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Record W2072899183 · doi:10.1016/j.ejheart.2008.02.014

Heart Failure in the Elderly: How Risky is it to be Discharged?

2008· letter· en· W2072899183 on OpenAlexaboutno aff
Markku S. Nieminen, Veli‐Pekka Harjola

Bibliographic record

VenueEuropean Journal of Heart Failure · 2008
Typeletter
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHeart failureCardiologyInternal medicineIntensive care medicineMedical emergency

Abstract

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Heart failure (HF) is an important and costly cause of hospitalisation. Hospital care represents almost 70% of total costs of heart failure for the community. Moreover, hospitalisation likely represents an impediment for patients in terms of quality of life. Thus the pressure to avoid hospitalisation is a major challenge for providers of care. Prevention of hospitalisation can be achieved with appropriate use of effective medication combined with a flexible follow-up system in order to prevent deterioration to a point which necessitates admission to the Emergency Department (ED). In European emergency services, most patients are seen in acute care by general practitioners either in health care centres or centralised general practice emergency services. Thus, European surveys on hospitalised patients represent pre-screened patient populations. The present literature on acute heart failure (AHF) has mainly focused on patients admitted to the ED and subsequently hospitalised [1,2]. Yet little is known about those patients who are discharged home from the ED without being admitted to in-hospital care. The systematic evaluation of elderly patients with multiple co-morbidities is a real challenge in the ED. A patient with acute HF requires a proper clinical examination and diagnostic work-up in the ED. An ED physician has to safely exclude precipitating factors underlying acute heart failure which would require in-hospital therapy. A hypertensive patient with diastolic dysfunction might be rapidly and safely discharged from the ED, whereas a patient with ischaemic heart disease and pulmonary oedema precipitated by acute infection would indeed need hospitalisation. A patient who is discharged needs to have proper follow-up and be given details of a health professional to contact (eg HF nurse) if his/her status starts to deteriorate. Otherwise the risk for rapid re-admission to the ED may not be reduced. The report by Ezekowitz et al in this issue of the European Journal of Heart Failure [3] is of special interest as it analyses heart failure admissions to emergency services based on hospital discharge records in an extremely large patient cohort with one-year follow-up. The report is an elegant population study combining various health care and social databases in Canada. In their cohort of 10,415 patients from Alberta, Canada, patients aged less than 65 years were excluded. Thus the mean age of patients was 79 years in both groups (discharged and admitted). The study is a retrospective review based on discharge data retrieved on all patients treated between 1998–2002 and covers provincial hospitals; of note, diagnostic coding allows the use of 6 discharge codes. The authors report a number of important and novel findings. First, more than one third of elderly heart failure patients are discharged from the ED after a visit for acute heart failure and this group has a high likelihood of attending the ED or being admitted in the next year. More importantly, they are also at risk for short-term (3.3% at 30 days) and long-term mortality (20% at one year). In addition, one-year mortality was significantly higher in those patients initially hospitalised. The rehospitalisation rate at 30 days in the Canadian study was around 20% and at one year was as high as 60%, which indeed is very high. By comparison, in the EuroHeart Failure Survey-I the rehospitalisation rate at 3 months was 24.2%. The authors also showed that patients were less likely to be admitted to small hospitals as compared to large hospitals — an issue which most likely reflects the more limited bed capacity in smaller hospitals. The authors also report that 88% of the patients seen in the ED were on ACE inhibitors (ACEIs) or angiotensin receptor blockers (ARBs) while in the EuroHeart Failure Survey-II (EHFS-II) the comparable figure was only 63% of patients on admission, but increased to 83% as reported at discharge. Since in EHFS-II the investigators also included patients with new onset HF (and 50% were due to acute coronary syndrome), the percentages do not reflect the same patient populations. Of the chronic HF patients in EHFS-II, 81.2% were on an ACEI or ARB. Use of beta blocking agents in the chronic HF patients in Europe was 48% and thus very similar to the Canadian population. Spironolactone use in Europe was as high as 34% compared to 23% in the Canadian population. However, when comparing practises in Europe there are great differences in prescription rates, for example in Finland 86% of admitted HF patients are on beta blocking agents [4]. One important facet of ED care for heart failure includes an evaluation of precipitating causes, something not feasible in administrative databases like the ones used by Ezekowitz et al. It is of interest that in EHFS-II, 31% of hospitalisations due to chronic heart failure were related to lack of compliance [2]. Compliance could not be analysed in the Canadian data set which was based on a retrospective analysis of patient data. There are probably several reasons for lack of compliance, such as age, social background and other illnesses. Lack of adherence to guidelines and secondly lack of clinical skills can also be major reasons, but are difficult to measure. Due to the retrospective nature of the study, Ezekowitz et al were not able to classify patients into clinical classes, as recommended in the European Guidelines [5]. The investigators could not evaluate the prognostic role of physiological variables, such as blood pressure on admission, or laboratory parameters like natriuretic peptides, haemoglobin, sodium or measures of renal function. Some of these measures are required for the application of risk stratification models [6,7] which could play an important role in ED decision making. We do not know which proportion of patients had new-onset, de-novo heart failure in contrast to those with a history of chronic heart failure. In addition, the authors do not provide information on the length of stay in the ED nor the treatment administered, so it is not possible to judge quality of care in this setting. Further, given the limitations of the dataset, the authors cannot address how outpatient follow-up was organised (or not), an important issue in light of the high recidivism rate with respect to ED visits. Nevertheless, the Canadian report is the first in which a large number of HF patient records are reviewed and is a good start for further analyses. Few would argue with the assertion that it is important for us to understand HF care in the ED setting with the same focus that we pay to acute coronary syndromes. In order to facilitate further research in this area, information entered into medical records should include structured data. Secondly the coding for discharge diagnosis should be systematic so that it reflects the main aetiology and other concomitant disorders. It is important to develop data mining programs based on diagnoses and structured data entry in patient's histories that will make possible reliable analyses of heart failure patient care in and out of hospital. The long term goal is to enable us to routinely analyse the implementation of clinical practice guidelines and disease management in the elderly. In conclusion, the findings of Ezekowitz et al. underscore the need for future evaluation of risk assessment models in AHF in prospective studies. Similarly, surveys like the EuroHeart Failure Survey allow for collection of large amount of data on clinical characteristics but the ability to collect samples, such as blood, is limited. Thus, prospective studies are required in order to assess the risks associated with rehospitalisation and to minimize unwanted outcomes in AHF patients. Such studies will require proper derivation and validation populations and a commitment to quality improvement in ED care delivery.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.005
Open science0.0010.001
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.033
GPT teacher head0.267
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2008
Admission routes1
Has abstractyes

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