Autor(es):
Santos, Ricardo ; Ribeiro, Bruno ; Sousa, Inês ; Santos, Jorge ; Guede-Fernández, Federico ; Dias, Pedro ; Carreiro, André V. ; Gamboa, Hugo ; Coelho, Pedro ; Fragata, José ; Londral, Ana
Data: 2024
Identificador Persistente: http://hdl.handle.net/10362/172576
Origem: Repositório Institucional da UNL
Assunto(s): Cardiothoracic surgery; Clinical decision support system; Complications prediction; Machine learning; Remote patient monitoring; Risk estimation; Health Informatics; SDG 3 - Good Health and Well-being
Descrição
Funding Information: This work was done under the project “CardioFollow.AI: An intelligent system to improve patients' safety and remote surveillance in follow-up for cardiothoracic surgery”, and supported by national funds through ‘FCT – Portuguese Foundation for Science and Technology , I.P.’, with reference DSAIPA/AI/0094/2020 . Publisher Copyright: © 2023 The Author(s)
Cardiac surgery patients are highly prone to severe complications post-discharge. Close follow-up through remote patient monitoring can help detect adverse outcomes earlier or prevent them, closing the gap between hospital and home care. However, equipment is limited due to economic and human resource constraints. This issue raises the need for efficient risk estimation to provide clinicians with insights into the potential benefit of remote monitoring for each patient. Standard models, such as the EuroSCORE, predict the mortality risk before the surgery. While these are used and validated in real settings, the models lack information collected during or following the surgery, determinant to predict adverse outcomes occurring further in the future. This paper proposes a Clinical Decision Support System based on Machine Learning to estimate the risk of severe complications within 90 days following cardiothoracic surgery discharge, an innovative objective underexplored in the literature. Health records from a cardiothoracic surgery department regarding 5 045 patients (60.8% male) collected throughout ten years were used to train predictive models. Clinicians' insights contributed to improving data preparation and extending traditional pipeline optimization techniques, addressing medical Artificial Intelligence requirements. Two separate test sets were used to evaluate the generalizability, one derived from a patient-grouped 70/30 split and another including all surgeries from the last available year. The achieved Area Under the Receiver Operating Characteristic curve on these test sets was 69.5% and 65.3%, respectively. Also, additional testing was implemented to simulate a real-world use case considering the weekly distribution of remote patient monitoring resources post-discharge. Compared to the random resource allocation, the selection of patients with respect to the outputs of the proposed model was proven beneficial, as it led to a higher number of high-risk patients receiving remote monitoring equipment.
