Prediction of Rehospitalization and Death in Severe Heart Failure by Physicians and Nurses of the ESCAPE Trial

Examining predictors for 6-month mortality and healthcare utilization for patients admitted for heart failure in the acute care setting

BACKGROUND

Acute heart failure (AHF) is a leading cause of mortality and hospitalization. Past studies reported increased healthcare spending in the last year of life in high-income countries, and this has been characterized as inappropriate healthcare resource utilization. The study aimed to examine potentially (in)appropriate healthcare utilization by comparing healthcare utilization patterns across predicted and observed 6-month mortality among patients admitted for HF.

METHODS

We conducted a retrospective cohort study among patients presenting at the emergency department (ED) of a tertiary hospital with HF as primary diagnosis and admitted after their ED discharge. We used LASSO Cox proportional hazards models to predict 6-month mortality, and estimated healthcare utilization patterns of predicted and observed mortality across inpatient healthcare services.

RESULTS

3946 patients were admitted into the emergency department with a primary diagnosis of HF. From 57 candidate variables, 17 were retained in the final 6- month mortality model (C-statistic 0.66). Patients who died within 6-months of ED admission had longer length of stay (LOS) and less inpatient surgeries than those who survived. Patients with a greater predicted mortality risk were admitted to the ICU more often and had a longer LOS than those with a lower predicted mortality risk.

CONCLUSIONS

There were significant differences in healthcare resource utilization in patients admitted for AHF across predicted versus actual mortality. Lack of information on patients' preferences prevents the estimation of (in)appropriateness. Future studies should account for these considerations to estimate inappropriate healthcare utilization among these patients.

Identification of elderly patients at risk for 30-day readmission: Clinical insight beyond big data prediction

AIM

This study explores the potential benefit of combining clinicians' risk assessments and the automated 30-day readmission prediction model.

BACKGROUND

Automated readmission prediction models based on electronic health records are increasingly applied as part of prevention efforts, but their accuracy is moderate.

METHODS

This prospective multisource study was based on self-reported surveys of clinicians and data from electronic health records. The survey was performed at 15 internal medicine wards of three general Clalit hospitals between May 2016 and June 2017. We examined the degree of concordance between the Preadmission Readmission Detection Model, clinicians' readmission risk classification and the likelihood of actual readmission. Decision trees were developed to classify patients by readmission risk.

RESULTS

A total of 694 surveys were collected for 371 patients. The disagreement between clinicians' risk assessment and the model was 34.5% for nurses and 33.5% for physicians. The decision tree algorithms identified 22% and 9% (based on nurses and physicians, respectively) of the model's low-medium-risk patients as high risk (accuracy 0.8 and 0.76, respectively).

CONCLUSIONS

Combining the Readmission Model with clinical insight improves the ability to identify high-risk elderly patients.

IMPLICATIONS FOR NURSING MANAGEMENT

This study provides algorithms for the decision-making process for selecting high-risk readmission patients based on nurses' evaluations.

Alerting Clinicians to 1-Year Mortality Risk in Patients Hospitalized With Heart Failure: The REVEAL-HF Randomized Clinical Trial

Importance

Heart failure is a major cause of morbidity and mortality worldwide. The use of risk scores has the potential to improve targeted use of interventions by clinicians that improve patient outcomes, but this hypothesis has not been tested in a randomized trial.

Objective

To evaluate whether prognostic information in heart failure translates into improved decisions about initiation and intensity of treatment, more appropriate end-of-life care, and a subsequent reduction in rates of hospitalization or death.

Design, Setting, and Participants

This was a pragmatic, multicenter, electronic health record-based, randomized clinical trial across the Yale New Haven Health System, comprising small community hospitals and large tertiary care centers. Patients hospitalized for heart failure who had N-terminal pro-brain natriuretic peptide (NT-proBNP) levels of greater than 500 pg/mL and received intravenous diuretics within 24 hours of admission were automatically randomly assigned to the alert (intervention) or usual-care groups.

Interventions

The alert group had their risk of 1-year mortality calculated using an algorithm that was derived and validated using similar historic patients in the electronic health record. This estimate, including a categorical risk assessment, was presented to clinicians while they were interacting with a patient's electronic health record.

Main Outcomes and Measures

The primary outcome was a composite of 30-day hospital readmissions and all-cause mortality at 1 year.

Results

Between November 27, 2019, through March 7, 2021, 3124 patients were randomly assigned to the alert (1590 [50.9%]) or usual-care (1534 [49.1%]) group. The alert group had a median (IQR) age of 76.5 (65-86) years, and 796 were female patients (50.1%). Patients from the following race and ethnicity groups were included: 13 Asian (0.8%), 324 Black (20.4%), 136 Hispanic (8.6%), 1448 non-Hispanic (91.1%), 1126 White (70.8%), 6 other ethnicity (0.4%), and 127 other race (8.0%). The usual-care group had a median (IQR) age of 77 (65-86) years, and 788 were female patients (51.4%). Patients from the following race and ethnicity groups were included: 11 Asian (1.4%), 298 Black (19.4%), 162 Hispanic (10.6%), 1359 non-Hispanic (88.6%), 1077 White (70.2%), 13 other ethnicity (0.9%), and 137 other race (8.9%). Median (IQR) NT-proBNP levels were 3826 (1692-8241) pg/mL in the alert group and 3867 (1663-8917) pg/mL in the usual-care group. A total of 284 patients (17.9%) and 270 patients (17.6%) were admitted to the intensive care unit in the alert and usual-care groups, respectively. A total of 367 patients (23.1%) and 359 patients (23.4%) had a left ventricular ejection fraction of 40% or less in the alert and usual-care groups, respectively. The model achieved an area under the curve of 0.74 in the trial population. The primary outcome occurred in 619 patients (38.9%) in the alert group and 603 patients (39.3%) in the usual-care group (P = .89). There were no significant differences between study groups in the prescription of heart failure medications at discharge, the placement of an implantable cardioverter-defibrillator, or referral to palliative care.

Conclusions and Relevance

Provision of 1-year mortality estimates during heart failure hospitalization did not affect hospitalization or mortality, nor did it affect clinical decision-making.

Trial Registration

ClinicalTrials.gov Identifier NCT03845660.

Criteria for Referral of Patients With Advanced Heart Failure for Specialized Palliative Care

BACKGROUND

Patients with advanced heart failure have substantial supportive care needs. Specialist palliative care can be beneficial, but it is unclear who is most appropriate for referral and when patients should be referred.

OBJECTIVES

We conducted a Delphi study of international experts to identify consensus referral criteria for specialist palliative care for patients with advanced heart failure.

METHODS

Clinicians from 5 continents with expertise in the integration of cardiology and palliative care were asked to rate 34 disease-based, 24 needs-based, and 9 time-based criteria over 3 rounds. Consensus was defined a priori as ≥70% agreement. A criterion was coded as major if the experts endorsed that meeting that criterion alone was adequate to justify a referral.

RESULTS

The response rate was 44 of 46 (96%), 41 of 46 (89%), and 43 of 46 (93%) in the first, second, and third rounds, respectively. Panelists reached consensus on 25 major criteria for specialist palliative care referral. The 25 major criteria were categorized under 6 topics, including "advanced/refractory heart failure, comorbidities, and complications" (eg, cardiac cachexia, cardiorenal syndrome) (n = 8), "advanced heart failure therapies" (eg, chronic inotropes, precardiac transplant) (n = 4), "hospital utilization" (eg, emergency room visits, hospitalization) (n = 2), "prognostic estimate" (n = 1), "symptom burden/distress" (eg, severe physical/emotional/spiritual distress) (n = 6), and "decision making/social support" (eg, goals-of-care discussions) (n = 4). The majority (68%) of major criteria had ≥90% agreement.

CONCLUSIONS

International experts reached consensus on a large number of criteria for referral to specialist palliative care. With further validation, these criteria may be useful for standardizing palliative care access in the inpatient and/or outpatient settings.

Definition and incidence of hypotension in intensive care unit patients, an international survey of the European Society of Intensive Care Medicine

INTRODUCTION

Although hypotension in ICU patients is associated with adverse outcome, currently used definitions are unknown and no universally accepted definition exists.

METHODS

We conducted an international, peer-reviewed survey among ICU physicians and nurses to provide insight in currently used definitions, estimations of incidence, and duration of hypotension.

RESULTS

Out of 1394 respondents (1055 physicians (76%) and 339 nurses (24%)), 1207 (82%) completed the questionnaire. In all patient categories, hypotension definitions were predominantly based on an absolute MAP of 65 mmHg, except for the neuro(trauma) category (75 mmHg, p < 0.001), without differences between answers from physicians and nurses. Hypotension incidence was estimated at 55%, and time per day spent in hypotension at 15%, both with nurses reporting higher percentages than physicians (estimated mean difference 5%, p = 0.01; and 4%, p < 0.001).

CONCLUSIONS

An absolute MAP threshold of 65 mmHg is most frequently used to define hypotension in ICU patients. In neuro(trauma) patients a higher threshold was reported. The majority of ICU patients are estimated to endure hypotension during their ICU admission for a considerable amount of time, with nurses reporting a higher estimated incidence and time spent in hypotension than physicians.

Referral Criteria to Palliative Care for Patients With Heart Failure: A Systematic Review

BACKGROUND

Patients with heart failure have significant symptom burden, care needs, and often a progressive course to end-stage disease. Palliative care referrals may be helpful but it is currently unclear when patients should be referred and by whom. We conducted a systematic review of the literature to examine referral criteria for palliative care among patients with heart failure.

METHODS

We searched Ovid, MEDLINE, Ovid Embase, and PubMed databases for articles in the English language from the inception of databases to January 17, 2019 related to palliative care referral in patients with heart failure. Two investigators independently reviewed each citation for inclusion and then extracted the referral criteria. Referral criteria were then categorized thematically.

RESULTS

Of the 1199 citations in our initial search, 102 articles were included in the final sample. We identified 18 categories of referral criteria, including 7 needs-based criteria and 10 disease-based criteria. The most commonly discussed criterion was physical or emotional symptoms (n=51 [50%]), followed by cardiac stage (n=46 [45%]), hospital utilization (n=38 [37%]), prognosis (n=37 [36%]), and advanced cardiac therapies (n=36 [35%]). Under cardiac stage, 31 (30%) articles suggested New York Heart Association functional class ≥III and 12 (12%) recommended New York Heart Association class ≥IV as cutoffs for referral. Prognosis of ≤1 year was mentioned in 21 (21%) articles as a potential trigger; few other criteria had specific cutoffs.

CONCLUSIONS

This systematic review highlighted the lack of consensus regarding referral criteria for the involvement of palliative care in patients with heart failure. Further research is needed to identify appropriate and timely triggers for palliative care referral.

Predicting High-Risk Patients and High-Risk Outcomes in Heart Failure

Identifying patients with heart failure at high risk for poor outcomes is important for patient care, resource allocation, and process improvement. Although numerous risk models exist to predict mortality, hospitalization, and patient-reported health status, they are infrequently used for several reasons, including modest performance, lack of evidence to support routine clinical use, and barriers to implementation. Artificial intelligence has the potential to enhance the performance of risk prediction models, but has its own limitations and remains unproved.

Can We Do More With Less While Building Predictive Models? A Study in Parsimony of Risk Models for Predicting Heart Failure Readmissions

Hospital readmission due to heart failure is a topic of concern for patients and hospitals alike: it is both the most frequent and expensive diagnosis for hospitalization. Therefore, accurate prediction of readmission risk while patients are still in the hospital helps to guide appropriate postdischarge interventions. As our understanding of the disease and the volume of electronic health record data both increase, the number of predictors and model-building time for predicting risk grow rapidly. This suggests a need to use methods for reducing the number of predictors without losing predictive performance. We explored and described three such methods and demonstrated their use by applying them to a real-world dataset consisting of 57 variables from health data of 1210 patients from one hospital system. We compared all models generated from predictor reduction methods against the full, 57-predictor model for predicting risk of 30-day readmissions for patients with heart failure. Our predictive performance, measured by the C-statistic, ranged from 0.630 to 0.840, while model-building time ranged from 10 minutes to 10 hours. Our final model achieved a C-statistic (0.832) comparable to the full model (0.840) in the validation cohort while using only 16 predictors and providing a 66-fold improvement in model-building time.

Critical Appraisal of Multivariable Prognostic Scores in Heart Failure: Development, Validation and Clinical Utility

Optimal management of heart failure requires accurate risk assessment. Many prognostic risk models have been proposed for patient with chronic and acute heart failure. Methodological critical issues are the data source, the outcome of interest, the choice of variables entering the model, the validation of the model in external population. Up to now, the proposed risk models can be a useful tool to help physician in the clinical decision-making. The availability of big data and of new methods of analysis may lead to developing new models in the future.

Predictive models for identifying risk of readmission after index hospitalization for heart failure: A systematic review

Aims:

Readmission rates for patients with heart failure have consistently remained high over the past two decades. As more electronic data, computing power, and newer statistical techniques become available, data-driven care could be achieved by creating predictive models for adverse outcomes such as readmissions. We therefore aimed to review models for predicting risk of readmission for patients admitted for heart failure. We also aimed to analyze and possibly group the predictors used across the models.

Methods:

Major electronic databases were searched to identify studies that examined correlation between readmission for heart failure and risk factors using multivariate models. We rigorously followed the review process using PRISMA methodology and other established criteria for quality assessment of the studies.

Results:

We did a detailed review of 334 papers and found 25 multivariate predictive models built using data from either health system or trials. A majority of models was built using multiple logistic regression followed by Cox proportional hazards regression. Some newer studies ventured into non-parametric and machine learning methods. Overall predictive accuracy with C-statistics ranged from 0.59 to 0.84. We examined significant predictors across the studies using clinical, administrative, and psychosocial groups.

Conclusions:

Complex disease management and correspondingly increasing costs for heart failure are driving innovations in building risk prediction models for readmission. Large volumes of diverse electronic data and new statistical methods have improved the predictive power of the models over the past two decades. More work is needed for calibration, external validation, and deployment of such models for clinical use.

Clinicians can independently predict 30-day hospital readmissions as well as the LACE index

BACKGROUND

Significant effort has been directed at developing prediction tools to identify patients at high risk of unplanned hospital readmission, but it is unclear what these tools add to clinicians' judgment. In our study, we assess clinicians' abilities to independently predict 30-day hospital readmissions, and we compare their abilities with a common prediction tool, the LACE index.

METHODS

Over a period of 50 days, we asked attendings, residents, and nurses to predict the likelihood of 30-day hospital readmission on a scale of 0-100% for 359 patients discharged from a General Medicine Service. For readmitted versus non-readmitted patients, we compared the mean and standard deviation of the clinician predictions and the LACE index. We compared receiver operating characteristic (ROC) curves for clinician predictions and for the LACE index.

RESULTS

For readmitted versus non-readmitted patients, attendings predicted a risk of 48.1% versus 31.1% (p < 0.001), residents predicted 45.5% versus 34.6% (p 0.002), and nurses predicted 40.2% versus 30.6% (p 0.011), respectively. The LACE index for readmitted patients was 11.3, versus 10.1 for non-readmitted patients (p 0.003). The area under the curve (AUC) derived from the ROC curves was 0.689 for attendings, 0.641 for residents, 0.628 for nurses, and 0.620 for the LACE index. Logistic regression analysis suggested that the LACE index only added predictive value to resident predictions, but not attending or nurse predictions (p < 0.05).

CONCLUSIONS

Attendings, residents, and nurses were able to independently predict readmissions as well as the LACE index. Improvements in prediction tools are still needed to effectively predict hospital readmissions.

Identifying Nonclinical Factors Associated With 30-Day Readmission in Patients with Cardiovascular Disease: Protocol for an Observational Study

Background

Cardiovascular disease (CVD) is the leading cause of hospitalization in older adults and high readmission rates have attracted considerable attention as actionable targets to promote efficiency in care and to reduce costs. Despite a plethora of research over the past decade, current strategies to predict readmissions have been largely ineffective and efforts to identify novel clinical predictors have been largely unsuccessful.

Objective

The objective of this study is to examine a wide array of socioeconomic, psychosocial, behavioral, and clinical factors to predict risks of 30-day hospital readmission in cardiovascular patients.

Methods

The study includes patients (aged 18 years and older) admitted for the treatment of cardiovascular-related illnesses at the Duke Heart Center, which is among the nation’s largest and top-ranked cardiovascular care hospitals. The study uses a novel standardized survey to ascertain data on a comprehensive array of patient characteristics that will be linked to their electronic medical records. A series of univariate and multivariate models will be used to estimate the associations between the patient-level factors and 30-day readmissions. The performance of the risk models will be examined based on 2 components of accuracy—model calibration and discrimination—to determine how closely the predicted outcome agrees with the observed (actual) outcome and how well the model distinguishes patients who were readmitted and those who were not. The purpose of this paper is to present the protocol for the implementation of this study.

Results

The study was launched in February 2014 and is actively recruiting patients from the Heart Center. Approximately 550 patients have been enrolled to date and the study is expected to continue recruitment until February 2018. Preliminary results show that participants in the study were aged 63.6 years on average (SD 14.0), predominately male (61.2%), and primarily non-Hispanic white (64.6%) or non-Hispanic black (31.7%). The demographic characteristics of study participants were not significantly different from all patients admitted to the Heart Center during this period with an average age of 65.0 years (SD 15.3) and predominately male (58.6%), non-Hispanic white (62.9%) or non-Hispanic black (31.8%) The integration of the interview data with clinical data from the patient electronic medical records is currently underway. The study has received funding and ethical approval.

Conclusions

Many US hospitals continue to struggle with high readmission rates in patients with cardiovascular disease. The primary objective of this study is to collect and integrate a comprehensive array of patient attributes to develop a powerful yet parsimonious model to stratify risks of rehospitalization in cardiovascular patients. The results of this research also have the potential to identify actionable targets for tailored interventions to improve patient outcomes.

End-of-Life Care in the Treatment of Heart Failure in Older Adults

Heart failure presents unique challenges to the clinician who desires to provide excellent and humane care near the end of life. Accurate prediction of mortality in the individual patient is complicated by a chronic disease that is punctuated by recurrent acute episodes and sudden death. Health care providers continue to have difficulty communicating effectively with terminally ill patients and their caregivers regarding end-of-life care preferences, all of which needs to occur earlier rather than later. This article also discusses various means of providing palliative care, and specific issues regarding device therapy, cardiopulmonary resuscitation, and palliative sedation with concurrent discussion of the ethical ramifications and pitfalls of each.

Early inpatient calculation of laboratory-based 30-day readmission risk scores empowers clinical risk modification during index hospitalization

Improving 30-day readmission continues to be problematic for most hospitals. This study reports the creation and validation of sex-specific inpatient (i) heart failure (HF) risk scores using electronic data from the beginning of inpatient care for effective and efficient prediction of 30-day readmission risk.

METHODS

HF patients hospitalized at Intermountain Healthcare from 2005 to 2012 (derivation: n=6079; validation: n=2663) and Baylor Scott & White Health (North Region) from 2005 to 2013 (validation: n=5162) were studied. Sex-specific iHF scores were derived to predict post-hospitalization 30-day readmission using common HF laboratory measures and age. Risk scores adding social, morbidity, and treatment factors were also evaluated.

RESULTS

The iHF model for females utilized potassium, bicarbonate, blood urea nitrogen, red blood cell count, white blood cell count, and mean corpuscular hemoglobin concentration; for males, components were B-type natriuretic peptide, sodium, creatinine, hematocrit, red cell distribution width, and mean platelet volume. Among females, odds ratios (OR) were OR=1.99 for iHF tertile 3 vs. 1 (95% confidence interval [CI]=1.28, 3.08) for Intermountain validation (P-trend across tertiles=0.002) and OR=1.29 (CI=1.01, 1.66) for Baylor patients (P-trend=0.049). Among males, iHF had OR=1.95 (CI=1.33, 2.85) for tertile 3 vs. 1 in Intermountain (P-trend <0.001) and OR=2.03 (CI=1.52, 2.71) in Baylor (P-trend < 0.001). Expanded models using 182-183 variables had predictive abilities similar to iHF.

CONCLUSIONS

Sex-specific laboratory-based electronic health record-delivered iHF risk scores effectively predicted 30-day readmission among HF patients. Efficient to calculate and deliver to clinicians, recent clinical implementation of iHF scores suggest they are useful and useable for more precise clinical HF treatment.

Hospice in heart failure: why, when, and what then?

Hospice is a model of care for patients nearing the end of their lives that emphasizes symptom management, quality of life (QOL), and support of the patient and caregiving family through the death of the patient and the family's bereavement. It is associated with high patient and caregiver satisfaction and appears to not shorten lifespan for appropriately referred patients. Patients with advanced heart failure are being referred to hospice care more often than in the past, but the majority of deaths occur without this benefit. Hospice care in the USA is defined by the Medicare Hospice Benefit and associated regulations. Hospice is appropriate for patients with an expected survival prognosis of 6 months or less, and multiple predictive factors and tools are available to assist in prognostication. Management of symptoms and specific drug therapy options are discussed. For many patients, deactivation of electronic cardiac devices is appropriate when the goals of care are comfort and QOL. Ongoing collaboration of the referring physician with the hospice agency and staff offers opportunities for seamless and quality care.

Utilizing Home Healthcare Electronic Health Records for Telehomecare Patients With Heart Failure: A Decision Tree Approach to Detect Associations With Rehospitalizations

Heart failure is a complex condition with a significant impact on patients' lives. A few studies have identified risk factors associated with rehospitalization among telehomecare patients with heart failure using logistic regression or survival analysis models. To date, there are no published studies that have used data mining techniques to detect associations with rehospitalizations among telehomecare patients with heart failure. This study is a secondary analysis of the home healthcare electronic medical record called the Outcome and Assessment Information Set-C for 552 telemonitored heart failure patients. Bivariate analyses using SAS and a decision tree technique using Waikato Environment for Knowledge Analysis were used. From the decision tree technique, the presence of skin issues was identified as the top predictor of rehospitalization that could be identified during the start of care assessment, followed by patient's living situation, patient's overall health status, severe pain experiences, frequency of activity-limiting pain, and total number of anticipated therapy visits combined. Examining risk factors for rehospitalization from the Outcome and Assessment Information Set-C database using a decision tree approach among a cohort of telehomecare patients provided a broad understanding of the characteristics of patients who are appropriate for the use of telehomecare or who need additional supports.

Predicting readmission risk following percutaneous coronary intervention at the time of admission

OBJECTIVE

To investigate whether a prediction model based on data available early in percutaneous coronary intervention (PCI) admission can predict the risk of readmission.

BACKGROUND

Reducing readmissions following hospitalization is a national priority. Identifying patients at high risk for readmission after PCI early in a hospitalization would enable hospitals to enhance discharge planning.

METHODS

We developed 3 different models to predict 30-day inpatient readmission to our institution for patients who underwent PCI between January 2010 and April 2013. These models used data available: 1) at admission, 2) at discharge 3) from CathPCI Registry data. We used logistic regression and assessed the discrimination of each model using the c-index. The models were validated with testing on a different patient cohort who underwent PCI between May 2013 and September 2015.

RESULTS

Our cohort included 6717 PCI patients; 3739 in the derivation cohort and 2978 in the validation cohort. The discriminative ability of the admission model was good (C-index of 0.727). The c-indices for the discharge and cath PCI models were slightly better. (C-index of 0.751 and 0.752 respectively). Internal validation of the models showed a reasonable discriminative admission model with slight improvement with adding discharge and registry data (C-index of 0.720, 0.739 and 0.741 respectively). Similarly validation of the models on the validation cohort showed similar results (C-index of 0.703, 0.725 and 0.719 respectively).

CONCLUSION

Simple models based on available demographic and clinical data may be sufficient to identify patients at highest risk of readmission following PCI early in their hospitalization.

Predicting readmission risk following coronary artery bypass surgery at the time of admission

BACKGROUND

Reducing readmissions following hospitalization is a national priority. Identifying patients at high risk for readmission after coronary artery bypass graft surgery (CABG) early in a hospitalization would enable hospitals to enhance discharge planning.

METHODS

We developed different models to predict 30-day inpatient readmission to our institution in patients who underwent CABG between January 2010 and April 2013. These models used data available: 1) at admission, 2) at discharge 3) from STS Registry data. We used logistic regression and assessed the discrimination of each model using the c-index. The models were validated with testing on a different patient cohort who underwent CABG between May 2013 and September 2015. Our cohort included 1277 CABG patients: 1159 in the derivation cohort and 1018 in the validation cohort.

RESULTS

The discriminative ability of the admission model was reasonable (C-index of 0.673). The c-indices for the discharge and STS models were slightly better. (C-index of 0.700 and 0.714 respectively). Internal validation of the models showed a reasonable discriminative admission model with slight improvement with adding discharge and registry data (C-index of 0.641, 0.659 and 0.670 respectively). Similarly validation of the models on the validation cohort showed similar results (C-index of 0.573, 0.605 and 0.595 respectively).

CONCLUSIONS

Risk prediction models based on data available early on admission are predictive for readmission risk. Adding registry data did not improved the performance of these models. These simplified models may be sufficient to identify patients at highest risk of readmission following coronary revascularization early in the hospitalization.

Physician predictions of length of stay of patients admitted with heart failure

Physicians' ability to predict length of stay is understudied, particularly for patients with heart failure (HF) admissions. The objective of this prospective, observational cohort study was to measure the accuracy of inpatient physicians' predictions of length of stay at the time of admission of patients admitted to an academic tertiary care hospital with HF and to determine whether level of experience improves accuracy. The patients included 165 adults consecutively admitted with heart failure, about whom 415 predictions were made within 24 hours of admission. Mean and median lengths of stay were 10.9 and 8 days, respectively. The mean difference between predicted and actual length of stay was statistically significant for all groups: interns, -5.9 days (95% confidence interval [CI]: -8.2 to -3.6, P < 0.0001); residents, -4.3 days (95% CI: -6.0 to -2.7, P = 0.0001); attending cardiologists, -3.5 days (95% CI: -5.1 to -2.0, P < 0.0001). There were no differences in accuracy by level of experience (P = 0.61). Physicians, regardless of experience, underestimate length of stay of patients admitted with HF. Journal of Hospital Medicine 2016;11:642-645. © 2016 Society of Hospital Medicine.

Rationale and Design of the ATHENA-HF Trial: Aldosterone Targeted Neurohormonal Combined With Natriuresis Therapy in Heart Failure

Although therapy with mineralocorticoid receptor antagonists (MRAs) is recommended for patients with chronic heart failure (HF) with reduced ejection fraction and in post-infarction HF, it has not been studied well in acute HF (AHF) despite being commonly used in this setting. At high doses, MRA therapy in AHF may relieve congestion through its natriuretic properties and mitigate the effects of adverse neurohormonal activation associated with intravenous loop diuretics. The ATHENA-HF (Aldosterone Targeted Neurohormonal Combined with Natriuresis Therapy in Heart Failure) trial is a randomized, double-blind, placebo-controlled study of the safety and efficacy of 100 mg/day spironolactone versus placebo (or continued low-dose spironolactone use in participants who are already receiving spironolactone at baseline) in 360 patients hospitalized for AHF. Patients are randomized within 24 h of receiving the first dose of intravenous diuretics. The primary objective is to determine if high-dose spironolactone, compared with standard care, will lead to greater reductions in N-terminal pro-B-type natriuretic peptide levels from randomization to 96 h. The secondary endpoints include changes in the clinical congestion score, dyspnea relief, urine output, weight change, loop diuretic dose, and in-hospital worsening HF. Index hospital length of stay and 30-day clinical outcomes will be assessed. Safety endpoints include risk of hyperkalemia and renal function. Differences among patients with reduced versus preserved ejection fraction will be determined. (Study of High-dose Spironolactone vs. Placebo Therapy in Acute Heart Failure [ATHENA-HF]; NCT02235077).

Accuracy of physician prognosis in heart failure and lung cancer: Comparison between physician estimates and model predicted survival

BACKGROUND

Anticipating adverse outcomes guides decisions but can be particularly challenging in heart failure.

AIM

We sought to assess the accuracy and comfort of physicians in predicting prognosis in heart failure.

DESIGN

Cross-sectional survey

PARTICIPANTS/SETTING

Faculty and trainees in internal medicine, cardiology, and oncology estimated survival for three standardized patients: (1) 59-year-old patient with stage IV lung cancer; (2) 79-year-old woman with New York Heart Association class 4 heart failure symptoms and preserved ejection fraction; and (3) 40-year-old man with New York Heart Association class 3 heart failure symptoms and reduced ejection fraction of 20%. Survival predictions were derived from surveillance, epidemiology, and end results-Medicare database and the Seattle Heart Failure Model. Accuracy was defined as <2-fold difference between the clinician and model estimate.

RESULTS

Totally, 79% (338/427) of participants responded. Physicians were more accurate in survival estimates for lung cancer than heart failure (74% vs 48%, respectively; p < 0.001). Cardiologists were more accurate in predicting survival in heart failure symptoms and reduced ejection fraction compared to generalists (67% vs 45%; p = 0.005) and oncologists (39%; p = 0.041) but no different at predicting heart failure symptoms and preserved ejection fraction. Cardiologists predicted longer survival in heart failure compared to others (p < 0.05). Physicians felt more uncomfortable discussing palliative care with heart failure patients compared to lung cancer.

CONCLUSIONS

Less than half of physicians accurately estimate survival in heart failure. Cardiologists were more accurate than other specialties for heart failure symptoms and reduced ejection fraction but no different for heart failure symptoms and preserved ejection fraction.

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

Heart failure (HF) patients are at high risk of hospital readmission, which contributes to substantial health care costs. There is great interest in strategies to reduce rehospitalization for HF. However, many readmissions occur within 30 days of initial hospital discharge, presenting a challenge for interventions to be instituted in a short time frame. Potential strategies to reduce readmissions for HF can be classified into three different forms. First, patients who are at high risk of readmission can be identified even before their initial index hospital discharge. Second, ambulatory remote monitoring strategies may be instituted to identify early warning signs before acute decompensation of HF occurs. Finally, strategies may be employed in the emergency department to identify low-risk patients who may not need hospital readmission. If symptoms improve with initial therapy, low-risk patients could be referred to specialized, rapid outpatient follow-up care where investigations and therapy can occur in an outpatient setting.

EVALUATING RISK-PREDICTION MODELS USING DATA FROM ELECTRONIC HEALTH RECORDS

The availability of data from electronic health records facilitates the development and evaluation of risk-prediction models, but estimation of prediction accuracy could be limited by outcome misclassification, which can arise if events are not captured. We evaluate the robustness of prediction accuracy summaries, obtained from receiver operating characteristic curves and risk-reclassification methods, if events are not captured (i.e., "false negatives"). We derive estimators for sensitivity and specificity if misclassification is independent of marker values. In simulation studies, we quantify the potential for bias in prediction accuracy summaries if misclassification depends on marker values. We compare the accuracy of alternative prognostic models for 30-day all-cause hospital readmission among 4548 patients discharged from the University of Pennsylvania Health System with a primary diagnosis of heart failure. Simulation studies indicate that if misclassification depends on marker values, then the estimated accuracy improvement is also biased, but the direction of the bias depends on the direction of the association between markers and the probability of misclassification. In our application, 29% of the 1143 readmitted patients were readmitted to a hospital elsewhere in Pennsylvania, which reduced prediction accuracy. Outcome misclassification can result in erroneous conclusions regarding the accuracy of risk-prediction models.

A simple validated method for predicting the risk of hospitalization for worsening of heart failure in ambulatory patients: the Redin-SCORE

AIMS

Prevention of hospital readmissions is one of the main objectives in the management of patients with heart failure (HF). Most of the models predicting readmissions are based on data extracted from hospitalized patients rather than from outpatients. Our objective was to develop a validated score predicting 1-month and 1-year risk of readmission for worsening of HF in ambulatory patients.

METHODS AND RESULTS

A cohort of 2507 ambulatory patients with chronic HF was prospectively followed for a median of 3.3 years. Clinical, echocardiographic, ECG, and biochemical variables were used in a competing risk regression analysis to construct a risk score for readmissions due to worsening of HF. Thereafter, the score was externally validated using a different cohort of 992 patients with chronic HF (MUSIC registry). Predictors of 1-month readmission were the presence of elevated natriuretic peptides, left ventricular (LV) HF signs, and estimated glomerular filtration rate (eGFR) <60 mL/min/m(2) . Predictors of 1-year readmission were elevated natriuretic peptides, anaemia, left atrial size >26 mm/m(2) , heart rate >70 b.p.m., LV HF signs, and eGFR <60 mL/min/m(2) . The C-statistics for the models were 0.72 and 0.66, respectively. The cumulative incidence function distinguished low-risk (<1% event rate) and high-risk groups (>5% event rate) for 1-month HF readmission. Likewise, low-risk (7.8%), intermediate-risk (15.6%) and high-risk groups (26.1%) were identified for 1-year HF readmission risk. The C-statistics remained consistent after the external validation (<5% loss of discrimination).

CONCLUSION

The Redin-SCORE predicts early and late readmission for worsening of HF using proven prognostic variables that are routinely collected in outpatient management of chronic HF.

Mineralocorticoid Receptor Antagonism in Acute Heart Failure

OPINION STATEMENT

Heart failure (HF) remains a tremendous burden to health care systems and patients worldwide. The cornerstone neurohormonal disruption that leads to the debilitating sequelae in HF patients revolves primarily around aldosterone and the renin-angiotensin-aldosterone system (RAAS). Aldosterone plays a detrimental role in tissue remodeling by inducing inflammation and fibrosis within the cardiovascular and renal systems, leaving mineralocorticoid receptor antagonists (MRAs) as key pharmacological tools to slow pathogenesis and improve patient outcomes. The role of MRA in improving morbidity and mortality in outpatients with chronic HF and low ejection fraction is well established and supported by large randomized controlled trials. However, evidence-based data relating to the use of MRA in acute HF (AHF) remain somewhat limited, and therefore, the use of MRA is not ubiquitously considered in the acute setting. Current studies for the use of MRA in AHF are limited by small sample size as well as safety concerns relating to the dose-dependent effects on electrolyte homeostasis and renal function. Here, we discuss the imperative need for additional trials elucidating the potential benefits of MRA in AHF as an adjunct diuretic therapy. We not only discuss the role of MRA in neurohormonal regulation of aldosterone but also highlight a potential dose-dependent role for MRA in natriuresis. Furthermore, we showcase existing and recent evidence-based data demonstrating the effectiveness of MRA in AHF and on long-term outcomes. Finally, we look at several treatment strategies and safety concerns as they relate to MRA use so as to aid in avoidance of MRA-related complications while facilitating achievement of treatment goals.

Education material for heart failure patients: what works and what does not?

Patient education is an important element of care, but evidence with regard to education material is not always apparent, as it is intertwined with educational strategies as components of heart failure management programs. Difficulties have arisen in determining the effectiveness of particular education strategies, as multiple strategies are commonly bundled together and packaged within research protocols. To further complicate this issue, the bundles are diverse, lack precision in describing their components, and report different outcomes. Despite these difficulties, clinicians can utilise a number of proven commonalities to deliver effective education: assessment of learning needs and style, verbal interaction with a healthcare professional, and a selection of multimedia patient education materials.

Strategies to Reduce 30-Day Readmissions in Older Patients Hospitalized with Heart Failure and Acute Myocardial Infarction

Readmission within 30 days after hospital discharge for common cardiovascular conditions such as heart failure and acute myocardial infarction is extremely common among older persons. To incentivize investment in reducing preventable rehospitalizations, the United States federal government has directed increasing financial penalties to hospitals with higher-than-expected 30-day readmission rates. Uncertainty exists, however, regarding the best approaches to reducing these adverse outcomes. In this review, we summarize the literature on predictors of 30-day readmission, the utility of risk prediction models, and strategies to reduce short-term readmission after hospitalization for heart failure and acute myocardial infarction. We report that few variables have been found to consistently predict the occurrence of 30-day readmission and that risk prediction models lack strong discriminative ability. We additionally report that the literature on interventions to reduce 30-day rehospitalization has significant limitations due to heterogeneity, susceptibility to bias, and lack of reporting on important contextual factors and details of program implementation. New information is characterizing the period after hospitalization as a time of high generalized risk, which has been termed the post-hospital syndrome. This framework for characterizing inherent post-discharge instability suggests new approaches to reducing readmissions.

Factors influencing the predictive power of models for predicting mortality and/or heart failure hospitalization in patients with heart failure

The present paper systematically reviews and compares existing prediction models in order to establish the strongest variables, models, and model characteristics in patients with heart failure predicting outcome. To improve decision making accurately predicting mortality and heart-failure hospitalization in patients with heart failure can be important for selecting patients with a poorer prognosis or nonresponders to current therapy, to improve decision making. MEDLINE/PubMed was searched for papers dealing with heart failure prediction models. To identify similar models on the basis of their variables hierarchical cluster analysis was performed. Meta-analysis was used to estimate the mean predictive value of the variables and models; meta-regression was used to find characteristics that explain variation in discriminating values between models. We identified 117 models in 55 papers. These models used 249 different variables. The strongest predictors were blood urea nitrogen and sodium. Four subgroups of models were identified. Mortality was most accurately predicted by prospective registry-type studies using a large number of clinical predictor variables. Mean C-statistic of all models was 0.66 ± 0.0005, with 0.71 ± 0.001, 0.68 ± 0.001 and 0.63 ± 0.001 for models predicting mortality, heart failure hospitalization, or both, respectively. There was no significant difference in discriminating value of models between patients with chronic and acute heart failure. Prediction of mortality and in particular heart failure hospitalization in patients with heart failure remains only moderately successful. The strongest predictors were blood urea nitrogen and sodium. The highest C-statistic values were achieved in a clinical setting, predicting short-term mortality with the use of models derived from prospective cohort/registry studies with a large number of predictor variables.

Resource use and cost implications of implementing a heart failure program for patients with systolic heart failure in Swedish primary health care

AIM

Heart failure (HF) is a common but serious condition which involves a significant economic burden on the health care economy. The purpose of this study was to evaluate cost and quality of life (QoL) implications of implementing a HF management program (HFMP) in primary health care (PHC).

METHODS AND RESULTS

This was a prospective randomized open-label study including 160 patients with a diagnosis of HF from five PHC centers in south-eastern Sweden. Patients randomized to the intervention group received information about HF from HF nurses and from a validated computer-based awareness program. HF nurses and physicians followed the patients intensely in order to optimize HF treatment according to current guidelines. The patients in the control group were followed by their regular general practitioner (GP) and received standard treatment according to local management routines. No significant changes were observed in NYHA class and quality-adjusted life years (QALY), implying that functional class and QoL were preserved. However, costs for hospital care (HC) and PHC were reduced by EUR 2167, or 33%. The total cost was EUR 4471 in the intervention group and EUR 6638 in the control group.

CONCLUSIONS

Introducing HFMP in Swedish PHC in patients with HF entails a significant reduction in resource utilization and costs, and maintains QoL. Based on these results, a broader implementation of HFMP in PHC may be recommended. However, results should be confirmed with extended follow-up to verify long-term effects.

Prospective development and validation of a model to predict heart failure hospitalisation

Objective

Acute heart failure syndrome (AHFS) is a major cause of hospitalisation and imparts a substantial burden on patients and healthcare systems. Tools to define risk of AHFS hospitalisation are lacking.

Methods

A prospective cohort study (n=628) of patients with stable chronic heart failure (CHF) secondary to left ventricular systolic dysfunction was used to derive an AHFS prediction model which was then assessed in a prospectively recruited validation cohort (n=462).

Results

Within the derivation cohort, 44 (7%) patients were hospitalised as a result of AHFS during 1 year of follow-up. Predictors of AHFS hospitalisation included furosemide equivalent dose, the presence of type 2 diabetes mellitus, AHFS hospitalisation within the previous year and pulmonary congestion on chest radiograph, all assessed at baseline. A multivariable model containing these four variables exhibited good calibration (Hosmer–Lemeshow p=0.38) and discrimination (C-statistic 0.77; 95% CI 0.71 to 0.84). Using a 2.5% risk cut-off for predicted AHFS, the model defined 38.5% of patients as low risk, with negative predictive value of 99.1%; this low risk cohort exhibited <1% excess all-cause mortality per annum when compared with contemporaneous actuarial data. Within the validation cohort, an identically applied model derived comparable performance parameters (C-statistic 0.81 (95% CI 0.74 to 0.87), Hosmer–Lemeshow p=0.15, negative predictive value 100%).

Conclusions

A prospectively derived and validated model using simply obtained clinical data can identify patients with CHF at low risk of hospitalisation due to AHFS in the year following assessment. This may guide the design of future strategies allocating resources to the management of CHF.

End-of-life care in the treatment of advanced heart failure in the elderly

Heart failure presents its own unique challenges to the clinician who desires to make excellent and humane care near the end of life a tangible reality. Accurate prediction of mortality in the individual patient is complicated by both the frequent occurrence of sudden death, both with and without devices, and the frequently chronic course that is punctuated by recurrent and more prominent acute episodes. A significant literature demonstrates that healthcare providers continue to have difficulty communicating effectively with terminally ill patients and their caregivers regarding end-of-life care preferences, and it is clear from the prognostic uncertainty of advanced heart failure that this kind of communication, and discussions regarding palliative care, need to occur earlier rather than later. This article discusses various means of providing palliative care, and specific issues regarding device therapy, cardiopulmonary resuscitation, and palliative sedation, with concurrent discussion of the ethical ramifications and pitfalls of each. A recent scientific statement from the American Heart Association begins to address some of the methodological issues involved in the care of patients with advanced heart failure. Above all, clinicians who wish to provide the highest quality of care to the dying patient need to confront the existential reality of death in themselves, their loved ones, and their patients so as to best serve those remanded to their care.

Clinical utility of N-terminal pro-B-type natriuretic peptide for risk stratification of patients with acute decompensated heart failure. Derivation and validation of the ADHF/NT-proBNP risk score

BACKGROUND

NT-proBNP has been associated with prognosis in acute decompensated heart failure (ADHF). Whether NT-proBNP provides additional prognostic information beyond that obtained from standard clinical variables is uncertain. We sought to assess whether N-terminal pro-B-type natriuretic peptide (NT-proBNP) determination improves risk reclassification of patients with ADHF and to develop and validate a point-based NT-proBNP risk score.

METHODS

This study included 824 patients with ADHF (453 in the derivation cohort, 371 in the validation cohort). We compared two multivariable models predicting 1-year all-cause mortality, including clinical variables and clinical variables plus NT-proBNP. We calculated the net reclassification improvement (NRI) and the integrated discrimination improvement (IDI). Then, we developed and externally validated the NT-proBNP risk score.

RESULTS

One-year mortalities for the derivation and validation cohorts were 28.3% and 23.4%, respectively. Multivariable predictors of mortality included chronic obstructive pulmonary disease, estimated glomerular filtration rate, sodium, hemoglobin, left ventricular ejection fraction, and moderate to severe tricuspid regurgitation. Adding NT-proBNP to the clinical variables only model significantly improved the NRI (0.129; p=0.0027) and the IDI (0.037; p=0.0005). In the derivation cohort, the NT-proBNP risk score had a C index of 0.839 (95% CI: 0.798-0.880) and the Hosmer-Lemeshow statistic was 1.23 (p=0.542), indicating good calibration. In the validation cohort, the risk score had a C index of 0.768 (95% CI: 0.711-0.817); the Hosmer-Lemeshow statistic was 2.76 (p=0.251), after recalibration.

CONCLUSIONS

The NT-proBNP risk score provides clinicians with a contemporary, accurate, easy-to-use, and validated predictive tool. Further validation in other datasets is advisable.

Electronically monitored medication adherence predicts hospitalization in heart failure patients

BACKGROUND

Hospitalization contributes enormously to health care costs associated with heart failure. Many investigators have attempted to predict hospitalization in these patients. None of these models has been highly effective in prediction, suggesting that important risk factors remain unidentified.

PURPOSE

To assess prospectively collected medication adherence, objectively measured by the Medication Event Monitoring System, as a predictor of hospitalization in heart failure patients.

MATERIALS AND METHODS

We used recently developed adaptive modeling methods to describe patterns of medication adherence in a sample of heart failure patients, and tested the hypothesis that poor medication adherence as determined by adaptive methods was a significant predictor of hospitalization within 6 months.

RESULTS

Medication adherence was the best predictor of hospitalization. Besides two dimensions of poor adherence (adherence pattern type and low percentage of prescribed doses taken), four other single factors predicted hospitalization: low hemoglobin, depressed ejection fraction, New York Heart Association class IV, and 12 or more medications taken daily. Seven interactions increased the predictive capability of the model: 1) pattern of poor adherence type and lower score on the Letter-Number Sequencing test, a measure of short-term memory; 2) higher number of comorbid conditions and higher number of daily medications; 3) higher blood urea nitrogen and lower percentage of prescribed doses taken; 4) lower hemoglobin and much worse perceived health compared to last year; 5) older age and lower score on the Telephone Interview of Cognitive Status; 6) higher body mass index and lower hemoglobin; and 7) lower ejection fraction and higher fatigue. Patients with none of these seven interactions had a hospitalization rate of 9.7%. For those with five of these interaction risk factors, 100% were hospitalized. The C-index (the area under the receiver-operating characteristics [ROC] curve) for the model based on the seven interactions was 0.83, indicating excellent discrimination.

CONCLUSION

Medication adherence adds important new information to the list of variables previously shown to predict hospitalization in adults with heart failure.

Predicting risk of hospitalization or death among patients with heart failure in the veterans health administration

Patients with heart failure (HF) are at high risk of hospitalization or death. The objective of this study was to develop prediction models to identify patients with HF at highest risk for hospitalization or death. Using clinical and administrative databases, we identified 198,460 patients who received care from the Veterans Health Administration and had ≥1 primary or secondary diagnosis of HF that occurred within 1 year before June 1, 2009. We then tracked their outcomes of hospitalization and death during the subsequent 30 days and 1 year. Predictor variables chosen from 6 clinically relevant categories of sociodemographics, medical conditions, vital signs, use of health services, laboratory tests, and medications were used in multinomial regression models to predict outcomes of hospitalization and death. In patients who were in the ≥95th predicted risk percentile, observed event rates of hospitalization or death within 30 days and 1 year were 27% and 80% respectively, compared to population averages of 5% and 31%, respectively. The c-statistics for the 30-day outcomes were 0.82, 0.80, and 0.80 for hospitalization, death, and hospitalization or death, respectively, and 0.82, 0.76, and 0.77, respectively, for 1-year outcomes. In conclusion, prediction models using electronic health records can accurately identify patients who are at highest risk for hospitalization or death. This information can be used to assist care managers in selecting patients for interventions to decrease their risk of hospitalization or death.

Renal dysfunction in heart failure

Renal dysfunction is a common, important comorbidity in patients with both chronic and acute heart failure (HF). Chronic kidney disease and worsening renal function (WRF) are associated with worse outcomes, but our understanding of the complex bidirectional interactions between the heart and kidney remains poor. When addressing these interactions, one must consider the impact of intrinsic renal disease resulting from medical comorbidities on HF outcomes. WRF may result from any number of important processes. Understanding the role of each of these factors and their interplay are essential in understanding how to improve outcomes in patients with renal dysfunction and HF.

Predicting the risk of unplanned readmission or death within 30 days of discharge after a heart failure hospitalization

BACKGROUND

The accuracy of current models to predict the risk of unplanned readmission or death after a heart failure (HF) hospitalization is uncertain.

METHODS

We linked four administrative databases in Alberta to identify all adults discharged alive after a HF hospitalization between April 1999 and 2009. We randomly selected one episode of care per patient and evaluated the accuracy of five administrative data-based models (4 already published, 1 new) for predicting risk of death or unplanned readmission within 30 days of discharge.

RESULTS

Over 10 years, 59652 adults (mean age 76, 50% women) were discharged after a HF hospitalization. Within 30 days of discharge, 11199 (19%) died or had an unplanned readmission. All 5 administrative data models exhibited moderate discrimination for this outcome (c-statistic between 0.57 and 0.61). Neither Centers for Medicare and Medicaid Services (CMS)-endorsed model exhibited substantial improvements over the Charlson score for prediction of 30-day post-discharge death or unplanned readmission. However, a new model incorporating length of index hospital stay, age, Charlson score, and number of emergency room visits in the prior 6 months (the LaCE index) exhibited a 20.5% net reclassification improvement (95% CI, 18.4%-22.5%) over the Charlson score and a 19.1% improvement (95% CI, 17.1%-21.2%) over the CMS readmission model.

CONCLUSIONS

None of the administrative database models are sufficiently accurate to be used to identify which HF patients require extra resources at discharge. Models which incorporate length of stay such as the LaCE appear superior to current CMS-endorsed models for risk adjusting the outcome of "death or readmission within 30 days of discharge".