The Cardio-Pal Score - A Novel Prognostication Tool For 6-Month Mortality In Heart Failure Inpatients
Bews, H.; tam, j.; Quan, S.; Dolovich, C.; Whalen, K.; Beaudry, S.-J.; Butterworth, S.; Southam, A.; Pilkey, J.
Show abstract
Background: Patients with heart failure (HF) experience high mortality, yet survival estimates remain challenging. Available prognostic tools commonly predict survival beyond 1-year, limiting goals of care discussions and enrollment on palliative care programs. The objective of this project was to develop a novel prognostic score to better predict 6-month mortality. Methods: Patients with HF admitted to the St. Boniface Hospital (Winnipeg, Canada) between January 1, 2019 and January 1, 2024 were identified. Patient characteristics, imaging parameters, laboratory values, and functional assessments (Palliative Performance Scale; PPSv2) were collected. Patients were randomly divided into development (80%) and validation (20%) cohorts. Penalized LASSO regression was used to select variables most predictive of 6-month mortality in the development cohort. Retained variables were incorporated into a multivariable logistic regression model to derive the CARDIO-PAL score, the performance of which was assessed in the holdout validation cohort. Results: Characteristics were similar between the development (n = 2036) and validation (n = 508) cohorts, with 6-month mortality rates of 29% and 31%, respectively. 8 candidate variables were retained: age (adjusted odds ratio [aOR] 1.01), PPSv2 (aOR 2.08), systolic blood pressure (aOR 1.14), discontinued use of angiotensin-converting enzyme inhibitor or angiotensin II receptor blocker (aOR 2.58), beta-blocker (aOR 1.59), and mineralocorticoid receptor antagonist (aOR 2.67), right ventricular systolic pressure (aOR 2.06), and urea (aOR 2.13). The CARDIO-PAL score demonstrated good discrimination, with an area under the curve of 0.80 (95% confidence interval 0.75 - 0.84) and good calibration (slope 0.84, intercept -0.04). 6-month mortality across low, intermediate, and high-risk groups was 2%, 15%, and 54%, respectively. Conclusion: The CARDIO-PAL score is a novel tool to predict 6-month mortality in HF inpatients. A collaborative approach integrating CARDIO-PAL with other prognostic scores and serial clinical assessments has the potential to promote patient-centered care and improve referral to palliative care programs.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Predicting 30-Day and 1-Year Mortality in Heart Failure with Preserved Ejection Fraction (HFpEF) 97%
- External validation of a claims-based model to predict left ventricular ejection fraction class in patients with heart failure 96%
- Perceived barriers and enablers influencing physical activity in heart failure: a qualitative one-to-one interview study 93%
Similar papers in this journal
- Development and Validation of a parsimonious AI-Based Risk Score for Mortality in Heart Failure: A UK cohort study 95%
- Using ECG Machine Learning for Detection of Cardiovascular Disease in African American Men and Women: the Jackson Heart Study 91%
- Simple Models Versus Deep Learning in Detecting Low Ejection Fraction From The Electrocardiogram 90%
Similar papers in this journal
- Impact of Advanced Therapy Centers on Characteristics and Outcomes of Heart Failure Admissions 94%
- Race, Sex and Age Disparities in the Performance of ECG Deep Learning Models Predicting Heart Failure 93%
- Biomarkers of RV dysfunction in HFrEF identified by direct tissue proteomics: extracellular proteins fibromodulin and fibulin-5 93%
Similar papers in this journal
- Multispecialty multidisciplinary input into comorbidities in heart failure reduces hospitalisation and clinic attendance 96%
- Vascular Comorbidities Worsen Prognosis of Patients with Heart Failure Hospitalized with COVID-19 93%
- Machine learning approaches to predict 30-day mortality following percutaneous coronary intervention in an Australian population 92%
Similar papers in this journal
- Artificial Intelligence Methods to Detect Heart Failure with Preserved Ejection Fraction (AIM-HFpEF) within Electronic Health Records: An equitable disease prediction model 95%
- Right Ventricular Dysfunction: An Overlooked Predictor of Sudden Cardiac Death and Arrhythmic Events -- A Meta-Analysis 91%
- Clinical presentation, disease course and outcome of COVID-19 in hospitalized patients with and without pre-existing cardiac disease – a cohort study across sixteen countries 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.