ArticleScandinavian journal of primary health care2020
Precipitating factors of heart failure decompensation, short-term morbidity and mortality in patients attended in primary care.
Article in Scandinavian journal of primary health care, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 17 citations in OpenAlex.
- The 'Chloride Theory' for Heart Failure Pathophysiology: An Updated Narrative Review 2025.Cardiology and therapy · 2026Review
- Learning Needs in Heart Failure Care Across Patients, Caregivers, and Healthcare Professionals: A Scoping Review of Asian Studies.Patient preference and adherence · 2026Review
- The differences between ambulatory and hospitalized heart failure patients who have mildly reduced or preserved ejection fractions: lessons learned from the HF-POL study.Cardiology journal · 2026Observational
- Health Care Professionals and Data Scientists' Perspectives on a Machine Learning System to Anticipate and Manage the Risk of Decompensation From Patients With Heart Failure: Qualitative Interview Study.Journal of medical Internet research · 2025Article
- A qualitative exploration of the educational needs of people living with heart failure: BANDAIDD-Explore study.PloS one · 2025Article
- Exploring the Relationship Between Ejection Fraction, Arterial Stiffness, NT-proBNP, and Hospitalization Risk in Heart Failure Patients.Diagnostics (Basel, Switzerland) · 2024Article
- Heart failure with mildly reduced and preserved ejection fraction: A review of disease burden and remaining unmet medical needs within a new treatment landscape.Heart failure reviews · 2024Review
- Heart failure patients without echocardiography are more commonly diagnosed in hospital care and are associated with higher mortality compared to primary care.Scandinavian journal of primary health care · 2024Article
- Gender-Related Differences in Patients with Acute Heart Failure: Observation from the Journey Heart Failure-Turkish Population Study.Anatolian journal of cardiology · 2023Article
- Psychometric Properties of a Machine Learning-Based Patient-Reported Outcome Measure on Medication Adherence: Single-Center, Cross-Sectional, Observational Study.Journal of medical Internet research · 2023Observational
- Prognostic Factors for Prolonged In-Hospital Stay in Patients with Heart Failure.Medicina (Kaunas, Lithuania) · 2023Article
- Evaluation of the HEFESTOS scale to predict outcomes in emergency department acute heart failure patients.Internal and emergency medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 12 institutions in 10 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo evaluate the precipitating factors for heart failure decompensation in primary care and associations with short-term prognosis.
settingPrimary care in ten European countries.
main outcome measuresPotential precipitating factors for decompensation of heart failure and their association with the event of hospitalisation or mortality 30 d after a decompensation.
resultsOf 692 patients 54% were women, mean age 81 (standard deviation [SD] 8.9) years; mean left ventricular ejection fraction (LVEF) 55% (SD 12%). Most frequently identified heart failure precipitation factors were respiratory infections in 194 patients (28%), non-compliance of dietary recommendations in 184 (27%) and non-compliance with pharmacological treatment in 157 (23%). The two strongest precipitating factors to predict 30 d hospitalisation or death were respiratory infections (odds ratio [OR] 2.8, 95% confidence interval [CI] (2.4-3.4)) and atrial fibrillation (AF) > 110 beats/min (OR 2.2, CI 1.5-3.2). Multivariate analysis confirmed the association between the following variables and hospitalisation/death: In relation to precipitating factors: respiratory infection (OR 1.19, 95% CI 1.14-1.25) and AF with heart rate > 110 beats/min (OR 1.22, 95% CI 1.10-1.35); and regarding patient characteristics: New York Heart Association (NYHA) III or IV (OR 1.22, 95% CI 1.15-1.29); previous hospitalisation (OR 1.15, 95% CI 1.11-1.19); and LVEF < 40% (OR 1.14, 95% CI 1.09-1.19).
conclusionsIn primary care, respiratory infections and rapid AF are the most important precipitating factors for hospitalisation and death within 30 d following an episode of heart failure decompensation. Key points Hospitalisation due to heart failure decompensation represents the highest share of healthcare costs for this disease. So far, no primary care studies have analysed the relationship between precipitating factors and short term prognosis of heart failure decompensation episodes. We found that in 692 patients with heart failure decompensation in primary care, the respiratory infection and rapid atrial fibrillation (AF) increased the risk of short-term hospital admission or death. Patients with a hospital admission the previous year and a decompensation episode caused by respiratory infection were even more likely to be hospitalized or die within 30 d.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.