Related Experiment Video
Updated: Feb 15, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Efficient selective screening for heart failure in elderly men and women from the community: A diagnostic individual
Rogier F Kievit1, Aisha Gohar1,2, Arno W Hoes1
11 Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, The Netherlands.
Insights
A new model using simple patient factors like age and shortness of breath can identify older adults likely to have heart failure. This aids early diagnosis and intervention for better health outcomes.
Area of Science:
- Cardiology
- Primary Care Medicine
- Geriatrics
Background:
- Undetected heart failure is common in older adults, increasing morbidity and mortality.
- Early identification is crucial for timely interventions and preventing disease progression.
- A validated model to pre-select patients for echocardiography is currently unavailable.
Purpose of the Study:
- To develop and validate a clinical model for identifying older individuals with potential heart failure.
- To enable earlier diagnosis and targeted interventions in community-dwelling older adults.
Main Methods:
- Analysis of individual patient data from four primary care screening studies (1941 participants >60 years old).
- Development of prediction models using five independent predictors: age, ischemic heart disease history, exercise-related shortness of breath, BMI, and apex beat.
- Cross-validation of models and assessment of performance using c-statistic and calibration.
Main Results:
- The model identified 462 participants with heart failure based on European Society of Cardiology guidelines.
- Key predictors included age, ischemic heart disease, shortness of breath, BMI, and apex beat.
- Model performance (c-statistic) ranged from 0.70 to 0.82 at cross-validation; adding N-terminal pro B-type natriuretic peptide improved it to 0.89.
Conclusions:
- Easily obtainable patient characteristics can effectively select older individuals for echocardiography.
- This model facilitates the confirmation or exclusion of heart failure in the community.
- Early detection through this model can lead to targeted interventions and improved patient management.
Abstract:
Background Prevalence of undetected heart failure in older individuals is high in the community, with patients being at increased risk of morbidity and mortality due to the chronic and progressive nature of this complex syndrome. An essential, yet currently unavailable, strategy to pre-select candidates eligible for echocardiography to confirm or exclude heart failure would identify patients earlier, enable targeted interventions and prevent disease progression. The aim of this study was therefore to develop and validate such a model that can be implemented clinically. Methods and results Individual patient data from four primary care screening studies were analysed. From 1941 participants >60 years old, 462 were diagnosed with heart failure, according to criteria of the European Society of Cardiology heart failure guidelines. Prediction models were developed in each cohort followed by cross-validation, omitting each of the four cohorts in turn. The model consisted of five independent predictors; age, history of ischaemic heart disease, exercise-related shortness of breath, body mass index and a laterally displaced/broadened apex beat, with no significant interaction with sex. The c-statistic ranged from 0.70 (95% confidence interval (CI) 0.64-0.76) to 0.82 (95% CI 0.78-0.87) at cross-validation and the calibration was reasonable with Observed/Expected ratios ranging from 0.86 to 1.15. The clinical model improved with the addition of N-terminal pro B-type natriuretic peptide with the c-statistic increasing from 0.76 (95% CI 0.70-0.81) to 0.89 (95% CI 0.86-0.92) at cross-validation. Conclusion Easily obtainable patient characteristics can select older men and women from the community who are candidates for echocardiography to confirm or refute heart failure.
More Related Videos
Related Concept Videos
01:30Heart Failure IV: Classification and Diagnostic Evaluation
01:29Heart Failure II: Pathophysiology
01:17Pathophysiology of Heart Failure
01:27Heart Failure I: Introduction
01:22Heart Failure VI: Adjunct Therapies
01:22Heart Failure Drugs: Diuretics