Abbreviated Duke Activity Status Index for Risk Stratification in Heart Failure

•We developed an abbreviated version of the Duke activity status index (DASI) questionnaire using the XGBoost algorithm as an alternative for assessing functional capacity.•We demonstrated that abbreviated DASI is accurate and consistent with the original DASI score.•Patients scoring in the lower qu...

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Bibliographic Details
Published in:The American journal of cardiology Vol. 237; pp. 54 - 59
Main Authors: Augusto, Silvio Nunes, Wu, Yuping, Chaikijurajai, Thanat, Hazen, Stanley L., Tang, W. H. Wilson
Format: Journal Article
Language:English
Published: United States Elsevier Inc 15.02.2025
Elsevier Limited
Subjects:
Aged
Algorithms
Biomarkers
Congestive heart failure
Congruences
Diabetes
Duke activity status index
Ejection fraction
Failure
Female
functional capacity
Heart failure
Heart Failure - diagnosis
Heart Failure - mortality
Heart Failure - physiopathology
Humans
Machine Learning
Male
Middle Aged
Mortality
Multicenter Studies as Topic
Patients
Peptides
Prognosis
Questionnaires
Randomized Controlled Trials as Topic
Reproducibility of Results
Risk Assessment - methods
Surveys and Questionnaires
Survival analysis
Survival Rate - trends
Validity
heart failure
functional capacity
Duke activity status index
prognosis
ISSN:0002-9149, 1879-1913, 1879-1913
Online Access:Get full text
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Summary:•We developed an abbreviated version of the Duke activity status index (DASI) questionnaire using the XGBoost algorithm as an alternative for assessing functional capacity.•We demonstrated that abbreviated DASI is accurate and consistent with the original DASI score.•Patients scoring in the lower quartile in abbreviated DASI are at a greater 5-year mortality risk. The Duke activity status index (DASI), a self-administered 12-item questionnaire has been used to estimate functional capacity and recently demonstrated prognostic information. We aimed to develop an abbreviated version for clinical applications. Leveraging the Cleveland Clinic GeneBank Study, we developed an abbreviated DASI questionnaire (aDASI) with the machine learning XGBoost algorithm, with the goal of maintaining the accuracy and reliability of the original DASI. We validated the prognostic value of aDASI in a subset of patients with heart failure from GeneBank and an independent data set from the GUIDE-IT (Guiding Evidence Based Therapy Using Biomarker Intensified Treatment in Heart Failure; ClinicalTrials.gov NCT01685840) trial. The results confirmed the congruence and accuracy between the original and the abbreviated scores while reducing the number of questions (R = 0.97, p <0.001). The original DASI score and the aDASI exhibited a strong correlation in the GeneBank and predictive value for all-cause mortality at different time points in the GUIDE-IT cohort. In conclusion, the abbreviated DASI tracks with original DASI assessment and performs comparably to the original DASI questionnaire in predicting all-cause mortality.
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ISSN:0002-9149
1879-1913
1879-1913
DOI:10.1016/j.amjcard.2024.11.029