Clinical risk prediction model for 30-day all-cause re-hospitalisation or mortality in patients hospitalised with heart failure.

Aftercare Aged Angiotensin-Converting Enzyme Inhibitors / therapeutic use Female Heart Failure / drug therapy Hospitalization Humans Male Patient Discharge
Heart failure Mortality Re-hospitalisation Risk prediction model

Journal

International journal of cardiology
ISSN: 1874-1754
Titre abrégé: Int J Cardiol
Pays: Netherlands
ID NLM: 8200291

Informations de publication

Date de publication:
01 Mar 2022
Historique:
received: 16 10 2021
revised: 18 12 2021
accepted: 28 12 2021
pubmed: 4 1 2022
medline: 15 3 2022
entrez: 3 1 2022
Statut: ppublish

Résumé

This study aimed to develop a risk prediction model (AUS-HF model) for 30-day all-cause re-hospitalisation or death among patients admitted with acute heart failure (HF) to inform follow-up after hospitalisation. The model uses routinely collected measures at point of care. We analyzed pooled individual-level data from two cohort studies on acute HF patients followed for 30-days after discharge in 17 hospitals in Victoria, Australia (2014-2017). A set of 58 candidate predictors, commonly recorded in electronic medical records (EMR) including demographic, medical and social measures were considered. We used backward stepwise selection and LASSO for model development, bootstrap for internal validation, C-statistic for discrimination, and calibration slopes and plots for model calibration. The analysis included 1380 patients, 42.1% female, median age 78.7 years (interquartile range = 16.2), 60.0% experienced previous hospitalisation for HF and 333 (24.1%) were re-hospitalised or died within 30 days post-discharge. The final risk model included 10 variables (admission: eGFR, and prescription of anticoagulants and thiazide diuretics; discharge: length of stay>3 days, systolic BP, heart rate, sodium level (<135 mmol/L), >10 prescribed medications, prescription of angiotensin converting enzyme inhibitors or angiotensin receptor blockers, and anticoagulants prescription. The discrimination of the model was moderate (C-statistic = 0.684, 95%CI 0.653, 0.716; optimism estimate = 0.062) with good calibration. The AUS-HF model incorporating routinely collected point-of-care data from EMRs enables real-time risk estimation and can be easily implemented by clinicians. It can predict with moderate accuracy risk of 30-day hospitalisation or mortality and inform decisions around the intensity of follow-up after hospital discharge.

Sections du résumé

BACKGROUND BACKGROUND
This study aimed to develop a risk prediction model (AUS-HF model) for 30-day all-cause re-hospitalisation or death among patients admitted with acute heart failure (HF) to inform follow-up after hospitalisation. The model uses routinely collected measures at point of care.
METHODS METHODS
We analyzed pooled individual-level data from two cohort studies on acute HF patients followed for 30-days after discharge in 17 hospitals in Victoria, Australia (2014-2017). A set of 58 candidate predictors, commonly recorded in electronic medical records (EMR) including demographic, medical and social measures were considered. We used backward stepwise selection and LASSO for model development, bootstrap for internal validation, C-statistic for discrimination, and calibration slopes and plots for model calibration.
RESULTS RESULTS
The analysis included 1380 patients, 42.1% female, median age 78.7 years (interquartile range = 16.2), 60.0% experienced previous hospitalisation for HF and 333 (24.1%) were re-hospitalised or died within 30 days post-discharge. The final risk model included 10 variables (admission: eGFR, and prescription of anticoagulants and thiazide diuretics; discharge: length of stay>3 days, systolic BP, heart rate, sodium level (<135 mmol/L), >10 prescribed medications, prescription of angiotensin converting enzyme inhibitors or angiotensin receptor blockers, and anticoagulants prescription. The discrimination of the model was moderate (C-statistic = 0.684, 95%CI 0.653, 0.716; optimism estimate = 0.062) with good calibration.
CONCLUSIONS CONCLUSIONS
The AUS-HF model incorporating routinely collected point-of-care data from EMRs enables real-time risk estimation and can be easily implemented by clinicians. It can predict with moderate accuracy risk of 30-day hospitalisation or mortality and inform decisions around the intensity of follow-up after hospital discharge.

Identifiants

DOI: 10.1016/j.ijcard.2021.12.051 PMID: 34979149
pubmed: 34979149
pii: S0167-5273(21)02053-2
doi: 10.1016/j.ijcard.2021.12.051
pii:
doi:

Substances chimiques

Angiotensin-Converting Enzyme Inhibitors 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

69-76

Commentaires et corrections

Type : CommentIn

Informations de copyright

Copyright © 2022 Elsevier B.V. All rights reserved.

Auteurs

A Driscoll (A)

Deakin University, School of Nursing and Midwifery, 1 Gheringhap Street, Geelong, VIC 3220, Australia; Austin Health, Dept of Cardiology, Studley Rd, Heidelberg, VIC 3081, Australia. Electronic address: andrea.driscoll@deakin.edu.au.

H Romaniuk (H)

Deakin University, Biostatistics Unit, Faculty of Health, 1 Gheringhap Street, Geelong, VIC 3220, Australia. Electronic address: h.romaniuk@deakin.edu.au.

D Dinh (D)

Monash University, School of Medicine and Preventive Health, Commercial Rd, Prahran, VIC 3121, Australia. Electronic address: diem.dinh@monash.edu.

J Amerena (J)

University Hospital Geelong, Cardiology Research Department, PO Box 281, Geelong 3220, Australia. Electronic address: johnam@barwonhealth.org.au.

A Brennan (A)

Monash University, School of Medicine and Preventive Health, Commercial Rd, Prahran, VIC 3121, Australia.

D L Hare (DL)

Austin Health, Dept of Cardiology, Studley Rd, Heidelberg, VIC 3081, Australia; University of Melbourne, School of Medicine, Swanson St, Melbourne, VIC 3001, Australia. Electronic address: David.hare@austin.org.au.

D Kaye (D)

Baker Heart and Diabetes Institute, Commercial Rd, Prahran, VIC 3121, Australia; Alfred Health, Department of Cardiology, Commercial Rd, Prahran, VIC 3121, Australia. Electronic address: david.kaye@bakeridi.edu.au.

J Lefkovits (J)

Monash University, School of Medicine and Preventive Health, Commercial Rd, Prahran, VIC 3121, Australia.

S Lockwood (S)

University Hospital Geelong, Cardiology Research Department, PO Box 281, Geelong 3220, Australia; Monash Health, Department of Cardiology, 246 Clayton Rd, Clayton, VIC 3168, Australia. Electronic address: Siobhan.Lockwood@monashhealth.org.

C Neil (C)

University Hospital Geelong, Cardiology Research Department, PO Box 281, Geelong 3220, Australia; Western Health, Department of Cardiology, 160 Gordon St, Footscray, VIC 3011, Australia. Electronic address: christopher.neil@wh.org.au.

D Prior (D)

St Vincents Hospital, Department of Cardiology, 41 Fitzroy Parade, Fitzroy, VIC 3065, Australia. Electronic address: David.prior@svha.org.au.

C M Reid (CM)

Curtin University, School of Public Health, NHMRC Centre for Research Excellence in Cardiovascular Outcomes Improvement, Kent St, Bentley, WA 6102, Australia. Electronic address: christopher.reid@curtin.edu.au.

L Orellana (L)

Deakin University, Biostatistics Unit, Faculty of Health, 1 Gheringhap Street, Geelong, VIC 3220, Australia.

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Classifications MeSH

questionsmedicales.fr › Administration des services de santé › Gestion des soins aux patients › Services de santé polyvalents › Soins de santé primaires › Continuité des soins › Post-cure › Clinical risk prediction model for 30-day...
questionsmedicales.fr › Personnes › Tranches d'âge › Adulte › Sujet âgé › Clinical risk prediction model for 30-day...
questionsmedicales.fr › Actions chimiques et utilisations › Actions pharmacologiques › Mécanismes moléculaires de l'action pharmacologique › Antienzymes › Inhibiteurs de protéases › Inhibiteurs de l'enzyme de conversion de l'angiotensine › Clinical risk prediction model for 30-day...
questionsmedicales.fr › Maladies cardiovasculaires › Cardiopathies › Défaillance cardiaque › Clinical risk prediction model for 30-day...
questionsmedicales.fr › Établissements, main d'oeuvre et services de soins de santé › Services de santé › Soins aux patients › Hospitalisation › Clinical risk prediction model for 30-day...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Clinical risk prediction model for 30-day...
questionsmedicales.fr › Administration des services de santé › Gestion des soins aux patients › Services de santé polyvalents › Soins de santé primaires › Continuité des soins › Sortie du patient › Clinical risk prediction model for 30-day...