Enhancing Palliative Care for Patients With Advanced Heart Failure Through Simple Prognostication Tools: A Comparison of the Surprise Question, the Number of Previous Heart Failure Hospitalizations, and the Seattle Heart Failure Model for Predicting 1-Year Survival

An retrospective study on the effects of deep learning model-based optimization emergency nursing on treatment compliance and curative effect of patients with acute left heart failure

BACKGROUND

Based on explainable DenseNet model, the therapeutic effects of optimization nursing on patients with acute left heart failure (ALHF) and its application values were discussed.

METHOD

In this study, 96 patients with ALHF in the emergency department of the Affiliated Hospital of Xuzhou Medical University were selected. According to different nursing methods, they were divided into conventional group and optimization group. Activity of daily living (ADL) scale was used to evaluate ADL of patients 6 months after discharge. Self-rating anxiety scale (SAS) and self-rating depression scale (SDS) were employed to assess patients' psychological state. 45 min improvement rate, 60 min show efficiency, rescue success rate, and transfer rate were used to assess the effect of first aid. Likert 5-level scoring method was adopted to evaluate nursing satisfaction.

RESULTS

The optimization group showed shorter durations for first aid, hospitalization, electrocardiography, vein channel establishment, and blood collection compared to the conventional group. However, their SBP, DBP, and HR were inferior. On the other hand, LVEF and FS were significantly better in the optimization group. After nursing intervention, SAS and SDS scores were lower in the optimization group. Additionally, the optimization group had higher 45-minute improvement rates, 60-minute show efficiency, rescue success, and transfer rates. They also performed better in 6-minute walking distance and ADL scores 6 months post-discharge. The optimization group had better compliance, total effective rates, and satisfaction than the conventional group.

CONCLUSION

It was demonstrated that explainable DenseNet model had application values in the diagnosis of ALHF. Optimization emergency method could effectively shorten the duration of first aid, relieve anxiety, and other adverse emotions, and improve rescue success rate and short-term efficacy. Nursing intervention has a positive impact on the total effective efficiency and patient satisfaction.

Integrating Palliative Care and Heart Failure: the PalliatHeartSynthesis realist synthesis

Background

Cardiovascular disease is the most common cause of death worldwide, highlighting the need for studies to determine options for palliative care within the management of patients with heart failure. Although there are promising examples of integrated palliative care and heart failure interventions, there is heterogeneity in terms of countries, healthcare settings, multidisciplinary team delivery, modes of delivery and intervention components. Hence, this review is vital to identify what works, for whom and in what circumstances when integrating palliative care and heart failure.

Objectives

To (1) develop a programme theory of why, for whom and in what contexts desired outcomes occur; and (2) use the programme theory to co-produce with stakeholders key implications to inform best practice and future research.

Design

A realist review of the literature underpinned by the Realist and Meta-narrative Evidence Syntheses: Evolving Standards quality and reporting standards.

Data sources

Searches of bibliographic databases were conducted in November 2021 using the following databases: EMBASE, MEDLINE, PsycInfo, AMED, HMIC and CINAHL. Further relevant documents were identified via alerts and the stakeholder group.

Review methods

Realist review is a theory-orientated and explanatory approach to the synthesis of evidence. A realist synthesis was used to synthesise the evidence as successful implementation of integrated palliative care and heart failure depends on the context and people involved. The realist synthesis followed Pawson's five iterative stages: (1) locating existing theories; (2) searching for evidence; (3) document selection; (4) extracting and organising data; and (5) synthesising the evidence and drawing conclusions. We recruited an international stakeholder group (n = 32), including National Health Service management, healthcare professionals involved in the delivery of palliative care and heart failure, policy and community groups, plus members of the public and patients, to advise and give us feedback throughout the project, along with Health Education England to disseminate findings.

Results

In total, 1768 documents were identified, of which 1076 met the inclusion criteria. This was narrowed down to 130 included documents based on the programme theory and discussions with stakeholders. Our realist analysis developed and refined 6 overarching context-mechanism-outcome configurations and 30 sub context-mechanism-outcome configurations. The realist synthesis of the literature and stakeholder feedback helped uncover key intervention strategies most likely to support integration of palliative care into heart failure management. These included protected time for evidence-based palliative care education and choice of educational setting (e.g. online, face to face or hybrid), and the importance of increased awareness of the benefits of palliative care as key intervention strategies, the emotive and intellectual need for integrating palliative care and heart failure via credible champions, seeing direct patient benefit, and prioritising palliative care and heart failure guidelines in practice. The implications of our findings are further outlined in the capability, opportunity, motivation, behaviour model.

Limitations

The realist approach to analysis means that findings are based on our interpretation of the data.

Future work

Future work should use the implications to initiate and optimise palliative care in heart failure management.

Conclusion

Ongoing refinement of the programme theory at each stakeholder meeting allowed us to co-produce implications. These implications outline the required steps to ensure the core components and determinants of behaviour are in place so that all key players have the capacity, opportunity and motivation to integrate palliative care into heart failure management.

Study registration

This study is registered as PROSPERO CRD42021240185.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: NIHR131800) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 34. See the NIHR Funding and Awards website for further award information.

Validation of the Surprise Question and the Development of a Multivariable Model

CONTEXT

The Surprise Question (SQ) (would you be surprised if this patient died within a year?) is a prognostic variable explored in chronic illnesses. Validation is limited to sensitivity, specificity, and predictive values.

OBJECTIVES

Our objective is to validate the SQ in cancer patients and develop a predictive model with additional variables.

METHODS

A prospective cohort study of adult (age>18) cancer patients seen between October 1, 2019, through March 31, 2021, undergoing systemic therapies had the SQ completed by oncologists prior to each change in systemic therapy. The primary outcome was survival for one year. Secondary outcomes were predictions of survival at three, six, and nine months. Patients were grouped into negative SQ (not surprised) and positive SQ (surprised). Sensitivity, specificity, predictive values, and likelihood ratios (LR) were calculated for the SQ. Additional prognostic variables were age, gender, cancer stage, line of therapy, Charleson Comorbid Index (CCI), palliative care consultation (prior to, after the SQ, or not at all), and healthcare utilization (outpatient, inpatient, and emergency department (ED). Logistic regression and receiver operating characteristics (ROC) were used for discrimination and modeling. Akaike information criterion (AIC) was used to compare the model fit as each predictor.

RESULTS

1366 patients had 1 SQ; 784 died within a year. The SQ predicted survival at one year (P = 0.008), with a positive LR of 1.459 (95%CI 1.316-1.602) and a c-statistic of 0.565 (95%CI 0.530-0.600). Additional variables increased the c-statistic to 0.648 (95% CI 0.608-0.686). The total model best predicted survival at three months, c-statistic of 0.663 (95% CI 0.616-0.706). However, the total model c-statistic remained <0.70.

CONCLUSIONS

The SQ, as a single factor, poorly predicts survival and should not be used to alter therapies. Adding additional objective variables improved prognostication, but further refinement and external validation are needed.