Machine learning-based model for prediction of clinical deterioration in hospitalized patients by COVID 19
Authors: Garcia-Gutiérrez Susana , Esteban-Aizpiri Cristobal , Lafuente Iratxe , Barrio Irantzu , Quiros Raul , Quintana Jose Maria , Uranga Ane ,   COVID-REDISSEC Working Group, García-Gutiérrez Susana , Lafuente Iratxe , Quintana Jose María , Orive Miren , Gonzalez Nerea , Anton Ane , Villanueva Ane , Muñoz Cristina , Legarreta Maria Jose , Quirós Raul , Yandiola Pedro Pablo España , Egurrola Mikel , Aramburu Amaia , Artaraz Amaia , Chasco Leire , Bronte Olaia , García Patricia , Jodar Ana , Fernandez Virginia , Esteban Cristobal , Mas Naia , Pulido Esther , Bengoetxea Itxaso , Martínez Antonio Escobar , Bilbao Amaia , Gorostiza Iñigo , Arriaga Iñaki , Zapiarain José Joaquín Portu , Parraza Naiara , Iriberri Milagros , Zalacain Rafael , Ruiz Luis Alberto , Serrano Leyre , Couto Adriana , Ateka Oier , Cano Arantza , Ibarra Maria Olatz , Millan Eduardo , Bacigalupe Mayte , Letona Jon , Arcelay Andoni , Berraondo Iñaki , Castells Xavier , Posso Margarita , Perestelo Lilisbeth , Acosta Guillermo Perez , Gonzñalez Candelaria Martín , Redondo Maximino , Padilla Maria , Muñoz Adolfo , de Madariaga Ricardo Saenz 
Published: May 2022

Despite the publication of great number of tools to aid decisions in COVID-19 patients, there is a lack of good instruments to predict clinical deterioration. COVID19-Osakidetza is a prospective cohort study recruiting COVID-19 patients. We collected information from baseline to discharge on: sociodemographic characteristics, comorbidities and associated medications, vital signs, treatment received and lab test results. Outcome was need for intensive ventilatory support (with at least standard high-flow oxygen face mask with a reservoir bag for at least 6 h and need for more intensive therapy afterwards or Optiflow high-flow nasal cannula or noninvasive or invasive mechanical ventilation) and/or admission to a critical care unit and/or death during hospitalization. We developed a Catboost model summarizing the findings using Shapley Additive Explanations. Performance of the model was assessed using area under the receiver operating characteristic and prediction recall curves (AUROC and AUPRC respectively) and calibrated using the Hosmer–Lemeshow test. Overall, 1568 patients were included in the derivation cohort and 956 in the (external) validation cohort. The percentages of patients who reached the composite endpoint were 23.3% vs 20% respectively. The strongest predictors of clinical deterioration were arterial blood oxygen pressure, followed by age, levels of several markers of inflammation (procalcitonin, LDH, CRP) and alterations in blood count and coagulation. Some medications, namely, ATC AO2 (antiacids) and N05 (neuroleptics) were also among the group of main predictors, together with C03 (diuretics). In the validation set, the CatBoost AUROC was 0.79, AUPRC 0.21 and Hosmer–Lemeshow test statistic 0.36. We present a machine learning-based prediction model with excellent performance properties to implement in EHRs. Our main goal was to predict progression to a score of 5 or higher on the WHO Clinical Progression Scale before patients required mechanical ventilation. Future steps are to externally validate the model in other settings and in a cohort from a different period and to apply the algorithm in clinical practice.

Registration: ClinicalTrials.gov Identifier: NCT04463706.