Machine learning-based forecast of Helmet-CPAP therapy failure in Acute Respiratory Distress Syndrome patients
Riccardo Campia, Antonio De Santisa, Paolo Colombob, Paolo Scarpazzab, Marco Masserolia
aDepartment of Electronics Information and Bioengineering, Politecnico di Milano, Piazza L. Da Vinci 32, Milano, MI, 20133, Italy
bAzienda Socio-Sanitaria Territoriale (ASST) della Brianza, Via Santi Cosma e Damiano 10, Vimercate, MB, 20871, Italy
Email addresses: [email protected] (Riccardo Campi), [email protected] (Antonio De Santis), [email protected] (Paolo Colombo), [email protected] (Paolo Scarpazza), [email protected] (Marco Masseroli)
Helmet-Continuous Positive Airway Pressure (H-CPAP) is a non-invasive respiratory support that is used for the treatment of Acute Respiratory Distress Syndrome (ARDS), a severe medical condition diagnosed when symptoms like profound hypoxemia, pulmonary opacities on radiography, or unexplained respiratory failure are present. It can be classified as mild, moderate or severe. H-CPAP therapy is recommended as the initial treatment approach for mild ARDS. Even though the efficacy of H-CPAP in managing patients with moderate-to-severe hypoxemia remains unclear, its use has increased for these cases in response to the emergence of the COVID-19 Pandemic.
Using the electronic medical records (EMR) from the Pulmonology Department of Vimercate Hospital, in this study we develop and evaluate a Machine Learning (ML) system able to predict the failure of H-CPAP therapy on ARDS patients.
The Vimercate Hospital EMR provides demographic information, blood tests, and vital parameters of all hospitalizations of patients who are treated with H-CPAP and diagnosed with ARDS. This data is used to create a dataset of 720 records and 38 features. Different ML models such as Random Forest, XGBoost, SVM, and Logistic Regression with LASSO are iteratively trained in a cross-validation fashion. We also apply a classification threshold calibration method and a feature selection algorithm to improve predictions quality and reduce the number of features.
The Random Forest and XGBoost models proved to be the most effective. They achieved final accuracies of 93.06% and 92.13% respectively. In terms of F1-score, the models scored 89.21% and 87.59% respectively. These models are trained with 13 features for Random Forest and 16 features for XGBoost. The PaO2/FiO2 Ratio, C-Reactive Protein, and O2 Saturation resulted as the most important features, followed by Heartbeats, Respiratory Rate, eGFR, Creatinine, and D-Dimer, in accordance with the medical scientific literature.
| File/folder | Description |
|---|---|
| /main.ipynb | code used to clean the datasets and train the models |
| /functions.py | accessory functions and classes |
| /models_vimercate/ | folder containing the trained models |
| /datasets/ | folder containing the dataset "dataset_vimercate.csv" used to train the models |
| /images/ | folder containing the obtained images |