Clinical risk prediction model for 30-day all-cause re-hospitalisation or mortality in patients hospitalised with heart failure
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: We a...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Journal Article |
| Language: | English |
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ELSEVIER IRELAND LTD
2022
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| Online Access: | http://purl.org/au-research/grants/nhmrc/1136372 http://hdl.handle.net/20.500.11937/93775 |
| _version_ | 1848765786652409856 |
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| author | Driscoll, A. Romaniuk, H. Dinh, D. Amerena, J. Brennan, A. Hare, D.L. Kaye, D. Lefkovits, J. Lockwood, S. Neil, C. Prior, D. Reid, Christopher Orellana, L. |
| author_facet | Driscoll, A. Romaniuk, H. Dinh, D. Amerena, J. Brennan, A. Hare, D.L. Kaye, D. Lefkovits, J. Lockwood, S. Neil, C. Prior, D. Reid, Christopher Orellana, L. |
| author_sort | Driscoll, A. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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: 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: 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: 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. |
| first_indexed | 2025-11-14T11:40:47Z |
| format | Journal Article |
| id | curtin-20.500.11937-93775 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:40:47Z |
| publishDate | 2022 |
| publisher | ELSEVIER IRELAND LTD |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-937752024-01-09T07:55:58Z Clinical risk prediction model for 30-day all-cause re-hospitalisation or mortality in patients hospitalised with heart failure Driscoll, A. Romaniuk, H. Dinh, D. Amerena, J. Brennan, A. Hare, D.L. Kaye, D. Lefkovits, J. Lockwood, S. Neil, C. Prior, D. Reid, Christopher Orellana, L. Science & Technology Life Sciences & Biomedicine Cardiac & Cardiovascular Systems Cardiovascular System & Cardiology Heart failure Risk prediction model Re-hospitalisation Mortality READMISSION VALIDATION GUIDELINES DERIVATION DIAGNOSIS SCORE RULE Science & Technology Life Sciences & Biomedicine Cardiac & Cardiovascular Systems Cardiovascular System & Cardiology Heart failure Risk prediction model Re-hospitalisation Mortality READMISSION VALIDATION DERIVATION SCORE RULE Heart failure Mortality Re-hospitalisation Risk prediction model Aftercare Aged Angiotensin-Converting Enzyme Inhibitors Female Heart Failure Hospitalization Humans Male Patient Discharge Humans Angiotensin-Converting Enzyme Inhibitors Aftercare Hospitalization Patient Discharge Aged Female Male Heart Failure 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: 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: 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: 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. 2022 Journal Article http://hdl.handle.net/20.500.11937/93775 10.1016/j.ijcard.2021.12.051 English http://purl.org/au-research/grants/nhmrc/1136372 ELSEVIER IRELAND LTD restricted |
| spellingShingle | Science & Technology Life Sciences & Biomedicine Cardiac & Cardiovascular Systems Cardiovascular System & Cardiology Heart failure Risk prediction model Re-hospitalisation Mortality READMISSION VALIDATION GUIDELINES DERIVATION DIAGNOSIS SCORE RULE Science & Technology Life Sciences & Biomedicine Cardiac & Cardiovascular Systems Cardiovascular System & Cardiology Heart failure Risk prediction model Re-hospitalisation Mortality READMISSION VALIDATION DERIVATION SCORE RULE Heart failure Mortality Re-hospitalisation Risk prediction model Aftercare Aged Angiotensin-Converting Enzyme Inhibitors Female Heart Failure Hospitalization Humans Male Patient Discharge Humans Angiotensin-Converting Enzyme Inhibitors Aftercare Hospitalization Patient Discharge Aged Female Male Heart Failure Driscoll, A. Romaniuk, H. Dinh, D. Amerena, J. Brennan, A. Hare, D.L. Kaye, D. Lefkovits, J. Lockwood, S. Neil, C. Prior, D. Reid, Christopher Orellana, L. Clinical risk prediction model for 30-day all-cause re-hospitalisation or mortality in patients hospitalised with heart failure |
| title | Clinical risk prediction model for 30-day all-cause re-hospitalisation or mortality in patients hospitalised with heart failure |
| title_full | Clinical risk prediction model for 30-day all-cause re-hospitalisation or mortality in patients hospitalised with heart failure |
| title_fullStr | Clinical risk prediction model for 30-day all-cause re-hospitalisation or mortality in patients hospitalised with heart failure |
| title_full_unstemmed | Clinical risk prediction model for 30-day all-cause re-hospitalisation or mortality in patients hospitalised with heart failure |
| title_short | Clinical risk prediction model for 30-day all-cause re-hospitalisation or mortality in patients hospitalised with heart failure |
| title_sort | clinical risk prediction model for 30-day all-cause re-hospitalisation or mortality in patients hospitalised with heart failure |
| topic | Science & Technology Life Sciences & Biomedicine Cardiac & Cardiovascular Systems Cardiovascular System & Cardiology Heart failure Risk prediction model Re-hospitalisation Mortality READMISSION VALIDATION GUIDELINES DERIVATION DIAGNOSIS SCORE RULE Science & Technology Life Sciences & Biomedicine Cardiac & Cardiovascular Systems Cardiovascular System & Cardiology Heart failure Risk prediction model Re-hospitalisation Mortality READMISSION VALIDATION DERIVATION SCORE RULE Heart failure Mortality Re-hospitalisation Risk prediction model Aftercare Aged Angiotensin-Converting Enzyme Inhibitors Female Heart Failure Hospitalization Humans Male Patient Discharge Humans Angiotensin-Converting Enzyme Inhibitors Aftercare Hospitalization Patient Discharge Aged Female Male Heart Failure |
| url | http://purl.org/au-research/grants/nhmrc/1136372 http://hdl.handle.net/20.500.11937/93775 |