Model Building as an Educational Hobby

Predictability of Inpatient Mortality of Different Comorbidities in Both Types of Acute Decompensated Heart Failure: Analysis of National Inpatient Sample

Background

Several prediction models have been proposed to assess the short outcomes and in-hospital mortality among patients with heart failure (HF). Several variables were used in common among those models. We sought to focus on other, yet important risk factors that can predict outcomes. We also sought to stratify patients based on ejection fraction, matching both groups with different risk factors.

Methods

We conducted a retrospective cohort study utilizing the Healthcare Cost and Utilization Project National Inpatient Sample (HCUP-NIS) 2016 database.

Results

There were totally 116,189 admissions for acute decompensated heart failure (ADHF). Of these, 50.9% were for heart failure with reduced ejection fraction (HFrEF) group (n = 59,195), and 49.1% were for heart failure with preserved ejection faction (HFpEF) group (n = 56,994). Overall, in-hospital mortality was 2.5% of admissions for ADHF (n = 2,869). When stratified by HF types, admissions for HFrEF had higher mortality rate (2.7%, n = 1,594) in comparison to admissions for HFpEF (2.2%, n = 1,275) (P < 0.001). Significantly associated variables in univariate analyses were age, race, hypertension, diabetes mellitus, chronic kidney disease (CKD), atrial fibrillation/flutter, obesity, and chronic ischemic heart disease (IHD), while gender and chronic obstructive pulmonary disease (COPD) did not achieve statistical significance (P > 0.1).

Conclusions

To our knowledge, this is the first study to stratify HF patients based on ejection fraction and utilizing different predictors and in-hospital mortality. These and other data support the need for future research to utilize these predictors to create more accurate models in the future.

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.

Risk scores did not reliably predict individual risk of mortality for patients with decompensated heart failure

OBJECTIVE

We investigated the performance of four prognostic tools in predicting 180-day mortality for patients admitted for acute decompensated heart failure (ADHF) by calculating sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) over a range of risk thresholds, in addition to discrimination and calibration.

STUDY DESIGN AND SETTING

We studied 1,458 patients. The risk assessment was performed using the Acute Decompensated Heart Failure National Registry (ADHERE) model and the Get With The Guidelines (GWTG), ADHF/NT-proBNP, and Acute Study of Clinical Effectiveness of Nesiritide in Decompensated Heart Failure (ASCEND) risk scores.

RESULTS

C-statistics ranged from 0.727 for the ADHERE model to 0.767 for the ADHF/NT-proBNP score. The ADHF/NT-proBNP risk score, the ADHERE model, and the ASCEND risk score, but not the GWTG risk score, were also well calibrated. Sensitivity and PPV were modest at the >30% risk threshold and ranged from 55% for the ADHF/NT-proBNP risk score to 38.8% for the ADHERE model and from 46.7% for the ADHF/NT-proBNP risk score to 42.1% for the ASCEND risk score, respectively. There was a modest agreement between the risk scores in classifying the patients across risk strata or in classifying those who died as being at >30% risk of death.

CONCLUSION

Although risk assessment tools work well for stratifying patients, their use in estimating the risk of mortality for individuals has limited clinical utility.

The prognosis of patients hospitalized with a first episode of heart failure, validation of two scores: PREDICE and AHEAD

Purpose

Heart failure (HF) is a chronic, frequent and disabling condition but with a modifiable course and a large potential for improving. The aim of this study was to validate the two available clinical prediction rules for mortality at one year in patients with primo-hospitalization for decompensated HF: PREDICE and AHEAD. The secondary aim was to evaluate in our setting the changes in the clinical pattern of HF in the last decade in patients hospitalized for a first episode of the disease.

Patients and methods

A prospective multicenter cohort study, which included 180 patients hospitalized with “de novo” HF was conducted to validate the PREDICE score. Calibration and discrimination measurements were calculated for the PREDICE model and the PREDICE score (using the validation cohort of the PREDICE) and the AHEAD score (using both the development and the validation cohort of the PREDICE).

Results

For the PREDICE models, the area under the curve (AUC) was 0.68 (95% confidence interval [CI]: 0.57–0.79) and the calibration slope 0.65 (95% CI: 0.21–1.20). For the PREDICE score AUC was 0.59 (95% CI: 0.47–0.71) and slope 0.42 (95% CI: −0.20–1.17). For the AHEAD score the AUC was 0.68 (95% CI: 0.62–0.73) and slope 1.38 (95% CI: 0.62–0.73) when used the development cohort of PREDICE and the AUC was 0.58 (95% CI: 0.49–0.67), and slope 0.68 (95% CI: −0.06 to 1.47) when used its validation cohort.

Conclusion

The present study shows that the two risk scores available for patients with primo-hospitalization for decompensated HF (PREDICE and AHEAD) are not currently valid for predicting mortality at one-year. In our setting the clinical spectrum of hospitalized patients with new-onset HF has been modified over time. The study underscores the need to validate the prognostic models before clinical implementation.

Primary palliative care for heart failure: what is it? How do we implement it?

Heart failure (HF) is a chronic and progressive illness, which affects a growing number of adults, and is associated with a high morbidity and mortality, as well as significant physical and psychological symptom burden on both patients with HF and their families. Palliative care is the multidisciplinary specialty focused on optimizing quality of life and reducing suffering for patients and families facing serious illness, regardless of prognosis. Palliative care can be delivered as (1) specialist palliative care in which a palliative care specialist with subspecialty palliative care training consults or co-manages patients to address palliative needs alongside clinicians who manage the underlying illness or (2) as primary palliative care in which the primary clinician (such as the internist, cardiologist, cardiology nurse, or HF specialist) caring for the patient with HF provides the essential palliative domains. In this paper, we describe the key domains of primary palliative care for patients with HF and offer some specific ways in which primary palliative care and specialist palliative care can be offered in this population. Although there is little research on HF primary palliative care, primary palliative care in HF offers a key opportunity to ensure that this population receives high-quality palliative care in spite of the growing numbers of patients with HF as well as the limited number of specialist palliative care providers.

The State of the Science on Integrating Palliative Care in Heart Failure

BACKGROUND

Heart failure (HF) is a chronic progressive illness associated with physical and psychological burdens, high morbidity, mortality, and healthcare utilization. Palliative care is interdisciplinary care that aims to relieve suffering and improve quality of life for persons with serious illness and their families. It is offered simultaneously with disease-oriented care, unlike hospice or end-of-life care. Despite the demonstrated benefits of palliative care in other populations, evidence for palliative care in the HF population is limited.

OBJECTIVE

The objective of this article is to describe the current evidence and the gaps in the evidence that will need to be improved to demonstrate the benefits of integrating palliative care into the care of patients with advanced HF and their family caregivers.

METHODS

We reviewed the literature to examine the state of the science and to identify gaps in palliative care integration for persons with HF and their families. We then convened an interdisciplinary working group at an NIH/NPCRC sponsored workshop to review the evidence base and develop a research agenda to address these gaps.

RESULTS

We identified four key research priorities to improve palliative care for patients with HF and their families: (1) to better understand patients' uncontrolled symptoms, (2) to better characterize and address the needs of the caregivers of advanced HF patients, (3) to improve patient and family understanding of HF disease trajectory and the importance of advance care planning, and (4) to determine the best models of palliative care, including models for those who want to continue life-prolonging therapies.

CONCLUSIONS

The goal of this research agenda is to motivate patient, provider, policy, and payor stakeholders, including funders, to identify the key research topics that have the potential to improve the quality of care for patients with HF and their families.