Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features.
Simone SchiaffinoMarina CodariAndrea CozziDomenico AlbanoMarco AlìRoberto ArioliEmanuele AvolaClaudio BnàMaurizio CariatiSerena CarrieroMassimo CressoniPietro S C DannaGianmarco Della PepaGiovanni Di LeoFrancesco DolciZeno FalaschiNicola FlorRiccardo A FoàSalvatore GittoGiovanni LeatiVeronica MagniAlexis E MalavazosGiovanni MauriCarmelo MessinaLorenzo MonfardiniAlessio PaschèFilippo PesapaneLuca M SconfienzaFrancesco SecchiEdoardo SegaliniAngelo SpinazzolaValeria TombiniSilvia TresoldiAngelo VanzulliIlaria VicentinDomenico ZagariaDominik FleischmannFrancesco SardanelliPublished in: Journal of personalized medicine (2021)
Pulmonary parenchymal and vascular damage are frequently reported in COVID-19 patients and can be assessed with unenhanced chest computed tomography (CT), widely used as a triaging exam. Integrating clinical data, chest CT features, and CT-derived vascular metrics, we aimed to build a predictive model of in-hospital mortality using univariate analysis (Mann-Whitney U test) and machine learning models (support vectors machines (SVM) and multilayer perceptrons (MLP)). Patients with RT-PCR-confirmed SARS-CoV-2 infection and unenhanced chest CT performed on emergency department admission were included after retrieving their outcome (discharge or death), with an 85/15% training/test dataset split. Out of 897 patients, the 229 (26%) patients who died during hospitalization had higher median pulmonary artery diameter (29.0 mm) than patients who survived (27.0 mm, p < 0.001) and higher median ascending aortic diameter (36.6 mm versus 34.0 mm, p < 0.001). SVM and MLP best models considered the same ten input features, yielding a 0.747 (precision 0.522, recall 0.800) and 0.844 (precision 0.680, recall 0.567) area under the curve, respectively. In this model integrating clinical and radiological data, pulmonary artery diameter was the third most important predictor after age and parenchymal involvement extent, contributing to reliable in-hospital mortality prediction, highlighting the value of vascular metrics in improving patient stratification.
Keyphrases
- pulmonary artery
- dual energy
- computed tomography
- pulmonary hypertension
- image quality
- coronary artery
- pulmonary arterial hypertension
- machine learning
- emergency department
- contrast enhanced
- positron emission tomography
- magnetic resonance imaging
- sars cov
- big data
- electronic health record
- end stage renal disease
- optic nerve
- newly diagnosed
- ejection fraction
- oxidative stress
- artificial intelligence
- prognostic factors
- magnetic resonance
- gene therapy
- peritoneal dialysis
- respiratory syndrome coronavirus
- deep learning
- data analysis
- aortic valve