The Effect of Missing Data when Predicting Readmission in Heart Failure Patients

0582863 - ÚPT 2024 RIV US eng C - Konferenční příspěvek (zahraniční konf.)
Plešinger, Filip - Koščová, Zuzana - Vargová, Enikö - Pavlus, Ján - Smíšek, Radovan - Viščor, Ivo - Bulková, V.
The Effect of Missing Data when Predicting Readmission in Heart Failure Patients.
2023 Computing in Cardiology (CinC). New York: IEEE, 2023, (2023). ISBN 979-8-3503-8252-5. ISSN 2325-8861. E-ISSN 2325-887X.
[Computing in Cardiology 2023 /50./. Atlanta (US), 01.10.2023-04.10.2023]
Grant CEP: GA TA ČR(CZ) FW06010766
Institucionální podpora: RVO:68081731
Klíčová slova: heart failure * modelling * readmission
Obor OECD: Medical engineering
https://ieeexplore.ieee.org/document/10364025 https://www.cinc.org/archives/2023/pdf/CinC2023-265.pdf

Background: The discharge of patients from hospital care is regulated by guidelines. Still, readmission of heart failure (HF) patients is a common issue, and several calculators have been published to predict it. Aims: We elaborate on how the prediction performance decreases when features become missing. We also elaborate on which features should a user include every time to reach acceptable prediction performance. Method: We prepared a balanced dataset from HF patients in the MIMIC-III database (N=2,204) with 16 features. Using training data (80%) in a four-fold cross-validation manner, we evaluated all feature combinations (N=Z^{16}-1) and found the optimal feature set for the logistic regression model. We also evaluated feature presence in top-performing models (N=655) and identified mandatory features. Finally, we trained the resultant model using all training data and evaluated the effect of missing features (N=2^{8} combinations) using separate test data (20%). Results: We identified three mandatory features (age, blood urea nitrogen, and systolic blood pressure) and eight optional. This led to a resultant model with eleven features. The hazard ratio (HR) using test data showed a value of 2.08 (95%CI 1.66-2.61) when all eleven features were present. It also showed an HR of 1.73 (95%CI1.39-2.17) when only three mandatory features were present, and others were missing (i.e., replaced by zeros).
Trvalý link: https://hdl.handle.net/11104/0351029