population.

Results

A total of 41 470 patients were eligible. After exclusion of minor injuries and patients not triaged, 19 325 patients were included. The 30-day mortality and 30-day readmission differed significantly among the chief complaints. The highest 30-day mortality was observed among patients presenting with altered level of conscousness (ALOC) (8.4%, OR=2.0, 95% CI 1.3 to 3.1) and dyspnea (8.0%, OR=2.1, 95% CI 1.6 to 2.6). 30-day readmission was highest among patients presenting with fever/infection (11.7%, OR=1.9, 95% CI 1.4 to 2.4) and dyspnea (11.2%, OR=1.7, 95% CI 1.4 to 2.0).

Discussion

Chief complaint is associated with 30-day mortality and readmission in a mixed ED population. ALOC and dyspnea had the highest mortality; fever/infection and dyspnea had the highest readmission rate. This knowledge may assist in improving and optimizing symptom-based initial diagnostic workup and treatment, and ultimately improve workflow and clinical outcome.

Level of evidence

Level III.

Rationale and design of the comparison of outcomes and access to care for heart failure (COACH) trial: A stepped wedge cluster randomized trial

BACKGROUND

Heart failure (HF) is an ambulatory care sensitive condition and a leading reason for emergency department (ED) visits and hospitalizations. Improved decision-making and care may enhance safety and efficiency for patients presenting to the ED with acute HF.

OBJECTIVES

We will evaluate an intervention comprised of 2 complementary components: (1) the Emergency Heart Failure Mortality Risk Grade simultaneous 7- and 30-day (EHMRG30-ST) risk scores, which will inform admission-discharge decisions, and (2) a rapid outpatient follow-up (RAPID-HF) clinic for low-to-intermediate risk patients on cardiovascular readmissions or death.

STUDY DESIGN

Stepped wedge cluster randomized trial with cross-sectional measurement at 10 acute care hospitals in Ontario, Canada. Patients presenting during control and intervention periods are eligible if they have a primary ED diagnosis of HF. In the intervention periods, access to the EHMRG30-ST web calculator will become available to hospitals' internet protocol (IP) addresses, and referral to the RAPID-HF clinic will be facilitated by a study nurse navigator. Patients with a high risk EHMRG30-ST score will be admitted to hospital. The RAPID-HF clinic will accept referrals for patients: (1) with low risk 7- and 30-day EHMRG30-ST scores who are discharged directly from the ED, or (2) intermediate risk patients with hospital length of stay < 72 hours. The RAPID-HF clinic, staffed by a nurse-clinician and cardiologist, will provide care during the 30-day transition after hospital separation.

CONCLUSION

This trial will determine whether novel risk stratification coupled with rapid ambulatory care achieves better outcomes than conventional decision-making and care for patients with HF.

MEESSI-AHF risk score performance to predict multiple post-index event and post-discharge short-term outcomes

BACKGROUND

The multiple estimation of risk based on the emergency department Spanish score in patients with acute heart failure (MEESSI-AHF) is a risk score designed to predict 30-day mortality in acute heart failure patients admitted to the emergency department. Using a derivation cohort, we evaluated the performance of the MEESSI-AHF risk score to predict 11 different short-term outcomes.

METHODS

Patients with acute heart failure from 41 Spanish emergency departments (n=7755) were recruited consecutively in two time periods (2014 and 2016). Logistic regression models based on the MEESSI-AHF risk score were used to obtain c-statistics for 11 outcomes: three with follow-up from emergency department admission (inhospital, 7-day and 30-day mortality) and eight with follow-up from discharge (7-day mortality, emergency department revisit and their combination; and 30-day mortality, hospital admission, emergency department revisit and their two combinations with mortality).

RESULTS

The MEESSI-AHF risk score strongly predicted mortality outcomes with follow-up starting at emergency department admission (c-statistic 0.83 for 30-day mortality; 0.82 for inhospital death, P=0.121; and 0.85 for 7-day mortality, P=0.001). Overall, mortality outcomes with follow-up starting at hospital discharge predicted slightly less well (c-statistic 0.80 for 7-day mortality, P=0.011; and 0.75 for 30-day mortality, P<0.001). In contrast, the MEESSI-AHF score predicted poorly outcomes involving emergency department revisit or hospital admission alone or combined with mortality (c-statistics 0.54 to 0.62).

CONCLUSIONS

The MEESSI-AHF risk score strongly predicts mortality outcomes in acute heart failure patients admitted to the emergency department, but the model performs poorly for outcomes involving hospital admission or emergency department revisit. There is a need to optimise this risk score to predict non-fatal events more effectively.

Role of a Brief Intensive Observation Area with a Dedicated Team of Doctors in the Management of Acute Heart Failure Patients: A Retrospective Observational Study

Background and objectives: Acute heart failure (AHF) is one of the main causes of hospitalization in Western countries. Usually, patients cannot be admitted directly to the wards (access block) and stay in the emergency room. Holding units are clinical decision units, or observation units, within the ED that are able to alleviate access block and to contribute to a reduction in hospitalization. Observation units have also been shown to play a role in specific clinical conditions, like the acute exacerbation of heart failure. This study aimed to analyze the impact of a brief intensive observation (OBI) area on the management of acute heart failure (AHF) patients. The OBI is a holding unit dedicated to the stabilization of unstable patients with a team of dedicated physicians. Materials and Methods: We conducted a retrospective and single-centered observational study with retrospective collection of the data of all patients who presented to our emergency department with AHF during 2017. We evaluated and compared two cohorts of patients, those treated in the OBI and those who were not, in terms of the reduction in color codes at discharge, mortality rate within the emergency room (ER), hospitalization rate, rate of transfer to less intensive facilities, and readmission rate at 7, 14, and 30 days after discharge. Results: We enrolled 920 patients from 1^st January to 31^st December. Of these, 61% were transferred to the OBI for stabilization. No statistically significant difference between the OBI and non-OBI populations in terms of age and gender was observed. OBI patients had worse clinical conditions on arrival. The patients treated in the OBI had longer process times, which would be expected, to allow patient stabilization. The stabilization rate in the OBI was higher, since presumably OBI admission protected patients from “worse condition” at discharge. Conclusions: Data from our study show that a dedicated area of the ER, such as the OBI, has progressively allowed a change in the treatment path of the patient, where the aim is no longer to admit the patient for processing but to treat the patient first and then, if necessary, admit or refer. This has resulted in very good feedback on patient stabilization and has resulted in a better management of beds, reduced admission rates, and reduced use of high intensity care beds.

The Association Between Patient-reported Clinical Factors and 30-day Acute Care Utilization in Chronic Heart Failure

BACKGROUND

Heart failure patients have high rates of repeat acute care use. Current efforts for risk prediction often ignore postdischarge data.

OBJECTIVE

To identify postdischarge patient-reported clinical factors associated with repeat acute care use.

RESEARCH DESIGN

In a prospective cohort study that followed patients with chronic heart failure for 30 days postdischarge, for 7 days after discharge (or fewer days if patients used acute care within 7 days postdischarge), patients reported health status, heart failure symptoms, medication management, knowledge of follow-up plans, and other issues using a daily interactive automatic phone call.

SUBJECTS

A total of 156 patients who had responded to phone surveys.

MEASURES

The outcome variable was dichotomous 30-day acute care use (rehospitalization or emergency department visit). We examined the association between each patient-reported issue and the outcome, using multivariable logistic regression to adjust for confounders.

RESULTS

Patients were 63 years old (SD=12.4), with 51% African-American and 53% women. Within 30 days postdischarge, 30 (19%) patients used acute care. After adjustment, poor health status [odds ratio (OR)=3.53; 95% confidence interval (CI), 1.06-11.76], pain (OR=2.44; 95% CI, 1.02-5.84), and poor appetite (OR=3.05; 95% CI, 1.13-8.23) were positively associated with 30-day acute care utilization. Among 58 reports of pain in follow-up nursing notes, 39 (67%) were noncardiac, 2 (3%) were cardiac, and 17 (29%) were indeterminate.

CONCLUSIONS

Patient-reported poor health status, pain, and poor appetite were positively associated with 30-day acute care utilization. These novel postdischarge markers require further study before incorporation into risk prediction to drive quality improvement efforts.

[Predictive factors of early readmission and mortality in patients with heart failure hospitalized in the Department of Internal Medicine of the San Carlos University Hospital, Spain]

INTRODUCTION

Heart failure (HF) is a health problem in Spain where the prevalence rate for this disease is correlated with aging. Heart failure-related mortality and hospital readmissions are high. The purpose of this study was to evaluate the clinical features of patients with HF hospitalized in the Department of Internal Medicine as well as factors associated with readmission and intra-hospital mortality.

METHODS

We conducted a cross-sectional, descriptive, and retrospective study based on the review of the clinical records of patients with primary diagnosis of HF in the Basic Minimum Set of Data (BMSD, Conjunto Mínimo Básico de Datos),who were discharged from the Department of Internal Medicine of the San Carlos Clinical Hospital (HCSC) in 2014.

RESULTS

The study involved 199 patients, with an average age of 82.7 years (61.8% were females); 85% of them had left ventricular ejection fraction (LVEF) > 40%, with an average pro-BNP of 9.101,3 pg/ml and 64.3% had ongoing atrial fibrillation. Thirty point two percent of patients were readmitted within 30 days, with an average rate of readmission/year of 1.45 (±0.86). Twenty five percent of patients died during the follow-up period in hospital. Among factors associated with intra-hospital mortality, older age was an associated variable (OR 1,050)(1,002-1,101) (p = 0.04). The most important factors associated with early readmission were polypharmacy (p = 0.024) as well as pluripathology based on Ollero criteria 4,974 (1,396-17,730) (p = 0.024). Patients hospitalized for HF in our Department are elderly patients treated with polymedication.

CONCLUSION

Patients hospitalized for cardiac insufficiency are older and are characterized by pluripathology and polypharmacy. Short-term prognosis is associated with high rates of readmission and mortality in hospitalmainly for patients suffering from kidney disease and/or neurological disorders.

Risk Prediction in Transition: MAGGIC Score Performance at Discharge and Incremental Utility of Natriuretic Peptides

BACKGROUND

Risk stratification for hospitalized patients with heart failure (HF) remains a critical need. The Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score is a robust model derived from patients with ambulatory HF. Its validity at the time of discharge and the incremental value of natriuretic peptides (NPs) in this setting is unclear.

METHODS

This was a single-center study examining a total of 4138 patients with HF from 2 groups; hospital discharge patients from administrative data (n = 2503, 60.5%) and a prospective registry of patients with ambulatory HF (n = 1635, 39.5%). The ambulatory registry patients underwent N-terminal pro-B-type NP (BNP) measurement at enrollment, and in the hospitalize discharge cohort clinical BNP levels were abstracted. The primary endpoint was all-cause mortality within 1 year. MAGGIC score performance was compared between cohorts utilizing Cox regression and calibration plots. The incremental value of NPs was assessed using calculated area under the curve and net reclassification improvement (NRI).

RESULTS

The hospitalized and ambulatory cohorts differed with respect to primary outcome (777 and 100 deaths, respectively), sex (52.1% vs 41.7% female) and race (35% vs 49.5% African American). The MAGGIC score showed poor discrimination of mortality risk in the hospital discharge (C statistic: 0.668, hazard ratio [HR]: 1.1 per point, 95% confidence interval [CI]: 0.652, 0.684) but fair discrimination in the ambulatory cohorts (C statistic: 0.784, HR: 1.16 per point, 95% CI: 0.74, 0.83), respectively, a difference that was statistically significant (P = .001 for C statistic, 0.002 for HR). Calibration assessment indicated that the slope and intercept (of MAGGIC-predicted to observed mortality) did not statistically differ from ideal in either cohort and did not differ between the cohorts (all P > .1). NP levels did not significantly improve prediction in the hospitalized cohort (P = .127) but did in the ambulatory cohort (C statistic: 0.784 [95% CI: 0.74, 0.83] vs 0.82 [95% CI: 0.78, 0.85]; P = .018) with a favorable NRI of 0.354 (95% CI: 0.202-0.469; P = .002).

CONCLUSION

The MAGGIC score showed poor discrimination when used in patients with HF at hospital discharge, which was inferior to its performance in patients with ambulatory HF. Discrimination within the hospital discharge group was not improved by including hospital NP levels.

Effect of early physician follow-up on mortality and subsequent hospital admissions after emergency care for heart failure: a retrospective cohort study

BACKGROUND

The 1-year mortality rate in patients with heart failure who are discharged from an emergency department is 20%. We sought to determine whether early follow-up after discharge from the emergency department was associated with decreased mortality or subsequent admission to hospital.

METHODS

This retrospective cohort study conducted in Ontario, Canada, included adult patients who were discharged from 1 of 163 emergency departments between April 2007 and March 2014 with a primary diagnosis of heart failure. Using a propensity score-matched landmark analysis, we assessed follow-up in relation to mortality and admissions to hospital for cardiovascular conditions.

RESULTS

Of 34 519 patients, 16 274 (47.1%) obtained follow-up care within 7 days and 28 846 (83.6%) within 30 days. Compared with follow-up between day 8 and 30, patients with follow-up care within 7 days had a lower rate of mortality over 1 year (hazard ratio [HR] 0.92; 95% confidence interval [CI] 0.87-0.97), and a reduced rate of admission to hospital over 90 days (HR 0.87, 95% CI 0.80-0.94) and 1 year (HR 0.92; 95% CI 0.87-0.97); the mortality rate over 90 days in this group trended to a lower rate (HR 0.90, 95% CI 0.10-1.00). Follow-up care within 30 days, compared with patients without 30-day follow-up, was associated with a reduction in 1-year mortality (HR 0.89, 95% CI 0.82-0.97) but not admission to hospital (HR 1.02, 95% CI 0.94-1.10). In this group, there was a trend toward an increase in 90-day admission to hospital (HR 1.14, 95% CI 1.00-1.29).

INTERPRETATION

Follow-up care within 7 days of discharge from the emergency department was associated with lower rates of long-term mortality, as well as subsequent hospital admissions, and a trend to lower short-term mortality rates. Timely access to longitudinal care for patients with heart failure who are discharged from the emergency setting should be prioritized.

Risk prediction for 30-day heart failure-specific readmission or death after discharge: Data from the Korean Acute Heart Failure (KorAHF) registry

BACKGROUND

Identifying patients with acute heart failure (HF) at high risk for readmission or death after hospital discharge will enable the optimization of treatment and management. The objective of this study was to develop a risk score for 30-day HF-specific readmission or death in Korea.

METHODS

We analyzed the data from the Korean Acute Heart Failure (KorAHF) registry to develop a risk score. The model was derived from a multiple logistic regression analysis using a stepwise variable selection method. We also proposed a point-based risk score to predict the risk of 30-day HF-specific readmission or death by simply summing the scores assigned to each risk variable. Model performance was assessed using an area under the receiver operating characteristic curve (AUC), the Hosmer-Lemeshow goodness-of-fit test, the net reclassification improvement (NRI), and the integrated discrimination improvement (IDI) index to evaluate discrimination, calibration, and reclassification, respectively.

RESULTS

Data from 4566 patients aged ≥40 years were included in the analysis. Among them, 446 (9.8%) had 30-day HF-specific readmission or death. The final model included 12 independent variables (age, New York Heart Association functional class, clinical history of hypertension, HF admission, chronic obstructive pulmonary disease, etiology of cardiomyopathy, systolic blood pressure, left ventricular ejection fraction, serum sodium, brain natriuretic peptide, N-terminal prohormone of brain natriuretic peptide at discharge, and prescription of β-blockers and angiotensin-converting enzyme inhibitors or angiotensin II receptor antagonists at discharge). The point risk score showed moderate discrimination (AUC of 0.710; 95% confidence interval, 0.685-0.735) and good calibration (χ²=8.540, p=0.3826).

CONCLUSIONS

The risk score for the prediction of the risk of 30-day HF-specific readmission or death after hospital discharge was developed using 12 predictors. It can be utilized to guide appropriate interventions or care strategies for patients with HF.

Early readmission for heart failure: An avoidable or ineluctable debacle?

Early hospital readmission after an episode of Acute Decompensated Heart Failure (ADHF) is an emerging issue that is causing a relevant clinical and economic burden. Although there might be several reasons for early readmissions, in many cases these might be effectively prevented by a more adequate post-discharge management, including recommendations on lifestyle and rehabilitation programs. However, almost half of hospitalizations are unrelated to specific cardiac causes and thus increases the difficulty in analyzing risks prediction. Many episodes are related to social environment, poor familiar assistance and inadequate followup program. In addition, the national and insurance companies constantly quest for a reduction of costs that could lead to inappropriately shortened hospital stays. Therefore, the suitability of early re-hospitalization as a correct target for good medical practice is highly debated. Nevertheless, the post-discharge phase after episodes of ADHF remains poorly analyzed in clinical trials and specific investigations should be considered during the transition period from acute to chronic status. A validated program, which focuses on an appropriate risk algorithm including cardiac and extracardiac precipitating factors is lacking. This is a necessary and it should become one of the most important targets to aim for in HF management and strategy.

Predictive biomarkers for death and rehospitalization in comorbid frail elderly heart failure patients

Background

Heart failure (HF) is associated with a high rate of readmissions within 30 days post-discharge and in the following year, especially in frail elderly patients. Biomarker data are scarce in this high-risk population. This study assessed the value of early post-discharge circulating levels of ST2, NT-proBNP, CA125, and hs-TnI for predicting 30-day and 1-year outcomes in comorbid frail elderly patients with HF with mainly preserved ejection fraction (HFpEF).

Methods

Blood samples were obtained at the first visit shortly after discharge (4.9 ± 2 days). The primary endpoint was the composite of all-cause mortality or HF-related rehospitalization at 30 days and at 1 year. All-cause mortality alone at one year was also a major endpoint. HF-related rehospitalizations alone were secondary end-points.

Results

From February 2014 to November 2016, 522 consecutive patients attending the STOP-HF Clinic were included (57.1% women, age 82 ± 8.7 years, mean Barthel index 70 ± 25, mean Charlson comorbidity index 5.6 ± 2.2). The composite endpoint occurred in 8.6% patients at 30 days and in 38.5% at 1 year. In multivariable analysis, ST2 [hazard ratio (HR) 1.53; 95% CI 1.19–1.97; p = 0.001] was the only predictive biomarker at 30 days; at 1 year, both ST2 (HR 1.34; 95% CI 1.15–1.56; p < 0.001) and NT-proBNP (HR 1.19; 95% CI 1.02–1.40; p = 0.03) remained significant. The addition of ST2 and NT-proBNP into a clinical predictive model increased the AUC from 0.70 to 0.75 at 30 days (p = 0.02) and from 0.71 to 0.74 at 1 year (p < 0.05). For all-cause death at 1 year, ST2 (HR 1.50; 95% CI 1.26–1.80; p < 0.001), and CA125 (HR 1.41; 95% CI 1.21–1.63; p < 0.001) remained independent predictors in multivariable analysis. The addition of ST2 and CA125 into a clinical predictive model increased the AUC from 0.74 to 0.78 (p = 0.03). For HF-related hospitalizations, ST2 was the only predictive biomarker in multivariable analyses, both at 30 days and at 1 year.

Conclusions

In a comorbid frail elderly population with HFpEF, ST2 outperformed NT-proBNP for predicting the risk of all-cause mortality or HF-related rehospitalization. ST2, a surrogate marker of inflammation and fibrosis, may be a better predictive marker in high-risk HFpEF.

Electronic supplementary material

The online version of this article (10.1186/s12877-018-0807-2) contains supplementary material, which is available to authorized users.

Rationale and design of the ICON-RELOADED study: International Collaborative of N-terminal pro-B-type Natriuretic Peptide Re-evaluation of Acute Diagnostic Cut-Offs in the Emergency Department

OBJECTIVES

The objectives were to reassess use of amino-terminal pro B-type natriuretic peptide (NT-proBNP) concentrations for diagnosis and prognosis of acute heart failure (HF) in patients with acute dyspnea.

BACKGROUND

NT-proBNP facilitates diagnosis, prognosis, and treatment in patients with suspected or proven acute HF. As demographics of such patients are changing, previous diagnostic NT-proBNP thresholds may need updating. Additionally, value of in-hospital NT-proBNP prognostic monitoring for HF is less understood.

METHODS

In a prospective, multicenter study in the United States and Canada, patients presenting to emergency departments with acute dyspnea were enrolled, with demographic, medication, imaging, and clinical course information collected. NT-proBNP analysis will be performed using the Roche Diagnostics Elecsys proBNPII immunoassay in blood samples obtained at baseline and at discharge (if hospitalized). Primary end points include positive predictive value of previously established age-stratified NT-proBNP thresholds for the adjudicated diagnosis of acute HF and its negative predictive value to exclude acute HF. Secondary end points include sensitivity, specificity, and positive and negative likelihood ratios for acute HF and, among those with HF, the prognostic value of baseline and predischarge NT-proBNP for adjudicated clinical end points (including all-cause death and hospitalization) at 30 and 180days.

RESULTS

A total of 1,461 dyspneic subjects have been enrolled and are eligible for analysis. Follow-up for clinical outcome is ongoing.

CONCLUSIONS

The International Collaborative of N-terminal pro-B-type Natriuretic Peptide Re-evaluation of Acute Diagnostic Cut-Offs in the Emergency Department study offers a contemporary opportunity to understand best diagnostic cutoff points for NT-proBNP in acute HF and validate in-hospital monitoring of HF using NT-proBNP.

Clinical presentation at first heart failure hospitalization does not predict recurrent heart failure admission

Aims

There are limited data on whether clinical presentation at first heart failure (HF) hospitalization predicts recurrent HF events. We aimed to assess predictors of recurrent HF hospitalizations in mild HF patients with an implantable cardioverter defibrillator or cardiac resynchronization therapy with defibrillator.

Methods and results

Data on HF hospitalizations were prospectively collected for patients enrolled in MADIT‐CRT. Predictors of recurrent HF hospitalization (HF2) after the first HF hospitalization were assessed using Cox proportional hazards regression models including baseline covariates and clinical presentation or management at first HF hospitalization. There were 193 patients with first HF hospitalization, and 156 patients with recurrent HF events. Recurrent HF rate after the first HF hospitalization was 43% at 1 year, 52% at 2 years, and 55% at 2.5 years. Clinical signs and symptoms, medical treatment, or clinical management of HF at first HF admission was not predictive for HF2. Baseline covariates predicting recurrent HF hospitalization included prior HF hospitalization (HR = 1.59, 95% CI: 1.15–2.20, P = 0.005), digitalis therapy (HR = 1.58, 95% CI: 1.13–2.20, P = 0.008), and left ventricular end‐diastolic volume >240 mL (HR = 1.62, 95% CI: 1.17–2.25, P = 0.004).

Conclusions

Recurrent HF events are frequent following the first HF hospitalization in patients with implanted implantable cardioverter defibrillator or cardiac resynchronization therapy with defibrillator. Neither clinical presentation nor clinical management during first HF admission was predictive of recurrent HF. Prior HF hospitalization, digitalis therapy, and left ventricular end‐diastolic volume at enrolment predicted recurrent HF hospitalization, and these covariates could be used as surrogate markers for identifying a high‐risk cohort.

Design and rationale for the Acute Congestive Heart Failure Urgent Care Evaluation: The ACUTE Study

BACKGROUND

Heart failure (HF) is one of the leading reasons for emergency department (ED) visits and hospitalization. However, externally validated risk algorithms for acute prognostication of heart failure patients are not available. Thus, many low-risk patients are hospitalized and some high-risk patients are discharged home, which, in some cases, may lead to death.

OBJECTIVES

The first objective of the ACUTE study is to perform a prospective validation of the Emergency Heart failure Mortality Risk Grade (EHMRG), which is a risk score derived to predict 7-day mortality in the ED setting. The second objective is to independently validate the 30-day model extension of the risk score (EHMRG30-ST) in the same cohort.

STUDY DESIGN

Patients with HF presenting to the ED will be recruited with a waiver of informed consent as a minimal risk study. The ED physician will calculate the EHMRG 7-day risk score, but treatment decisions will not be influenced by the predictive models. Follow-up will be obtained using probabilistic linkage with the Registered Persons Database of vital statistics, whereby deaths will be ascertained. We will examine mortality rates according to EHMRG and EHMRG30-ST algorithms. We will also compare physician-judged risk estimates, based on clinical judgment alone, with the EHMRG score.

CONCLUSION

The ACUTE study will determine if a retrospectively derived algorithm for simultaneous estimation of 7-day and 30-day mortality risk can accurately identify low- and high-risk patients with acute HF and improve upon physician-judged risk estimation.